FRONTIERS EBOOK COPYRIGHT STATEMENT

The copyright in the text of individual articles in this ebook is the property of their respective authors or their respective institutions or funders. The copyright in graphics and images within each article may be subject to copyright of other parties. In both cases this is subject to a license granted to Frontiers.

The compilation of articles constituting this ebook is the property of Frontiers.

Each article within this ebook, and the ebook itself, are published under the most recent version of the Creative Commons CC-BY licence. The version current at the date of publication of this ebook is CC-BY 4.0. If the CC-BY licence is updated, the licence granted by Frontiers is automatically updated to the new version.

When exercising any right under the CC-BY licence, Frontiers must be attributed as the original publisher of the article or ebook, as applicable.

Authors have the responsibility of ensuring that any graphics or other materials which are the property of others may be included in the CC-BY licence, but this should be checked before relying on the CC-BY licence to reproduce those materials. Any copyright notices relating to those materials must be complied with.

Copyright and source acknowledgement notices may not be removed and must be displayed in any copy, derivative work or partial copy which includes the elements in question.

All copyright, and all rights therein, are protected by national and international copyright laws. The above represents a summary only. For further information please read Frontiers’ Conditions for Website Use and Copyright Statement, and the applicable CC-BY licence.

ISSN 1664-8714
ISBN 978-2-8325-6131-7
DOI 10.3389/978-2-8325-6131-7

About Frontiers

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals.

Frontiers journal series

The Frontiers journal series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too.

Dedication to quality

Each Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world’s best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public - and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

What are Frontiers Research Topics?

Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers editorial office: frontiersin.org/about/contact

Unconventional resources: provenance analysis, sediment transport, reservoir evaluation, geo-energy

This ebook is split into several files; this is part two of the ebook, please see the overview text of the Research Topic page to access the first part of the ebook.

Topic editors

Wenguang Wang – Northeast Petroleum University, China

Xixin Wang – Yangtze University, China

Chao Liu – Northeast Petroleum University, China

Yongqiang Chen – Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia

Yihuai Zhang – University of Glasgow, United Kingdom

Citation

Wang, W., Wang, X., Liu, C., Chen, Y., Zhang, Y., eds. (2025). Unconventional resources: provenance analysis, sediment transport, reservoir evaluation, geo-energy. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-8325-6131-7

Table of Contents

Sedimentary architecture of thin-layer beach bar sand bodies in the G oilfield, Niger

Jianing Wang, Cheng Lei, Yanshu Yin and Zhenhua Xu

Determining complex petroleum migration pathways along faults and sandbodies: a new method and application

Shi Jijian, Fu Guang and Li Lili

Sedimentary evolutionary processes, architecture, and sedimentary model of a lobate shallow-water delta: insights from flume simulation experiments

You Qi, Youjing Wang, Guosheng Qin, Yanchen Song, Hualing Zhang and Fanle Meng

3-D forward modeling of shale gas fracturing dynamic monitoring using the borehole-to-ground transient electromagnetic method

Weihao Zhang, Xingbing Xie, Lei Zhou, Xinyu Wang and Liangjun Yan

Simulation and optimization of unstable dynamic propagation of multiple fractures in the shale formation

Jue Wang, Genbo Peng, Lei Zhang, Ziyuan Cong and Buqin Hu

Distribution patterns and controlling factors of beach bars in Qinghai Lake, NW China

Biao Peng, Zhengjian Xu, Xiaowan Chen and Yonghua Zhao

Identification of Milankovitch sedimentary cycle in Fengcheng Formation, Mahu depression: a case study of well Maye 1

Ran Wang, Xuan Chen, Qiusheng Chang and Xinmei Zhao

Evaluating and study of natural gas injecting in Shunbei-1 block fault-controlled fractured-cavity type reservoir

Yunfeng He, Yaoyu Liu, Wen Chen, Linxian Chi, Yujun He, Hao Li, Yuxuan Sun and Lixin Wang

Influence of extensional strike-slip fault systems on hydrocarbon accumulation: case studies from Huanghekoudong and Miaoxi’nan sub-sags

Sheng Fu, Deyu Wang, Haifeng Yang, Lijun Song, Ming Ma and Kaiyu Wang

Utilizing logs to identify complex lithology of tight marl reservoir in the Leikoupo Formation 3² Submember (T₂l₃²) of the Sichuan Basin, China

Rui Yuan, Zhiwei Wu, Yongguang Xin, Hao Zhang, Saijun Wu and Siyue Yang

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Wei Wei, Yanlin Shao, Zhonggui Hu, Qing Wang, Fan Deng, Yu Huang and Kunpeng Zhao

Paleoenvironment and shale gas potential of the Carboniferous Dawuba and the Cambrian Niutitang shales in the Upper Yangtze Platform, South China

Ting Wang, Zhengjian Xu, Kun Yuan, Xunlian Wang and Mingna Ge

Experimental and numerical studies on CO₂ injectivity in low permeability oil reservoirs

Fankun Meng, Lin Cao, Yuhui Zhou, Botao Liu, Chengyue Wen and Jia Liu

The differential collapse–filling evolution process of the paleo-underground river in the Northern Uplift of the Tarim Basin

Hong-Qi Dong, Jia-Peng Liang, Qing-Yu Zhang, Jing-Rui Li, Jun-Wei Wan, Guo-Quan Nie and Yu-Lu Li

Coupling noble gas and alkane gas isotopes to constrain normally pressured shale gas expulsion in SE Sichuan Basin, China

Jiaxin Li, Yunhai Wang, Dazhi Fang, Zhigang Wen and Chenjun Wu

ORIGINAL RESEARCH
published: 27 March 2024
doi: 10.3389/feart.2024.1394612

Sedimentary architecture of thin-layer beach bar sand bodies in the G oilfield, Niger

Jianing Wang¹, Cheng Lei², Yanshu Yin¹* and Zhenhua Xu³

¹School of Earth Sciences, Yangtze University, Wuhan, Hubei, China

²Research Institute of Petroleum Exploration and Development, Beijing, China

³School of Earth Science and Technology, China University of Petroleum, Beijing, Beijing, China

Edited by:
Wenguang Wang, Northeast Petroleum University, China

Reviewed by:
Muming Wang, University of Calgary, Canada
Jiagen Hou, China University of Petroleum, Beijing, China
Mei Chen, University of Bremen, Germany

* Correspondence: Yanshu Yin, 2021710328@yangtzeu.edu.cn

Received: 01 March 2024
Accepted: 18 March 2024
Published: 27 March 2024

Citation: Wang J, Lei C, Yin Y and Xu Z (2024) Sedimentary architecture of thin-layer beach bar sand bodies in the G oilfield, Niger. Front. Earth Sci. 12:1394612. doi: 10.3389/feart.2024.1394612

The G oilfield in the southeastern Termit Basin of Niger is characterized by thin-bed beach bar deposits exhibiting strong reservoir heterogeneity and suboptimal production efficiency, necessitating internal structural dissection of the beach bar sand bodies. Employing a well-seismic integration approach, we systematically dissect the architecture of these sand bodies layer by layer to determine their spatial distribution. A classification scheme for beach bar architecture is proposed, with core and log data analysis identifying essential architectural elements and their logging responses. Seismic amplitudes, thin bed delineation, frequency decomposition inversion attributes, and attribute fusion technology delineate the architectural boundaries. Integrating five indicators from four-level architectural recognition at wellbores—shallow lake mudstone appearance, bar margin/beach microfacies occurrence, logging curve morphology differences, beach bar thickness variations, and elevation differences between adjacent bars—enables detailed dissection of the beach bar architecture, corroborated by connectivity analysis. In the study area, beach bar distribution primarily develops in two modes: vertical stacking (accumulation of multiple main bars from different episodes) and isolated (stable mudstone interlayers between main bar sand bodies appearing relatively isolated). This research provides a basis for dissecting beach bar architecture reservoirs under sparse well conditions.

Keywords: beach bar, sedimentary architecture, reservoir characterization, wellseismic integration, termit basin

1 INTRODUCTION

Beach-bar sand bodies are formed by the reworking and redeposition of sandy sediments carried along the periphery of lakes under the influence of hydrodynamic forces such as waves and lake currents (Jiang et al., 2015; Qiu et al., 2022). Comprising two types, beach and bar (Zhu et al., 1994), they represent a significant sedimentary type in lakes. Due to their geological conditions favoring proximity to oil sources, well-developed source-reservoir-cap rock combinations, and conducive conditions for oil and gas accumulation, they can give rise to sizable oil and gas fields (Zhang, 2012). Beach-bar reservoirs have been identified in various locations globally, including the Uinta Basin in the United States, Melut Basin in South Sudan, and the Bohai Bay Basin, Dongying Sag, Junggar Basin, and Tarim Basin in China, resulting in substantial research outcomes (Guan et al., 2021; Hou et al., 2022). Many scholars have conducted in-depth research on beach-bar sand bodies, exploring aspects such as genetic mechanisms (Di Celma et al., 2020), sedimentary environment (Aleman et al., 2015), and patterns (Yin et al., 2021). Significant achievements have been made in the classification of beach-bar sand bodies, the distribution patterns of sand bodies, as well as the geological significance and reservoir characteristics of oil and gas in beach-bar sand bodies.

In recent years, successful advancements have been made in reservoir architecture research, particularly in the detailed dissection and prediction of sand body distribution in continental reservoirs. From fluvial environments (Yue et al., 2008; Hu et al., 2017; Zhou et al., 2017; Wang et al., 2022) to deltaic environments (Feng et al., 2018; Ren et al., 2022), fan-delta environments (Lin et al., 2013; Sun et al., 2017; Shang et al., 2020), and alluvial fan environments (Yi et al., 2010; Zhang et al., 2023), researchers have delved into various settings. In the detailed dissection of beach-bar reservoirs, Shang et al. dissected the architecture of beach-bar sand bodies based on dense well networks, core data, and identified six signs for recognizing individual bars. They elucidated the distribution patterns of three types of individual bars: lateral migrating, vertically superimposed, and isolated. Building on this, the internal architecture of individual bars was dissected, and predictions were made for interbedded distributions within the bars, providing a systematic analysis method for beach-bar architecture dissection. Xia et al., based on drilling and core data, analyzed the relationship between the morphology of beach-bar sand bodies and reservoir characteristics, summarizing three types of beach-bar sand body morphologies: rock type extinction within a single sand body, isolated single sand body, and physical property masking. They clarified that within the bars, the main oil-bearing architecture unit is the beach-bar.

The G oil field in Niger is situated in the central Agadam block of the Termit Basin. Drilling data indicates beach-bar deposition in this block, with thin thickness of beach-bar sand bodies, rapid variations in reservoir lithology, and complex distributions of reservoirs and fluids. Due to sparse well networks, large well spacing, and deep burial of reservoirs, accurately predicting the architecture units of subsurface beach-bar reservoirs solely relying on well data is challenging. How to accurately characterize beach-bar sand bodies has become a significant issue in oil field development. This paper addresses this challenge by employing seismic data fusion methods to determine the macroscopic architecture unit’s planar distribution boundaries of beach-bar sand bodies. Building on this, detailed characterizations of beach-bar sand bodies and their internal structural units were conducted using well logging data. The well-seismic fusion achieved a refined dissection of the structural elements of beach-bar sand bodies. Utilizing connectivity analysis results for validation, a set of thin-layer beach-bar well-seismic fusion dissection technologies was established, laying the foundation for the efficient development of the G oil field in Niger. Additionally, it provides technical references for similar block dissections of beach-bar reservoirs.

2 GEOLOGICAL SETTING

Geographically, the Termit Basin is located in the southeastern region of the Republic of Niger, within the Sahara Desert hinterland (Figure 1). It stands as a prominent oil and gas basin within Niger’s borders. Positioned on the western side of the central Agadem block within the Termit Basin, the G oilfield is a noteworthy hydrocarbon-bearing fault block. It stretches in a northwest-southeast direction, forming an elongated structure with an area of 72 km².

FIGURE 1 | Structural location and stratigraphic column of G Oilfield, Niger.

During the Early Paleogene to Oligocene, the Termit Basin experienced two phases of rifting, with the sedimentation period of the G oilfield occurring during the early phase of rifting (Yuan et al., 2023). In this early rifting phase, the Sokor Formation’s first segment primarily witnessed the development of river delta, nearshore lake, and semi-deep lake to deep lake sediments. The G oilfield, positioned predominantly at the far end of the deltaic margin, belongs to the favorable facies belt of nearshore lake beach bar. The predominant source direction is southwest, characterized by quartz-rich fine to medium sandstone, fine sandstone, siltstone, and shale. The reservoirs mainly consist of intergranular permeability, with the development of both high porosity and high permeability reservoirs, while low porosity and low permeability reservoirs are relatively less common. The lithology displays interbedded sandstone and mudstone, with a maturity level ranging from low to medium. The primary reservoir space type is dominated by primary porosity, followed by intergranular dissolution pores, with sedimentary processes playing a crucial role in reservoir development. The primary oil-bearing interval in the G oilfield is the Sokor 1 segment, which, based on depositional cycles, can be further subdivided into E0, E1, E2, E3, E4, and E5, comprising a total of six stratigraphic units. Among these, E1 to E5 represent five oil-bearing units, while E0 at the top consists of mudstone. E1 and E2 layers constitute the main oil-bearing sequences in the G oilfield, with smaller-scale oil reservoirs distributed in E3 and E0 layers.

3 BEACH BAR SAND BODY ARCHITECTURE DIVISION

3.1 Beach bar architecture interface division

The architecture interface refers to a series of stratigraphic contact surfaces with different hierarchical levels, based on which the strata can be divided into stratigraphic blocks of different levels with causal relationships (Wu et al., 2021). The identification of architecture interfaces is the foundation for reservoir architecture studies (Wang et al., 2019). In the study area, the predominant sedimentary type is beach bar, and beach bar sand bodies undergo frequent migration and oscillation during their development due to source, hydrodynamic changes, and periodic fluctuations in lake levels. Considering the study area’s conditions, definitions of architecture levels, and detailed stratigraphic correlations, we establish the architecture unit division scheme for the G oilfield in Niger (Table 1). The beach bar sedimentation in the G oilfield is classified into composite beach bar sedimentation at a layer scale, single episode beach bar sedimentation at a single layer level, single microfacies within the beach bar, and single intra-bar accretion sand bodies (corresponding to Miall’s architecture system levels 6 to 3) (Andrew, 1985). In this paper, the 6th-level architecture interface is considered the boundary between composite beach bar sediment bodies and surrounding rocks, representing composite beach bar sand bodies. The 5th-level architecture interface represents the boundary of single episode beach bar sediment bodies, representing single episode beach bar sand bodies. The 4th-level architecture interface marks the boundary of a single microfacies within the beach bar, corresponding to the architecture unit of a single microfacies within the beach bar. The 3rd-level architecture interface is the interface of a single bar’s internal accretion body, corresponding to the architecture unit of accretion body within a single bar. Vertically, each composite beach bar consists of multiple single episode beach bars, and each single episode beach bar comprises various sedimentary microfacies. Intervals between single episode beach bars of different periods are filled with lacustrine mud, forming stable interlayers vertically. Proliferations only exist within the bar body, separated by muddy or calcareous interbeds. As the 5th-level architecture interface defines single episode beach bar sand bodies as composite bodies, which makes it challenging to reflect the internal architecture characteristics of the reservoir, the distribution range of the 3rd-level architecture interface is limited to the intra-bar area. Its thickness is thin and constrained by the core data in this study. Guidance on the division of the 3rd-level architecture in the G oilfield can be drawn from previous studies (Shang et al., 2014) on the dissection of beach bar 3rd-level architecture. Within the beach bar single microfacies constrained by the 4th-level architecture interface, the main bar has the best physical properties and is a single-genetic sand body, representing a favorable reservoir for remaining oil. It can be effectively identified and divided based on existing data. Therefore, the focus of this study is to identify and clarify the distribution characteristics of the main bar sand body within the composite beach bar sand body.

TABLE 1 | Boundary classification of reservoir architectural units with different scales in beach bar.

3.2 Types and characteristics of beach bar architecture elements

Upon determining the architecture interface division scheme, the identification of beach bar architecture elements can be carried out through core and well logging data. This involves establishing the electrical response characteristics of architecture elements and interfaces, achieving the recognition and division of individual well architecture elements. This process lays the foundation for the spatial distribution analysis of architecture elements and the analysis of overlay patterns. Through core observations and calibration of well logs, combined with conventional well logging features, the study summarizes the sedimentary microfacies types and characteristics within the research area. In the G Oilfield, beach bar deposition can be classified into bar sand deposition and beach sand deposition, with bar sand deposition further categorized into main bars and bar margins (Table 2).

TABLE 2 | Logging interpretation template for architecture units.

The logging characteristics of the main bar microfacies include a low natural gamma-ray value (GR < 60 API), a negative natural potential anomaly with generally larger amplitudes, higher resistivity (RD > 20 Ω m), and typical box-shaped or funnel-shaped curve features, occasionally exhibiting a bell-shaped curve. The curves are smooth, indicating strong hydraulic dynamics and sufficient sediment supply. The main composition is medium to fine sandstone, displaying typical low-angle crossbedding, occasional thin gravel layers (1–2 cm thick). The thickness of a single main bar body is generally more than 2 m, located in the upper part of the sand body, with good physical properties, primarily characterized by primary pores and a few fractures, typically representing oil reservoirs.

The logging characteristics of the bar margin microfacies include a medium to low natural gamma-ray value (GR > 60 API), a negative natural potential anomaly with a lesser degree than the main bar, and relatively high resistivity (RD ∈ [12, 20] Ω.m). The curve exhibits a higher frequency, low amplitude notched box or bell shape, indicating unstable sediment supply. The predominant composition is fine to silty sandstone, displaying typical ripple bedding, common bioturbation, and insect holes. The thickness of a single bar margin is generally around 1–2 m, located at the top or bottom of the sand body. While the porosity is comparable to the main bar, the permeability is lower, often representing non-productive layers.

The logging characteristics of the beach microfacies include a medium natural gamma-ray value to a low natural gamma-ray value (GR > 60 API), a negative natural potential anomaly, and relatively high resistivity (RD ∈ [8, 15] Ω.m). The curve exhibits minor notches, teeth, or finger shapes. The predominant composition is fine sandstone, with occasional sandy mudstone and muddy sandstone. Vertically, it is characterized by thin interbeds of sandy mudstone. It displays typical ripple bedding, common bioturbation, and insect holes. The thickness of a single beach sand is generally around 0.1 m, located at the top or bottom of the sand body, with comparatively poor physical properties.

The logging characteristics of the nearshore shallow lake mud include a high natural gamma-ray value (GR > 100 API), either no negative or positive natural potential anomaly, and low resistivity (RD < 8 Ω m). The curve exhibits minor notches. It represents pure mudstone or carbonaceous mudstone.

4 BEACH BAR ARCHITECTURE DISSECTION

In the study area, there are a total of 25 wells with uneven spacing and a sparse well network, resulting in limited data availability. Conducting reliable reservoir dissection solely based on well logging data is challenging. The seismic main frequency in the target block is 30 Hz, with limited vertical resolution. However, there is dense lateral sampling, effectively compensating for the shortcomings of well logging in predicting the planar distribution, extension, orientation, and position of beach-bar sand bodies between wells. Additionally, the seismic effective bandwidth is 0–60 Hz, making it suitable for identifying the spatial distribution of beach-bar sand bodies through frequency division. This has led to a specific approach for dissecting beach-bar sand bodies in the study area: (1) Utilizing seismic inversion to enhance seismic resolution, simultaneously selecting diverse seismic attributes, and generating seismic attribute slices. By merging these slices through the fusion of seismic attributes, predicting the planar distribution of unconditioned architecture units. Integrating well logging data to ensure alignment between architecture predictions and well conditions, forecasting intricately layered composite bar sands at a layer level (sixth-level architecture), and delineating the planar characteristics of fifth level and fourth level architecture. (2) Building upon the study of architecture unit distribution, integrating seismic results and well logging data. Employing methods such as vertical staging and lateral demarcation to dissect individual sand bodies in the study area. Predicting the distribution of architecture units within the reservoir.

4.1 Seismic attribute fusion for identifying beach bar boundaries

The seismic attribute analysis encounters challenges in accurately capturing the comprehensive characteristics of reservoirs (Yue et al., 2022). To overcome these limitations, a method of seismic attribute fusion is employed by selectively choosing one or several attributes most sensitive to the target reservoir. Through mathematical operations, various seismic attributes are fused together to reflect geological features comprehensively and reliably, leveraging the physical significance of different attributes (Li et al., 2015). For the studied block, seismic attribute fusion is utilized to predict the planar distribution of architecture units and enhance vertical resolution. Before the fusion process, seismic attributes are carefully selected. In this study, based on the characteristics of seismic data in the research area, three seismic attributes—amplitude, thin layer identification, and frequency division inversion (Xie et al., 2023)—are preferred. Amplitude is sensitive to thick and laterally continuous sand bodies in the vertical direction but less effective in identifying thin-layered sand bodies. Thin layer identification is based on the oscillation of instantaneous frequency to capture local information, reflecting changes in adjacent reflection phase axes. While effective in identifying thin-layered sand bodies, it performs poorly in recognizing superimposed thick-layered sand bodies. Seismic frequency division inversion leverages different frequency information in seismic data, providing better insights into sand bodies at different scales. Analysis of single-frequency band data in the research area reveals that seismic attributes at 10 Hz reflect the distribution of relatively thick superimposed sand bodies. Attributes at 30 Hz can to some extent simultaneously reflect the distribution of both thick and thin-layered sand bodies, while attributes at 60 Hz primarily depict the distribution characteristics of thin-layered sand bodies (Figure 2). In this study, the three central frequency bands of 10 Hz (Figure 2A), 30 Hz (Figure 2B), and 60 Hz (Figure 2C) are employed for wavelet frequency division of the original seismic data, ensuring the completeness of seismic information while enhancing resolution. However, due to the influence of the terrain and seismic data in the research area, there is a certain impact on predicting sand bodies in areas without wells, and inversion results may contain errors. Considering the strengths and weaknesses of these three attributes, they are fused using the RGB fusion method, aiming to capitalize on their respective advantages and compensate for their limitations. This approach aims to reflect the distribution characteristics of different architecture units.

FIGURE 2 | GR log of G3 well correlated with seismic attributes at 10 Hz (A), 30 Hz (B) and 60 Hz (C).

The seismic fusion slices consist of four colors: red, green, blue, and black. The red, blue, and green regions respectively reflect high attribute values of seismic amplitude (>8,000, thick sand bodies), thin layer detection (>300, thin sand bodies), and frequency division inversion (<85, thick-thin sand bodies). The black region reflects the common low values of the three attributes. The fusion results of these three attributes can effectively capture the macroscopic distribution characteristics of beach-bar sand bodies, while the black region primarily represents the distribution of shallow lake mud (Figure 3). Taking the E2-9 layer as an example, the development of beach-bar sand bodies in this layer is relatively high, and shallow lake mud is mainly distributed in the western and northern regions of the study area, both reflected in the RGB fusion slice. In the fusion slice, the overlap of red and green areas mainly reflects the deposition of bar sand. For instance, the development of four lens-shaped bar sand deposits near the northern part of the E2-9 layer is well represented in the RGB fusion slice. The boundaries of bar sand are clearly delineated, and the limits between the four adjacent bar sands are distinct. The beach sand, as a thin layer around the bar sand and a more continuous sand body, is primarily reflected by the overlap of blue and green areas in the RGB fusion slice, providing a good representation on the plane. Based on seismic slices and in conjunction with the well-based interpretation of architecture units and the development model of beach-bar patterns (Han et al., 2016), the overlaid planar distribution of beach-bar sand bodies for each layer is predicted.

FIGURE 3 | Seismic RGB fusion slice (A) and architecture plan (B) of the E2-9 layer.

4.2 Well logging-based architecture distribution determination

Drawing from the sedimentary evolution of bars and the superposition relationships of sand bodies, and utilizing seismic attribute fusion in conjunction with well logging data, we applied the ‘vertical staging, lateral delineation’ method to articulate five key identification indicators for architecture units, as illustrated in Figure 4.

(1) Appearance of shallow lake mudstone: Based on the formation model of bars, the deposition of a bar leads to a local topographic uplift, and a trough appears behind the bar where low-energy conditions prevail. Fine-grained mud sediments filled in this trough form inter-bar mudstones. The appearance of shallow lake mudstone indicates the outer side of the bar and is an important sign for determining the boundary of composite bars. For example, in Figure 4A, between the bar bodies of well G8 and well G10, there is inter-bar mud.

(2) Appearance of bar margin/beach microfacies: Based on the formation model of bars, waves undergo a series of changes when propagating to the gentle slope zone due to topographical influences. Sand bars form at the breaker lines and shorelines where sediment is concentrated, while beaches mainly occur in the wave shoal zone, wave rebound zone, and surf backflow zone. Bar margin/beach microfacies can be used to identify the edges of bars and are characterized by a belt-shaped distribution around the main bar. According to the pattern in this study, the beach is the outer edge of the bar, and the bar margin is the inner edge of the bar, both of which can serve as indicators for determining lateral boundaries. Figure 4B shows changes in sedimentary facies between wells G2, G16, and G1.

(3) Differences in well logging curve morphology: In different parts of a single bar, the sedimentary environment and sedimentary cycles have some similarities, and there are also similarities in well logging curve morphology. If there is a significant difference in the morphology of well logging curves between adjacent wells, it can be determined that they belong to different bar depositions, thus delineating the lateral boundaries of bars. In Figure 4C, wells G8 and G9 are adjacent, and bars develop in the same formation. However, the well logging curves of the two wells in the same formation show significant differences in morphology, indicating two separate bars.

(4) Thickness differences in the development of bars: In different parts of bars, the sedimentary thickness should gradually decrease uniformly from the main bar towards the periphery, transitioning gradually from the main bar to the bar margin, beach, and shallow lake mud. There should be no sudden thickening or thinning. At the same time, the most obvious difference between the main bar, bar margin, and beach is also reflected in the thickness of the sand bodies. In Figure 4D, two bars of the same layer show a sudden change in thickness, indicating two separate bars.

(5) Elevation differences between adjacent bar bodies: In a fine-scale comparison framework, the top and bottom surfaces of finely subdivided layers represent isochronous surfaces, and in high-energy environments such as wave and lake flow action, the strata quickly “fill and level.” Isochronous surfaces are almost consistent with isobaths. If there is a significant difference in elevation between the bottom surfaces of two adjacent bar bodies, reflecting their inconsistent sedimentation times, it is highly likely that they do not belong to the same sand bar. Therefore, when there is a significant elevation difference in the sand body bottom between two neighboring wells, it can be used as an indicator to judge that the sand bodies belong to different periods (Figure 4D).

FIGURE 4 | Identification criteria for single main bar in G oilfield. (A) Appearance of shallow lake mudstone; (B) Appearance of bar margin/beach microfacies; (C) Differences in well logging curve morphology; (D) Thickness differences in the development of bars; Elevation differences between adjacent bar bodies.

5 BEACH BAR RESERVOIR ARCHITECTURE DISTRIBUTION CHARACTERISTICS

5.1 Sedimentary patterns of beach bar

Using the above-mentioned methods, an analysis was conducted on the E2-9 beach-bar reservoir (Figure 5). E2-9 can be divided into two episodes: E2-9-1 and E2-9-2. Along the AA’ profile and combining the planar development characteristics of the beach-bar, a consensus was reached, identifying 4 beach-bar episodes, 10 individual bars, and 6 main bars. E2-9-1 represents the first episode of the E2-9 layer, developing 2 single-episode beach-bars, 7 individual bars, and 3 main bars. The main bars are concentrated near wells G3, G14, G17, and G4. The internal beach-bars in this single layer exhibit a banded distribution with a northwest-southeast orientation. Along the AA’ profile, 2 beach-bar episodes and 2 individual bars are developed, with no main bars. Stable nearshore mud is deposited between the two beach-bar episodes, and the boundary between the two episodes lies between wells G15 and G1.

FIGURE 5 | Results of the dissection for the E2-9 layer.

The single layer E2-9-2 represents the second episode of the E2-9 layer, developing 2 single-episode beach-bars, 3 individual bars, and 4 main bars. Among the 3 individual bars, two of them each develop a main bar, while the other individual bar hosts the development of two main bars. Internally, the beach-bars in this single layer exhibit a banded and lens-shaped distribution with a northwest-southeast orientation. Along the AA’ profile, 2 beach-bar episodes, 2 individual bars, and 3 main bars are developed. Notably, the main bars developed in well G9 and well G10 each occur on different individual bars, while the main bars in G10 and G11 co-develop on a single individual bar.

5.2 Connectivity analysis

To validate the sand body architecture results of the E2-9-2 single layer, considering the connectivity analysis results (Figure 6), G6 well, an oil production well within the G oilfield, is adjacent to other oil production wells, including G4, G10, G11, G14, and G17. Based on this, an architecture grid map for the E2-9 and E2-10 layers was established (Figure 6). Inter-well water absorption tests were conducted on G6 well, and the results showed that the main water-absorbing layers in G6 well were the E2-9-2 single layer and E2-10-1 single layer, with water absorption intensities of 77.12% and 11.66%, respectively. The water absorption intensity of other layers was 0. According to the architecture anatomy results, G6 well is connected through the main bar sand body with G4, G17, and G11 wells in the E2-9-2 single layer, and it is connected to G10 well through the bar margin sand body. The main bar is the best reservoir microfacies type within the beach bar, indicating good sand body connectivity and high water absorption intensity between these wells, consistent with the architecture anatomy results.

FIGURE 6 | Well distribution map of E2-9-2 and grid pattern of E2-9 and E2-10 well group.

At the same time, the relationship between the daily oil production of individual wells and the maximum permeability of the perforation interval, single sand body thickness, and overlapping sand body thickness was statistically analyzed (Figure 7). The results show a strong positive correlation between daily oil production, maximum permeability of the perforation interval (Figure 7A), and single sand body thickness (Figure 7B). There is no significant correlation with the thickness of overlapping sand bodies (Figure 7C). This further supports the conclusion that the main bar is the primary oil-bearing architecture unit.

FIGURE 7 | Daily oil production correlation diagram: max permeability (A), single sand body thickness (B), and overlapping sand bodies thickness (C) in perforation interval.

5.3 Quantitative parameter analysis of beach bar architecture elements

Building on the detailed subdivision of the main bars within the G oilfield, the dimensions of the sand bars (including main bars and bar margins) were statistically analyzed (Figure 8). The length of the sand bars mostly ranged from 1,000 to 3,000 m, with an average of 2,000 m; the width varied from 500 to 1,000 m, with an average of 700 m. The length-to-width ratio was mostly between 1 and 4, averaging 2.5. There is a certain linear positive correlation between the length and width of the sand bars, while there is no apparent correlation between width and thickness.

FIGURE 8 | Length-width relationship of sand bars (A) and main bars (B) in G Oilfield.

The main bars were relatively smaller, with lengths mostly ranging from 500 to 1,000 m, averaging about 700 m; widths ranging from 200 to 400 m, averaging about 300 m; and thickness exceeding 2 m, averaging about 4 m. The length-to-width ratio of the main bars was mostly between 1 and 3, averaging 2. There is a certain linear positive correlation between the length and width of the main bars, while there is no apparent correlation between width and thickness.

6 BEACH BAR ARCHITECTURE ELEMENT DISTRIBUTION PATTERNS

By characterizing the sand body features of the reservoir architecture in G Oilfield on both the plan view and the profile, and referring to previous studies (Xia et al., 2019) on the contact styles of single sand bodies in sand bars, the spatial distribution patterns of the main sand bars in G Oilfield were summarized into two types: vertical stacking type and isolated type (Figure 9).

(1) Vertical Stacking Type: This involves the vertical accumulation of multiple single main bars from different periods. Most of these main bars are well developed, with significant thickness. They exhibit continuous vertical stacking. This type is mainly distributed in the E1-1 to E1-4 oil groups and E2-4 to E2-8 oil groups. Based on previous research, the main development periods of the sand bar system in a rift basin are during the early rifting and fault transition periods. During these periods, tectonic movement is relatively stable, and the impact of faulting is weaker. The paleogeomorphology is relatively gentle, favoring the formation of sand bar bodies (Lin et al., 2010). Additionally, influenced by the base-level cycle, when the base level is lower, the water body is shallower, promoting the development of sand bars. G Oilfield is mainly developed in the Sokor1 group, which is the initial faulting period, providing a good foundation for the development of sand bar reservoirs in G Oilfield.

(2) Isolated Type: This involves the development of stable mudstone interlayers between main sand bars, presenting a relatively isolated spatial pattern. Laterally and vertically, it usually exhibits poor connectivity in the vertical direction due to episode changes causing poor vertical connectivity between multiple periods of sand bars. This type is mainly influenced by the ancient water depth. Due to deeper water bodies and higher base levels, most of the source materials are below the wave base and are less affected by lake waves, thereby limiting the development of sand bar bodies. Typically, this type exhibits smaller and scattered distributions of main sand bars.

FIGURE 9 | Distribution model of beach bar sand bodies in G oilfield.

7 DISCUSSION

This study employed a well-seismic integrated dissection method specifically tailored for thin-layer beach-bar sand bodies, resulting in a detailed characterization and the development of a thin-layer beach-bar well-seismic fusion dissection technique. In previous studies (Shang et al., 2014) on the distribution of beach-bar sand bodies, researchers primarily relied on the combination of abundant well data and core information to depict the spatial distribution. However, in cases with a limited number of wells, there has been a relative lack of corresponding research support. The innovation of this paper lies in the establishment of an architecture dissection method suitable for sparse well networks and deeper burial conditions of beach-bar sand bodies. This method excels in predicting the spatial distribution of thin-layer beach-bar sand bodies with limited well data. It particularly focuses on the detailed characterization of the main bar sand bodies, highlighting their superior physical properties within the beach-bar sand bodies. This innovation provides a new perspective and feasibility for addressing the distribution of beach-bar sand bodies under similar geological conditions, holding significant implications for the efficient development of oil fields. However, it should be noted that the method has certain requirements for the quality of seismic data, and in practical applications, attention must be paid to the quality of seismic data to ensure the credibility of the architecture dissection results.

Simultaneously, in delineating the boundaries of composite beach bars and single bars, the seismic RGB fusion was employed in this study. During this process, amplitude, thin-layer identification, and frequency division inversion were selected as the seismic attributes, based on various factors such as the characteristics of the study area and seismic data. It is crucial to emphasize that these three attributes are not the sole options, as their selection depends on the differences in the study area and the diversity of seismic data. Therefore, future applications of this method in similar areas should carefully consider the characteristics of the specific study area and the quality of seismic data for optimizing seismic attributes, aiming to achieve more effective boundary recognition results in accordance with scientific paper standards.

8 CONCLUSION

This paper presents a scheme for the sedimentary architecture division of beach-bar sand bodies in the G oil field. The 6th-level architecture unit represents composite beach-bar deposition, the 5th-level unit signifies a single-episode beach-bar deposition, and the 4th-level unit indicates the individual microfacies within the beach-bar, including the main bar, bar margin, and beach sand bodies. The 3rd-level unit corresponds to the intra-main bar accretionary bodies. An interpretative template for the elements of beach-bar architecture has been established, forming a thin-layer beach-bar sand body architecture dissection technology using well-seismic fusion. The well-seismic fusion method was employed to identify beach-bar sand bodies, integrating amplitude, thin layer identification bodies, and frequency decomposition inversion attributes to identify the spatial distribution boundaries of individual beach-bars. Five identification indicators for the architecture units were summarized based on well logging data, determining the spatial distribution of the main bar, bar margin, and beach sand body architecture. The accuracy of the dissection was validated using connectivity analysis results. Quantitative analysis of the beach-bar architecture elements in the G oil field revealed that the length of the bar sands ranges mostly from 1,000 to 3,000 m, averaging around 2000 m. The width typically falls between 500 and 1,000 m, with an average of 700 m. The length-to-width ratio varies from 1 to 4, averaging at 2.5. The main bars tend to be smaller, with lengths ranging from 500 to 1,000 m and averaging around 700 m. Width falls mostly between 200 and 400 m, averaging around 300 m, and a thickness greater than 2 m, averaging about 4 m. The length-to-width ratio varies from 1 to 3, averaging at 2. Finally, the spatial distribution patterns of beach-bar architecture elements were summarized, indicating two main types: vertical superposition and isolated. During the initial faulting period, beach-bar sand bodies widely developed, influenced by the rotation of base level. When the base level is low, the primary development is vertical superposition, while at a higher base level, the main development is isolated.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

AUTHOR CONTRIBUTIONS

JW: Methodology, Validation, Writing–original draft, Writing–review and editing. CL: Data curation, Writing–review and editing. YY: Data curation, Project administration, Validation, Writing–review and editing. ZX: Data curation, Formal Analysis, Methodology, Project administration, Supervision, Validation, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by National Natural Science Foundation of China (No. 42372137).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Aleman, N., Robin, N., Certain, R., Anthony, E. J., and Barusseau, J.-P. (2015). Longshore variability of beach states and bar types in a microtidal, storm-influenced, low-energy environment. Geomorphology 241, 175–191. doi:10.1016/j.geomorph.2015.03.029

Andrew, D. M. (1985). Architectural-element analysis: a new method of facies analysis applied to fluvial deposits. J. Earth-Science Rev. 22, 261–308. doi:10.1016/0012-8252(85)90001-7

Di Celma, C., Pitts, A., Jablonska, D., and Haynes, J. T. (2020). Backset lamination produced by supercritical backwash flows at the beachface-shoreface transition of a storm-dominated gravelly beach (middle Pleistocene, central Italy). Mar. Petroleum Geol. 112, 103987–104011. doi:10.1016/j.marpetgeo.2019.08.015

Feng, W. J., Lu, F. M., Wu, S. H., et al. (2018). Reservoir architecture analysis of braided delta front developed in the long-axis gentle slope of faulted basin: a case study of the fifth Zaoyuan formation, Zaonan fault block, Zaoyuan Oilfield, Dagang. J. J. China Univ. Min. Technol. 47 (2), 367–379. doi:10.13247/j.cnki.jcumt.000746

Guan, X., Pan, S. X., Qu, Y. Q., Xu, D. N., Zhang, H., Ma, Y. P., et al. (2021). Discovery and hydrocarbon exploration potential of beach-bar sand in Shawan Sag, Junggar Basin. J. Lithol. Reserv. 33 (1), 90–98. doi:10.12108/yxyqc.20210109

Han, Y. H., Hao, L. W., Wang, Qi., et al. (2016). Sedimentary characteristics and types of beach bars deposited in modern times in nearshore of Qinghai Lake. J. JOPC. 18 (5), 759–768. doi:10.7605/gdlxb.2016.05.057

Hou, Q. J., Liu, X. T., Han, H. W., Liu, H. J., Wei, G. H., Chen, Y. M., et al. (2022). Fractal characteristics, prediction and application for pores in a tight beach-bar sand reservoir: a case study for dongying Sag. J. Acta Sedimentol. Sin. 40 (5), 1439–1450. doi:10.14027/j.issn.1000-0550.2021.060

Hu, G. Y., Fan, T. E., Chen, F., Jing, Y. Q., Wang, H., and Song, L. M. (2017). From reservoir architecture to seismic architecture facies: characteristic method of a high-resolution fluvial facies model. J. Acta Geol. Sin. 91 (2), 465–478. doi:10.3969/j.issn.0001-5717.2017.02.012

Jiang, Z. X., Wang, J. h., and Zhang, Y. F. (2015). Advances in beach-bar research: a review. J. J. Of Palaeogeogr. 17 (4), 427–440. doi:10.7605/gdlxb.2015.04.036

Li, T. T., Wang, Z., Ma, S. Z., Wang, Z., and Yuan, Z. L. (2015). Summary of seismic attributes fusion method. J. Prog. Geophys. 30 (1), 378–385. doi:10.6038/pg20150155

Lin, H. X., Deng, H. W., Qin, Y. Q., Gao, X. P., Long, G. Q., Zhao, N., et al. (2010). Control of sequence stratigraphic evolution on the distribution and hydrocarbon accumulation of beach and bar reservoirs. Petroleum Explor. Dev. 37 (6), 1882–1890.

Lin, Yu., Wu, S. H., Yue, D. L., Yan, J. S., Li, B., and Wang, L. Q. (2013). Fine anatomizing reservoir architecture of fan-delta front:A case study on dujiatai reservoir in shu2-6-6 Block,Liaohe oilfield. J. Nat. Gas. Geosci. 24 (2), 335–344.

Qiu, L. W., Li, X., Yang, Y. Q., et al. (2022). Research progresses of the genetic mechanism and architecture characterization on lake bars. J. J. Of Palaeogeogr. 24 (2), 191–209. doi:10.7605/gdlxb.2022.02.015

Ren, Y. L., Zhao, J. F., Chen, J. Y., Guan, X., and Song, J. G. (2022). Sedimentary characteristics and sand body architecture of shallow delta front in ordos basin: a case study of chang 9 member in shiwanghe section in yichuan. J. Xinjiang Pet. Geol. 43 (3), 310–319. doi:10.7657/XJPG20220307

Shang, J. X., Zhang, H., Ye, Q., Li, H., Wang, M. W., and Ruan, H. J. (2020). Architecture analysis of fan delta thick reservoir and its application in potential tapping of remaining oil at high water cut stage: taking the Wenchang B oilfield in Pearl River Mouth Basin as an example. J. China Offshore Oil Gas 32 (02), 96–105. doi:10.11935/j.issn.1673-1506.2020.02.011

Shang, X. F., Hou, J. G., Sun, F. T., Tang, L., Liu, Y. M., Li, Y. Q., et al. (2014). Architectural characteristics and sedimentary models of beach-bar sandstone reservoirs: a case study of the Paleogene Shahejie Formation in Banqiao oilfield, Huanghua depression. [J]. ACTA PET. SIN. 35 (6), 1160–1171. doi:10.7623/syxb201406014

Sun, L., Wang, Z. Z., Yu, X. H., Ma H, Y., and Cao, Q. (2017). Study on reservoir architecture of fan delta in the upper karamay formation of eastern block Wu2, karamay oilfield. J. Petroleum Geol. Recovery Effic. 24 (4), 8–15. doi:10.13673/j.cnki.cn37-1359/te.2017.04.002

Wang, J., Li, X. B., Liu, H. Q., Zhang, Z. J., Wanyan, R., Wang, H. B., et al. (2019). Study of the development and preservation of lacustrine beach and bar based on the modern sedimentary characteristics of qinghai lake. J. Acta Sedimentol. Sin. 37 (5), 1016–1030. doi:10.14027/j.issn.1000-0550.2019.038

Wang, X., Yu, S., Li, S., and Zhang, N. (2022). Two parameter optimization methods of multi-point geostatistics. J. Petroleum Sci. Eng. 208, 109724. doi:10.1016/j.petrol.2021.109724

Wu, S. H., Yue, D. L., Feng, W. J., et al. (2021). Research progress of depositional architecture of clastic systems. J. JOPC 23 (2), 245–262. doi:10.7605/gdlxb.2021.02.018

Xia, X. M., Wu, Y. X., Zhang, S. Q., Xue, J. Q., Wang, L., Shi, Q., et al. (2019). Study on sand body architecture of lacustrine beach-bar and significance for development of tight oil sweet areas: case study of Well Za-2 area in Zahaquan area, Qaidam Basin. J. Nat. Gas. Geosci. 30 (8), 1158–1167. doi:10.11764/j.issn.1672-1926.2019.07.006

Xie, P. F., Hou, J. G., Duan, D. P., Yao, Y. R., Yang, W. Z., Liu, Y. M., et al. (2023). A novel genetic inversion workflow based on spectral decomposition and convolutional neural networks for sand prediction in Xihu Sag of East China Sea. Geoenergy Sci. Eng. 231, 212331. doi:10.1016/j.geoen.2023.212331

Yi, Z. L., Wu, S. H., Du, Q. L., Zhang, B. G., Yue, D. L., and Li, Y. P. (2010). An accurate anatomizing method for structure of reservoir of alluvial fan: a case study on lower karamay formation, liuzhong area, karamay oilfield. J. J4. 40 (4), 939–946. doi:10.3969/j.issn.1671-5888.2010.04.024

Yin, S., Zhu, B., Wu, Y., and Xu, F. (2021). Lithofacies architecture and distribution patterns of lacustrine mixed fine-grained rocks —a case study of permian lucaogou formation in jimsar Sag, NW China. Front. Earth Sci. 9, 782208. doi:10.3389/feart.2021.782208

Yuan, S. Q., Dou, L. R., Cheng, D. S., Mao, F. j., Pan, C. F., Zheng, F. Y., et al. (2023). New understanding and exploration direction of hydrocarbon accumulation in Termit Basin, Niger. J. Petroleum Explor. Dev. 50 (2), 268–280. doi:10.1016/s1876-3804(23)60386-6

Yue, D. l., Li, W., Du, Y. S., Hu, G. Y., Wang, W. F., Wang, W. R., et al. (2022). Review on optimization and fusion of seismic attributes for fluvial reservoir characterization. Earth Sci. 47 (11), 3929–3943. doi:10.3799/dqkx.2022.221

Yue, D. L., Wu, S. H., Tan, Q. H., et al. (2008). An anatomy of paleochannel reservoir architecture of meandering river reservoir——a case study of Guantao formation,the west 7th block of Gudong oidfield. J. Earth Sci. Front. 15 (1), 101–109. doi:10.3799/dqkx.2022.221

Zhang, S. W. (2012). Thinking and practice of Tertiary oil and gas exploration of maturing region in eastern China: a case study of Jiyang depression. J. ACTA Pet. Sin. 33 (S1), 53–62. doi:10.7623/syxb2012S1007

Zhang, Y., Li, J., Song, B. H., Huang, J. F., and Wu, X. W. (2023). Progress and development direction of researches on remaining oil in alluvial fan reservoir. J. J. Southwest Petroleum Univ. Sci. Technol. Ed. 45 (6), 1–17. doi:10.11885/j.issn.1674-5086.2022.01.02.01

Zhou, Y. B., Zhao, S. X., Zhang, Q. X., and Zhong, X. X. (2017). Progress of the outcrop analogy method application in the modeling of underground reservoirs. J. Bull. Geol. Sci. Technol. 36 (1), 247–254.

Zhu, X. M., Xin, Q. L., and Zhang, J. R. (1994). Sedimentary characteristics and models of the beach-bar reservoirs in faulted down lacustrine basins. J. Acta Sedimentol. Sin. 12 (2), 20–28. doi:10.14027/j.cnki,cjxb.1994.02.003

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer JH declared a shared affiliation with the author ZX to the handling editor at time of review.

Copyright © 2024 Wang, Lei, Yin and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 28 March 2024
doi: 10.3389/feart.2024.1371864

Determining complex petroleum migration pathways along faults and sandbodies: a new method and application

Shi Jijian1,²*, Fu Guang1,² and Li Lili³

¹School of Earth Sciences, Northeast Petroleum University, Daqing, Heilongjiang, China

²Key Laboratory of Natural Gas Sealing Mechanism, CNPC Key Laboratory of Gas Reservoir Formation and Development, Daqing, Heilongjiang, China

³Exploration and Development Institute of Daqing Oilfield, China National Petroleum Corporation, Daqing, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Xiaoming Zhao, Southwest Petroleum University, China
Fangwen Chen, China University of Petroleum (East China), China
Xia Guoqing, Chengdu University of Technology, China

* Correspondence: Shi Jijian, shijijian@163.com

Received: 17 January 2024
Accepted: 04 March 2024
Published: 28 March 2024

Citation: Jijian S, Guang F and Lili L (2024) Determining complex petroleum migration pathways along faults and sandbodies: a new method and application. Front. Earth Sci. 12:1371864. doi: 10.3389/feart.2024.1371864

Introduction: The reservoir-forming rule in the paleo-slope zone outside petroliferous basins’ source kitchens of the eastern basin in China is unclear, manifested in the distribution of oil and gas in complex multi-layers. This study focuses on petroleum migration along faults and sandbodies to clarify this question and improve the exploration efficiency of petroleum in areas with similar geological conditions.

Method: We summarized two models of petroleum migration based on two factors: the connected sandbodies and the vertical unsealing faults. Model I is conducive to the migration of hydrocarbons within a single layer, while Model II is conducive to the vertical migration of hydrocarbons across layers. Combining the means for evaluating fault sealing and sandbody connectivity, integrating these models could form a set of characterization methods for the distribution of the complex fluid migration pathways.

Results: This method is applied to characterize the complex multilayer migration pathways distribution in the first and second members of the Shahejie Formation in the central-southern area of the Wen’an Slope in the Baxian Sag, Jizhong Depression, Bohai Bay Basin, China. The results show that, in the first and second members of the Shahejie Formation in the central-southern area of the Wen’an Slope, the area for Model II is mainly distributed in the central-eastern regions. The second member has a Model I area distributed in its western region, and the first member has a Model I area distributed in its centraleastern parts. The latter distribution area is larger than the former. According to hydrocarbon migration analysis, the first member is more conducive to large regional migration and accumulation of hydrocarbons than the second member.

Discussion: The predicted result of petroleum migration is consistent with the hydrocarbon distribution found in the central-southern areas of the Wen’an Slope, which is that oil and gas distribution in the first member is significantly greater than that in the second member. Thus, our method is feasible for characterizing the regional distribution of complex multi-layer migration pathways in simple tectonic setting and oil source conditions. However, further research is still needed to increase the effectiveness of present petroleum migration analysis.

Keywords: sandbody, fault, caprock, migration pathways, hydrocarbon migration, paleo-slope zone

1 INTRODUCTION

The slope zone is one of the structural units of a basin. It is a gentle monocline formed during the development of the basin and is usually distributed at the edges, outside of the deep source rock kitchens of the basin. According to the theory of oil and gas accumulation, the geological structure background during the large-scale generation and migration of oil and gas has a controlling effect on the migration characteristics and accumulation locations of oil and gas. In the paleo-slope zone, the accumulated oil and gas all come from the kitchens in the center of the basin, and the oil and gas have undergone remarkable lateral and vertical migration. The relationship among sandbodies, faults, and the caprock can provide conduits for the migration of oil and gas within a single layer and between layers, resulting in a significantly more complex oil and gas accumulation distribution (Wang, 2007; Berndt et al., 2012; Foschi et al., 2014; Liu et al., 2017; Bertoni et al., 2018; Sun et al., 2021; Zhu et al., 2021; Wan et al., 2022).

Complex multi-layer migration pathways mean a three-dimensional oil and gas migration network composed of sandbodies, faults, and other factors (Wang, 2007; Berndt et al., 2012). It is important to evaluate these factors when studying the characteristics of oil and gas migration within the paleo-slope zone outside of source rock kitchens: whether the faults that break through the caprock are vertically sealed, whether the faults are laterally sealed, and whether the formation sandbodies are connected (Allen, 1978; Aydin, 2000; Ligtenberg, 2005; Fu and Zhan, 2017; Zhu et al., 2021).

Previous scholars have conducted in-depth research evaluating fault vertical and lateral sealing capacity (Allan, 1989; Knipe, 1992; Lindsay et al., 1993; Yielding et al., 2010; Fu and Zhan, 2017; Nicol and Childs, 2018). Four types of fault sealing in siliciclastic sequences have already been recognized. These are juxtaposition sealing, shale-smear sealing, fault-rock sealing, and diagenetic sealing (Yielding et al., 2010).

Juxtaposition seals can be produced by a permeability gradient between the different rocks juxtaposed across the fault plane when the hanging wall of a normal fault moves downward. Allan (1989), Knipe (1992), and Lindsay et al. (1993) identified reservoir juxtaposition areas across a fault surface to qualitatively evaluate the sealing ability of a fault by stratigraphic juxtaposition diagrams called an Allan Map (Allan, 1989) and a Knipe Diagram (Knipe, 1992; Knipe, 1997).

Clay smear (i.e., entrainment of clay or shale) into the fault zone gives the fault itself a high entry pressure to form an extremely effective fluid flow barrier. Several algorithms have been developed to predict fault seal capacity using these geological properties, including the shale-smearing factor (SSF) (Lindsay et al., 1993), the clay-smear potential (CSP) (Bouvier et al., 1989; Fulljames et al., 1997; Nicol and Childs, 2018), and caprock juxtaposition thickness (CJT) (Fu and Zhan, 2017).

Fault-rock sealing is a type of fault sealing that relies on fault rock having a lower permeability than the reservoir rock. Fault rocks are composed of different types of host rocks entrained into the fault zone (Schultz and Fossen, 2008). The shale-gouge ratio (SGR) (Yielding et al., 1997; Yielding, 2002; Yielding, 2009; Choi et al., 2015; Bretan, 2017) is a useful algorithm to evaluate fault-sealing ability by predicting the average proportion of fine-grained material entrained into the fault gouge from the host rocks. Another algorithm has been developed by evaluating the fault-rock-reservoir displacement pressure difference (DPD) (Bretan, 2017; Shi, 2019).

When the porosity of fault rocks was partially or completely removed by preferential cementation along a permeable fault plane, a diagenesis seal was ultimately created. Cemented faults or fractures are a common result of deformation (Fisher and Knipe, 1998; Fisher and Knipe, 2001; Fisher et al., 2009). However, due to the complex source timing and mechanisms of cementation, there is no effective algorithm for evaluating the effectiveness of the cementation process on fault sealing.

The above algorithms have successfully evaluated the vertical and lateral sealing of faults from different perspectives. Geologists can apply suitable methods based on their own work conditions.

Success has also been achieved via research into sandbody connectivity. The overlapping and connecting of sheet-like, wedge-shaped (fan-shaped), banded, and complex branching sandbodies are the basis for the long-distance migration of oil and gas along a single formation. Allen (1978) put forward a quantitative model to determine the connectivity of sandbodies in the 1970s. Additional methods to determine the connectivity of sandbodies include seismic, dynamic, and statistical analysis and reservoir geochemical methods (Kaufman et al., 1987; King, 1990; Johnson et al., 1996; Jackson et al., 2005). Determining the connectivity of sandbodies according to their density threshold is a simple and widely used method. The density threshold is usually represented by the sandstone-formation ratio. The sandstone-formation ratio is a ratio of the sum thickness of sandstone to the total thickness of a stratum, which is a parameter that characterizes the relative quantity of sandbodies within a set of strata and can indirectly reflect the connectivity conditions of individual sandbodies deposited in river and lake sedimentary environments (Jackson et al., 2005).

The above-discussed research methods have played a vital role in correctly understanding oil and gas migration and accumulation behavior in sandbodies and faults. According to the combined characteristics of the fault’s vertical and lateral seals and sandbody connectivity, oil and gas migration can be divided into two types: single-layer and cross-layer migration. However, relatively few studies have accurately characterized the distribution of oil and gas cross-layer migration pathways. The main research focus has been on the required conditions for oil and gas cross-layer migration. Thus, there is a gap in the literature regarding the prediction of multi-layer oil and gas migration pathways. Predicting these pathways would be conducive to accurately reflecting the oil and gas distribution pattern in the slope area outside the deep source rock kitchen and guiding oil and natural gas exploration. Therefore, it is of great significance to investigate the characterization of multi-layer complex migration pathways through faults and sandbodies to correctly understand the oil and gas accumulation distribution patterns in the paleo-slope zone of a petroliferous basin to guide oil and gas exploration.

This paper takes the first and second members of the Shahejie Formation in the central-southern area of the Wen’an Slope in the Baxian Sag as an example. The geological situation of the study area is introduced in Section 2. The migration models and integrated method for the distribution of the complex fluid migration pathways on the basin paleo-slope zone outside petroliferous basins’ source kitchens are presented in Section 3. In Section 4, the distribution area of the connected sandbodies located above and below the regional caprock and the vertical unsealed area of the conducting faults that break through the regional caprock linking the sandbodies for the first and second members of the Shahejie Formation in the central-southern area of the Wen’an Slope were evaluated. The distribution of the complex fluid migration pathways was characterized. The relationship with the oil and gas distribution in the first and second members of the Shahejie Formation is discussed. The results illustrate the rationality and applicability of the proposed method.

2 GEOLOGICAL SETTING

The central-southern area of the Wen’an Slope in the Baxian Sag, Jizhong Depression, Bohai Bay Basin, China, was selected as a case study example. The central-southern area of the Wen’an Slope is located east of the Baxian Sag, adjoins the Dacheng Uplift in the east, the Baxian Depression in the west, the Wuqing Depression in the north, and the Raoyang Depression in the south. It is an inherited monoclinic structure trending NE-SW. The Wen’an Slope is filled with Eocene and Oligocene strata, containing the Kongdian, Shahejie and Dongying formations from bottom to top (Figure 1). The Kongdian Formation (abbreviated for Ek) developed river and delta facies composed of dark mudstone with a small amount of sandstone interbedded. The Shahejie Formation also developed river and delta facies, and it is mainly composed of dark mudstone and oil shale. It is usually divided into four submembers, numbered from fourth to first from bottom to top (abbreviated for Es₄, Es₃, Es₂, and Es1). The sandstones are well-developed in Es₃ and Es₂; meanwhile, a large amount of dark mudstone and oil shale is developed in Es₄, Es₃, and the lower part of Es₁. Dongying Formation (abbreviated as Ed) river facies are characterized by a sandstone-mudstone interbed.

FIGURE 1 | Simplified geologic background of the Wen’an Slope location map of the Bohai Basin in east China and the tectonic location of the study area (A). (B) Stratigraphic sequences and lithology of the interval of interest and a map showing petroleum geological element details of the study area.

The Wen’an Slope has undergone three distinct stages of tectonic evolution: early Paleogene, late Paleogene, and late Neogene (Wang, 2017). Multiple periods of tectonic evolution have led to well-developed faults. The fault strike is mainly north-east and north-northeast (Wang, 2017).

The oil and gas accumulations discovered in this area are mainly distributed in Es₁ and Es₂. A small number of accumulations is distributed in the Dongying Formation. The oil and gas mainly originate from the Es₁ source rock on the west side of the Baxian Depression (Liu et al., 2017). The oil and gas were generated by the Es₃ source rock kitchen located in the Baxian Depression and migrated to the sandbodies of Es₂ through a consequent fault and the sandbodies to the central-southern area of Wen’an Slope under the effect of buoyancy (Wang, 2017). In this area, it is recognized that the migration pathways changed between the sandbodies and faults. However, how to determine the conditions and combination models for the complex migration pathways in sandbodies and faults is still unclear. Therefore, characterizing the Es₁ and Es₂ fault and sandbody migration pathways in the central-southern area of the Wen’an Slope is crucial for correctly understanding the oil and gas distribution pattern and guiding the exploration for oil and gas.

3 METHODS

3.1 Model of complex multi-layer migration pathways

Under the influence of buoyancy and due to the limitation of the regional continuous caprock at the top, hydrocarbons will be limited to continuous lateral migration within a single layer within the sandbodies of a monoclinic formation unless there are lateral closed faults and pinch-out sandstone in the upward dip direction of the strata (Fu and Zhan, 2017). If the fault breaks the integrity of the caprock of the layer, the hydrocarbons are more prone to vertical migration through the fault than lateral migration. Thus, the hydrocarbons enter the sandbodies in the upper strata of the cap rock and multi-layer migration pathways are formed.

Of course, there is an extreme situation. Hydrocarbon leakage occurs when a fault that destroys the integrity of the only caprock extends to the surface or the integrity of all the caprocks above is destroyed.

Based on the composition and conditions of the complex multi-layer migration pathways, we summarize two models (Figure 2). Three parameters must be evaluated to clarify which model the migration of hydrocarbons in the paleo-slope belongs to. The first parameter is the connectivity of the sandbodies located above and below the regional caprock. The second parameter is related to the lateral sealing capacity of the faults that cut the sandbodies. The third is the vertical sealing capacity of the faults that cut through the caprock.

FIGURE 2 | Model of the complex multi-layer migration pathway. (A) Model I is conducive to the migration of hydrocarbons within a single layer. The hydrocarbons can migrate laterally across the fault but not vertically. (B) Model II is conducive to the vertical migration of hydrocarbons across layers.

3.2 Connected sandbodies

Due to limited exploration data and research methods, studying the subsurface sandbody connectivity is challenging. Therefore, only drilling data can provide the sandstone-formation ratio distribution of strata above and below the caprock. As shown in Figure 3A, the sandstone-formation ratio and hydrocarbon indication characteristics of the sandbody at known well points in the study area are gathered and analyzed. In view of the theory that oil and gas can only be transported in connected sandbodies and thus can be found by drilling in the paleo-slope zone outside source rock kitchens (Wang, 2007), we take the minimum sandstone-formation ratio of sandbodies bearing oil and gas as the minimum sandstone-formation ratio required for sandbody connectivity (Figure 3B). Thus, the area where the sandstone-formation ratios of the layers above and below the caprock are greater than the minimum sandstone-formation ratio required for Model I is delineated and combined with the evaluation results of the fault lateral sealing in the region.

FIGURE 3 | Schematic diagram of the prediction of connected sandbody distribution area in the slope area outside the source area. (A) By collecting the sandstone-formation ratios and containing fluids of drilling wells within the same reservoir in the study area, we can determine the minimum sandstone-formation ratio using the short plate theory. (B) According to the minimum sandstone-formation ratio, we can define the range of connected sandbodies on the distribution map of the sandstone-formation ratio of the reservoir.

3.3 Lateral sealing ability of the fault cutting the sandbodies

A lateral sealed fault in the upward dip direction of a monoclinic formation can stop the lateral migration of oil and gas within a single layer. Thus, determining the fault’s lateral sealing and laying a foundation for characterizing the single-layer migration pathway boundaries in the regional sandbodies is critical.

Based on the difference between the regional fault throw and the thickness of the caprocks and a single sandbody, the fault sealing principle can be inferred (Choi et al., 2015). We can select the Knipe graphical method or the shale-gouge ratio (SGR) standard method to evaluate the lateral sealing ability of the fault cutting the sandbody. In this study, we determined the limits of the SGR (Eq. 1) values that can form a petroleum fault trap through the corresponding relationship among the trap oil and gas-bearing property, trap depth, and SGRs using statistical methods. Based on the above data, a fault lateral sealing ability discrimination chart for the study area was drawn, and the fault lateral sealing was judged according to the attribute value of the target fault depth, the SGR of the fault rock, and the properties of the fluids in the target layer (Figure 4).

where SGR is the shale-gouge ratio, Vcl× ∆z is the volume fraction of clay in an interval of thickness ∆z, and T is the throw across the fault.

FIGURE 4 | Fault lateral sealing discrimination chart. The burial depths and calculated SGRs of fault rocks and containing fluid in the juxtaposition reservoirs were organized. The chart uses different symbols located within the two-dimension coordinates of SGR and depth. The vectorized envelope curve was determined based on the minimum SGR data on fault rocks placed against oil and gas layers at different depths. It can be used to predict the minimum SGR value of fault rock at different depths to seal hydrocarbons laterally.

3.4 Vertical sealing ability of the fault cutting the caprock

The mudstone forming the caprock undergoes plastic deformation in the faulting process in this study area. Thus, we used the CJT to evaluate the vertical sealing ability of the fault across the caprock. The CJT can be calculated as the difference between the thickness of the cap rock and the vertical displacement of the fault across the cap rock.

The paleo-CJT is more significant than the present-CJT for hydrocarbon migration. The process of obtaining the various parameters to calculate the paleo-CJT is as follows. Present fault vertical displacement and caprock thickness data were collected from drilling and seismic testing. On this basis, the paleo-fault throw in the oil and gas accumulation period was determined using the maximum fault displacement subtraction method (Dutton and Trudgill, 2009), and the paleo-caprock thickness was obtained using the paleo-formation-thickness recovery method (Tan et al., 2005).

The paleo-CJTs and hydrocarbon distributions above and below the caprock at the known well points in the study area were collected. The minimum CJT of oil and gas distributed only under the caprock was considered after statistical analysis, as shown in Figure 5A. When the minimum CJT is reached, the segmented growth of the fault in the caprock begins to connect, and the oil and gas can migrate upward through the caprock at the fault, accumulating and distributing above and below the caprock. The minimum caprock juxtaposition thicknesses of different caprocks are different because of the different geological conditions, which means Hfmin1 is not necessarily the same as Hfmin2.

FIGURE 5 | Schematic diagram of the prediction of the vertically sealed area. (A) CJTs with different hydrocarbon distributions above and below the caprock were arranged in numerical order within the coordinate axis at the known well points in the study area. (B) The vertical sealing ability of faults within the caprock can be evaluated using the minimum caprock juxtaposition thicknesses of the caprock. Then, the range of the vertically sealed area in the fault-caprock collocation can be defined.

Finally, the Model II hydrocarbon migration area is shown in Figure 5B.

3.5 Characterization of complex multi-layer migration pathways

The complex multi-layer migration contains elements of both Model I and Model II. The process of formation can be described as shown in Figure 6. First, the connected sandbody distribution area in a single layer above and below the caprock should be determined. Sandstone-formation ratio threshold calibration presented earlier can determine the connected sandbodies in reservoirs without fault for the study area. If there was any fault in the reservoir, the lateral sealing ability of the fault should be evaluated. The fault lateral unseal means the sandbodies on both sides of the fault could be connected. Otherwise, local unconnected sandbody areas surround the fault. Second, the activities of faults linking different layers of reservoirs should be carefully analyzed. Active faults are more favorable to migration pathways during the period of large-scale oil and gas production in source rocks. The vertical sealing ability of these faults in caprocks should be determined. The vertical unsealed faults are the main vertical migration pathways for oil and gas to cross layers, and a Model II area on the plane can be defined around the distribution of vertical unsealed faults. Third, the area distribution characteristics of the complex multi-layer migration pathways can be obtained by overlaying the distribution ranges of Models I and II above and below the caprock (Figure 7).

FIGURE 6 | Schematic diagram of multi-layer migration pathways in the paleo-slope zone. There are four strata and three faults. Caprock I and Caprock II are two regional caprocks that are mainly composed of thick mudstone. Sandbody I and Sandbody II are connected, and oil and gas could migrate in them laterally from the lower to the upper position of the slope. Three faults (abbreviated as F1, F2, and F3) cut different strata. F1 cuts Caprock 1 and Sandbody I, while F2 and F3 cut four strata simultaneously. The CJTs for three faults in two caprocks were calculated as HF1, HF2I, HF2II, and HF3. HF2I is smaller than the minimum CJT of Caprock I, and HF1 is larger than the minimum CJT of Caprock I. Therefore, a vertical unsealed section exists in the lower caprock. Hydrocarbons from outside the deep source rock kitchens in the basin could only migrate within Sandbody I in the downward direction of the slope on the left side of F2. Then, they migrated upward into Sandbody II above Caprock I through F2. HF2II and HF3 are larger than the minimum CJTs of Caprock II. Hydrocarbon migration shifted toward the upward slope direction in Sandbody II on the right side of F2.

FIGURE 7 | Schematic diagram of the prediction of the distribution area of the complex multi-layer migration pathways in the slope area outside the source. Model I areas of different reservoirs above and below the caprock and a Model II area were overlaid in the paleo-slope. Then, we can establish the complex multi-layer migration pathways by combining the Model 1 and Model 2 areas chosen by oil and gas under migration regularity, which are the areas marked with red arrows and bullseye symbols.

4 RESULTS AND DISCUSSION

The methods described above were used to characterize the distribution of the complex multi-layer migration pathways in the first and second members of the Shahejie Formation (Es₁ and Es₂) in the central-southern area of the Wen’an Slope in the Baxian Sag, Jizhong Depression, Bohai Bay Basin, China.

4.1 Determination of Model I distribution

First, we selected the test wells in the study area and screened the minimum SGR value of the fault rock related to the traps in the Es1 and Es2 members. According to the correlation between the SGR in the fault rock and the depth of the faulted strata, the lower limit value of the SGR for the lateral fault sealing was determined using the statistical method. The fault lateral sealing discrimination chart of target layers in the study area was drawn based on this limit value. Statistically, the lower limit value of the SGR in the Wen’an Slope is about 20%–25% between the depths of 1,000 m and 3,500 m (Figure 8). The fault traps that exceed the lower limit are laterally sealed and can simultaneously form fault lateral sealing and oil and gas accumulation.

FIGURE 8 | Determination of the lateral sealing threshold of the late active faults in Es₁ and Es₂ in the Wen’an Slope. Drilling data obtained from the Wen’an Slope are displayed in a two-dimensional coordinate system using depth as the Y-axis and SGR as the X-axis. According to the method shown in Figure 4, the vectorized envelope curve was used to predict the minimum SGR value of fault rock at different depths to seal hydrocarbons laterally in the Wen’an Slope.

Then, we determined the distribution range of connected sandbodies in the formations above and below the caprock based on fault lateral sealing evaluation. The Es₁ and Es₂ sandstone-formation ratios in the central-southern area of the Wen’an Slope were calculated using drilling data. The planar distribution map was drawn, as shown in Figure 9. As the figure illustrates, the Es₂ sandbodies in the central-southern area of the Wen’an Slope are more developed than the Es₁. The area with a high sandstone-formation ratio in Es₂ is widely distributed in the central part, while the area with a high sandstone-formation ratio in Es1 is only distributed in the eastern part. The Es₂ sandstone-formation ratios reach a minimum at the southwestern and eastern boundaries, while Es₁ sandstone-formation ratios reach a minimum at the southwest. According to the relationship between the Es₁ and Es₂ sandstone-formation ratios and the oil and gas distribution in the sandbodies (Figure 10), the minimum sandstone-formation ratio of the formation required for sandbody connectivity was determined to be about 20%. Based on this, the Es₁ and Es₂ distribution area of connected sandbodies in the central-southern area of the Wen’an Slope can be obtained. Figure 9 shows that the Es₂ connected sandbodies in the central-southern area of the Wen’an Slope are mainly distributed in the central area. In contrast, the connected sandbodies of Es₁ are mainly distributed in the eastern part, and the distribution area of the former is larger than the latter.

FIGURE 9 | Distribution map of the sand-formation ratios of the Es₂ (A) and the Es₁ (B) in the central-southern part of the Wen’an Slope. These maps were drawn mainly by seismic attributes and corrected using the actual sand-formation ratio data from wells. The development of the sandbodies in Es₂ and Es₁ is shown clearly through the two maps. It is possible to determine the planar area of the connected sandbody by the sandstone-formation ratio thresholds within Es₂ and Es₁.

FIGURE 10 | Determination diagram of the minimum sand-formation ratio required for the sandbody connectivity of Es2 in the Wen’an Slope area. The Y-axis of this graph represents the average sand-formation ratio, and the X-axis represents the formations developed in the Wen’an Slope. We analyzed the sand-formation ratios and bearing hydrocarbons of formations from all exploration wells in the Wen’an Slope. The average sand-formation ratios are drawn in the form of bar charts with different colors in two-dimensional coordinates. Finally, the threshold for sandbody connectivity was determined based on the minimum sand-formation ratio.

4.2 Determination of Model II distribution

According to core logging, the oil and gas caprock of Es2 in the Wen’an Slope area is mudstone in the lower part of Es1. The caprock of oil and gas in Es₁ is mudstone in the upper part of Es₁. In addition, the distribution of mudstone caprocks in the lower and upper parts of Es1 in the central-southern area of the Wen’an Slope was evaluated based on drilling and seismic data. The research results indicate that the mudstone caprocks in the lower and upper parts of Es₁ in the Wen’an Slope area are thick in the west and thin in the east. However, the mudstone cap in the upper part of Es₁ is thicker than that in the lower part. The mudstone caprock thickness in the lower and upper parts of Es₁ decreases gradually from the west to the east. It decreases to 100 m and 0 m in the eastern boundary of the central-southern area of the Wen’an Slope. Based on seismic data interpretation, faults have developed in the mudstone caprock of the lower and upper parts of Es₁ in the Wen’an Slope area. Only the faults active during the oil and gas accumulation phases could accommodate the migration of hydrocarbons. According to the tectonic evolution, the main active period of the faults developed within the mudstone caprocks was the deposition period of the middle and upper Minghuazhen Formation (Wang, 2017). The distribution of the hydrocarbon transporting faults in Es1 and Es2 was identified (Figure 11).

FIGURE 11 | Plane distribution map of the vertical migrating faults in Es₂ (A) and Es₁ (B) in the middle-south area of the Wen’an Slope. We drew all the fault-cutting Es₂ and Es₁ in the plane map. The vertical unsealed fault is shown in red. The distribution area of Model II for vertical migrating pathways is delineated by the dashed red line.

Figure 11 shows that the hydrocarbon migration faults that developed in the mudstone caprock in the lower and upper parts of Es₁ are in the central-southern area of the Wen’an Slope. They are mainly distributed in the central part, followed by the south, and few are distributed in the northern part of the slope zone. These faults are mainly N-NE striking, followed by NE-E striking, and some are S-N striking. The throw of the fault and the thickness of the cut mudstone caprock in the lower and upper parts of Es₁ in the Wen’an Slope area were used to determine the paleo-fault-throw and paleo-thickness of the caprock in the oil and gas accumulation period using the maximum fault displacement subtraction method (Dutton and Trudgill, 2009) and the paleo-thickness recovery method (Xu et al., 2018). These two methods allow the calculation of the migration fault and paleo-CJTs of the mudstone caprock in the lower and upper parts of Es₁. Based on the statistics of the relationship between the fault-contact thickness of the upper and lower parts of Es₁ at the known wells in the Wen’an Slope and the distribution of the oil and gas above and below the caprocks, the minimum paleo-CJTs were determined through the oil and gas distributed only under the caprocks at approximately 133 m and 137 m (Figure 12). Based on the paleo-CJT threshold, the hydrocarbon migration faults that are vertically sealed are mainly distributed in the western part of the Wen’an Slope (Figure 11).

FIGURE 12 | We determined the minimum CJTs in the lower (A) and upper (B) parts of Es₁ in the central-south part of the Wen’an Slope in tabular form. In these tables, the Y-axis represents the layers developed in the Wen’an Slope, and the X-axis represents the wells whose CJTs were calculated. The wells are arranged from left to right in the order of CJTs from small to large. In the table, we marked the hydrocarbons shown in different layers of each well with different symbols and also marked the target caprock with a gray stripe. Two types of oil and gas distribution are shown in the table. One is that there are both oil and gas shows above and below the caprock, and the other is that there are oil and gas shows only below the caprock. Observing the table from left to right, when Type one oil and gas shows become Type two, the CJT of the corresponding well undergoing type change is the minimum CJT.

4.3 Distribution of complex multi-layer migration pathways

The Model I and Model II migration pathways in Es₂ and Es₁ were determined by superimposing the above-determined distribution area of the connected sandbodies and the fault’s vertical sealing area in the lower and upper parts of the Es₁ caprocks in the central-southern area of the Wen’an Slope (Figure 13).

FIGURE 13 | We overlaid the Model 1 area in Figure 9 on the Model 2 area in Figure 11 using the method of determining complex oil and gas migration pathways. (A) shows the complex multi-layer pathway distribution in the central-southern part of the Wen’an Slope. The oil and gas shows of drilling wells in Es1 and Es2 in the study area are marked with different symbols on the graph. In order to more clearly depict the complex migration pathways for oil and gas, we have drawn a profile diagram of (B). The position of (B) is marked with black lines on (A).

It can be concluded that the Model I and Model II areas in Es₂ are mainly distributed in the western and central parts, respectively. In contrast, the migration pathways in Es₁ are more complex. The hydrocarbons migrated through the Model II pathways in the central part of the study area from Es2 to Es₁. Considering that the connected sandbodies and vertical unsealed faults coexist in the central part, the Model I migration pathways in Es₁ and Model II migration pathways in Es₁ to Ed are complicated and combined.

The discovered hydrocarbon accumulations in Es₁ and Es₂ in the central-western part of the Wen’an Slope are closely related to the distribution of the complex multi-layer migration pathways (Figure 13). The hydrocarbon accumulations in Es₂ are mainly distributed in the area of the Model I migration pathways in Es₂.

Similarly, the hydrocarbons in Es₁ are mainly distributed in the combined Model I and Model II pathways in Es₁. The west boundary of the Model II migration pathways in Es₂ divides the hydrocarbon distribution range into two layers. This is because oil and gas can only laterally transport and accumulate along Es₁ or Es₂ in the distribution area of the Model I migration pathways, and oil and gas drilling could be successful. Due to the smaller distribution area of the Model I migration pathways in Es₂ than in Es₁, oil and gas migration and accumulation in Es₁ is more favorable.

The feasibility of using this integrated approach to characterize the distribution of multi-layer migration pathways for hydrocarbons can be verified by calibrating the prediction results with the current distribution of the hydrocarbon accumulations found in Es₁ and Es₂ in the central area of the Wen’an Slope.

The results from applying our method to the Wen’an Slope highlight the possibility of successfully predicting the complex multi-layer migration pathways for hydrocarbon. Faults and sandbodies are the main conduits in the study area. However, two issues require further discussion.

One issue is the parameters we used to evaluate the sealing ability of the fault. SGR is a parameter that characterizes the shale content of fault rocks at specific locations under ideal conditions. It only characterizes the quantity of the clay-size -debris particles from host rocks and cannot quantitatively reflect various diagenetic processes that occurred during the formation of fault rocks, such as cataclasis, cementation, and dissolution (Pei et al., 2015). Thus, predicting the fault-sealing ability should be done with care in the basins characterized by strong tectonic movement and hydrothermal activity. In addition, CJT is a statistical parameter reflecting the vertical sealing ability of faults within the regional cap rock and is influenced by factors such as the lithology, thickness of the regional cap rock, and fault activity and scale (Fu and Zhan, 2017). It is only effective for faults within a specific caprock in a period. The minimum CJT for faults in one caprock cannot be used to evaluate faults in another caprock.

The other one is the parameter of sandbody connectivity. Due to the complexity of the sandbody development and distribution of river facies, geologists have creatively proposed a sandstone-formation ratio parameter (Kaufman et al., 1987). It has universal applicability in describing reservoir development, identifying sedimentary environments, and predicting sandbody connectivity. However, we should recognize that the algorithm of a sandstone-formation ratio is an indirect way to determine the connectivity of sandbodies from a probabilistic perspective. The minimum sandstone-formation ratio representing the connectivity of sandbodies has certain limitations and can only be used for a certain basin. Some geologists believe that there are still effective migration pathways for oil and gas in the areas of connected sandbodies, for instance, a sandbody with higher porosity and permeability or structural ridges (Fu and Zhan, 2017; wang, 2017). The mechanism of oil and gas migration is basically clear, but further research is needed to establish more effective methods for evaluating sandbody migration pathways based on the characterization of strata development.

These problems could be solved if we take the effect of fault development mechanism, sedimentation analysis, and diagenesis into account based on the idea and workflow of our methodology. Determining complex petroleum migration pathways could be more practical and accurate in the future.

5 CONCLUSION

(1) Three factors that affect oil and gas migration within the paleo-slope area outside the source kitchen are the sandbody connectivity, the fault’s lateral sealing ability, and the fault’s vertical sealing ability. By integrating these three factors, the complex migration pathways can be summarized into two models.

(2) Sandbody connectivity can be determined by the minimum sandstone-formation ratio of the reservoirs bearing oil and gas. A fault’s lateral sealing ability can be judged by the limit value of the SGR for the fault rock in the target layer. The caprock juxtaposition thickness (CJT) can be used to evaluate the vertical sealing ability of the fault cutting the caprock. Then, the distribution areas of the Model I and Model II migration pathways are determined. Integrating the definitions of Model I and Model II forms a set of characterization methods for determining the distribution of the complex migration pathways in the slope area outside the source kitchens. The feasibility of using this approach to predict the distribution of complex multi-layer migration pathways is verified using a case study example.

(3) In the area of the Wen’an Slope in the Baxian Sag, Jizhong Depression, Bohai Bay Basin, China, the Model II migration pathways in Es₁ and Es₂ are mainly distributed in the central-eastern regions. The Model I migration pathways in Es₂ are mainly distributed in the western region. In contrast, the Model I migration pathways of Es₁ are mainly distributed in the central and eastern regions. The distribution area of the latter is larger than the former. Therefore, Es₁ is more favorable for oil and gas migration and accumulation than Es₂, which is consistent with the fact that the oil and gas distribution of Es₁ is evidently more than that of Es₂ in the central-southern area of Wen’an Slope.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary material; further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

SJ: Writing–original draft, Writing–review and editing. FG: Methodology, Writing–review and editing. LL: Software, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The work was supported by the National Natural Science Foundation of China (No. 41702153), the National Science and Technology Major Project (2016ZX05007-003-003), and the Natural Science Foundation of Heilongjiang Province (QC2017034).

ACKNOWLEDGMENTS

The authors thank LetPub (www.letpub.com) for linguistic assistance and pre-submission expert review.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Allan, U. S. (1989). Model for hydrocarbon migration and entrapment within faulted structures. Am. Assoc. Petroleum Geol. Bull. 73 (7), 803–811. doi:10.1306/44b4a271-170a-11d7-8645000102c1865d

Allen, J. R. (1978). Studies in fluviatile sedimentation: an exploratory quantitative model for the architecture of avulsion-controlled alluvial suites. Sediment. Geol. 21 (2), 129–147. doi:10.1016/0037-0738(78)90002-7

Aydin, A. (2000). Fractures, faults, and hydrocarbon entrapment, migration and flow. Mar. Petroleum Geol. 17, 797–814. doi:10.1016/S0264-8172(00)00020-9

Berndt, C., Costa, S., Canals, M., Camerlenghi, A., de Mol, B., and Saunders, M. (2012). Repeated slope failure linked to fluid migration: the Ana submarine landslide complex, Eivissa Channel, Western Mediterranean Sea. Earth Planet. Sci. Lett. 319–320, 65–74. doi:10.1016/j.epsl.2011.11.045

Bertoni, C., Cartwright, J., Foschi, M., and Martin, J. (2018). Spectrum of gas migration phenomena across multilayered sealing sequences. AAPG Bull. 102, 1011–1034. doi:10.1306/0810171622617210

Bouvier, J. D., Kaarssijpesteijn, C. H., Kluesner, D. F., Onyejekwe, C. C., and Vanderpal, R. C. (1989). Three-dimensional seismic interpretation and fault sealing investigations, Nun River Field, Nigeria. Am. Assoc. Pet. Geol. Bull. 73 (11), 1397–1414. doi:10.1306/44b4aa5a-170a-11d7-8645000102c1865d

Bretan, P. (2017). Trap analysis: an automated approach for deriving column height predictions in fault-bounded traps. Petroleum Geoscience J. 23 (1), 56–69. doi:10.1144/10.44petgeo2016-022

Choi, J. H., Yang, S. J., Han, S. R., and Kim, Y. S. (2015). Fault zone evolution during cenozoic tectonic inversion in SE korea. J. Asian Earth Sci. 98, 167–177. doi:10.1016/j.jseaes.2014.11.009

Dutton, D. M., and Trudgill, B. D. (2009). Four-dimensional analysis of the Sembo-relay system, offshore Angola: implications for fault growth in salt-detached settings. AAPG Bull. 93 (6), 763–794. doi:10.1306/02230908094

Fisher, Q. J., Casey, M., Harris, S. D., and Knipe, R. J. (2003). Fluid-flow properties of faults in sandstone: the importance of temperature history. Geology 31 (11), 965–968. doi:10.1130/g19823.1

Fisher, Q. J., and Knipe, R. J. (1998). Fault sealing processes in siliciclastic sediments. Geol. Soc. Lond., Spec. Publ. 147, 117–134. doi:10.1144/gsl.sp.1998.147.01.08

Fisher, Q. J., and Knipe, R. J. (2001). The permeability of faults within siliciclastic petroleum reservoirs of the north sea and Norwegian continental shelf. Mar. Petroleum Geol. 18 (10), 1063–1081. doi:10.1016/s0264-8172(01)00042-3

Fisher, Q. J., Knipe, R. J., and Worden, R. H. (2009). “Microstructures of deformed and nondeformed sandstones from the North Sea: implications for the origins of quartz cement in sandstones,” in Quartz cementation in sandstones 14 ed . Editors R. H. Worden, and S. Morad (Oxford, UK: Blackwell Publishing Ltd), 129–146.

Foschi, M., Cartwright, J. A., and Peel, F. J. (2014). Vertical anomaly clusters: evidence for vertical gas migration across multilayered sealing sequences. AAPG Bull. 98, 1859–1884. doi:10.1306/04051413121

Fu, G., and Zhan, M. W. (2017). Conversion condition analysis for vertical migration along faults and lateral migration along sandbodies of oil and gas:taking the local structure of Bohai Bay basin as an example. J. China Univ. Min. &Technology 46 (2), 336–344. doi:10.13247/j.cnki.jcumt.000581

Fulljames, J. R., Zijerveld, L. J. J., and Franssen, R. C. M. W. (1997). in Fault seal processes: systematic analysis of fault seals over geological and production time scales ed . Editors P. Møller-Pedersen, and A. G. Koestler (China: Norwegian Petroleum Society Special Publications), 7, 51–59.

Jackson, M. D., Yoshida, S., Muggeridge, A. H., and Johnson, H. D. (2005). Three-dimensional reservoir characterization and flow simulation of heterolithic tidal sandstones. Bulletin 89 (4), 507–528. doi:10.1306/11230404036

Johnson, C. R., Greenkorn, R. A., and Woods, E. G. (1996). Pulse testing:a new method for describing reservoir flow properties between wells. JPT 18 (12), 1599–1604. doi:10.2118/1517-pa

Kaufman, R. L., Ahmed, A. S., and Hempkins, W. B. (1987). A new technique for the analysis of commingled oils and its application to production allocation calculations. 16th Annu. Indones. Petro.Assoc , 247–268. Paper IPA 87-23/21. doi:10.29118/ipa.13.247.268

King, P. R. (1990). The connectivity and conductivity of overlapping sandbodies A T.North Sea oil and gas reservoirs II. London: Graham & Trotman, 353–358.

Knipe, R. J. (1992). Faulting processes and fault seal. Struct. Tect. Model. Its Appl. Petroleum Geol. 1, 325–342. doi:10.1016/b978-0-444-88607-1.50027-9

Knipe, R. J. (1997). Juxtaposition and seal diagrams to help analyze fault seals in hydrocarbon reservoirs. Am. Assoc. Pet. Geol. Bull. 81 (2), 187–195. doi:10.1306/522b42df-1727-11d7-8645000102c1865d

Ligtenberg, J. H. (2005). Detection of fluid migration pathways in seismic data: implications for fault seal analysis. Basin Res. 17, 141–153. doi:10.1111/j.1365-2117.2005.00258.x

Lindsay, N. G., Murphy, F. C., Walsh, J. J., Watterson, J., Flint, S., et al. (1993). Outcrop studies of shale smears on fault surfaces. Geol. Model. Hydrocarbon Reservoirs Outcrop Analogues 15, 113–123. doi:10.1002/9781444303957.CH6

Liu, N., Qiu, N., Chang, J., Shen, F., Wu, H., Lu, X., et al. (2017). Hydrocarbon migration and accumulation of the suqiao buried-hill zone in wen’an Slope, jizhong subbasin, bohai bay basin, China. Mar. Petrol. Geol. 86, 512–525. doi:10.1016/j.marpetgeo.2017.05.040

Nicol, A., and Childs, C. (2018). Cataclasis and silt smear on normal faults in weakly lithified turbidites. J. Struct. Geol. 117, 44–57. doi:10.1016/j.jsg.2018.06.017

Pei, Y. W., Paton, D. A., Knipe, R. J., and Wu, K. Y. (2015). A review of fault sealing behaviour and its evaluation in siliciclastic rocks. Earth-Science Rev. 150, 121–138. doi:10.1016/j.earscirev.2015.07.011

Schultz, R. A., and Fossen, H. (2008). Terminology for structural discontinuities. AAPG Bull. 92 (7), 853–867. doi:10.1306/02200807065

Shi, J. J. (2019). Quantitative evaluation of the lateral sealing ability of normal faults in siliciclastic sequences: implication for fault trap in well gang 64, in the west qikou sag, China. J. Petroleum Sci. Technol. 9 (3), 27–45. doi:10.1016/S1876-3804(16)30040-4

Shi, J. J., and Guo, F. K. (2020). Analysis of lateral blocking of faults in the saertu reservoir in xiaolinke-halahai fault zone. J. Oil Gas Technol. 42 (4), 34. doi:10.12677/JOGT.2020.424153

Sun, Q., Cartwright, J., Foschi, M., Lu, X., and Xie, X. (2021). The interplay of stratal and vertical migration pathways in shallow hydrocarbon plumbing systems. Basin Res. 33, 2157–2178. doi:10.1111/bre.12552

Tan, K. J., Wei, P. S., Lü, X. M., et al. (2005). Research on quantitative resumption method of stratum paleothickness and its application——taking Ludong area in Zhunge’er Basin as an example. Nat. Gas. Ind. 25 (10), 24–26.

Wan, J. L., Gong, Y. J., Huang, W. H., Zhuo, Q., and Lu, X. (2022). Characteristics of hydrocarbon migration and accumulation in the lower jurassic reservoirs in the tugerming area of the eastern kuqa depression, tarim basin. J. Petroleum Sci. Eng. 208, 109748–109815. doi:10.1016/j.petrol.2021.109748

Wang, B. (2007). Hydrocarbon migration pathway system of panyu low Uplift in the pearl river mouth basin (Wuhan: China University of Geosciences), 1–70. Master Thesis.

Wang, W. (2017). Hydrocarbon migration pattern of sandstone conduit layer and its control on accumulation in Wen’an slope area of Ba sag. Northeast Pet. Univ .

Xu, S., Hao, F., Xu, C., Zou, H., Zhang, X., Zong, Y., et al. (2018). Hydrocarbon migration and accumulation in the northwestern bozhong subbasin, bohai Bay Basin, China. J. Petroleum Sci. Eng. 172, 477–488. doi:10.1016/j.petrol.2018.09.084

Yielding, G. (2002). “Shale gouge ratio – calibration by geohistory,” in Hydrocarbon seal quantification ed . Editors A. G. Koestler, and R. Hunsdale (Amsterdam: Norwegian Petroleum Society (NPF) Special Publications), 11, 1–15.

Yielding, G. (2009). “Using probabilistic shale smear factor to relate SGR predictions of column height to fault-zone heterogeneity,” in 2nd EAGE International Conference on Fault and Top SealsFrom Pore to Basin Scale, (USA, 21 September 2009) ( IEEE), 93–95.

Yielding, G., Bretan, P., and Freeman, B. (2010). Fault seal calibration: a brief review. Reserv. Compart. Spec. Publ. 347, 243–255. doi:10.1144/sp347.14

Yielding, G., Freeman, B., and Needhanm, D. T. (1997). Quantitative fault seal prediction. AAPG Bull. 81, 897–917. doi:10.1306/522b498d-1727-11d7-8645000102c1865d

Zhu, W. Q., Wu, K. Q., Ke, L., Chen, K., and Liu, Z. F. (2021). Study on fault-controlled hydrocarbon migration and accumulation process and models in Zhu I Depression. Acta Oceanol. Sin. 40, 107–113. doi:10.1007/s13131-021-1755-9

Conflict of interest: Author LL was employed by China National Petroleum Corporation.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Jijian, Guang and Lili. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 09 April 2024
doi: 10.3389/feart.2024.1393211

Sedimentary evolutionary processes, architecture, and sedimentary model of a lobate shallow-water delta: insights from flume simulation experiments

You Qi, Youjing Wang*, Guosheng Qin, Yanchen Song, Hualing Zhang and Fanle Meng

PetroChina Research Institute of Petroleum Exploration and Development, Beijing, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Xianguo Zhang, China University of Petroleum, China
Wenjie Feng, Yangtze University, China
Gang Hui, University of Calgary, Canada

* Correspondence: Youjing Wang, wangyoujing@petrochina.com.cn

Received: 28 February 2024
Accepted: 26 March 2024
Published: 09 April 2024

Citation: Qi Y, Wang Y, Qin G, Song Y, Zhang H and Meng F (2024) Sedimentary evolutionary processes, architecture, and sedimentary model of a lobate shallow-water delta: insights from flume simulation experiments. Front. Earth Sci. 12:1393211. doi: 10.3389/feart.2024.1393211

By using equipment such as time-lapse cameras and 3D laser scanners, this study conducted flume simulation experiments to obtain topographic elevation, the amount of sediment increment, sedimentary cross sections, and other related data and diagrams. Quantitative software was then utilized for sedimentological analysis to clarify the depositional evolution, architecture, and depositional model of lobate shallow-water deltas. The research results show that under the influence of hydraulic conditions and main distributary channels, lobate shallow-water deltas primarily undergo three evolution stages: initial channel formation, distributary channel system formation, and continuous stable evolution stage. The architecture elements of lobate shallow-water deltas can be divided into distributary channels dominated by channel deposition, proximal river mouth bars, transitional bars, and distal bars. The lateral composition and downstream deposition of the river-bar combination are the main structural units of the shallow water delta. The proximal profile distributary channels exist in the form of thick channel sand bodies with multiple repetitive intercalation cycles, and thick proximal bar deposits form on both sides of the distributary channels. The bifurcation of distributary channels in the middle profile leads to a reduction in channel scale, while large-scale proximal bars still develop on both sides of the channel. In the distal profile, a substantial river mouth bar complex is formed at the break of the delta front slope, and with the gradual progradation of the delta, the river mouth bar deposition zone becomes flattened. Based on the insights of the flume simulation experiments, a sedimentary model for lobate shallow-water delta was established.

Keywords: lobate delta, flume experiments, evolution processes, architecture, sedimentary model

1 INTRODUCTION

Fisk et al. (1954,1961) first proposed the concept of shallow-water deltas in the study of the Mississippi River Delta. Subsequent scholars, through comprehensive research in delta formation dynamics, lithological characteristics, and other areas (Hoy and Ridgway, 2003; Olariu and Bhattacharya, 2006), have summarized the geological background for the formation of lacustrine shallow-water deltas. It is characterized by its formation in gently sloping, slowly subsiding depression basins, with relatively shallow water and a stable tectonic backdrop, forming deltas within plateaus and continental shelves, distinct from typical Gilbert-type deltas.

Shallow-water deltaic depositional systems have been successively discovered in multiple continental basins in China, including the Songliao Basin (Zhu et al., 2012; Deng et al., 2015; Hu et al., 2015), Bohai Bay Basin (Zhu et al., 2008; Xu et al., 2019), Ordos Basin (Liu et al., 2011; Chen et al., 2023), and many modern lakes such as the Poyang Lake. The shallow-water delta is divided into delta plain subfacies, delta front subfacies, and pro-delta subfacies. Using the shallow-water delta in the Sanzhao Depression of the Songliao Basin as an example, Zhu Xiaomin (Zhu et al., 2012) suggested that the webbed deltaic depositional front facies developed under humid climate conditions, with a broad front facies zone that can be divided into inner and outer fronts. Zhang (2010) used modern sedimentation in Poyang Lake as a reference and proposed that distributary channel-type deltas with lobate and lobe-shaped skeletal sand bodies and delta plain sedimentation are well developed, occupying over 3/4 of the delta area, while the delta front facies zone is relatively less developed. Fu et al. (2021) and Chen et al. (2021) analyzed the application of seismic sedimentology in subfacies division of shallow-water deltas and the identification of distributary channel sand bodies. Wang et al. (2020) and Qin et al. (2020) respectively discussed the classification of distributary channel sedimentation models and the sedimentary characteristics and models of front tabular sand in shallow-water deltas. In the Ordos Basin, Li et al. (2019), Liu et al. (2021) and Zhu et al. (2021) conducted comprehensive analyses of shallow-water delta reservoir lithological characteristics, sand body distribution patterns, and front mouth bar deposition characteristics in the Yanchang Formation. Shallow-water deltas have been classified based on different source systems, control factors, and sand body morphologies (Heerden and Roberts, 1988; Lemons and Chan, 1999; Longhitano, 2008; Nagy et al., 2010; Zhang et al., 2010; Cahoon et al., 2011; Yin et al., 2012; Zhu et al., 2013; Yin et al., 2014; Wu et al., 2019). Sand body morphology is a relatively direct classification method, dividing shallow-water deltas into tabular, lobate, braided, and bird’s foot types based on sand body distribution patterns. Among them, lobate shallow-water deltas often have a large plan-view scale and longitudinally rich sand body development and have been found to be ideal reservoirs among several billion-ton scale oilfields globally, making them a very important type of shallow-water delta.

With the advancement of computer technology, geomorphological measurements, and recording techniques (Woodward et al., 2003; Feng et al., 2011; Qiao et al., 2015; Zeng et al., 2017b), flume simulation experiments have gradually become a reliable means of sedimentological research (Zeng C. et al., 2017; Bai et al., 2018; Wei et al., 2020; Wang et al., 2023). Scholars initiated flume simulation experiments as early as the 1960s (McKee, 1957). Compared to studies based on geological outcrops and modern sedimentation (Zhang, 1996; Jia et al., 2000; Jiao et al., 2005), their primary advantage lies in the convenience of measurements (easily slice and sample), detailed recording of sedimentary processes, and the significant implications for sedimentological research and determining the general distribution patterns of reservoirs. Flume simulation experiments can provide a large amount of sand body model parameters at different sedimentary stages, and can more intuitively and three-dimensionally depict the sedimentation process.

The Triassic Yanchang Formation in the Yishan Slope of the Ordos Basin, China, has gentle structural slopes with dips of less than 1 and a stable distribution of stratum thickness (Fu et al., 2017). It is divided into ten sand groups from bottom to top, undergoing complete cyclical changes from lake transgression to regression. Among these, the Chang 6 sand group develops thick oil-rich lobate shallow-water deltaic front sand bodies, which are the main reservoir units (Figure 1). In the middle to late stages of development, a finer characterization of the internal configuration of sand bodies is necessary to correspond to later oilfield development, given the complex and intricate sedimentary interfaces within lobate shallow-water deltas. Advanced experimental and analytical methods are urgently required to clarify the sedimentary evolution of this type of delta and to better identify its internal structural characteristics. Taking the Chang 6 lobate delta in the Wuliwan area of the Jing’an Oilfield in the Yishan Slope of the Ordos Basin as an example, this study sets parameters including sediment grain size, hydraulic conditions, and slope gradient similar to those underground to simulate the evolution of the shallow-water delta in the research area through flume experiments, dissect its internal configuration characteristics, establish a sedimentary model, and provide guidance for water flooding adjustments in the oilfield.

FIGURE 1 | Comprehensive histogram of the location and stratigraphy of the study area modified based on Fu et al. (2017).

2 EXPERIMENTAL DESIGN AND METHODOLOGY

2.1 Experimental device design

The shallow-water delta simulation device consists of a bedform, water supply unit, sand supply unit, water level-controlled boundary, and laser scanner, etc. (Figure 2). Previous flume simulation experiments used sandy platforms as the bedform to simulate a flat basin (lv, 2020). In order to simulate the sedimentary evolution of the shallow-water delta as accurately as possible and to replicate its sediment characteristics, this simulation device features a different bed shape design compared to previous shallow-water delta flume experiments. The design of the base of the simulated region is characterized by a plain-slope-break-front style to better observe the evolution processes of the delta distributary channels and to focus on researching the bar combinations of the shallow-water delta. The slope of the base is set at 0.55° in accordance with the ancient slope of underground reservoirs. The length of the delta plain sedimentary area is 2.9 m, which matches the ancient sedimentary conditions of the reservoir’s long gentle slope and short lake area. On the plan view, the slope-break area serves as the boundary, reflecting the sedimentary characteristics of the delta plain subfacies from the source area to the slope-break area, and the sedimentary characteristics of the pro-delta subfacies from the slope-break area to the boundary.

FIGURE 2 | Schematic diagram of the flume simulation experiment facility.

In the downstream, a water level control boundary was set to maintain the stability of the water level. Additionally, a constant-speed water pump was placed in the water body of the delta front deposition area as the experimental water supply device to ensure stable water conditions through a circulated water supply. In order to obtain images and topography information during the sedimentary evolution of the shallow-water delta, a time-lapse photography device was installed above the flume setup to capture real-time image information during the sedimentary evolution process. Furthermore, one spatial reference information collection point (alignment point) and 4 laser scanning stations were set up. After each period, the sedimentary area was scanned using the FARO-S70 laser scanner to obtain sub-millimeter laser scans for three-dimensional coordinates, images, and terrain topography records of the sedimentary bodies.

During the flume simulation process, in order to ensure accurate measurement of sedimentary terrain topography by the laser scanner, the simulation was conducted in cycles of 60 min/120 min, after the water was stopped and drained at the end of each cycle, the laser scanning was performed to avoid measurement errors caused by water refraction.

2.2 Preparation of simulation parameters

Based on the experimental analysis results of Chang 6 reservoir and the outcrop of the Yanchang Formation in the Wuliwan area of the Jing’an Oilfield in the Ordos Basin, and regarding similar international experiment parameters and sediment numerical simulation parameter values, experimental parameters such as the slope of the base, sediment composition, water flow velocity and discharge, sand supply, and water viscosity were determined using a proportional reduction method (Table 1).

2.3 Simulation data processing

The 138-h experiment was monitored throughout the process. After the completion of the simulation experiment, real-time image information of the sediment evolution process recorded by the time-lapse photography equipment can be obtained. The three-dimensional spatial information calculation and alignment were conducted using SCENE software for the point cloud data generated from each simulation period regarding the coordinates of the reference collection point. Utilizing the QuantSedAnaV2.0 programming software, the aligned billion-level three-dimensional point cloud data were gridded, and operations such as outlier handling and image correction were performed. After data gridding, error analysis and data volume correction were carried out. Measurement error trends were obtained based on the calibration objects at the edges of the experimental setup, and the data volume was corrected for errors in each period to obtain the simulated data volume of the experiment. The experimental data volume includes coordinates, grid numbers, simulation periods, elevation, image information, etc., and can generate topographic elevation maps, sediment increment maps, and cross-sections for further analysis of the sediment evolution process.

3 ANALYSIS OF EXPERIMENTAL RESULTS

3.1 Sedimentary evolution process during each stage

The experiment recorded the evolving orthophoto images (Figure 3), de-trended topographic maps (Figure 4), and the sedimentation thickness increment within a specified observation timeframe (Figure 5) (positive values representing sedimentation, negative values representing erosion). De-trending helped eliminate the influence of long-profile elevation differences and primarily reflected the geomorphology of the delta.

FIGURE 3 | Orthorectified image of the evolution of the lobate-shaped shallow water delta in the simulation of the flume experiment. Blue dashed lines indicated the flow path during each simulation period. (A) Experiment time: 0 hour; (B) Experiment time: 3-4 hours; (C) Experiment time: 9-10 hours; (D) Experiment time: 13-14 hours; (E) Experiment time: 21-22 hours; (F) Experiment time: 29-30 hours; (G) Experiment time: 37-38 hours; (H) Experiment time: 45-46 hours; (I) Experiment time: 57-58 hours; (J) Experiment time: 65-66 hours; (K) Experiment time: 83-84 hours; (L) Experiment time: 93-94 hours; (M) Experiment time: 103-104 hours; (N) Experiment time: 113-114 hours; (O) Experiment time: 133-134 hours.

FIGURE 4 | Topographic map (downstream trend was de-trended) of the lobate-shaped shallow water delta in the simulation. Blue dashed lines indicated the flow path during each simulation period. (A) Experiment time: 0 hour; (B) Experiment time: 3-4 hours; (C) Experiment time: 9-10 hours; (D) Experiment time: 13-14 hours; (E) Experiment time: 21-22 hours; (F) Experiment time: 29-30 hours; (G) Experiment time: 37-38 hours; (H) Experiment time: 45-46 hours; (I) Experiment time: 57-58 hours; (J) Experiment time: 65-66 hours; (K) Experiment time: 83-84 hours; (L) Experiment time: 93-94 hours; (M) Experiment time: 103-104 hours; (N) Experiment time: 113-114 hours; (O) Experiment time: 133-134 hours.

FIGURE 5 | Sediment thickness increment for each run step. A value greater than 0 indicates that sedimentation has occurred within one run step, with an increase in sedimentation thickness. If the value is less than 0, it means that erosion has occurred within one simulation period, and the sediment thickness has decreased. Magenta dashed lines indicated the flow path during each simulation period. (A) Experiment time: 0 hour; (B) Experiment time: 3-4 hours; (C) Experiment time: 9-10 hours; (D) Experiment time: 13-14 hours; (E) Experiment time: 21-22 hours; (F) Experiment time: 29-30 hours; (G) Experiment time: 37-38 hours; (H) Experiment time: 45-46 hours; (I) Experiment time: 57-58 hours; (J) Experiment time: 65-66 hours; (K) Experiment time: 83-84 hours; (L) Experiment time: 93-94 hours; (M) Experiment time: 103-104 hours; (N) Experiment time: 113-114 hours; (O) mainstream line distribution.

In the laboratory flume simulation, the initial bed was a gently sloping sandy layer to simulate a relatively flat terrain before the formation of a shallow-water delta. The evolution of the lobate shallow-water delta from nonexistence to stability evolution mainly underwent three stages: the initial channel formation stage, distributary channel system formation stage, and continuous stable evolution stage.

3.1.1 Initial channel formation stage

In the early stages of the sediment simulation (Figures 3A–D, 4A–D), sediment primarily developed in the plain region. As the simulated river entered the water body, the expansion of water flow resulted in a decrease in flow velocity due to the top support effect. With the decrease in river energy, some immobile sediment was unloaded in the river mouth area. Gradually, this resulted in the formation of subaqueous shoals due to the deposition of bedload sediments from the river, which gradually increased in height, forming river mouth sand bars. The obstruction by the river mouth sand bars led to the branching of the original single river channel, forming initial distributary channels. These initial distributary channels then formed secondary river mouth sand bars, leading to further channel bifurcation and the formation of the initial shape of the lobate shallow-water delta.

3.1.2 Distributary channel system formation

After a period of continuous evolution (Figures 3E–H, 4E–H), the delta area expanded gradually, the number of distributary channels increased, and under the constraint of various rank estuarine sand bars, the delta extended downstream. This included the development of wider, deeply incising main distributary channels, as well as narrower, frequently meandering distributary channels. The main and distributary channels together formed the river network system on the delta surface, which gradually became more complex as the delta continued to grow.

3.1.3 Continuous stable evolution stage

Under sustained water flow and sediment supply, the delta continued to evolve (Figures 3I–O, 4I–O). Distributary channels further migrated, evolved, and amalgamated, and after nearly a hundred simulation cycles, the delta area was stabilized. The growth of the delta’s edges slowed down, while the distributary channels on its surface migrated rapidly and frequently, leading to further rapid changes in the relationship between distributary channels and river mouth bars. However, during this period, the overall sedimentary characteristics of the delta remained stable.

3.2 Sedimentary architecture patterns

Based on high-precision experimental geomorphological laser scanning data and comprehensive sediment simulation experiments, each period of the experimental flume simulation results was de-trended to highlight the morphological and distribution characteristics of channels and sandbars and to conduct architecture dissection. With an increase in the number of experiments, the delta on the plain gradually transformed from a simple pattern of deltaic forms into a complex architecture of compound deltaic-estuarine systems. On the cross-section, sediment deposition evolved from simple vertical stacking to a more complex structure due to channel erosion.

3.2.1 Basic configuration unit combination

The morphological units of a lobate-shaped shallow water delta can be divided into distributary channels dominated by river sediment, proximal river mouth bars, transitional bars, and distal bars. A typical lobate-shaped shallow water delta is mainly composed of large-scale multi-branched deltaic-estuarine systems, which gradually grow in size as they extend towards the water body. Throughout the entire sedimentary evolution of the shallow water delta, the sediment characteristics of the deltaic-estuarine systems display different features at various stages of the experiment:

Initial channel formation stage: During this stage (Figures 5A–D), sediment deposition is primarily incremental and fans out in the plain region around the river mouth. The channels exhibit low sinuosity and experience rapid sediment filling. A small number of the channels have incision capabilities (Figure 5B). Overall, the distribution of the channels and river mouth bars is relatively concentrated. The river mouth bars mainly distribute on both sides of the initial channel and secondary distributary channels, with minimal incision caused by the channels.

Distributary channel system formation stage: At this stage (Figures 5E–H), although the delta continues to grow, the rate of growth is slower than it was in the initial stage. The erosive capacity of the channels increases, with two types of erosion observed: incision and lateral cutting due to the lateral migration of the river, forming migrating side-cut channels. Both types of channels coexist during this stage. The river mouth bars formed in the previous stage increase in size and extend towards the distal end. As the distributary channels extend forward and enter deeper water bodies, the hydrodynamic force decreases rapidly, resulting in the formation of distal bars at the termination of the channels (Figures 5G, I, J).

Continuous stable evolution stage: In this stage (Figures 5I–N), due to continuous water flow and sediment supply, the delta development gradually stabilizes, with a slow increase in area. The incision effect of the channels becomes more pronounced. As the distributary channels gradually extend forward and the river network (Figure 5O) becomes more complex, the cutting effect of the channels becomes more apparent. The stability of the distal channel system is lower than that of the proximal channel system, with a prevalence of migrating side-cut channels. The distribution of previously deposited river mouth bars becomes more complex due to the influence of the changing network of the river systems, and together with the distributary channel deposit end bars that extend continuously, formed the multiple fringe-shaped river mouth bar combinations. Under a stable sedimentation background, the delta system exhibits periodic cycles of progradation and retrogradation.

3.2.2 Proximal depositional configuration

The proximal cross-sectional profile is selected from sediment deposits close to the source in the upper plain (Figure 6), distributary channels often exist in the form of multi-period repetitive and overlapping wide-banded thick-layer channel sand bodies, with thick deposits of proximal bars formed on both sides of the distributary channels. Under a stable tectonic and sediment supply background, the delta progrades continuously, transitioning from front-edge deposition to plain deposition from bottom to top. The plain distributary channels mainly include incised channels (IC) and lateral cut channels (LC). The development of distributary channels gradually increases, transitioning from isolated to lateral stacking pattern, while the development and continuity of river mouth bars reduce progressively.

FIGURE 6 | Sedimentary architecture of the experimental delta at the upstream part. The upper figure was the reproduced strata based on the topography data. The bottom figure was the sedimentary architecture interpreted based on the upper figure. LC means the lateral migration channel; IC means the incised channel.

3.2.3 Mid-range depositional configuration

The mid-range cross-sectional profile is located in the upper zone of the entire shallow water delta-front deposit (Figure 7), and the sedimentation shifts from delta-front edge deposits to plain deposits from bottom to top. The development of distributary channels gradually increases, transitioning from isolated channels into laterally stacked ones, while the development and continuity of river mouth bars decrease gradually. The distributary channels still exhibit lateral stacking migration but at a significantly smaller scale compared to the upstream profile, indicating that the branching of the distributary channels results in a smaller channel scale. Large-scale proximal bars continue to develop on both sides of the channels, and these proximal bars are often incised by other distributary channels, leading to the presence of multiple distributary channels within a single proximal bar. Compared to the proximal cross-sectional profile, the scale of the bar combinations decreases, while the number of depositional periods increases in the mid-range cross-sectional profile.

FIGURE 7 | Sedimentary architecture of the experimental delta at the middle part. The upper figure was the reproduced strata based on the topography data. The bottom figure was the sedimentary architecture interpreted based on the upper figure. LC means the lateral migration channel; IC means the incised channel.

3.2.4 Distal deposition configuration styles

The distal profile is located in delta fronts because most sediment develops underwater (Figure 8), and it shows the formation of large-scale river mouth bar complexes at the sediment slope break near the delta front, generally distributed in the lower part of the profile. As the delta gradually progrades, the river mouth bar deposition rapidly transitions into a plain. Even after plain formation, deltaic sedimentation still develops bar combinations, although the scale of these bar combinations is relatively smaller compared to the proximal profile. Along the front slope break zone, thick river mouth bar complexes are developed at the base, with lateral high stacking forming extensive interconnected thick river mouth bars and bodies. Delta progradation leads to their top plain formation, resulting in laterally amalgamated thin sand layers. The development of distributary channels in the plain sedimentation layer increases from bottom to top, with enhanced stacking. In the distal portion, distributary channels primarily migrate laterally, with lower incision depth.

FIGURE 8 | Sedimentary architecture of the experimental delta at the distal part. The upper figure was the reproduced strata based on the topography data. The bottom figure was the sedimentary architecture interpreted based on the upper figure. LC means the lateral migration channel; IC means the incised channel.

We have highlighted the new insights from the experimental delta. Firstly, the distributary channels dominated the deposition and evolution. For a distributary channel, its upper reach is incised into the previously deposited sediments, and transported the sediments to its distal reach to form channel mouth bars. Therefore, the sedimentary architecture of the finally preserved experimental delta was complicated by the migration and avulsion of the distributary channels.

Secondly, the channel mouth bars were cut and transformed by the later distributary channels, the preservation of channel mouth bars, especially for the proximal bars were poorly preserved in the experimental delta.”

3.3 Sedimentary model

Based on the identification of configuration units, a clear combination pattern between different configuration units was established through a detailed interpretation of multiple typical cross-sectional profiles. The main channels are mostly isolated within extensively developed floodplains, with nearly no contact with other configuration units. Distributary channels, proximal river mouth bars, transitional bars, and distal bars are the primary configuration units at the delta front. The combination pattern of river mouth bar configuration units is complex, with the potential for self-stacking to form different types of river mouth bar complexes. Additionally, river mouth bars can combine with distributary channels to form various patterns, with the most common being the downcutting of distributary channels into the lower deposited river mouth bars.

Based on the above understanding, a sedimentary configuration model for the Triassic Yanchang Formation Chang 6 Sand Formation in Jing’an Oilfield, Yishan Slope, Ordos Basin was established (Figure 9). Based on changes in the lake basin water level, the overall sedimentary environment can be divided into delta plains, delta fronts, and pre-deltas from the land towards the lake basin direction. The delta plain can be further divided into upper and lower plains. The main sedimentary configuration unit evolves from the upper plain backbone channel to the lower plain distributary channel and ultimately to the delta front river mouth bars. Distributary channels and river mouth bars are the main genetic sand bodies, with distributary channels often showing lateral migration and superposition along with remodeling the early river mouth bars, ultimately leading to the development of thick layers of sand bodies.

FIGURE 9 | Sedimentary model of the Triassic Yanchang Formation Chang 6 Sand Formation in Jing’an Oilfield, Yishan Slope, Ordos Basin.

TABLE 1 | Parameter settings for flume simulation experiments.

4 CONCLUSION

Based on flume simulation experiments, the sedimentary process of a lobate shallow-water delta mainly goes through three evolutionary stages: initial channel formation, distributary channel system formation, and continuous stable evolution stage. The configuration units of the lobate shallow-water deltas can be divided into distributary channels, proximal river mouth bars, transitional bars, and distal bars. Lateral amalgamation of bar complexes and progradation are the main structural units of the shallow water deltas. During the continuous growth of the delta, distributary channels extend forward gradually, and the river network becomes more complex. Multiple fringe-like river mouth bar combinations can be formed at the delta front.

In the proximal profile, distributary channels often exist in the form of multiple stages of repeated overlapping thick channel sandbodies, while thick proximal bar deposition forms on both sides of the distributary channels. In the mid-range profile, the sedimentation changes from front-end deposition to plain deposition from bottom to top. The development of distributary channels gradually increases, and the channels shift from isolated to lateral superposition. The development and continuity of river mouth bars gradually decrease. In the distal profile, a large-scale river mouth bar complex is observed at the sediment slope break at the distal end of the delta front, distributed in the lower part of the profile. However, as the delta gradually progrades, the river mouth bar deposition area becomes a plain. Based on the identification of configuration units and the systematic interpretation of typical profiles, a sedimentary model of the Triassic Yanchang Formation Chang 6 Sand Formation in Jing’an Oilfield, Yishan Slope, Ordos Basin was established.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

YQ: Conceptualization, Formal Analysis, Resources, Writing–original draft. YW: Conceptualization, Funding acquisition, Project administration, Supervision, Visualization, Writing–review and editing. GQ: Data curation, Methodology, Writing–original draft. YS: Investigation, Writing–review and editing. HZ: Validation, Writing–review and editing. FM: Validation, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by “Research and demonstration of key technologies of ballast engineering in mature oil fields of PetroChina,” grant number 2023YQX10201.

ACKNOWLEDGMENTS

The authors gratefully acknowledge the comments from reviewers and the academic editors for their constructive reviews of the manuscript.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Bai, Y. C., Hu, X., Xu, H. Y., Zou, D. S., and Bai, Y. (2018). Experimental simulation analysis of shallow water delta formation process in lake. J. Hydraulic Eng. 49 (05), 549–560. doi:10.13243/j.cnki.slxb.20171157

Cahoon, D. R., White, D. A., and Lynch, J. C. (2011). Sediment infilling and wetland formation dynamics in an active crevasse splay of the Mississippi River delta. Geomorphology 131 (3-4), 57–68. doi:10.1016/j.geomorph.2010.12.002

Chen, C., Qi, Y., Yu, Z. L., and Wang, B. (2021). Seismic identification of channel sand body superposition in shallow water delta: a case study of Linxing S area, eastern Ordos Basin. Nat. Gas. Geosci. 32 (5). doi:10.11764/j.issn.1672-1926.2021.02.003

Chen, X., Xiao, L., Wang, M. Y., Hao, C. X., Wang, F., and Tang, H. N. (2023). Provenance and palaeo-sedimentary environment restoration of Chang 8 oil-bearing Formation in southwestern margin of Ordos Basin: evidence from rock geochemistry. Curr. Geol. 37 (05), 1264–1281. doi:10.19657/j.geoscience.1000-8527.2023.037

Deng, Q. J., Hu, M. Y., Hu, Z. G., and Wu, Y. K. (2015). Sequence stratigraphic framework and sedimentary system distribution of Fuyu oil layer in Shuangcheng area, northern Songliao Basin. Curr. Geol. 29 (03), 609–622. doi:10.3969/j.issn.1000-8527.2015.03.013

Feng, X., Zou, L. L., Liu, C., Lu, Q., Liang, W. J., Li, L. L., et al. (2011). Forward modeling of fully polarimetric GPR. J. Geophys. Eng. 54 (2), 349–357. doi:10.3969/j.issn.0001-5733.2011.02.011

Fisk, H. N. (1961). Bar-finger sands of Mississippi delta. New York, NY, USA: Springer.

Fisk, H. N., Kolb, C. R., McFarlan, E., and Wilbert, L. J. (1954). Sedimentary framework of the modern Mississippi delta [Louisiana]. J. Sediment. Res. 24 (2), 76–99. doi:10.1306/d4269661-2b26-11d7-8648000102c1865d

Fu, J., Deng, X., Wang, Q., Li, J., Qiu, J., Hao, L., et al. (2017). Densification and hydrocarbon accumulation of triassic Yanchang Formation Chang 8 member, Ordos Basin, NW China: evidence from geochemistry and fluid inclusions. Petroleum Explor. Dev. 44 (1), 48–57. doi:10.1016/s1876-3804(17)30007-1

Fu, X., Du, X. F., Guan, D. Y., Li, J., and Li, X. H. (2021). Application of seismosedimentology to the study of sedimentary facies of river-shallow water delta: a case study of guantao Formation in penglai A structure area, Bohai sea. Bull. Geol. Sci. Technol. 40 (3), 96–108. doi:10.19509/j.cnki.dzkq.2021.0304

Heerden, I. L. V., and Roberts, H. H. (1988). Facies development of Atchafalaya delta, Louisiana: a modern bayhead delta. Aapg Bull. 72. doi:10.1306/703c8eb1-1707-11d7-8645000102c1865d

Hoy, R. G., and Ridgway, K. D. (2003). Sedimentology and sequence stratigraphy of fan-delta and river-delta deposystems, Pennsylvanian Minturn Formation, Colorado. Aapg Bull. 87 (7), 1169–1191. doi:10.1306/03110300127

Hu, M. Y., Sun, C. Y., Xue, D., and Zhang, H. J. (2015). High resolution sequence study of the 4th member of the quansi Formation in the Sanzhao area, northern Songliao. Curr. Geol. 29 (04), 765–776. doi:10.3969/j.issn.1000-8527.2015.04.006

Jia, A. L., Mu, L. X., Chen, L., and Huang, S. Y. (2000). Fine research method of fan delta reservoir outcrop. J. Petroleum Sci. Eng. 43 (04), 105–108+125.

Jiao, Y., Yan, J., I, S. L., Yang, R., Lang, F., and Yang, S. (2005). Architectural units and heterogeneity of channel reservoirs in the Karamay Formation, outcrop area of Karamay oil field, Junggar basin, northwest China. Aapg Bull. 89 (4), 529–545. doi:10.1306/10040400955

Lemons, D. R., and Chan, M. A. (1999). Facies architecture and sequence stratigraphy of fine-grained lacustrine deltas along the eastern margin of late Pleistocene Lake Bonneville, northern Utah and southern Idaho. AAPG Bull. 83 (4), 635–665. doi:10.1306/00aa9c14-1730-11d7-8645000102c1865d

Li, Y., Zhang, Y. F., Wang, T., Zhang, Q., Wang, M., Li, R. Z., et al. (2019). Sedimentary facies of Chang 1 member of upper triassic Yanchang Formation and development law of shallow delta mouth bar in zizhou area, Ordos Basin. J. Palaeogeogr. 21 (5), 757–766. doi:10.7605/gdlxb.2019.05.051

Liu, H. L., Qiu, Z., Xu, L. M., Qin, W. F., Tong, Q., Lin, J. H., et al. (2021). Distribution of shallow water delta sand bodies and the genesis of thick layer sand bodies of the Triassic Yanchang Formation, Longdong Area, Ordos Basin. PETROLEUM Explor. Dev. 48 (1), 123–135. doi:10.1016/s1876-3804(21)60009-5

Liu, H. Q., Li, X. B., Wan, Y. R., Wei, L. H., and Liao, J. B. (2011). Paleogeographic environment and sedimentary characteristics of Chang 8 reservoir Formation in Ordos Basin. J. Sediment. Res. 29 (06), 1086–1095. doi:10.14027/j.cnki.cjxb.2011.06.005

Longhitano, S. G. (2008). Sedimentary facies and sequence stratigraphy of coarse-grained gilbert-type deltas within the pliocene thrust-top potenza basin (southern apennines, Italy). Sediment. Geol. 210 (3-4), 87–110. doi:10.1016/j.sedgeo.2008.07.004

Lv, J. L., Zhu, Y. J., Xia, R., Zheng, Y. K., Liu, C. H., Feng, W. J., et al. (2020). Sedimentary characteristics and evolution process of arid branch river system: experimental study of flume sedimentation simulation. J. Sediment. Res. 38 (05), 994–1005. doi:10.14027/j.issn.1000-0550.2019.101

McKee, E. D. (1957). Flume experiments on the production of stratification and cross-stratification. J. Sediment. Res. 27 (2), 129–134. doi:10.1306/74d70678-2b21-11d7-8648000102c1865d

Nagy, J., Hess, S., and Alve, E. (2010). Environmental significance of foraminiferal assemblages dominated by small-sized Ammodiscus and Trochammina in Triassic and Jurassic delta-influenced deposits. Earth-Science Rev. 99 (1-2), 31–49. doi:10.1016/j.earscirev.2010.02.002

Olariu, C., and Bhattacharya, J. P. (2006). Terminal distributary channels and delta front architecture of river-dominated delta systems. J. Sediment. Res. 76 (2), 212–233. doi:10.2110/jsr.2006.026

Qiao, H., Wang, Z. Z., Li, L., Li, H. M., Pan, L., and Wang, K. Y. (2015). The establishment of a knowledge base on meandering river geology using satellite imagery and its practical applications. Curr. Geol. 29 (6), 1444. doi:10.3969/j.issn.1000-8527.2015.06.020

Qin, R. S., Yue, H. L., Zhou, F. J., Wu, Q. Y., and Lei, Y. (2020). Sedimentary characteristics and models of the ribbon sand in the foreland of a river-controlled shallow water delta: a case study of the lower ming member in block 34 of bozhong area, yellow river mouth sag. Acta Sedimentol. Sin. 38 (2). doi:10.14027/j.issn.1000-0550.2019.027

Wang, X., Wei, S. Y., Lu, Q. Q., Dai, R., Hu, H., Bi, Y. H., et al. (2023). Development process and formation mechanism of transverse sand bar in delta front based on physical simulation. J. Sediment. Res. 41 (03), 818–827. doi:10.14027/j.issn.1000-0550.2021.150

Wang, X. B., Jiang, Z. X., Hu, G. Y., Fan, T. E., Fan, H. J., He, M. W., et al. (2020). Classification of sedimentary patterns of distributary channels in shallow water deltas. J. Earth Sci. Environ. 42 (5). doi:10.19814/j.jese.2020.04053

Wei, S. Y., Liu, Z. B., Wu, S. H., and Wang, X. T. (2020). Simulation experiments on alluvial fan sedimentation process and sedimentary configuration controlled by normal faults. J. Palaeogeogr. 22 (06), 1095–1108. doi:10.7605/gdlxb.2020.06.074

Woodward, J., Ashworth, P. J., Best, J. L., Sambrook Smith, G. H., and Simpson, C. J. (2003). The use and application of GPR in sandy fluvial environments: methodological considerations. Geol. Soc. Lond. Spec. Publ. 211 (1), 127–142. doi:10.1144/gsl.sp.2001.211.01.11

Wu, S. H., Xu, Z. H., and Liu, Z. (2019). river-controlled shallow water delta sedimentary architecture. J. Palaeogeogr. 21 (02), 202–215.

Xu, Z., Wu, S., Liu, Z., Zhao, J., Geng, H., Wu, J., et al. (2019). Configuration characteristics of finger sand bars in shallow water delta front: a case study of the lower member of Neogene Minghuazhen Formation in Bohai BZ25 oilfield, Bohai Bay Basin. PETROLEUM Explor. Dev. 46(02), 322–333. doi:10.11698/PED.2019.02.12

Yin, T. J., Li, X. Y., Zhang, C. M., Zhu, Y. J., and Gong, F. H. (2012). Morphological characteristics of sedimentary sandbodies in modern shallow lake basin deltas: a case study of dongting lake and Poyang Lake. J. Petroleum Nat. Gas Technol. 34 (10), 1–7+166. doi:10.3969/j.issn.1000-9752.2012.10.001

Yin, T. J., Zhang, C. M., Zhu, Y. J., Yang, W., Ye, J. G., Cai, W., et al. (2014). A special type of shallow delta--the superimposed delta. J. Geol. 88 (02), 263–272. doi:10.19762/j.cnki.dizhixuebao.2014.02.009

Zeng, C., Yin, T. J., and Song, Y. K. (2017a). Numerical simulation experiment of sedimentation of shallow water delta under lake level rise and fall. Earth Sci. 42 (11), 2095–2104. doi:10.3799/dqkx.2017.134

Zeng, Q. L., Zhang, R. H., lu, W. Z., Wang, B., and Wang, C. Y. (2017b). Study on the development law and controlling factors of fractures based on 3D laser scanning technology: a case study of the outcrop profile of suohan village in kuqa foreland area, tarim basin. Nat. Gas. Geosci. 28 (3). doi:10.11764/j.issn.1672-1926.2017.02.004

Zhang, C. M. (2010). Shallow water delta sedimentary model. J. Sediment. Res. 28 (5), 933–944. doi:10.14027/j.cnki.cjxb.2010.05.006

Zhang, C. M., Xu, L., Lin, K. X., Liu, H. B., and Qiu, Y. N. (1996). The anatomical study of the distributary channel sands in the 68th layer of the Oil Sand Mountain oilfield in Qinghai. J. Sediment. Res. 55 (04), 71–77.

Zhang, C. M., Yin, T. J., Zhu, Y. J., and Ke, L. M. (2010). Shallow water delta sedimentary model. J. Sediment. Res. 28 (05), 933–944. doi:10.14027/j.cnki.cjxb.2010.05.006

Zhu, S. F., Cui, H., Chen, J. H., Luo, G. J., Wang, W. Y., Yang, Y., et al. (2021). Sedimentary system and reservoir petrology of shallow water delta--A case study of the 8th member of the Shan 1 yihe Formation in the western Ordos Basin. Acta Sedimentol. Sin. 39 (1). doi:10.14027/j.issn.1000-0550.2020.115

Zhu, W. L., Li, J. P., Zhou, X. H., and Guo, Y. H. (2008). Neogene shallow water delta sedimentary system and large oil and gas field exploration in Bohai Sea. J. Sediment. Res. 48 (04), 575–582.

Zhu, X. M., Liu, Y., Fang, Q., Li, Y., Liu, Y. Y., Wang, R., et al. (2012). Formation conditions and sedimentary model of shallow water delta in large depression lacustrine basin: a case study of Fuyu oil reservoir in Sanzhao sag, Songliao Basin. Front. earth Sci. 19 (01), 89–99.

Zhu, X. M., Pan, R., Zhao, D. N., Liu, F., Wu, D., Li, Y., et al. (2013). Formation and development of shallow water delta in lake basin and case analysis. J. China Univ. Petroleum Nat. Sci. Ed. 37 (05), 7–14. doi:10.3969/j.issn.1673-5005.2013.05.002

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Qi, Wang, Qin, Song, Zhang and Meng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 09 April 2024
doi: 10.3389/feart.2024.1394094

3-D forward modeling of shale gas fracturing dynamic monitoring using the borehole-to-ground transient electromagnetic method

Weihao Zhang1,², Xingbing Xie1,², Lei Zhou1,²*, Xinyu Wang³ and Liangjun Yan²

¹Key Laboratory of Exploration Technology for Oil and Gas Resources (Yangtze University), Ministry of Education, Wuhan, China

²Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University, Wuhan, Hubei, China

³School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China

Edited by:
Wenguang Wang, Northeast Petroleum University, China

Reviewed by:
Xushan Lu, Shandong University, China
Zhengyu Xu, Chongqing University, China

* Correspondence: Lei Zhou, 501161@yangtzeu.edu.cn

Received: 01 March 2024
Accepted: 21 March 2024
Published: 09 April 2024

Citation: Zhang W, Xie X, Zhou L, Wang X and Yan L (2024) 3-D forward modeling of shale gas fracturing dynamic monitoring using the borehole-to-ground transient electromagnetic method. Front. Earth Sci. 12:1394094. doi: 10.3389/feart.2024.1394094

The borehole-ground transient electromagnetic method enhances the detection range and resolution by placing the transmitter electrode within the borehole, allowing for close-proximity excitation of subsurface targets and compensating for the shortcomings of traditional ground-based methods. Utilizing an unstructured vector finite element method combined with a second-order backward Euler variable time-stepping difference scheme and the MUMPS solver, we have achieved three-dimensional forward modeling simulation of the transient electromagnetic field for a borehole-ground electric source. Based on the validation of its accuracy, we analyzed the effectiveness of this method for dynamic monitoring of shale gas reservoir fracturing. Through forward modeling, we examined the characteristics of the radial electric field response during shale gas reservoir fracturing monitoring and evaluated the effectiveness of the borehole-ground TEM method in dynamic monitoring of shale gas reservoirs. The research results indicate that this method can meet the requirements for reservoir fracturing dynamic monitoring and has a promising application prospect.

Keywords: borehole transient electromagnetic method, finite element, three-dimensional forward modeling, dynamic monitoring, shale gas fracturing

1 INTRODUCTION

In recent years, as unconventional oil and gas utilization enter a late stage of development into areas where the water cut is high, research on the identification of oil, gas, and water; the prediction of the residual oil and gas distribution; and the dynamic monitoring of oil reservoirs has become crucial (Hu et al., 2014; Zhao et al., 2019). Hydraulic fracturing is a key technology in the development of unconventional oil and gas reservoirs that directly affects the production of unconventional oil and gas. The forward simulation of hydraulic fracturing dynamic monitoring models can not only verify the effectiveness of hydraulic fracturing, but also provides important guidance for the hydraulic fracturing of unconventional oil and gas. Although techniques such as the time-lapse seismic method have matured, during hydraulic fracturing, the small difference in the acoustic impedance caused by the displacement of reservoir fluids by fracturing fluids makes data interpretation difficult, and this method is also costly. Therefore, geophysical methods such as time-lapse gravity and time-lapse electromagnetic models are beginning to be developed (Peng et al., 2012). Notably, the displacement of fracturing fluids can cause significant changes in reservoir electrical properties, providing a theoretical basis for the application of electromagnetic exploration techniques in the dynamic monitoring of unconventional oil and gas reservoirs.

With the increasing demand for the exploration of deep-seated resources, several limitations in conventional ground-based electromagnetic surveying techniques for deep resource exploration have been identified. To address these issues, two approaches can be pursued. First, increasing the transmit power of the excitation field source to improve the signal-to-noise ratio of the received electromagnetic signals can enhance the practicality and effectiveness of electromagnetic methods. Second, facilities such as tunnels, mines, and boreholes can be used to place a transmitter under-ground and close to the target body for emission stimulation. This not only allows for the acquisition of stronger abnormal response signals, thereby increasing the detection depth, but also enhances the resolution of the abnormal body.

The transient electromagnetic method has been widely applied in environmental surveys, oil and gas exploration, mineral exploration, geothermal resource exploration, and deep crustal studies. Compared to controlled-source electromagnetic (CSEM) and magnetotelluric (MT) methods, this method has several advantages, such as a greater detection depth and a higher signal-to-noise ratio (Keller et al., 1984; Strack, 1984; Hördt et al., 1992; Mitsuhata et al., 2002; Müller et al., 2002; Commer et al., 2006; Haroon et al., 2015; He and Xue, 2018). The rational interpretation of electromagnetic data relies on forward and inversion techniques, and forward simulation plays a crucial role by providing important guidance for oil and gas reservoir development. Therefore, this topic has been the focus of widespread attention by scholars.

Current research on time-domain electromagnetic methods primarily focuses on numerical simulation and feasibility analysis. In the field of borehole transient electromagnetic technology, the borehole electrical method was initially proposed by researchers in the former Soviet Union for the delineation of coal seam horizons. He Zhanxiang further developed the borehole time-frequency electromagnetic method based on previous research, deriving a three-dimensional numerical simulation algorithm for vertical dipole sources using the volume integral equation method, and achieved three-dimensional forward and inversion modeling of borehole electromagnetic methods (Wang et al., 2006; Wang et al., 2007). Ke Ganpan and Huang Qinghua used finite element methods to implement three-dimensional forward simulation of vertical line source electric fields (Ke and Huang, 2009). Cuevas established a borehole electromagnetic model that involved placing vertical electric dipole sources as excitation sources at the bottom of the metal casing. In addition, Cuevas analyzed the electromagnetic response characteristics at the radial distance measurement points on the ground and studied the effects of factors such as the length and conductivity of the metal casing on the response (Cuevas, 2012). Ghada and Sami conducted time-domain numerical simulations of infinitely long wire sources in two-layered formations and analyzed the influences of different layer conductivities on the response results (Ghada and Al-Najim, 2015). Zhang Jiaqi integrated the expressions for each component of the dipole electromagnetic field along the direction of the wire to obtain analytical expressions for the vertical long wire source’s electromagnetic field, and compared the responses of this field with the response of the ground horizontal electric dipole as part of a three-dimensional forward simulation of a borehole. This demonstrated the significant advantages of borehole electromagnetic methods in deep resource detection compared to conventional ground-based methods (Zhang, 2018). In terms of dynamic monitoring, Orange et al. analyzed the feasibility of using marine controlled-source electromagnetic methods to monitor the process of oil and gas reservoir development based on a classic two-dimensional oil and gas model (Orange et al., 2009). Furthermore, Wirianto et al. studied the feasibility of using land-based controlled-source electromagnetic methods to monitor changes in oil and gas reservoirs through numerical simulations of complex three-dimensional resistivity models (Wirianto et al., 2010). Meanwhile, Xie Xingbing et al. conducted experiments on residual oil monitoring using time-domain long-offset transient electromagnetic methods and preliminarily verified the effectiveness of this method in detecting residual oil boundaries (Xie et al., 2016). Li et al. proposed a multi-source, multi-directional ground-to-well vertical electromagnetic profiling method and used the integral equation method to simulate the electromagnetic response characteristics of oil and gas reservoir resistivity and oil saturation changes during water injection and production processes (Li et al., 2017). Liu et al. conducted numerical simulations and field experiments on the hydraulic fracturing process of shale gas fields and confirmed the good effect of controlled-source electromagnetic methods in monitoring shale gas production (Liu R. et al., 2020). Wang Xinyu et al. conducted a simulation and performed an analysis of the dynamic monitoring of oil reservoirs using electrical source transient electromagnetic methods and verified the significant response effects of this method in the dynamic monitoring of oil and gas reservoirs (Wang et al., 2022).

Early studies have demonstrated that, compared to conventional ground-based electromagnetic methods, borehole electromagnetic methods have significant advantages in deep exploration. Additionally, electrical source transient electromagnetic methods have been proven to be feasible for the dynamic monitoring of reservoirs. Therefore, based on the non-structured vector finite element method, in this paper, a borehole model is constructed, and the finite element results are compared with numerical filtering solutions to validate the algorithm’s effectiveness. Subsequently, we establish a three-dimensional model and calculate the electric field components for dynamic monitoring of shale gas hydraulic fracturing within the model and analyze the relative anomaly changes to verify the effectiveness of borehole transient electromagnetic methods in the dynamic monitoring of shale gas hydraulic fracturing.

2 METHOD AND TECHNIQUE OF THE DYNAMIC MONITORING OF SHALE GAS FRACTURING USING THE TIME DOMAIN ELECTROMAGNETIC METHOD

In recent years, the borehole electromagnetic method has emerged as a widely applied electromagnetic method in the exploration of oil and gas reservoirs, residual oil detection in oil fields, and the dynamic monitoring of reservoirs. It mainly consists of two forms: 1) the well-surface electromagnetic method (WSEM), in which electromagnetic sources are emitted from within the well and received on the surface; and 2) the surface-well-electromagnetic method (SWEM), in which electromagnetic sources are emitted from the surface and received within the well. In the borehole electromagnetic method, the casing of the well can be used as the emitting electrode, or else dipole/magnetic sources can be placed near the geological target layer within the well for close-range stimulation. The excitation signal can be single-frequency or multi-frequency domain electromagnetic waves, or alternatively, it can be time-domain square waves. Array observations are conducted on the ground to measure the intensity of the electric field and the induced electromotive force components. The measured signal strength depends on the distribution of the formation resistivity and other physical parameters within the electromagnetic field’s range of action determined by the observation method (Hu et al., 2022).

The transmitter of the borehole transient electromagnetic method is either an electrode (electrical source) or a magnetic dipole source (magnetic source) located in the well. The waveform of the transmitted current can be a zero-based square wave or a pseudo-Gaussian pulse, and the source can be moved within the well to conduct measurements. The receiving array can be deployed in various layouts depending on the surface conditions. At each excitation point, signals from multiple excitation cycles need to be observed and stacked to improve the signal-to-noise ratio, which requires more measurement time than the charging method. Both the electric field components and magnetic field components can be measured simultaneously to compensate for the insensitivities of electric and magnetic fields to different electrical differences. In addition to imaging the intensity and gradient of the electric field, processing of the observation data can also yield images of the apparent resistivity and polarization parameters of the target interval. The borehole transient electromagnetic method has been widely used in the study of various factors, such as the layering of oil and gas reservoirs, the delineation of reservoirs, and the exploration of underground water. It is characterized by its high efficiency, easy operation, and fast exploration speed. Compared to surface-well electromagnetic methods and airborne electromagnetic methods, borehole electromagnetic methods have a broader exploration range and are more sensitive to high-resistivity anomalies. The borehole transient electromagnetic detection method is an effective electromagnetic exploration method (Figure 1) (Hu et al., 2022).

FIGURE 1 | Schematic diagram of borehole-to-ground transient electromagnetic method device.

3 IMPLEMENTATION OF 3-D FORWARD ALGORITHM

3.1 Time domain finite element forward modeling theory of electromagnetic fields

In the quasi-static state, the time domain Maxwell equations of the active region can be expressed as follows:

Starting from the Maxwell equations in the time domain, the magnetic field is eliminated, and the electric field diffusion equation, which ignores the displacement current term, is established:

where is the permeability; is the position vector; is the electric field intensity at position , at time ; is the magnetic field intensity at position , at time ; is the magnetic induction intensity at position , at time ; is the conduction current density at position , at time ; is the applied source current density at position , at time ; and is the electrical conductivity.

An unstructured tetrahedral grid is used to discretely calculate the region, and the Nédélec vector interpolation basis function, which automatically satisfies the continuity and non-dispersion of the tangential component of the electric field, is used to approximate the linear distribution of the electric field within the element. (Figure 2) The electric field inside any tetrahedral element can be expressed as follows:

where is the tangential electric field value on the j edge of the tetrahedral element, which can be called the degree of freedom, and is the vector interpolation basis function.

FIGURE 2 | Electric field distribution of edges of the tetrahedral element.

Based on Galerkin’s weighted margin method and Eq. 5, the following equation can be obtained:

Finally, an equation in the following form is obtained:

where and are the global stiffness matrix and mass matrix, and is the source term. The specific expression is as follows:

where is the volume of the unit. For a grounded vertical long wire source, we approximate it to a superposition of multiple segments of electric dipoles (Ansari and Farquharson, 2014), each of which can be expressed as follows:

where is the current intensity at time t, dl is the length of the electric dipole, is the current direction vector, is the position of the emission dipole source, and is an impulse function.

In order to improve the numerical accuracy, the time is discretized using the second-order backward Euler difference scheme, and simultaneous Eq. 7 (Um et al., 2010):

Finally, large linear equations are formed, which can be expressed as follows:

In order to solve Eq. 13, the initial electric field at time t = 0 should be given. For a series of current sources with an initial current of zero, such as a grounded long wire source, square wave, triangle wave, sine wave, or trapezoid wave, the initial electric field is . The initial electric field of the next step waveform is composed of two parts: the spatial electric field distribution caused by the long wire source and the stable direct current electric field formed by the positive and negative electrodes.

can be calculated from Ohm′s law, and can be calculated from the negative gradient of the potential .

If the point source current strength is , is located at , and is obtained according to the continuity of the electric field by using the differential form of Ohm’s law , then

where is the impulse function. The three-dimensional Poisson equation satisfied by the point source field can be obtained by combining the above equations:

The same grid is used to ensure the consistency of the boundary between the direct current electric field and the vector electric field, and the total field method is used to solve both the direct current (DC) field and the transient electromagnetic field. Due to the large solution area, the Dirichlet boundary condition is applied to the external boundary in the calculation process.

The tangential electric fields of the edges of all of the tetrahedrons are obtained using the lower-upper (LU) decomposition and quick back substitution of the above equations using a multi-wavefront direct solver; namely, the multifrontal massively parallel sparse direct solver (MUMPS). The electric field response , , and at any point in space is obtained via linear interpolation of the basis function. The response of the magnetic field component is obtained using Faraday’s law of electromagnetic induction.

4 VALIDATION OF THREE-DIMENSIONAL MODEL OF TIME-DOMAIN ELECTROMAGNETIC METHOD IN BOREHOLE AND ON THE GROUND

4.1 Verification of algorithm’s accuracy

To validate the accuracy of the algorithm proposed in this paper, a comparison verification is conducted using a homogeneous half-space model. Assuming that the resistivity of air is 10⁶ Ω⋅m and the resistivity of the homogeneous half-space is 100 Ω⋅m, a vertical electric dipole with a depth range of 100 m–101 m underground is considered. The transmitting current is set to 1 A. The receiving point is located on the ground along the x-direction at a distance of 1,500 m from the transmitter. Schematic diagrams of the model and mesh partition are presented in Figure 3

FIGURE 3 | (A) Schematic diagram of the vertical electric dipole model in a uniform half-space, and (B) schematic diagram of its mesh discretization.

The computational results of the homogeneous half-space are compared and analyzed against the results obtained using COMSOL (Zheng and Zhao, 2023). As shown in Figure 4, the finite element solution closely matches the curve obtained using COMSOL. Based on calculations, the relative error between the two is within ±5%. Therefore, it can be concluded that the algorithm proposed in this paper is reliable and trustworthy.

FIGURE 4 | (A) Comparison of the electric field results of the vertical electric dipoles in a uniform half space and (B) the relative error.

4.2 3-D model verification

To verify the computational accuracy of the algorithm for complex geoelectric models, a three-dimensional model (Liu Y. J. et al., 2020) is designed. The electrical source has a length of 1,000 m, is arranged along the y-direction, and its center point is located at the surface (0,0,0). The transmitting current is set to 1 A. Three measurement points are arranged along the x-direction, and they have coordinates of A (−500,0,0), B (−1050,0,0), and C (−2000,0,0). Local refinement is applied to the mesh at the transmitter and measurement points (Figure 5).

FIGURE 5 | (A) Section diagram of a three-dimensional complex model and (B) schematic diagram of its mesh discretization.

Figure 6 compares the numerical results of the electric field component Ey at the three measurement points with the finite volume calculation results of Liu et al. (Liu Y. J. et al., 2020). It can be observed from Figure 6 that the results fit well, with errors all less than 5%. This demonstrates the accuracy of the algorithm proposed in this paper.

FIGURE 6 | Results of the Ey component of the 3-D model in ground mode.

5 RESPONSE ANALYSIS OF SHALE GAS FRACTURING MONITORING MODEL

To theoretically explore the capability of the borehole transient electromagnetic method for the dynamic monitoring of shale gas hydraulic fracturing, a three-dimensional model (Figure 7) is designed. In this model, a layer of shale with a thickness of 300 m and resistivity of 40 Ω·m is located 2000 m below sea level. Above the shale layer, there is limestone with a thickness of 700 m and a resistivity of 2000 Ω·m, as well as mudstone with a thickness of 1,300 m and resistivity of 100 Ω·m. Below the shale layer, a layer of limestone with a resistivity of 2000 Ω·m and thickness of 4,300 m overlies a limestone substrate with a resistivity of 1,000 Ω·m. The wellhead is located at (0,0), where a transmitting source with a length of 2000 m along the z-direction is deployed. The transmitting current is set to 1 A. The measurement points are distributed within an x range of 200 m–5,000 m and a y range of −1,500 m–1,500 m, with a mesh size of 200 m × 200 m. Two hydraulic fracturing stages are conducted within the shale layer, each with a fracture range of 1,200 m × 1,000 m × 300 m. After fracturing, the resistivity changes to 5 Ω·m. Three observations are made at the surface to observe the response of the electric field before hydraulic fracturing, after the first fracturing stage, and after the second fracturing stage. The radial electric field is analyzed.

FIGURE 7 | Schematic diagram of the shale fracturing model.

Figure 8 shows the response curve at observation point (2,200, 300, 0) and the difference curve between after two hydraulic fracturing stages and before fracturing. It can be observed that significant changes in the electric field response occur at 30 ms and 50 ms. To better illustrate the distribution pattern of the underground electric field at different times, the response profiles of the electric field at 10 ms, 30 ms, 50 ms, 100 ms, and 500 ms are plotted in Figure 9.

FIGURE 8 | (A) Er response curve observed at the observation point (2200, 300, 0) at three times, and (B) the difference in Er between the first and second stages of fracturing and before fracturing.

FIGURE 9 | Response profiles of the electric field Er at 10 ms, 30 ms, 50 ms, 100 ms, and 500 ms, (rows one through five, respectively), and the response in the pre-fracturing, first-stage fracturing, and second-stage fracturing periods (columns one through three, respectively).

Figure 9 displays the response profiles of the electric field before and after fracturing at 10 ms, 30 ms, 50 ms, 100 ms, and 500 ms. It can be seen from Figure 9 that before fracturing, the contours of the electric field are relatively smooth from the early to late stages, and the electric field response exhibits faster attenuation in the near field and slower attenuation in the far field. The contours of the electric field before and after fracturing are similar at 10 ms. After 10 ms, significant differences in the electric field response values can be observed between the first and second fracturing stages. After 30 ms, clear boundaries of the resistivity changes can be seen (Figure 9). At 500 ms, it is evident that the electric field values are smaller in the low-resistivity body than in the surrounding area, demonstrating the capability of this method in detecting anomalies.

To further analyze the changes in the electric field response values before and after hydraulic fracturing and to verify the applicability of this method in the dynamic monitoring of shale gas fracturing, we created difference profile maps of the electric field response at 10 ms, 30 ms, 50 ms, 100 ms, and 500 ms (Figure 10). As can be seen from Figures 10, at 10 ms, there is a significant change in the field values in the anomaly area, and the change is greater in the second fracturing stage compared to the first fracturing stage and is greater in the fracturing stages compared to no fracturing. The size and location of the anomaly body can be distinguished based on the field values. At 30 ms, the decay rate of the electric field values near the anomaly body is slower than that without fracturing. Similarly, it can be observed that the decay rate of the electric field response values near the anomaly is slower in the second fracturing stage than in the first fracturing stage. This is because the shale becomes a low-resistivity body after fracturing, leading to a decrease in its resistivity and the formation of significant current pathways in the low-resistivity body, resulting in significant changes in the electric field in the fracturing area. In the late stage, the residual values near the low-resistivity body are smaller than those in the surrounding area, which is consistent with the decay law of the electromagnetic field near the low-resistivity body. In conclusion, based on the contour maps of and the residual contour maps of , the significant changes in the electric field response before and after hydraulic fracturing in a shale gas reservoir can be clearly observed, confirming the effectiveness and feasibility of using the borehole transient electromagnetic method for dynamic monitoring of shale gas fracturing.

FIGURE 10 | Response profiles of the electric field Er fracturing differential value in the first stage (column one) and second stage (column two) of Er fracturing at 10 ms, 30 ms, 50 ms, 100 ms, and 500 ms (rows one through five, respectively).

6 CONCLUSION

In this study, we applied the unstructured vector finite element method to three-dimensional forward simulation of the borehole transient electromagnetic (TEM) method for the dynamic monitoring of shale gas fracturing. The effectiveness of the borehole TEM method in monitoring the dynamic changes in underground mineral deposits and oil and gas reservoirs with resistivity differences was demonstrated. Based on the analysis of the response, the following conclusions were attained:

(1) Under the condition of a homogeneous half-space, the algorithm’s correctness was verified by comparing the finite element solution proposed in this study with the COMSOL solution. To further validate the accuracy of the algorithm, comparisons were made with the finite volume method in surface mode. It was demonstrated that the algorithm can effectively perform three-dimensional forward simulations of the borehole TEM method in complex models.

(2) Numerical analysis of underground shale gas reservoir models revealed that the borehole TEM method can effectively characterize the electrical differences before and after shale gas fracturing, thereby achieving high-precision dynamic monitoring of shale gas fracturing.

At present, many scholars have carried out the research of borehole to ground transient electromagnetic method, but it is rarely applied to the dynamic monitoring of shale gas fracturing. With the help of this algorithm proposed in this study allows for the analysis of the patterns of the borehole TEM method in the dynamic monitoring of unconventional oil and gas fracturing. This provides theoretical guidance for the dynamic monitoring of unconventional oil and gas fracturing in practical production processes.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

WZ: Conceptualization, Formal Analysis, Methodology, Software, Writing–original draft. XX: Conceptualization, Methodology, Resources, Writing–review and editing. LZ: Formal Analysis, Resources, Writing–review and editing. XW: Software, Validation, Writing–review and editing. LY: Supervision, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Natural Science Foundation of Key Project of China, grant number 42030805; the National Natural Science Foundation of China, grant number 42274103; and the Science and Technology Department Project of China Petroleum & Chemical Corporation, grant number P22163.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Ansari, S., and Farquharson, C. G. (2014). 3D finite–element forward modeling of electromagnetic data using vector and scalar potentials and unstructured grids. Geophysics 79, 149–165. doi:10.1190/geo2013-0172.1

Commer, M., Helwig, S. L., Hördt, A., Scholl, C., and Tezkan, B. (2006). New results on the resistivity structure of Merapi Volcano (Indonesia), derived from three-dimensional restricted in version of long-offset transient electromagnetic data. Geophys. J. Int. 167, 1172–1187. doi:10.1111/j.1365-246x.2006.03182.x

Cuevas, N. (2012). “Casing effect in borehole-surface (surface-borehole) EM fields,” in 74th Annual International Conference and Exhibition, EAGE//Extended Abstracts, P201.

Ghada, M. S., and Al-Najim, F. A. (2015). The transient electromagnetic field created by electric line source on a plane conducting Earth. Int. J. Mech. Appl. 5, 16–22. doi:10.5923/j.mechanics.20150501.03

Haroon, A., Adrian, J., Bergers, R., Gurk, M., Tezkan, B., Mammadov, A. L., et al. (2015). Joint inversion of long-offset and central-loop transient electromagnetic data: application to a mud volcano exploration in Perekishkul, Azerbaijan. Geophys. Prospect. 63, 478–494. doi:10.1111/1365-2478.12157

He, J. S., and Xue, G. Q. (2018). Review of the key techniques on short-offset electromagagnetic detection. Chin. J. Geophys. 61, 1–8. doi:10.6038/cig2018L0003

Hördt, A., Druskin, V. L., Knizhnerman, L. A., and Strack, K. M. (1992). Interpretation of 3-D effects in long-offset transient electromagnetic (LOTEM) soundings in the Münsterland area/Germany. Geophysics 57, 1127–1137. doi:10.1190/1.1443327

Hu, W. B., Shen, J. S., and Yan, L. J. (2022). Principle and application of controllable source electromagnetic method for reservoir fluid prediction. China: Science Press.

Hu, Z. Z., He, Z. X., Li, D. C., Liu, Y. X., Sun, W. B., Wang, C. F., et al. (2014). Reservoir monitoring feasibility study with time lapse magnetotellurics survey in Sebei Gas Field. Oil Geophys. Prospect. 49, 997–1005. doi:10.13810/j.cnki.issn.1000-7210.2014.05.051

Ke, G. P., and Huang, Q. H. (2009). 3D forward and inversion problems of borehole-to-surface electrical method. Acta Sci. Nat. Univ. Pekin. 45, 264–272. doi:10.13209/j.0479-8023.2009.041

Keller, G. V., Pritchard, J. I., Jacobson, J. J., and Harthill, N. (1984). Megasource time-domain electromagnetic sounding methods. Geophysics 49, 993–1009. doi:10.1190/1.1441743

Li, J. H., He, Z. X., and Xu, Y. X. (2017). Three-dimensional numerical modeling of surface-to-borehole electromagnetic method for monitoring reservoir. Appl. Geo-physics 14, 101–111. doi:10.1007/s11770-017-0643-8

Liu, R., Liu, J., Wang, J., Liu, Z., and Guo, R. W. (2020a). A time-lapse CSEM monitoring study for hydraulic fracturing in shale gas reservoir. Mar. Petroleum Geol. 120, 104545. doi:10.1016/j.marpetgeo.2020.104545

Liu, Y. J., Yogeshwar, P., Hu, X. Y., Peng, R. H., Tezkan, B., Mörbe, W., et al. (2020b). Effects of electrical anisotropy on long-offset transient electromagnetic data. Geophys. J. Int. 222, 1074–1089. doi:10.1093/gji/ggaa213

Mitsuhata, Y. J., Uchida, T., and Amano, H. (2002). 2.5-D in-version of frequency-domain electromagnetic data generated by a grounded-wire source. Geophysics 67, 1753–1768. doi:10.1190/1.1527076

Müller, M., Hördt, A., and Neubauer, F. (2002). Internal structure of Mount Merapi, Indonesia, derived from long-offset transient electromagnetic data. J. Geophys. Res. Solid Earth 107. doi:10.1029/2001jb000148

Orange, A., Key, K., and Constable, S. (2009). The feasibility of reservoir monitoring using time-lapse marine CSEM. Geophysics 74, 21–29. doi:10.1190/1.3059600

Peng, R. H., Hu, X. Y., Liu, Y. X., and He, Z. X. (2012). “The feasibility study of time-lapse two dimensional magnetotelluric for reservoir monitoring,” in The 28th Annual Meeting of the Chinese Geophysical Society, Beijing, China.

Strack, K. M. (1984). The deep transient electromagnetic sounding technique: first field test in Australia. Explor. Geophys. 15, 251–259. doi:10.1071/eg984251

Um, E. S., Harris, J. M., and Alumbaugh, D. L. (2010). 3D time–domain simulation of electromagnetic diffusion phenomena: a finite–element electric–field approach. Geophysics 75, 115–126. doi:10.1190/1.3473694

Wang, X. Y., Yan, L. J., and Mao, Y. R. (2022). Simulation and analysis of dynamic monitoring of oil and gas reservoir based on grounded electric source TEM. Oil Geophys. Prospect. 57, 459–466. doi:10.13810/j.cnki.issn.1000-7210.2022.02.023

Wang, Z. G., He, Z. X., and Liu, Y. (2006). Research of three-dimension modeling and anomalous rule on borehole-ground DC method. Chin. J. Eng. Geophys. 3, 87–92.

Wang, Z. G., He, Z. X., and Wei, W. B. (2007). Research on 3D modeling of borehole vertical bipole using body integral equation. Prog. Geophys. 22, 1802–1808.

Wirianto, M., Mulder, W. A., and Slob, E. C. (2010). A feasibility study of land CSEM reservoir monitoring in a complex 3-D model. Geophys. J. Interna-tional 181, 741–755. doi:10.1111/j.1365-246x.2010.04544.x

Xie, X. B., Zhou, L., Yan, L. J., and Hu, W. B. (2016). Remaining oil detection with time-lapse long offset& window transient electromagnetic sounding. Oil Geophys. Prospect. 51, 605–612. doi:10.13810/j.cnki.issn.1000-7210.2016.03024

Zhang, J. Q. (2018). Finite-length wire Source3D electromagnetic forward modeling of vertical. Beijing, China: China University of Geosciences.

Zhao, P. Q., Hao, H. M., Ni, T. L., Li, H. G., Tao, Z. Q., and Ma, Y. H. (2019). A novel technology: reservoir seepage geophysics. Oil Geophys. Prospect. 54, 719–728. doi:10.13810/j.cnki.issn.1000-7210.2019.03.028

Zheng, H. B., and Zhao, X. M. (2023). Application of transient electromagnetic method based on COMSOL simulation in tunnel advance geological prediction. Highway 68, 469–474.

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Copyright © 2024 Zhang, Xie, Zhou, Wang and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 10 April 2024
doi: 10.3389/feart.2024.1394491

Simulation and optimization of unstable dynamic propagation of multiple fractures in the shale formation

Jue Wang¹*, Genbo Peng², Lei Zhang³, Ziyuan Cong² and Buqin Hu²

¹Department of Petroleum Engineering, Northeast Petroleum University, Daqing, China

²School of Environment, Liaoning University, Shenyang, China

³Luliang Oilfield Operation Area of Xinjiang Oilfield Branch Company, PetroChina, Karamay, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Xiaoran Wang, China University of Mining and Technology, China
Jia Tao, SINOPEC Petroleum Exploration and Production Research Institute, China
Sai Wang, New Mexico Institute of Mining and Technology, United States

* Correspondence: Jue Wang, wangjue@nepu.edu.cn

Received: 01 March 2024
Accepted: 18 March 2024
Published: 10 April 2024

Citation: Wang J, Peng G, Zhang L, Cong Z and Hu B (2024) Simulation and optimization of unstable dynamic propagation of multiple fractures in the shale formation. Front. Earth Sci. 12:1394491. doi: 10.3389/feart.2024.1394491

Multi-cluster hydraulic fracturing of horizontal wells is a well-adopted technique with high efficiency to increase the production of tight and shale formations. However, the stress shadows among clusters pose challenges to the synchronous propagation of hydraulic fractures during multi-cluster fracturing. In order to explore the fracture propagation mechanism and characteristics under the influence of stress shadow in low-spacing staged multi-cluster fracturing, a three-dimensional hydraulic fracturing model was generated using a lattice-based method. This model considered the impact of geological and engineering parameters on the propagation behavior of multiple fractures in shale formation. A variable pumping approach is adopted, where the fracturing fluid is initially injected at a high rate and then transitioned to a lower rate. Afterward, a method was proposed to quantitatively assess the extent of fracturing in a specific area (i.e., the stimulated area), considering the impact of stress shadow within a single stage. The simulation results demonstrated significant differences in the fracture stimulation area due to the influence of each parameter in the case of uncontrollable geological factors and controllable engineering factors. An increase in both Young’s modulus and stress anisotropy of the reservoir results led to a corresponding increase in the total fracture stimulation area. As the principal stress orientation increased, the fracture stimulation area gradually decreased. In terms of operational parameters, the stimulated area of hydraulic fractures gradually decreased as the fracture spacing increased. With increasing injection rate, the stimulated area initially expanded and then decreased, peaking at an injection rate of 0.04 m³/s. These findings can provide valuable insights into the propagation behavior of multi-cluster hydraulic fractures under uncertain parameters, with significant implications for future engineering applications.

Keywords: lattice-based method, stress shadow, multi-cluster fractures, stimulated area, pumping mode

1 INTRODUCTION

Shale and tight reservoirs are preferred for the exploration and development of unconventional oil and gas resources. In order to increase production, horizontal wellbores with multi-cluster hydraulic fracturing need to be widely adopted. As the key factor affecting horizontal well multi-cluster fracturing technology, fracture stress interference has been emphasized in several studies. Nevertheless, an inadequate quantitative understanding of the impact of fracture stress shadows between fractures results in a weak foundation for designing fracture cluster spacing.

Numerous studies have been conducted on stress shadow during the propagation of multiple hydraulic fracturing (Geyer and Nemat-Nasser, 1982; Pollard and Aydin, 1988; Abass et al., 1996; Olson and Dahi-Taleghani, 2009; Bunger et al., 2011; Miller et al., 2011; Xing et al., 2018; Cong et al., 2022; Zhang et al., 2022; Li et al., 2023a). Bunger et al. (2011) investigated the effect of fracture initiation sequence on the deflection angle during fracture propagation through laboratory model experiments. These short fractures were deflected due to stress interference caused by long fractures. Zheng et al. (2020) explored the fracture extension in sandstone specimens under true triaxial stress conditions, considering both simultaneous and sequential fracturing. The experimental results showed that the distribution of fractures exhibited asymmetry due to the influence of pore pressure and stress field factors. Zhou et al. (2018) improved the pumping system to fracture large shale samples. The initiation and propagation of single fractures were observed in most cases. In addition to inhibiting the propagation of adjacent fractures, the stress shadow effect between fractures also causes the branching of adjacent fractures (Sani et al., 2015; Ugueto et al., 2016; Fu et al., 2019; Xiao and Xiao, 2019; Xie et al., 2019; Cong et al., 2021; Tian et al., 2022; Li et al., 2023b). The fracture initiation sequence can change the original stress field of the formation and generate stress shadows, thus influencing the morphology of the subsequent fractures. In contrast, the pumping mode can alleviate the effect of stress shadow to some extent. However, physical experiments can only provide qualitative insights into fracture propagation and are subject to several restrictions, including complex experimental preparation, high costs, lab sample scale effects, and monitoring tools.

A series of numerical methods and models have been proposed to explore the effects of stress shadows and complex stress fields on fracture propagation (Bakhshi et al., 2019; Zhang et al., 2024). Current research primarily focuses on geological conditions and operational means. Sheng et al. (2018) analyzed the interaction between different fractures by simulating the five-stage hydraulic fracturing treatment using the extended finite element method. The results showed that the fracture deflection increased with decreasing fracture spacing and stress difference, and the fracture deflection was insensitive to the increase of Young’s modulus. Based on the extended finite element method, Wang et al. (2018) investigated the effect of pre-existing fractures on the initiation and expansion of the subsequent fracture using the cohesion unit. The results showed that the propped width of the previous fracture, the fracture spacing, and Young’s modulus of the reservoir were the main factors in controlling the injection pressure of the subsequent fracture. Liu et al. (2020) studied shale multi-cluster hydraulic fracturing using XSite software based on a particle-based simulation calculation method. The results showed significant effects of stress anisotropy and cluster spacing on the geometry of the extended fracture. Multi-cluster fractures exhibit varying extension conditions in different geological settings. Previous scholars have conducted several studies on the effects of operational engineering parameters to enhance the uniform extension of multi-cluster fractures. Han et al. (2020) investigated the fracture extension paths under neighboring perforations at different injection rates by extended finite elements. The simulation results suggested that a closer increment of the injection rate of neighboring perforations increases the stress-shadow area between the fractures. Duan et al. (2020) investigated the evolutionary characteristics of simultaneously induced fractures using a discrete element method. The effects of field stress and engineering parameters on fracture morphology were also explored. The results showed that the higher effective stress anisotropy counteracted the stress-shadow effect, forcing the fractures to propagate in the direction of maximum stress and forming relatively long parallel fractures. Increasing the spacing can alleviate the stress-shadow effect, while the effects of injection rate and fluid viscosity on middle fracture interactions are not significant (Yamamoto et al., 2004; Kumar and Ghassemi, 2006; Lecampion, 2008; Ooi et al., 2012; Wu et al., 2012; Dohmen et al., 2014; Sobhaniaragh et al., 2018). Researchers have conducted numerous studies on geological and engineering parameters. However, the fracturing effect has only been studied from the viewpoint of a single factor. In reality, hydraulic fracturing with multiple clusters of fracture extensions is the outcome of the combined effects of various engineering parameters under specific formation conditions. Hence, this study examines the impact of geological factors on fracture extension and proposes a method to enhance multi-cluster fracture extension. It involves optimizing the spacing of fracture clusters, injection rate, and pumping method based on controllable engineering parameters corresponding to the specific geological conditions of the reservoir. Since operational parameters and geological properties are key factors affecting the interactions between fractures, it is necessary to comprehensively investigate the effects of these parameters on the shape and geometry of fractures.

In this work, a three-dimensional model is constructed using hydraulic fracturing software based on the discrete lattice theory and synthetic rock mass method. The effect of a relatively short cluster spacing (5 m or less) on fracture propagation behavior was investigated through a series of case studies, considering operational parameters (injection rate, the number of fractures) and reservoir parameters (reservoir Young’s modulus, stress anisotropy, principal stress direction). The findings can provide valuable theoretical insights for the fracturing spacing design in horizontal shale wells.

2 SIMULATION METHOD

The XSite lattice simulator developed by the Itasca Consulting Group is employed to simulate the propagation of hydraulic fracture using the discrete element method (DEM) and synthetic rock mass (SRM) (Damjanac and Cundal, 2016; Xing et al., 2018). This advanced simulator allows for dynamic simulation of the fracturing process without predefining the propagation path. The smooth joint model (SJM) represents pre-existing joints in the simulator, while the lattice effectively simulates fracture propagation in rock materials.

2.1 Mechanical model

The lattice nodes are connected by normal and shear springs and randomly distributed within the simulator to accurately simulate the behavior of rock masses. In order to replicate the translational motion of each node, a precise central difference equation for the linear momentum equilibrium and displacement-velocity relationships was adopted in Eqs 1, 2 (Damjanac and Cundal, 2016):

where and represent the velocity and position of component i (i = 1, 3) at time t, respectively; represents the sum of all force components acting on mass m at the time step of . Similarily, the angular velocities of the component can be obtained as Eq. 3:

where represents the sum of all moment components acting on the moment of inertia I node. Changes in relative displacement and spring force are determined by the node velocities in Eqs 4, 5:

where N denotes “Normal”; S denotes “shear”; and represent the spring normal and shear stiffnesses, respectively.

The following equations show the correlation between the micro spring and the macro rock mass in terms of tensile strength and shear strength in Eqs 6, 7:

where FNmax represents the spring tensile strength; FSmax denotes the spring shear strength; and represent the calibration coefficient of tensile and shear strength, respectively; T represents the tensile strength of macro rock mass; indicates the friction coefficient; C represents the shear strength of macro rock; R denotes the resolution.

2.2 Flow model

Fluid flow is simulated within the intact rock matrix and the joints, where the flow in the fractures can be resolved through a network of fluid nodes connected by pipes. Additionally, the matrix flow representing the leakage to the intact rock is considered, which is dependent on the permeability of the rock. The estimation of the fracture flow can be approximated using the lubrication equation. On this basis, the flow down from node “A” to node “B” can be determined in Eq. 8 (Damjanac and Cundal, 2016; Wan et al., 2020):

where a is the hydraulic aperture; μf is the viscosity of the fluid; PA and PB represent fluid pressures at nodes “A” and “B,” respectively; Z is the elevations of nodes; ρw represents fluid density; g represents the acceleration due to gravity; kr represents the relative permeability; represents a dimensionless calibrated parameter, which is a calibrated function of model resolution (Damjanac and Cundal, 2016).

2.3 Hydro-mechanical coupling

In XSite, hydraulic fracture propagation is characterized by the coupling of mechanical and fluid flow processes. Both the aperture and deformation mechanism of the model have an impact on the permeability of the cracks. The mechanical model calculates deformation and damage based on changes in fluid pressure, and the changes in fluid pressure are also affected by deformation (Damjanac and Cundal, 2016). The hydro-mechanical coupling incorporated within these models is described as follows:

1. The pore size or the deformation of the solid model determines the fracture permeability.

2. The deformation and strength of the solid model are influenced by fluid pressure, making it necessary to calculate the effective stresses.

3. The fluid pressures are influenced by the deformation of the solid model. Specifically, the code can predict alterations in fluid pressure during undrained conditions.

3 MODEL SETUP

In this study, an ideal model is developed with reference to actual geological conditions. The detailed geometry of the model is depicted in Figure 1 (three-dimensional stereogram), with a model size of 40*40*40 m³ and four initiation fractures along the horizontal wellbore in the center of the model. Table 1 provides comprehensive information on the physical properties and parameters for fracturing fluid injection in this model. Since the model was specifically designed to visualize the multiple fracture propagation in the shale-rich area, it is considered a pure shale without a barrier layer. All cases in Section 4 are presented in the main view to visualize the extended morphology of the multi-stage fractures more clearly.

FIGURE 1 | Numerical model of multi-cluster hydraulic fracture propagation.

TABLE 1 | Rock mechanical parameters and fracturing fluid pumping parameters used for the simulation study.

4 RESULTS AND DISCUSSION

4.1 Geological setting effect

4.1.1 Stress anisotropy effect

Differences in principal stress in the reservoir can significantly affect the complexity of the fracture formation during the fracturing. Stress anisotropy (SA) is usually used to characterize the horizontal principal stress difference. In order to investigate the influence of reservoir stress anisotropy on fracture extension, the maximum principal stress is kept constant at 8 MPa, and the minimum principal stress is set at levels of 7 MPa, 6 MPa, 5 MPa, 4 MPa, and 3 MPa. The injection rate is constant at 0.04 m³/s. Figure 2 reveals that the deflection degree of fracture extension is significantly higher in low-stress anisotropy cases compared to high-stress anisotropy cases. This result indicates that fracture propagation in a low-stress anisotropy field is susceptible to external stress field disturbance. In a high-stress anisotropy field, cracks are resistant to external stress fields and continue to propagate along their original direction.

FIGURE 2 | The propagation model of multi-fractures in the formation at varying levels of horizontal principal stress differences of 1 MPa (A), 2 MPa (B), 3 MPa (C), 4 MPa (D), and 5 MPa (E).

As shown in Figure 3A, the total stimulated area of the fracture increases with increasing stress anisotropy. Comparison of the stimulated area of the fracture at the same point in each case reveals that the stimulated area of each fracture consistently increases with increasing stress anisotropy. When there is a high level of stress anisotropy, the fracture is more likely to propagate along the direction of maximum principal stress. Additionally, a larger stimulated area allows the fracture to contact more reservoirs, thereby increasing the overall output. Figure 3B demonstrates that the fracture area expands rapidly with time, indicating that the fracture extension is greatly facilitated by the high-stress anisotropy of the fracture.

FIGURE 3 | (A) The quantitative analysis of the multi-fracture area at varying horizontal principal stress differences. (B) The dynamic curve of the stimulated area during multi-cluster fracturing in the formation at varying horizontal stress anisotropy.

4.1.2 Principal stress orientation effect

In most studies, the orientation of the horizontal wellbore is assumed to coincide with the minimum horizontal principal stress. However, horizontal wellbores are usually oriented at an angle to the minimum principal stress in actual oilfield operations. In this section, the effect of stress field direction on fracture extension is analyzed by changing the direction of the horizontal principal stress. Here, the principal stress orientation (PSO) is defined as the acute angle between the direction of the horizontal minimum principal stress and the direction of the wellbore. For example, when the PSO is 0°, the direction of the minimum principal stress is consistent with the direction of the wellbore, as shown in Figure 4A. Notably, when the PSO increases, the number of interconnected fractures increases accordingly. As a result, the overall length of the fracture extension is affected.

FIGURE 4 | The propagation model of multi-fractures in the formation at different principal stress orientations of 0° (A), 20° (B), 40° (C), and 60° (D).

Figure 5A shows that the total stimulated area decreases with increasing stress direction, where the stimulated area of the cases with the PSO of 20° and 40° are numerical approximations. It can be seen from Figure 4B that when PSO is 20°, the fracture is minimally influenced by the orientation of the principal stress. Furthermore, its propagation direction remains perpendicular to the wellbore. Figure 4C shows that fractures 1 and 4 experience significant bending when the fracture PSO is 40°. This bending gradually occurs along the direction of the maximum principal stress because these fractures are influenced by the principal stress field. An intricate stress distribution consisting of a principal stress field and fracture stress shadow arises between fractures 2 and 3, resulting in the contact of these fractures throughout the extension stage. Hence, the primary difference between the two cases is that the middle two fractures span from being separate to interconnected. However, this alteration does not significantly affect the final area of fracture stimulation. Consequently, the fracture stimulation areas are similar in both cases. When the PSO is 60°, side fractures propagate parallel to the maximum principal stress, whereas the two middle fractures interconnect with fractures near the wellbore after reaching a certain length due to the stress shadow effect. Ultimately, the case of 60° PSO has the least stimulation area (Figure 4D). Figure 5B depicts the gradual increase in the stimulation area over time. In the initial fracture extension stage, the direction of the principal stress has a negligible effect on the fracture propagation. As a result, the stimulation area of the fracture remains closed. When the fracture extends beyond a certain distance, the stress shadows gradually increase, and the interference becomes more pronounced as the PSO increases.

FIGURE 5 | (A) The quantitative analysis of the multi-fractures area at different principal stress orientations. (B) The dynamic curve of the stimulation area during multi-cluster fracturing in the formation at different principal stress orientations.

4.1.3 Young’s modulus effect

The primary reason for employing fracturing treatments to stimulate unconventional reservoirs is their inherent brittleness, which significantly influences the propagation of hydraulic fractures and the fracture shape. The conventional methods for assessing shale brittleness rely on Young’s modulus and Poisson’s ratio. Thus, Young’s modulus is used as the variable to study the impact of Young’s modulus on fracture propagation in the reservoir. The study investigates the impact of reservoir brittleness on the propagation of multi-cluster fractures by changing the reservoir modulus. Figure 6 shows that the fracture length gradually increases with increasing Young’s modulus. In low Young’s modulus reservoirs, the limited fracture extension despite the same injection volume can be attributed to the increased brittleness of the reservoir caused by high Young’s modulus (Qiu et al., 2021). This brittleness leads to greater fracture extension. Additionally, the low Young’s modulus reservoirs exhibit larger fracture width but shorter fracture length.

FIGURE 6 | The propagation model of multi-fractures in the formation at Young’s moduli of 10 MPa (A), 20 MPa (B), 27.7 MPa (C), 40 MPa (D), and 50 MPa (E).

Figure 7A demonstrates that the total stimulation area significantly increases with increasing Young’s modulus. This phenomenon can be attributed to the impact of Young’s modulus on fracture generation. The low brittleness reservoir promotes the generation of fractures with shorter lengths but wider widths, whereas the high brittleness reservoir exhibits the opposite characteristics. Moreover, the comparison shows that the difference in fracture area is small at lower Young’s modulus and large at higher Young’s modulus. This difference can be attributed to differences in reservoir stress conductivity. Since the reservoirs with a high Young’s modulus facilitate the stress conduction, the stress shadow near the fracture has a significant impact on fracture extension. Figure 7B depicts the relationship between the total stimulated fracture area and the duration of fracture fluid injection in reservoirs with different Young’s modulus values throughout the fracturing process. It can be seen that the fracture propagation is facilitated by a high Young’s modulus.

FIGURE 7 | (A) The quantitative analysis of the multi-fractures area across different formations with varying Young’s moduli. (B) The dynamic curve of the stimulated area during multi-cluster fracturing in the formation at different reservoir Young’s moduli.

4.2 Operational parameters effect

4.2.1 Fracture number effect

The number of fractures at each stage of the fracturing process is one of the most important factors affecting production. The number of fractures within a fixed distance affects the spacing between each fracture, resulting in stress shadows of different intensities. Cluster spacing for hydraulic fracturing in the Eagle Ford and Denver-Julesburg (DJ) basins is 5 m (Cheng, 2012; Lu, 2016; Somanchi et al., 2018). Thus, to analyze the impact of stress shadows and provide guidance for future fracturing treatments with relatively low cluster spacing, the propagation behaviors of fractures need to be examined. Four cases are illustrated in Figure 8, each with the same fluid injection volume. The number of fractures is set to 2, 3, 4, and 5, with corresponding fracture spacing of 15 m, 7.5 m, 5 m, and 3.75 m, respectively.

FIGURE 8 | Multi-cluster hydraulic fracturing numerical models with different fracture numbers: (A) Case of two fractures. (B) Case of three fractures. (C) Case of four fractures. (D) Case of five fractures.

Figure 9 shows the width and geometry of the fractures after extension in each case. Different colors of the fracture surface correspond to distinct distributions of fracture widths. It can be seen that the fractures with a large fracture spacing are symmetrically distributed on both sides of the wellbore, and side fractures extend outwards due to the stress shadow. However, when the fracture spacing is limited, the fracture tends to extend unevenly. Figure 9C illustrates that fractures propagate on both sides of the wellbore, while the central section of the fractures can only extend along one side due to the constraint of stress shadow. Figure 9D shows that in the case of five fractures, each fracture has a distinct propagation pattern. Overall, as the number of fractures increases within a fixed length stage, the effects of fracture stress shadows become more serious. As a result, the distribution of fracture fluid along the wellbore becomes uneven, leading to varying patterns and morphologies of fracture propagation.

FIGURE 9 | The propagation model of multi-fractures in the formation with different numbers of fractures: (A) Case of two fractures. (B) Case of three fractures. (C) Case of four fractures. (D) Case of five fractures.

The stimulated area of multi-stage hydraulic fracturing is shown in Figure 10. With the increase of fractures, the stimulated area of the fractures first increases and then decreases. When there are four fractures in a single stage (a fracture spacing of 5 m), the total stimulated area reaches its maximum. In the cases involving two fractures, the stimulated area of each fracture is nearly identical, suggesting a uniform dispersion of the fluid in both fractures. However, a greater number of fractures within a certain length results in more irregular dimensions of the fractures. As shown in Figure 9D, in the case of five fractures, the middle fracture (fracture 3) stops propagating under the influence of the strongest stress shadow, resulting in a stimulated area of zero. This result indicates that increasing the number of fractures in a single stage is not necessarily optimal when considering a certain stage length. Therefore, it is crucial to effectively exploit the stress shadow that exists between fractures.

FIGURE 10 | The quantitative analysis of the multi-fracture area with different numbers of fractures.

Figure 11 illustrates the temporal evolution of the cumulative total stimulated area for each fracturing case. It can be seen that the curves for the two fracturing cases are increasingly distant from the curves for other cases. It indicates that fewer fractures and longer fracture spacing cannot release the stimulation potential. However, as shown in the magnified image in the yellow section, factures grow slightly faster in the two-fracture case than in all other cases during the early extension stage. In the later stages of extension, the five-fracture case exhibits a greater fracture growth area than the other cases. This difference is because the wide fracture spacing produces less interference from fracture stress shadows in the early stage of fracture extension, enabling the fracture to grow rapidly. Moreover, in the late extension stage, the greater number of initiation fractures provides a larger stimulated area for the entire fracturing stage, even producing stronger stress shadow effects.

FIGURE 11 | The dynamic curve of the stimulated area during multi-cluster fracturing in the formation with different numbers of fractures.

4.2.2 Injection rate effect

Fracturing fluid injection rate is a crucial factor affecting fracture morphology. In this part, the number of fractures in a single stage is set to 4 (fracture spacing is 5 m). Discussions of final fracturing patterns and production rates are only meaningful if the total fracturing fluid injection is the same. Therefore, to ensure the total injection volume is consistent, different injection times are set for each case.

Figure 12 shows the differences in fracture morphology for different injection rates. As the fracture injection rate increases, the fracture extends symmetrically to both sides of the wellbore with a more balanced distribution, especially for the middle two fractures in all cases. For side fractures, the deflection angle of the fractures transitions from nearly straight to a certain level of curvature with increasing fracture injection rate. The difference between intermediate and side fractures can be attributed to variations in the stress disturbance level. As shown in Figure 12A, all three fractures exhibit asymmetric extension at an injection rate of 0.02 m³/s, except for the fracture on the left side. However, when the injection rate increases to 0.1 m³/s, the fractures extend more evenly with a greater degree of deflection, as shown in Figure 12E. In conclusion, a high injection rate can benefit the generation of symmetrical fractures. The high injection rate facilitates rapid fracture formation during the fracture initiation stage, allowing the fracture to have enough energy to expand towards the pre-set direction and reducing the effect of stress shadows.

FIGURE 12 | The propagation model of multi-fractures in the formation at different injection rates: (A) Injection rate of 0.02 m³/s. (B) Injection rate of 0.04 m³/s. (C) Injection rate of 0.06 m³/s. (D) Injection rate of 0.08 m³/s. (E) Injection rate of 0.1 m³/s.

Figure 13 illustrates the stimulated area for each fracture under the influence of different injection rates. Four fracture sizes are quantitatively analyzed for each case, and the effect of fracture injection rate on the stimulated area is compared. At low injection rates, the stimulated area of the side fracture is larger than that of the middle fractures. With the increase of injection rate, the stimulation area of side fractures decreases, and that of the middle fractures increases, resulting in a gradual approximation of the two fracture propagation modes. Therefore, a high injection rate is beneficial in promoting uniform distribution of multiple fracturing fluids. However, the uniform distribution of fluid also leads to increased stress interference between fractures, thereby reducing the total fracture stimulation area. Conversely, at a low injection rate case, although the fluid cannot be evenly distributed to each fracture, well-developed fractures can extend further due to reduced stress interference from other fractures, resulting in the largest total fracture area.

FIGURE 13 | The quantitative analysis of the multi-fractures area at different injection rates.

4.2.3 Pumping mode of fracturing fluid effect

Previous research has demonstrated that changing the injection rate of the fracturing fluid can effectively reduce the fracture initiation pressure and enhance the propagation capacity of hydraulic fractures (Patel et al., 2017; Ripudaman et al., 2018). Nevertheless, the suitability of a pumping mode with variable injection rates was barely investigated. Thus, this work analyzes the propagation behavior of multiple fractures under different pumping modes using a 3D lattice-based model. The fracturing fluid is pumped with four major models, as shown in Figure 14.

FIGURE 14 | The four fracturing fluid pumping models: (A) Model A; (B) Model B; (C) Model C; (D) Model D.

Figure 15 shows that the propagation of multiple fractures with a short spacing distance is significantly affected by the pumping mode. A study of the dynamic fracture propagation process shows that relatively high constant rate pumping results in a uniform distribution of fluid across the four fractures compared to low-rate pumping. As a result, by continuously pumping a fluid with a high injection rate into the fracture, the fluid in the fracture can be ensured to retain sufficient energy to resist the formation stress exerted by the surrounding rock. As a result, uniform fracture evolution can be achieved even under the effect of fracture interference of multiple fractures. In model C, fractures 1 and 4 exhibit a consistent and steady extension, while fractures 2 and 3 present a distorted condition. Under model D, all fractures except for fracture 3 are symmetrically propagate along both sides of the wellbore. The wellbore is pressurized at a high injection rate, leading to high breakdown pressures during the fracture initiation. It creates a tensile fracture that can quickly penetrate the stress-shadowed area without being affected by stress shadows. Additionally, the high injection rate generates high fluid pressures within the fracture, which further helps to overcome the influence of stress shadows (Lecampion, 2012).

FIGURE 15 | The propagation model of multi-fractures in the formation under different pumping modes: (A) Model A: low constant rate pumping of 0.02 m³/s; (B) Model B: high constant rate pumping of 0.1 m³/s; (C) Model C: increased pumping rate from 0.02 m³/s to 0.1 m³/s; (D) Model D: decreased pumping rate from 0.1 m³/s to 0.02 m³/s.

Figure 16 shows that the stimulation area of Model A is larger than that of Model B in the constant injection rate pumping mode. It indicates that a lower injection rate results in a larger fracture area, facilitating enhanced contact between the reservoir and wellbore. However, a low injection rate is not conducive to the uniform growth of adjacent fractures, which can also be proved by Figure 15. Under varying injection rates, the stimulation area of model D is larger compared to model C. Combined with Figures 15C, D, the pumping mode of model D facilitates the prompt evolution of fractures during the fracture initiation stage and mitigates the fracture interference. The above analysis demonstrates that the variable injection pumping mode with a decreasing injection rate of the fracturing fluid can achieve the optimal stimulation effect.

FIGURE 16 | The quantitative analysis of the multi-fractures area at different injection rates.

4.3 Parameters importance analysis

The key factors determining fracture propagation in unconventional reservoirs can be briefly classified into geological and engineering factors. The engineering factors are associated with engineering strategies, and the geological factors refer to the geomechanical properties of reservoirs. Therefore, the identification of reservoirs with favorable geological and mechanical characteristics as the target layer, in conjunction with suitable fracturing treatment, can facilitate the evolution of hydraulic fractures to enhance hydrocarbon recovery.

A weighted study of the impact of geological features on fracture propagation is useful for optimizing the selection of target reservoirs. In order to evaluate the effects of the stress anisotropy, principal stress orientation, and Young’s modulus on the fracture geometry and production, the dimensionless stimulated area is defined as the ratio of the stimulated area of all cases to that in the baseline case. A value of 1 represents the fracture dimensionless stimulated area for the baseline case. Table 2 presents the range of geological uncertain parameters of this study. The horizontal coordinate in Figure 17 represents the dimensionless stimulated area, illustrating the effect of uncertainty parameters on the growth of the fracture stimulation area. The order of uncertainty parameters on the vertical coordinate is determined by the absolute difference between the maximum and minimum deviation of the normalized fracture stimulation area, indicating the effectiveness of the fracturing treatment. The orange bar indicates a positive effect, whereas the blue bar represents a negative effect. The black vertical line in the center represents the baseline in all cases. It can be seen from Figure 17 that Young’s modulus of the reservoir is the most important factor affecting fracture growth, followed by reservoir stress anisotropy and principal stress direction. Therefore, Young’s modulus of the reservoir will be the primary consideration when selecting the target reservoir.

TABLE 2 | The range of uncertain parameters on the fracture growth.

FIGURE 17 | Tornado chart of the sensitivity studies based on the data of simulation cases.

Engineering parameters as operational controls provide the most effective means to optimize technology and enhance reservoir production. The perforation cluster spacing determines the fracture number at each stage. To increase the initial production rate of a single well, the spacing between perforation clusters is progressively reduced. However, the presence of stress shadows leads to uneven growth of multiple clusters in a single segment, which has been demonstrated in several studies (Miller et al., 2011; Bunger and Cardella, 2015). Generally, approximately 50% of the clusters are responsible for nearly 70% of the production. A similar phenomenon has been observed in this study (Figure 10).

In the early stage of hydraulic fracturing, a high fluid injection rate can help resist stress shadow around the wellbore. By gradually decreasing the fluid injection rate at the later stages, sufficient time can be reserved for further crack expansion. Furthermore, pumping modes with different injection rates can decrease the initiation pressure and facilitate the continuous propagation of fractures. The timing and magnitude of alterations in fluid injection rate must be carefully coordinated with the geological characteristics of the reservoir. Therefore, extensive experimental and theoretical investigations are needed to determine its optimal value to guide future studies.

5 CONCLUSION

In this paper, the lattice spring code XSite is used to study the influence of stress shadow between multiple hydraulic fractures during synchronous propagation. The simulation considers various factors, including stress anisotropy, principal stress orientation, reservoir Young’s modulus, fracture number, injection flow rate, and pumping mode. The conclusion of this study are as follows:

(1) The simultaneous propagation of several fractures results in stress shadow, which changes the local stress field and subsequently influences the direction of fracture propagation. Consequently, the fractures separate from each other and form complex non-planar shapes with branches in the cracks. The fractures are primarily perpendicular to the minimum principal stress, but side fractures are more noticeably deviated in the direction of the wellbore.

(2) Reservoir Young’s modulus has the greatest effect on fracture stimulation among all the factors that have been investigated. Enhancing Young’s modulus can enlarge the stimulated area for optimal production increase. Therefore, while selecting target reservoirs, fracturing reservoirs with a relatively high Young’s modulus can result in longer and larger stimulation fractures.

(3) The size of the fracture stimulation area initially increases and then decreases with increasing injection rate. At an injection rate of 0.04 m³/s, the maximum stimulation area is achieved. Additionally, higher injection rates facilitate uniform distribution of fluid volume within each fracture.

(4) The overall stimulated area of a single-stage fracture expands when a pumping pattern with decreasing injection rates is used for fracturing treatment. As a result, the extension capacity of hydraulic fracture is enhanced at higher and then lower injection rates.

This study focuses on the growth of multi-cluster fractures within a single stage. Subsequent research will be conducted on multi-stage hydraulic fracturing models for field scale.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

JW: Conceptualization, Writing–original draft. GP: Investigation, Writing–review and editing. LZ: Methodology, Writing–review and editing. ZC: Software, Writing–review and editing. BH: Formal Analysis, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the National Natural Science Foundation of China (52174024) and The research was supported by the Shenyang Science and Technology Talent Project of China (No. RC230286).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Abass, H. H., Hedayati, S., and Meadows, D. L. (1996). Nonplanar fracture propagation from a horizontal wellbore: experimental study. SPE Prod. Facil. 11, 133–137. doi:10.2118/24823-pa

Bakhshi, E., Rasouli, V., Ghorbani, A., Fatehi Marji, M., Damjanac, B., and Wan, X. (2019). Lattice numerical simulations of lab-scale hydraulic fracture and natural interface interaction. Rock Mech. Rock Eng. 52, 1315–1337. doi:10.1007/s00603-018-1671-2

Bunger, A. P., and Cardella, D. J. (2015). Spatial distribution of production in a Marcellus Shale well: evidence for hydraulic fracture stress interaction. J. Pet. Sci. Eng. 133, 162–166. doi:10.1016/j.petrol.2015.05.021

Bunger, A. P., Jeffrey, R. G., Kear, J., Zhang, X., and Morgan, M. (2011). “Experimental investigation of the interaction among closely spaced hydraulic fractures,” in 45th US Rock Mechanics/ Geomechanics Symposium, (San Francisco, California, USA, 26-29 June 2011).

Cheng, Y. (2012). Impacts of the number of perforation clusters and cluster spacing on production performance of horizontal shale-gas wells. SPE Reserv. Eval. Eng. 15, 31–40. doi:10.2118/138843-pa

Cong, Z., Li, Y., Pan, Y., Liu, B., Shi, Y., Wei, J., et al. (2022). Study on CO₂ foam fracturing model and fracture propagation simulation. Energy 238, 121778. doi:10.1016/j.energy.2021.121778

Cong, Z., Li, Y., Liu, Y., and Xiao, Y. (2021). A new method for calculating the direction of fracture propagation by stress numerical search based on the displacement discontinuity method. Comput. Geotech. 140, 104482. doi:10.1016/j.compgeo.2021.104482

Damjanac, B., and Cundall, P. (2016). Application of distinct element methods to simulation of hydraulic fracturing in naturally fractured reservoirs. Comput. Geotechnics 71, 283–294. doi:10.1016/j.compgeo.2015.06.007

Dohmen, T., Zhang, J., and Blangy, J. P. (2014). “Measurement and analysis of 3D stress shadowing related to the spacing of hydraulic fracturing in unconventional reservoirs,” in SPE annual technical conference and exhibition (Malaysia: Society of Petroleum Engineers). doi:10.2118/170924-ms

Duan, K., Kwok, F., Zhang, Q., and Shang, J. (2020). On the initiation, propagation and reorientation of simultaneously-induced multiple hydraulic fractures. Comput. Geotechnics 117, 103226. doi:10.1016/j.compgeo.2019.103226

Fu, W., Savitski, A. A., Damjanac, B., and Bunger, A. P. (2019). Three-dimensional lattice simulation of hydraulic fracture interaction with natural fractures. Comput. Geotechnics 107, 214–234. doi:10.1016/j.compgeo.2018.11.023

Geyer, J. F., and Nemat-Nasser, S. (1982). Experimental investigation of thermally induced interacting cracks in brittle solids. Int. J. Solids Struct. 18, 349–356. doi:10.1016/0020-7683(82)90059-2

Han, W., Cui, Z., and Zhang, J. (2020). Fracture path interaction of two adjacent perforations subjected to different injection rate increments. Comput. Geotechnics 122, 103500. doi:10.1016/j.compgeo.2020.103500

Kumar, D., and Ghassemi, A. (2016). A three-dimensional analysis of simultaneous and sequential fracturing of horizontal wells. J. Petroleum Sci. Eng. 146, 1006–1025. doi:10.1016/j.petrol.2016.07.001

Lecampion, B. (2008). An extended finite element method for hydraulic fracture problems. Commun. Numer. Methods Eng. 25, 121–133. doi:10.1002/cnm.1111

Lecampion, B. (2012). Hydraulic fracture initiation from an open-hole: wellbore size, pressurization rate and fluid-solid coupling effects. Pap. Present. A. T. 46th U.S. Rock Mechanics/Geomechanics Symposium, Chic. Ill .

Li, Y., Peng, G., Tang, J., Zhang, J., Zhao, W., Liu, B., et al. (2023a). Thermo-hydro-mechanical coupling simulation for fracture propagation in CO₂ fracturing based on phase-field model. Energy 284, 128629. doi:10.1016/j.energy.2023.128629

Li, Y., Wu, J., Zhang, J., Yang, H., Zeng, B., Xiao, Y., et al. (2023b). Optimization method of oriented perforation parameters improving uneven fractures initiation for horizontal well fracturing. Fuel 349, 128754. doi:10.1016/j.fuel.2023.128754

Liu, X., Rasouli, V., Guo, T., Qu, Z., Sun, Y., and Damjanac, B. (2020). Numerical simulation of stress shadow in multiple cluster hydraulic fracturing in horizontal wells based on lattice modelling. Eng. Fract. Mech. 238, 107278. doi:10.1016/j.engfracmech.2020.107278

Lu, Q. (2016). Unconventional reservoir perforating cluster spacing optimization method for staged-fracturing horizontal well. Malaysia: SPE Annu Tech. Conf. Exhib.doi:10.2118/184483-STUD023S099R025

Miller, C., Waters, G., and Rylander, E. (2011). “Evaluation of production log data from horizontal wells drilled in organic shales,” in North American Unconventional Gas Conference and Exhibition, (The Woodlands, Texas, USA, 14–16 June 2011).

Olson, J. E., and Dahi-Taleghani, A. (2009). Modeling simultaneous growth of multiple hydraulic fractures and their interaction with natural fractures. SPE Hydraulic Fracturing Technology Conference.

Ooi, E. T., Song, C., Tin-Loi, F., and Yang, Z. (2012). Polygon scaled boundary finite elements for crack propagation modelling. Int. J. Numer. Methods Eng. 91 (3), 319–342. doi:10.1002/nme.4284

Patel, S. M., Sondergeld, C. H., and Rai, C. S. (2017). Laboratory studies of hydraulic fracturing by cyclic injection. Int. J. Rock Mech. Min. Sci. 95, 8–15. doi:10.1016/j.ijrmms.2017.03.008

Pollard, D. D., and Aydin, A. (1988). Progress in understanding jointing over the past century. Geol. Soc. Am. Bull. 100, 1181–1204. doi:10.1130/0016-7606(1988)100<1181:piujot>2.3.co;2

Qiu, D., Zhang, J., Lin, Y., Liu, J., Rabiei, M., Rasouli, V., et al. (2021). Lattice numerical simulations of hydraulic fracture propagation and their geometry evolution in transversely isotropic formations. Front. Earth Sci. 9, 787736. doi:10.3389/feart.2021.787736

Ripudaman, M., Eric, C., Prateek, B., Philip, C., and Mukul, M. (2018). Strategies for effective stimulation of multiple perforation clusters in horizontal wells. SPE Prod. Operations 33 (3), 539–556. doi:10.2118/179126-pa

Sani, A. M., Podhoretz, S. B., and Chambers, B. D. (2015). “The use of completion diagnostics in haynesville shale horizontal wells to monitor fracture propagation, well communication, and production impact,” in Proceedings of the SPE/CSUR Unconventional Resources Conference, Calgary, AB, Canada.

Sheng, M., Li, G., Sutula, D., Tian, S., and Bordas, S. P. (2018). XFEM modeling of multistage hydraulic fracturing in anisotropic shale formations. J. Petroleum Sci. Eng. 162, 801–812. doi:10.1016/j.petrol.2017.11.007

Sobhaniaragh, B., Mansur, W. J., and Peters, F. C. (2018). The role of stress interference in hydraulic fracturing of horizontal wells. Int. J. Rock Mech. Min. Sci. 106, 153–164. doi:10.1016/j.ijrmms.2018.04.024

Somanchi, K., Brewer, J., and Reynolds, A. (2018). Extreme limited-entry design improves distribution efficiency in plug-and-perforate completions: insights from FiberOptic diagnostics. SPE Drill. Complet 33, 298–306. doi:10.2118/184834-PA

Tian, F., Jin, Y., Jin, F., Ma, X., Shi, L., Zhang, J., et al. (2022). Multi-fracture synchronous propagation mechanism of multi-clustered fracturing in interlayered tight sandstone reservoir. Sustainability 14 (14), 8768. doi:10.3390/su14148768

Ugueto, C., Gustavo, A., Huckabee, P. T., Molenaar, M. M., Wyker, B., and Somanchi, K. (2016). “Perforation cluster efficiency of cemented plug and perf limited entry completions; Insights from fiber optics diagnostics,” in Proceedings of the SPE Hydraulic Fracturing Technology Conference, The Woodlands, TX, USA.

Wan, X., Rasouli, V., Damjanac, B., and Pu, H. (2020). Lattice simulation of hydraulic fracture containment in the North Perth Basin. J. Petroleum Sci. Eng. 188, 106904. doi:10.1016/j.petrol.2020.106904

Wang, B., Zhou, F., Zou, Y., Liang, T., Wang, D., Hu, J., et al. (2018). Effects of previously created fracture on the initiation and growth of subsequent fracture during TPMSF. Eng. Fract. Mech. 200, 312–326. doi:10.1016/j.engfracmech.2018.08.002

Wu, R., Kresse, O., Weng, X., Cohen, C. E., and Gu, H. (2012). “Modeling of interaction of hydraulic fractures in complex fracture networks,” in SPE Hydraulic Fracturing Technology Conference, The Woodlands, TX, USA.

Xiao, X., and Xiao, C. (2019). Analysis of initiation angle for fracture propagation considering stress interference. Energies 12 (10), 1841. doi:10.3390/en12101841

Xie, J., Huang, H., Sang, Y., Fan, Y., Chen, J., Wu, K., et al. (2019). Numerical study of simultaneous multiple fracture propagation in changning shale gas field. Energies 12 (7), 1335. doi:10.3390/en12071335

Xing, P., Yoshioka, K., Adachi, J., El-Fayoumi, A., Damjanac, B., and Bunger, A. P. (2018). Lattice simulation of laboratory hydraulic fracture containment in layered reservoirs. Comput. Geotechnics 100, 62–75. doi:10.1016/j.compgeo.2018.03.010

Yamamoto, K., Shimamoto, T., and Sukemura, S. (2004). Multiple fracture propagation model for a three-dimensional hydraulic fracturing simulator. Int. J. Geomechanics 4 (1), 46–57. doi:10.1061/(asce)1532-3641(2004)4:1(46)

Zhang, H., Chen, J., Li, Z., Hu, H., and Mei, Y. (2024). Numerical simulation of multi-cluster fracturing using the triaxiality dependent cohesive zone model in a shale reservoir with mineral heterogeneity. Rock Mech. Rock Eng. 57, 325–349. doi:10.1007/s00603-023-03527-5

Zhang, J., Li, Y., Pan, Y., Wang, X., Yan, M., and Shi, X. (2021). Experiments and analysis on the influence of multiple closed cemented natural fractures on hydraulic fracture propagation in a tight sandstone reservoir. Eng. Geol. 281, 105981. doi:10.1016/j.enggeo.2020.105981

Zheng, H., Pu, C., Wang, Y., and Sun, C. (2020). Experimental and numerical investigation on influence of pore pressure distribution on multi-fractures propagation in tight sandstone. Eng. Fract. Mech. 230, 106993. doi:10.1016/j.engfracmech.2020.106993

Zhou, J., Zeng, Y., Jiang, T., and Zhang, B. (2018). Laboratory scale research on the impact of stress shadow and natural fractures on fracture geometry during horizontal multi-staged fracturing in shale. Int. J. Rock Mech. Min. Sci. 107, 282–287. doi:10.1016/j.ijrmms.2018.03.007

Conflict of interest: Author LZ was employed by Luliang Oilfield Operation Area of Xinjiang Oilfield Branch Company, PetroChina.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Wang, Peng, Zhang, Cong and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 16 April 2024
doi: 10.3389/feart.2024.1390039

Distribution patterns and controlling factors of beach bars in Qinghai Lake, NW China

Biao Peng1,2,³, Zhengjian Xu2,4,5,⁶*, Xiaowan Chen1,⁷ and Yonghua Zhao³

¹Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd., Xi’an Jiaotong University, Xi’an, China

²School of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing, China

³Chang’an University, Xi’an, China

⁴Chongqing Institute of Geology and Mineral Resources, Chongqing, China

⁵Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education, Wuhan, China

⁶Key Laboratory of Sedimentary Basin and Oil and Gas Resources, Ministry of Natural Resources, Chengdu, China

⁷Shaanxi Fruit Industry Development Co., Ltd., Xi’an, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Chaojin Lu, University of Miami, United States
Xiaomng Sun, University of Science and Technology of China, China
Hao Wu, Lanzhou University, China

* Correspondence: Zhengjian Xu, xumou08@sina.com

Received: 22 February 2024
Accepted: 18 March 2024
Published: 16 April 2024

Citation: Peng B, Xu Z, Chen X and Zhao Y (2024) Distribution patterns and controlling factors of beach bars in Qinghai Lake, NW China. Front. Earth Sci. 12:1390039. doi: 10.3389/feart.2024.1390039

The significance of beach-bar reservoirs, which are intricately linked to unconventional hydrocarbon reservoirs, has gained increased attention. This study elucidates the complex mechanisms governing the formation, spatial distribution, and morphological characteristics of beach bars in Qinghai Lake, NW China, highlighting the interplay of lake-level fluctuations, wind wave dynamics, sediment provenance, and terrain gradient. Our findings reveal that large-scale lake-level fluctuations primarily dictate the initial formation settings of beach bars, while small-scale fluctuations refine their internal stratification and sedimentary features. Wind wave dynamics play a critical role in defining suitable depth zones for beach bar development, influencing their morphology and resulting in distinct sedimentary characteristics across different lake zones. Furthermore, the study categorizes beach bars into three main systems based on sediment provenance—bedrock, alluvial fan (fan delta), and braided river systems—each exhibiting unique compositional and structural attributes. Terrain gradient around the lake significantly affects beach bar formation, with areas having a gradient less than 0.7° being particularly favorable. This research advances our understanding of lacustrine beach bar dynamics, offering insights into their potential as indicators of past environmental conditions and their implications for hydrocarbon exploration. The comprehensive analysis of controlling factors provides a foundation for predicting beach bar distribution and contributes to the broader field of sedimentary geology by integrating multi-disciplinary approaches to study lacustrine sedimentary processes.

Keywords: Qinghai Lake, beach bars, sediment provenance, lake-level fluctuations, wind and wave dynamics, topography

1 INTRODUCTION

Beach bar sandstones, predominantly found in shore and shallow lake areas (Wu, 1986; Zhu, 2008; Jiang, 2009), have become increasingly significant in the context of oil and gas exploration due to their association with subtle hydrocarbon reservoirs (Ji et al., 2013; Shang et al., 2014a; Zhao et al., 2014). These reservoirs are distinguished by their favorable geological conditions, such as proximity to oil sources, substantial storage capacity, and well-organized source-reservoir-cap assemblages, making them potential sites for large oil and gas fields (Ji et al., 2013; Shang et al., 2014a; Zhao et al., 2014). Recent breakthroughs in exploring beach-bar hydrocarbon reservoirs across various geological periods and basins underscore their importance. Noteworthy examples include discoveries in the lower Cretaceous of the Chepaizi area in the Junggar basin (Zhao et al., 2014), the Paleogene in the Gaoyou sag of the Subei basin (Zhu et al., 1994; Ji et al., 2013), and the Middle Permian in the north-central part of the Yishan slope of the Ordos Basin (Yang et al., 2007), with the Paleogene Shahejie Formation in the Dongying sag of the Bohai Bay basin being a significant example (Hongwen et al., 2011; Jiang et al., 2011).

A thorough understanding of the distribution and controlling factors of beach bars is essential for predicting the distribution of beach bar sandstone and for the restoration of paleoenvironment and paleoclimate, critical aspects of oil and gas exploration. Although current research has extensively examined the structural, landform, sequence, water depth, and provenance factors of beach bars (Shang et al., 2014b; Liu Q. H. et al., 2015; Jiang et al., 2015), it has primarily focused on ancient lacustrine basin beach bars, with modern lacustrine basin beach bars receiving less attention. The exploration of ancient lacustrine beach bars, relying on seismic, well logging, rock core data, and other methods, contrasts with the direct measurement approach possible in modern lacustrine basins, offering a more intuitive understanding and the opportunity to refine the research framework.

Qinghai Lake, the largest saline lake in China, located in the northeastern part of the Qinghai-Tibet Plateau, serves as an invaluable natural laboratory for studying lacustrine sedimentary processes. The lake’s beach bars, due to their relevance to understanding lacustrine dynamics, sedimentary environments, and potential climate change implications, have garnered significant research interest. Early studies provided foundational insights into the sedimentary structures and compositions of beach bars, highlighting their significance as indicators of lake-level fluctuations and sediment supply dynamics. Later research expanded these findings through geomorphological and sedimentological analyses to map beach bar distribution and infer past hydrological conditions (Wang et al., 2001; Zhou and Li, 2005).

Recent investigations have shifted towards a multi-scale analysis of beach bars, integrating sedimentological data with geophysical surveys and remote sensing techniques. Ground-penetrating radar (GPR) studies have unveiled the complex internal structures of beach bars, linking their episodic growth phases to climatic oscillations. Additionally, the assessment of wind regimes and wave energy’s impact on beach bar spatial variability has underscored the significant influence of prevailing winds on their orientation and morphology (Zhang et al., 2010; Li, 2015).

Our comprehensive research, encompassing extensive field surveys, profile analysis, and indoor analysis, has elucidated the distribution patterns and controlling factors of beach bars in Qinghai Lake. These findings not only advance our understanding of paleogeomorphology and paleoclimate but also have practical implications for predicting beach-bar reservoir distribution in oil and gas exploration, thereby bridging the gap between theoretical research and applied geoscience.

2 GEOLOGICAL BACKGROUND

Qinghai Lake, the largest inland brackish lake in China, is situated on the northeast margin of the Qinghai-Tibet Plateau, located between 99°36′E to 100°47′E and 36°32′N to 37°15′N (Chinese Academy of Sciences, 1979). The lake is encircled by the Datong Mountain, Riyue Mountain, and Qinghai South Mountain, which together delineate a closed inland fault basin that stretches from northwest to southeast. This basin exhibits a gradual decline in altitude from northwest to southeast (Chinese Academy of Sciences, 1979).

The lake spans approximately 4,264–4,473 km², with its long axis extending about 106 km in a near east-west direction and its horizontal axis about 63 km in a near south-north direction, resulting in an elliptical shape. The orientation of the long axis is approximately NWW 315°, making the lake surface elongated from east to west. Qinghai Lake’s elevation ranges from 3,193 m to 3,198 m above sea level, with a circumference of around 360 km. The average depth of the lake is 21 m, with the deepest point reaching up to 32 m. The total volume of the lake exceeds 100 × 10⁹ m³ (Chinese Academy of Sciences, 1979; An et al., 2006) (Figure 1).

FIGURE 1 | The distribution of sedimentary facies around The Qinghai Lake.

3 METHODS

Based on the field investigation of the Qinghai Lake, the authors have carried out the elaborate dissection of profile sections around the Qinghai Lake. Except for the profile sections, forming beach-bars in the Qinghai Lake had been used to investigate the formation mechanisms of beach-bar sedimentary succession. Through the application of remote sensing technology and extensive field research at Qinghai Lake, a comprehensive survey identified a significant presence of beach-bar deposits around the northeast, southeast, south, southwest, and northwest margins of the lake (Figure 1). Notably, the highest altitude recorded for these beach bars reached 3,215 m.

The grain size distribution was measured by a Malvern 3000 laser diffraction instrument at the State Key Laboratory of Petroleum Resources and Engineering, China. The sorting and skewness are based on (Folk 1966) equation (Folk, 1966), where:

here, is the sediment sorting, and is the sediment skewness, and is the grain diameter (in phi units) at the cumulative percentile value of x. Other parameters were measured on the field sites.

4 DISTRIBUTION CHARACTERISTICS OF BEACH BAR IN QINGHAI LAKE

In the northeast and southern margins of Qinghai Lake, a distinctive pattern of beach-bar deposits has been observed. These deposits, which run parallel to the lake’s shoreline, predominantly consist of small to semi-closed lagoons, as well as cross-shore deposits, indicating a varied depositional environment influenced by the lake’s dynamics. Conversely, the beach-bar deposits identified along the southwest and northwest margins are characterized by their lack of significant topographic uplift, with a relatively lower incidence of lagoons and cross-shore deposits (Figure 1). This suggests a more uniform depositional environment in these areas.

Meanwhile, the southeastern margin of Qinghai Lake showcases a different pattern, where beach-bar deposits also align parallel to the shoreline. However, in this region, the deposits primarily form completely enclosed lagoons and cross-shore deposits. This variation underscores the influence of local topographical and hydrodynamic conditions on the formation and distribution of beach-bar deposits around Qinghai Lake (Figure 1).

5 THE CONTROL EFFECT OF LAKE-LEVEL FLUCTUATION ON BEACH BAR

Lake-level fluctuation plays a pivotal role in determining the characteristics and distribution of beach-bar deposits. These fluctuations can be categorized into two types: large-scale and small-scale. For the purposes of this research, the distinction between large-scale and small-scale fluctuations is based on the extent of horizontal migration of the lake shoreline, with 150 m serving as the threshold. Specifically, fluctuations resulting in a shoreline migration greater than 150 m are considered large-scale, while those causing migration less than 150 m are deemed small-scale. The choice of 150 m as the cutoff is grounded in the observation that the width of beach-bar deposits in Qinghai Lake typically does not exceed this dimension. Meanwhile, the topographic slope of Qinghai Lake was about 0.5°, and the extent of horizontal migration of the lake shoreline, 150 m, was just the average wave base of Qinghai Lake, about 1.5 m. As a result, large-scale lake-level fluctuations can lead to the development of independent beach bars, so taking 150 m as the threshold.

Large-scale lake-level fluctuations primarily influence the location where beach bars develop. In contrast, small-scale fluctuations play a crucial role in shaping the internal structure and superimposed styles of beach bars. This distinction is crucial for understanding the formation and evolution of beach bars in response to varying lake levels. Examples in the subsequent sections will provide detailed illustrations of how these two types of fluctuations exert their control over beach-bar development and characteristics.

5.1 The characteristics of lake-level fluctuation in Qinghai Lake

Recent advancements in research methods have enriched our understanding of Qinghai Lake’s lake-level fluctuations. A series of studies have contributed to this body of knowledge:

Zhang et al. (1988) and Zhang et al. (1994) utilized stable isotopes and trace elements in Ostracoda from Qinghai Lake’s core samples to infer water level fluctuations over the past 12,000 years.

Yuan et al. (1990) categorized Qinghai Lake’s development history into three epochs based on ancient shoreline data, geomorphologic characteristics, the neotectonic framework, and limited carbon isotope dating: the maximal epoch of the Middle Pleistocene, the stable epoch of the late Pleistocene, and the contraction epoch of the Holocene.

Chen et al. (1990) and Lister et al. (1991) proposed that the highest lake levels since the Holocene, approximately 13 m above current levels, occurred between 7.4 kyr and 6 kyr, based on seismic and shallow drilling data analysis.

Yu and Kelts (2002), through core sample analysis and 14C dating, discovered that the lake level was nearly dry in the early Holocene.

Madsen et al. (2008), Liu et al. (2010), Rhode et al. (2010), Liu et al. (2011), and Liu X. J. et al. (2015) reconstructed ancient lake shoreline fluctuations using optically stimulated luminescence (OSL) dating of sand deposits and eolian loess around Qinghai Lake.

Liu et al. (2013) examined the organic carbon isotopic composition of aquatic and terrestrial plants and sediments in and around Qinghai Lake. By comparing these with drilling well data, it was concluded that the lake level was low in the early Holocene.

These diverse methodologies and objectives have led to varied analyses and interpretations of Qinghai Lake’s lake-level fluctuations across different periods. Despite this, there has been a concerted effort to study the continuous fluctuation of Qinghai Lake’s levels since nearly 18,000 years ago. This paper critiques the strengths and weaknesses of previous studies, incorporating published dating data and other relevant evidence, to reconstruct the lake-level fluctuation curve of Qinghai Lake over this period.

The sediment dating data from Qinghai Lake, alongside the spatial positioning of the samples, allowed for a categorization into four distinct types: shore or shallow water deposits, loess or eolian deposits, nearshore deposits, and deep lacustrine deposits. These categories were then visually represented in a scatter diagram. Utilizing the spatial relationships among these data types, we reconstructed the plane up-down curve of Qinghai Lake’s water level since 18 kyr (as shown in Figure 2).

FIGURE 2 | Lake level change curve since the last 18 kyr.

The reconstructed curve reveals that the water level of Qinghai Lake generally fluctuated around an elevation of 3,200 m from 18 kyr to 11.7 kyr. This period was characterized by a stable lake level, during which a significant number of beach-bar deposits formed at this elevation. The research of Liu et al. (2011) and Liu X. J. et al. (2015) supports this finding, having identified numerous beach bars from this timeframe that were approximately 6 m above the current lake level, as determined through OSL dating.

Between 11.7 kyr and 8 kyr, the lake experienced a general decline in water level, reaching a low point near 3,165 m above sea level in the early Holocene. This was a critical juncture where the lake almost dried up, only for the water level to experience a rapid ascent to a relatively high level between 8 kyr and 7 kyr.

The period from 8 kyr to 5 kyr saw a swift rise in the lake level from 3,165 m to 3,212 m. The analyses of shallow drilling data and ostracod shell analysis concluded that this was a high-water period for Qinghai Lake since the Holocene, with levels reaching approximately 18 m above the present day (Zhang et al., 1988; Zhang et al., 1994). This assessment was corroborated by Chen et al. (1990), Lister et al. (1991), and Wang and Shi (1992), who all agreed that the lake level reached its peak since the Holocene during this interval.

Between 5 kyr and 2.5 kyr, OSL dating of shore and nearshore deposits showed that the lake level experienced fluctuations around 3,202 m.

Over the last 2.5 kyr, the trend was generally towards a reduction in lake level, with a period of stability around 3,202 m observed from 2.5 kyr to 1.5 kyr, followed by a more accelerated decline.

5.2 Large-scale lake-level fluctuation and its control on beach-bar development

Beach bars, essential features along the shoreline of Qinghai Lake, are directly influenced by lake-level fluctuations. These fluctuations dictate the migration of the shoreline, thereby determining the positioning of beach-bar development. Despite the tectonic stability of Qinghai Lake since 18 kyr, its water level has experienced significant changes, which have been a primary driver in the distribution of beach bars. This text outlines the evolution of beach bars at Qinghai Lake over four distinct stages, using continuous measurements from Yilangjian as a case study (Figure 3).

FIGURE 3 | Distribution of beach-bars along the survey line in the Yilangjian of the Qinghai Lake. (A) for the satellite photograph of Yilangjian; (B) for the distribution diagram of beach-bars along the survey line.

5.2.1 Stage 1: warm-wet stage in the late Pleistocene (18 kyr to 11.7 kyr)

During this period, Qinghai Lake’s elevation ranged from 3,197 m to 3,202 m, approximately 3–8 m above the current lake level, over a span of about 6 kyr. The climate transitioned from dry and cold to warm and wet, leading to increased monsoon precipitation (Zeng, 2007). With ample material supply and gentle terrain, beach-bar deposits formed at elevations between 3,197 m and 3,202 m. Fluctuating lake levels resulted in the formation of lagoon deposits behind the beach bars in less elevated areas (Figure 4).

FIGURE 4 | Diagrams showing the first-stage evolution at the Yilangjian profile. (A) for the early Warm-wet stage in the Late Pleistocene; (B) for the late Warm-wet stage in the Late Pleistocene.

5.2.2 Stage 2: cold-dry stage in the Holocene epoch (11.7 kyr to 8 kyr)

At this stage, the lake’s elevation dropped to 3,165 m, about 30 m below the current level, nearly drying up. The climate was characterized by dry and cold conditions (Zhao et al., 2009), leading to a lack of beach-bar deposits. Instead, extensive loess and lagoon deposits developed across the basin (Figure 5) (Liu et al., 2003; Zeng, 2007; Zhao et al., 2009).

FIGURE 5 | Diagrams showing the second-stage evolution at the Yilangjian profile. (A) for the early Cold-dry stage in the Holocene; (B) for the late Cold-dry stage in the Holocene.

5.2.3 Stage 3: megathermal stage in the Holocene epoch (8 kyr to 5 kyr)

This middle Holocene stage saw the peak monsoon intensity at Qinghai Lake, resulting in significant water recharge and a rapid rise in lake level. From a nearly dry state, the lake level surged to about 18 m above the current level, reaching its highest point at 3,212 m since the Holocene (Zhang et al., 1994). Consequently, beach bars formed at this elevation, marking the furthest development from the current lake level (Figure 6).

FIGURE 6 | Diagrams showing the third-stage evolution at the Yilangjian profile. (A) for the early Megathermal stage in the Holocene; (B) for the late Megathermal stage in the Holocene.

5.2.4 Stage 4: cold-wet stage in the Holocene epoch (5 kyr to present)

This final stage is divided into two phases. From 5 kyr to 2.5 kyr, the climate alternated between cool-wet and warm-dry (Zhang et al., 1994), with the lake level dropping rapidly from its peak in the Megathermal Stage to stabilize around 3,200 m. From 2.5 kyr onwards, the climate shifted towards cool-dry (Zhang et al., 1994), and the lake level’s decline rate accelerated. Beach bars formed during this stage, along with those from the late Pleistocene, became composite beach bars. The receding lake level led to the development of lagoon deposits behind the bars. Over time, as the water evaporated, these lagoons became shallower, resulting in the formation of typical wet deposits seen today. Modifications in the lake level also led to the creation of small lagoons and alluvial fan deposits within the composite beach bars (Figure 7).

FIGURE 7 | Diagrams showing the fourth-stage evolution at the Yilangjian profile. (A) for the early Cold-wet stage in the Holocene; (B) for the late Cold-wet stage in the Holocene.

5.3 Small-scale lake-level fluctuations and their influence on beach-bar depositional patterns

Small-scale fluctuations in lake level have triggered the lateral migration of various facies zones within the beach-bar system, including the swash zone, breaker zone, and washover zone. This migration critically influences the vertical stacking relationships among these facies zones, which in turn dictates the overall stacking patterns of depositional units within the beach-bar. Such dynamics are essential in understanding the internal architecture of beach-bar systems.

Moreover, the internal stratification patterns within beach bars are influenced not only by small-scale lake-level fluctuations but also by the dynamics of wind and wave action. The varying strength of wind and waves over time contributes to the lateral migration of different facies zones, further complicating the depositional patterns within beach bars.

To illustrate these processes, consider the sedimentary profile located in the middle of a beach bar on the southeast edge of Qinghai Lake (Figure 8). This profile, standing 2 m tall and capped with a 10 cm layer of loess, is primarily composed of medium to fine gravel, with minor coarse sand. Based on criteria such as lithology, sedimentary structures, lamina thickness, lateral continuity, and top-bottom contact relationships, the profile has been subdivided into 19 distinct sub-layers. These sub-layers represent a sequence of depositional environments ranging from the swash zone to the washover zone, labeled from interval A to B as the swash zone, breaker zone, surf zone, and washover zone, respectively (Figure 8).

FIGURE 8 | Vertical evolution of beach-bars in the southeastern of the Qinghai Lake.

A interval is dominated by gravel and sand, which are sub-rounded, poorly-sorted, massive bedding, and gravels oriented floating in the coarse sands. The mean grain size is between −2 and −1 phi, and the sorting is more than 2. B interval is dominated by gravel, which are sub-rounded, well-sorted, massive bedding. The mean grain size is between −4 and −1.5 phi, and the sorting is less than 2. C interval is dominated by sand, which are the sub-rounded, well-sorted, normal grading bedding. The mean grain size is between −1 and 0, and the sorting is less than 2. Moving from section A to D, the profile demonstrates a gradational shallowing of water depth, vividly capturing the frequent lake-level changes experienced at this site.

6 THE INFLUENCE OF WIND WAVES ON BEACH BAR FORMATION

Wind waves serve as the primary energy source for beach-bar formation. Within a lake basin, it is the wave action and longshore currents that primarily influence the formation and distribution of beach bars. The term “control functions of wind waves” refers to the generation of longshore currents by waves. These currents represent the net movement along the shoreline, driven by waves that propagate at an angle to the shore in the wave action zone. Rather than a true parallel shoreline current, the longshore current behaves like an asymmetric wave moving parallel to the shoreline, with sediment particles advancing in a “two steps forward, one step back” motion. Wind predominantly controls waves, with the wind’s direction influencing wave propagation and its strength, along with the fetch distance, determining wave power.

The strength and direction of wind waves directly impact beach-bar development: the former dictates the depth range where beach bars can form, while the latter shapes the beach bars and their associated features.

6.1 Hydrodynamic characteristics of Qinghai Lake

Qinghai Lake, the largest lake in China, is significantly influenced by the Buha River, a 300 km long river that flows into its western part. This river is a key source of energy, generating currents within the lake. The annual runoff amount is 818 million cubic meters, and far greater than the total runoff amount of all the rivers that feed into Qinghai Lake. The lake’s hydrodynamics are also shaped by the prevailing winds from the west and northwest (Table 1). These winds create friction with the lake’s surface, leading to the formation of waves. Additionally, variations in water temperature and the process of evaporation alter the lake’s water density, which in turn triggers vertical circulation. Together, these elements—river inflow, wind action, and temperature-evaporation dynamics—form the cornerstone of Qinghai Lake’s hydrodynamic characteristics.

TABLE 1 | Maximum wind speed of each month from 1961 to 1970 in the Qinghai Lake (Lanzhou Branch Chinese Academy of Sciences, 1994).

Data collected from 65 measurement points across the lake reveal a consistent pattern in the water flow direction, primarily influenced by the inflow from the Buha River and the northwest winds. The predominant movement of lake water is from the northwest towards the southeast. However, there are exceptions in certain areas where lake bay reflux occurs, notably in the northern and southern bay areas near the Buha River, as well as in the Heima River district, Erhai bay, and the eastern bay areas (Figure 9).

FIGURE 9 | Lake currents orientation of the Qinghai Lake (Ji et al., 2013).

The influence of the northwest wind is a constant feature throughout the year at Qinghai Lake. Records from 1961 to 1970 indicate that monthly maximum wind speeds ranged between 13 m/s and 22 m/s, with average speeds fluctuating from 3.4 m/s to 4.3 m/s (Table 1). This consistent wind pattern plays a crucial role in shaping the lake’s hydrodynamic environment.

6.2 Influence of wind waves on the water depth range and sedimentary structure for beach-bar formation

The creation of a beach bar is dependent on strong hydrodynamic forces, typically situated above the wave base. The wave base is defined as the maximum depth at which wave action can influence the movement of sediment particles in both sea and lake environments. Beyond this depth, the sediment remains unaffected by wave action. Consequently, the strength of wind waves plays a crucial role in determining the water depth that waves can influence, thereby dictating the depth range conducive to beach bar development.

Meanwhile, the strength of wind waves can determine the sediment structure of beach bars. The higher strength, the better sorting and the coarser particle size, such as the windward zone and the leeward zone.

6.3 Role of wind wave direction in shaping beach bars

The growth direction and pattern of beach bars are heavily influenced by the angle of wave approach relative to the lake’s shoreline. This angle, determined by the prevailing wind direction, dictates the morphology of beach bars, as exemplified by the Yilangjian beach bar in Qinghai Lake (Figure 10). Qinghai Lake, for instance, experiences a dominant northwest wind, leading to distinct windward, leeward, and lateral zones within the lake basin. These zones, characterized by their respective positions (northwest, southeast, southwest, and northeast margins), exhibit unique beach bar morphologies and associations due to variations in wind direction and fetch distance.

FIGURE 10 | Control of derection of winds and waves on the morphology of beach-bars in the Qinghai Lake.

Windward Zone Characteristics: In the windward zone, where the wind originates, the interaction between the wind and the water body is minimal, resulting in the shortest fetch length, less than 10 km. The shoreline is almost perpendicular to the wind direction (Figure 11). This minimal interaction leads to low wave energy, and consequently, features such as lagoon deposits, cross-shore deposits, and bars are infrequent. Instead, flat beaches without significant bulges are more common (Figure 12A).

FIGURE 11 | Location of wind areas in the Qinghai Lake.

FIGURE 12 | Beach-bar characteristics in the different wind area of the Qinghai Lake ((A) for Windward Zone; (B, C) for Lateral Wind Zone; (D) for Leeward Zone).

Lateral Zone Dynamics: The lateral zone experiences wind passing through the basin, with a moderate fetch length due to a larger area of wind-water interaction, about 10–80 km. The shoreline and wind direction are nearly parallel or at a slight angle (Figure 11). The oblique wave approach in this zone favors the development of coastal flow-type beach bars, semi-enclosed lagoons, and visible cross-shore deposits (Figures 12B, C).

Leeward Zone Features: The leeward zone is directly downwind, characterized by the largest interaction area between the wind and the water, resulting in the longest fetch length, greater than 80 km. Here, the shoreline is almost perpendicular to the wind direction, leading to the strongest wave energy and wave approach almost perpendicular to the shoreline (Figure 11). This environment is conducive to the formation of numerous cross-shore deposits, large fully enclosed lagoon deposits, and a multitude of bars, with fewer beaches being present (Figure 12D).

7 INFLUENCE OF PROVENANCE ON BEACH BAR FORMATION

7.1 Provenance characteristics and the source-sink system

The concept of a “source-sink” system, which traces sediment from its origin (source) to its final deposition area (sink), is instrumental in understanding how provenance controls beach bar formation. This approach is particularly useful in dissecting the complex interactions that lead to beach bar development in various parts of Qinghai Lake.

Beach bar deposits in Qinghai Lake are predominantly found along its northwest, northeast, southeast, and south margins. To analyze these features effectively, Qinghai Lake can be divided into four distinct “source-sink” depositional systems. These systems are defined by a combination of structural, landform, geographical, and provenance characteristics, as well as by the specific locations of beach bar development, the hydrodynamic environment of the lake basin, and sedimentary traits (Figure 13). The four systems are:

FIGURE 13 | “Source to sink” sedimentary system in the Qinghai Lake.

Datong Mountain - Buha River (Delta) – Wuha’alan River (Delta) - Northwest Margin System: This system is primarily composed of braided river deltas, fan deltas, and beach bars, with sediment originating from Datong Mountain. The lithology of Datong Mountain in the vicinity of the Buha and Wuha’alan rivers includes lower Paleozoic hypometamorphic rocks and granite. Analysis of hydrodynamic and “source-sink” characteristics indicates that the beach bars on the northwest margin primarily derive from the fan delta of the Wuha’alan River (Figures 9, 13).

Datong Mountain - Shaliuhe River (Delta) - Northeast Margin System: Characterized by fan deltas, wave-dominated deltas, and beach bars, this system’s sediments also originate from Datong Mountain, where Sinian metamorphic rocks predominate near the Shaliu River. The source of beach bars on the northeast margin is mainly the Shaliu River fan delta, as suggested by the lake’s hydrodynamic and “source-sink” dynamics (Figures 9, 13).

Riyue Mountain - Daban Mountain - Eolian Deposits–Daotang River - Southeast Margin System: This system, comprising eolian deposits, fan deltas, wave-dominated deltas, and beach bars, is influenced by the consistent northwest and west winds in Qinghai Lake. The Tuanbao and Daban mountains block these winds, causing fine sand to accumulate in front of the mountains. The primary sources of eolian deposits are the Hubei and western Qinghai marginal mountains. The beach bars in the southeast margin are mainly sourced from the Riyue Mountain Delta and eolian deposits, with Riyue Mountain consisting of Pre-Sinian gneiss, granite, and granodiorite (Figures 9, 13).

Qinghai Southern Mountain - Impact Fan (Fan Delta) - South Margin System: Comprising alluvial fans, fan deltas, and beach bars, the sediments here primarily originate from the Qinghai South Mountain, characterized by metamorphic rocks and granite. The beach bars on the southern margin are largely sourced from the adjacent alluvial fan in Qinghai South Mountain (Figures 9, 13).

7.2 Influence of provenance types on beach bar development

The availability of sufficient provenance material is essential for the formation of beach bars, as it determines the structural maturity of these features. Beach bars are typically formed through the secondary transport and redistribution of sediments, initially shaped by wave action. This process underscores the importance of provenance type in the development of beach bars. Consequently, beach bars in Qinghai Lake can be categorized into three distinct sedimentary systems based on their provenance: bedrock, alluvial fan (fan delta), and braided river beach-bar systems (Figure 14).

FIGURE 14 | Distribution of different provence beach-bars in the Qinghai Lake.

Bedrock Beach-Bar System: This system includes beach bars located on the southwest margin of Qinghai Lake and those on the east side of Yilangjian. Characterized by their proximity to the sediment source—less than 2 km away—these beach bars are primarily composed of medium-sized gravel, with the largest particles reaching up to 10 cm in diameter. The sediment mix is coarse and poorly sorted, featuring angular gravel pieces (Figures 14, 15A).

FIGURE 15 | Different provence beach-bars in the Qinghai Lake. (A) for Bedrock Beach-Bar; (B) for Alluvial Fan (Fan Delta) Beach-Bar; (C) for Braided River Beach-Bar.

Alluvial Fan (Fan Delta) Beach-Bar System: Beach bars on the east, southeast, and northeast margins of Qinghai Lake fall into this category. Their proximity to the provenance varies between 5 km and 40 km. The dominant sediments are fine gravel and coarse sand, with the largest particles measuring up to 5 cm. These sediments are moderately well sorted, exhibiting sub-rounded gravel shapes and featuring structures such as swash cross-bedding and plate cross-bedding (Figures 14, 15B).

Braided River Beach-Bar System: This system is exemplified by the beach bars on the northwest margin of Qinghai Lake, where the distance to the provenance exceeds 40 km. Here, coarse sand is the predominant sediment, supplemented by fine gravel with the largest particles up to 1 cm in diameter. The gravel is sub-circular, and the sediments show medium to good sorting, with common occurrences of swash cross-bedding and plate cross-bedding (Figures 14, 15C).

8 THE CONTROL OF TOPOGRAPHY ON BEACH BARS

The topographic and geomorphological characteristics of Qinghai Lake are key factors in controlling the development and distribution of beach bars. Understanding these characteristics through the reconstruction of the sedimentary topography of Qinghai Lake’s beach bars is crucial for analyzing their sedimentary features, distribution patterns, and controlling factors.

In this study, we have utilized remote sensing and bathymetric data to reconstruct the contour map of Qinghai Lake’s basin elevation (Figure 16). The surface elevation of Qinghai Lake stands at 3,194 m, descending to its deepest point at 3,166 m. At the lake’s center, Haixin Mountain reaches the highest elevation of 3,260 m. The terrain gently slopes on the lake’s eastern shore, ranging from 3,194 m to 3,270 m, in contrast to the steeper terrain of the southern shore, which varies from 3,194 m to 3,300 m. Notably, Tuanbao Mountain on the eastern shore and Qinghai South Mountain on the southern shore exhibit elevations ranging from 3,269 m to 4,025 m and 3,300 m to 4,500 m, respectively. The western and northern shores present very gentle and gentle terrains, with elevations ranging from 3,194 m to 3,245 m and 3,194 m to 3,294 m, respectively, highlighting the varied topographical features surrounding the lake.

FIGURE 16 | Altitude contour map of the Qinghai Lake.

Further analysis was conducted on the slope of Qinghai Lake’s basin terrain using the elevation difference and horizontal distance, employing the arctangent function for reconstruction (Figure 17). This analysis revealed that the slopes of the eastern and western shores are exceptionally gentle, all below 0.3°. The northern shore’s slope is relatively gentle, not exceeding 0.7°, while the southern shore displays a moderate slope, which can be segmented into five distinct sections. The extremities of the southern shore are notably steep, with slopes reaching up to 3.3° where the lake abuts directly against mountains. The central sections exhibit slopes of 0.7°, 1.3°, and again 0.7°, respectively. Additionally, three areas within the lake basin exhibit anomalous slopes: the frontal slope break zone of the Buha River delta in the west at 0.3°; the frontal fracture zone of the Shaliu River delta in the north at 0.5°; and the uplift area of Haixin Mountain in the center with a slope exceeding 0.5°.

FIGURE 17 | Slope gradient contour map of the Qinghai Lake.

The presence of gentle terrain around Qinghai Lake, a rift lake basin currently experiencing a rift-transform transition, provides favorable conditions for the development of beach bars. The lake basin’s overall geomorphology is relatively gentle due to stable tectonic movements and weaker fault activity. Through the detailed reconstruction of the sedimentary topography and slope of Qinghai Lake’s beach bars (Figures 16, 17), it has been determined that the beach bars predominantly develop on terrain slopes ranging between 0.1° and 0.7°. This finding underscores the significant influence of topography on the formation and distribution of beach bars in the area.

The provenance and topography are closely related. Three beach-bar systems, bedrock beach-bar system, alluvial fan (fan delta) beach-bar system, and braided river beach-bar system has the distinct slopes (Figures 14, 17). The bedrock beach-bar system is more than 1.5°. The alluvial fan (fan delta) beach-bar system is 0.3°–1.5°. The braided river beach-bar system is less than 0.3°.

9 CONCLUSION

(1) The formation and spatial distribution of beach bars in Qinghai Lake are governed by an intricate interplay of factors, including lake-level fluctuations, wind wave dynamics, sediment provenance, and terrain gradient. This complex interaction dictates not only the positioning and development of beach bars but also their internal stratification, morphology, and longevity.

(2) Large-scale fluctuations in lake level are the primary drivers behind the positioning of beach bars, essentially determining the initial settings for their formation. In contrast, small-scale fluctuations play a more nuanced role, refining the internal stratification and superimposed patterns within the beach bars, thereby influencing their detailed sedimentary features.

(3) Wind wave dynamics are crucial for defining the depth zones suitable for beach bar development. The direction and intensity of wind waves not only determine the depth at which beach bars can form but also significantly shape their overall morphology and arrangement. This results in distinct sedimentary characteristics across different zones of the lake, including the windward, lateral wind, and leeward zones, each exhibiting unique sedimentary signatures.

(4) Sediment provenance is another key factor that influences the compositional maturity and structural integrity of beach bars. Depending on their source, beach bars in Qinghai Lake can be categorized into three main systems: bedrock, alluvial fan (fan delta), and braided river beach-bar systems. Each system showcases specific compositional and structural attributes that reflect its provenance.

(5) The terrain gradient around Qinghai Lake plays a pivotal role in identifying potential locations for beach bar formation. Areas with a gradient less than 0.7° are particularly favorable, as they provide the optimal conditions for beach bar development. This factor, combined with the aforementioned elements, orchestrates the complex internal structure and distribution patterns of beach bars in the lake.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

BP: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing. ZX: Formal Analysis, Funding acquisition, Methodology, Validation, Writing–review and editing. XC: Formal Analysis, Validation, Writing–review and editing. YZ: Formal Analysis, Validation, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research was conducted with the support of several funding sources, including Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd and Xi’an Jiaotong University (No. 2021WHZ0090), Innovation Capability Support Program of Shaanxi (No. 2024RS-CXTD-55), the project of Shaanxi Province Land Engineering Construction Group (No. DJTD2023-02), the National Natural Science Foundation of China (No. 42002167), the Natural Science Foundation of Chongqing, China (No. cstc2021jcyj-msxmX0733), the youth project of the science and technology research program of the Chongqing Education Commission of China (No. KJQN202101530), the China Postdoctoral Science Foundation (No. 2023MD734109), the Open Fund of Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education (No. TPR-2023-06), and the Open Fund of Key Laboratory of Sedimentary Basins and Petroleum Resources, Ministry of Natural Resources (No. cdcgs2023009).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

An, Z. S., Wang, P., Shen, J., Zhang, Y. X., Zhang, P. Z., Wang, S. M., et al. (2006). Geophysical exploration research on the lakebed structure and sediment distribution of Qinghai Lake. Science in China ser. D. Earth Sci. 36 (04), 332–341.

Chen, K. Z., Bowler, J. M., and Kelts, K. (1990). Palaeoclimatic evolution within the Qinghai-Xizang (Tibet) plateau in the last 40 000 years. Quat. Sci. 10 (1), 21–31.

Chinese Academy of Sciences (1979). Comprehensive survey report on Qinghai Lake. Beijing: Science Press.

Folk, R. L. (1966). A review of grain-size parameters. Sedimentology 6 (2), 73–93. doi:10.1111/j.1365-3091.1966.tb01572.x

Hongwen, D., Yi, X., Li, X. M., and Zhenglong, J. (2011). Genetic type, distribution patterns and controlling factors of beach and bars in the second member of the Shahejie formation in the Dawangbei Sag, Bohai Bay, China. Geol. J. 46 (4), 380–389. doi:10.1002/gj.1290

Ji, Y. L., Lu, H., and Liu, Y. R. (2013). Sedimentary model of shallow water delta and beach bar in the member 1 of Paleogene funing Formation in Gaoyou sag, Subei basin. J. Palaeogeogr. 15 (05), 729–740.

Jiang, Z., Liu, H., Zhang, S., and Su, X. (2011). Sedimentary characteristics of large-scale lacustrine beach-bars and their Formation in the eocene boxing sag of Bohai Bay basin, east China. Sedimentology 58 (5), 1087–1112. doi:10.1111/j.1365-3091.2010.01196.x

Jiang, Z. X. (2009). Sedimentary. Beijing: Petroleum Industry Press.

Jiang, Z. X., Wang, J. H., and Zhang, Y. F. (2015). Advances in beach-bar research: a review. J. Palaeogeogr. 17 (04), 427–440.

Lanzhou Branch Chinese Academy of Sciences (1994). Evolution and prediction of the recent environment of Qinghai Lake. Beijing: Science Press.

Li, P. (2015). Influence of wind regimes on the formation of beach bars in Qinghai Lake, NW China. Sediment. Geol. 319, 134–145.

Lister, G. S., Kelts, K., Zao, C. K., Yu, J. Q., and Niessen, F. (1991). Lake Qinghai, China: closed-basin like levels and the oxygen isotope record for ostracoda since the latest Pleistocene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 84 (1), 141–162. doi:10.1016/0031-0182(91)90041-o

Liu, Q. H., Zhu, H. T., Shu, Y., Zhu, X. M., Yang, X. H., and Fu, X. (2015a). Provenance systems and their control on the beach-bar of Paleogene enping formation, enping sag, pearl river mouth basin. ACTA Pet. Sin. 36 (03), 286–299.

Liu, W. G., Li, X. Z., An, Z. S., Xu, L. M., and Zhang, Q. L. (2013). Total organic carbon isotopes: a novel proxy of lake level from Lake Qinghai in the Qinghai-Tibet Plateau, China. Chem. Geol. 347 (6), 153–160. doi:10.1016/j.chemgeo.2013.04.009

Liu, X. J., Lai, Z., Madsen, D., and Zeng, F. (2015b). Last deglacial and Holocene Lake level variations of Qinghai Lake, north-eastern Qinghai–Tibetan plateau. J. Quat. Sci. 30 (3), 245–257. doi:10.1002/jqs.2777

Liu, X. J., Lai, Z. P., Fan, Q. S., Long, H., and Sun, Y. (2010). Timing for high lake levels of Qinghai Lake in the Qinghai-Tibetan plateau since the last interglaciation based on quartz OSL dating. Quat. Geochronol. 5 (2), 218–222. doi:10.1016/j.quageo.2009.03.010

Liu, X. J., Lai, Z. P., Madsen, D., Yu, L., Liu, K., and Zhang, J. (2011). Lake level variations of Qinghai Lake in northeastern Qinghai-Tibetan Plateau since 3.7ka based on OSL dating. Quat. Int. 236 (1-2), 57–64. doi:10.1016/j.quaint.2010.08.009

Liu, X. Q., Wang, S. M., and Shen, J. (2003). The grainsize of the core QH-2000 in Qinghai Lake and its implication for paleoclimate and paleoenvironment. J. Lake Sci. 15 (2), 112–117. doi:10.18307/2003.0203

Madsen, D. B., Ma, H., David, R., Brantingham, P. J., and Forman, S. L. (2008). Age constraints on the late quaternary evolution of Qinghai Lake, Tibetan plateau. Quat. Res. 69 (2), 316–325. doi:10.1016/j.yqres.2007.10.013

Rhode, D., Ma, H., Madsen, D. B., Brantingham, P. J., Forman, S. L., and Olsen, J. W. (2010). Paleoenvironmental and archaeological investigations at Qinghai Lake, western China: geomorphic and chronometric evidence of lake level history. Quat. Int. 218 (1), 29–44. doi:10.1016/j.quaint.2009.03.004

Shang, X. F., Hou, J. G., Dong, Y., Li, Y., Tang, L., Deng, X. L., et al. (2014b). Sedimentary mechanism and distribution pattern of beach-bar sandbodies mainly dominated by contemporaneous faults in Banqiao Sag. J. China Univ. Petroleum Ed. Nat. Sci. 38 (6), 32–39.

Shang, X. F., Hou, J. G., Sun, F. T., Tang, L., Liu, Y. M., Li, Y. Q., et al. (2014a). Architectural characteristics and sedimentary models of beach-bar sandstone reservoirs: a case study of the Paleogene Shahejie Formation in Banqiao oilfield, Huanghua depression. ACTA Pet. Sin. 35 (6), 1160–1171.

Wang, J., et al. (2001). Sedimentary characteristics of beach bars in Qinghai Lake. J. Lake Sci. 13 (4), 297–304.

Wang, S. M., and Shi, Y. F. (1992). Review and discussion on the late quaternary evolution of Qinghai Lake. J. Lake Sci. 4 (3), 1–9. doi:10.18307/1992.0301

Wu, C. J. (1986). Sandbodies in lake basin. ACTA Sedimeatologica Sin. 04, 1–27.

Yang, X. Y., Shen, Z. G., Fang, S. X., Hou, F. H., Li, A. Q., Fu, S. T., et al. (2007). Sedimentary characteristics of beach and bar sandbodies in the lower submember of Member 8 of Xiashihezi Fomation of Midd e Pem an in W shenqi Gasfield, Ordos Basin. J. Palaeogeogr. 9 (02), 175–183.

Yu, J. Q., and Kelts, K. (2002). Climatic change in the northeast Qinghai-Tibet Plateau during the late glacial/holocene transition. Quat. Sci. 22 (5), 413–423.

Yuan, B. Y., Chen, K. Z., Bowler, J. M., and Ye, S. J. (1990). The formation and evolution of the Qinghai Lake. Quat. Sci. 03, 233–243.

Zeng, C. (2007). Oxygen isotope records of carbonates from lake Qinghai as environmental archives: Re-explanation. J. Salt Lake Res. 15 (1), 16–19.

Zhang, P. X., Zhang, B. Z., Qian, G. M., Li, H. J., and Xu, L. M. (1994). The study of paleoclimatic parameter of Qinghai Lake since Holocene. Quat. Sci. 03, 225–238.

Zhang, P. X., Zhang, B. Z., and Yang, W. B. (1988). The evolution of the water body environment in Qinghai Lake since the postglacial age. ACTA Sedimeatologica Sin. 6 (2), 5–18.

Zhang, Y., et al. (2010). Ground-penetrating radar investigation of sedimentary structures of beach bars in Qinghai Lake. Geophys. Res. Lett. 37, L12402.

Zhao, C. F., Lu, H. Y., Zhou, Y. L., Yi, S. W., and Mason, J. (2009). Palaeoclimate changes recorded by loess deposit around Qinghai Lake (northeastern Qinghai-Tibetan plateau) since last deglaciation. Geol. J. China Univ. 15 (1), 135–140.

Zhao, D. N., Zhu, X. M., Dong, Y. L., Wu, D., and Zhu, M. (2014). Application of seismic sedimentology to prediction of beach and bar sandbodies in gentle slope of lacustrine basin: a case study of the Lower Cretaceous in Chepaizi area, Junggar Basin, NW China. Petroleum Explor. Dev. 41 (01), 60–67. doi:10.1016/s1876-3804(14)60006-9

Zhou, W., and Li, X. (2005). Geomorphological and sedimentological evidence of lake-level changes in Qinghai Lake in the Holocene. Quat. Sci. 25 (1), 98–107.

Zhu, X. M. (2008). Sedimentary rock. Beijing: Petroleum Industry Press.

Zhu, X. M., Xin, Q. L., and Zhang, J. R. (1994). Sedimentary characteristics and models of the beach -bar reservoirs in faulted down lacustrine basins. ACTA Sedimeatologica Sin. 02, 20–28.

Conflict of interest: Authors BP and XC were employed by Shaanxi Land Engineering Construction Group Co., Ltd.
Authors XC were employed by Shaanxi Fruit Industry Development Co., Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Peng, Xu, Chen and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 13 May 2024
doi: 10.3389/feart.2024.1390929

Identification of Milankovitch sedimentary cycle in Fengcheng Formation, Mahu depression: a case study of well Maye 1

Ran Wang¹, Xuan Chen²*, Qiusheng Chang¹ and Xinmei Zhao¹

¹PetroChina Xinjiang Oilfield Company, Karamay, China

²School of Geosciences, Yangtze University, Wuhan, China

Edited by:
Chao Liu, Northeast Petroleum University, China

Reviewed by:
Fang Zeng, Northeast Petroleum University, China
Fei Ren, Origin Energy, Australia
Chunqi Xue, China University of Petroleum, China
Ziru Zhao, China University of Petroleum (East China), China, in collaboration with reviewer (CX)
Jingjing Liu, University of Barcelona, Spain, in collaboration with reviewer (FR)

* Correspondence: Xuan Chen, 27573832@qq.com

Received: 24 February 2024
Accepted: 02 April 2024
Published: 13 May 2024

Citation: Wang R, Chen X, Chang Q and Zhao X (2024) Identification of Milankovitch sedimentary cycle in Fengcheng Formation, Mahu depression: a case study of well Maye 1. Front. Earth Sci. 12:1390929. doi: 10.3389/feart.2024.1390929

The sedimentary cycle of Fengcheng Formation in Junggar Basin changes rapidly and is difficult to identify. The low classification accuracy of high-frequency fine-grained sedimentary cycles restricts the exploration of unconventional shale oil and gas. Full-interval coring of well Maye 1 provides a solid foundation for small-layer research. In this paper, the high-frequency sedimentary cycle of Milan kovitch in Fengcheng Formation is studied by means of spectral analysis and wavelet transform, and the shale sweet spot is identified. The long period of eccentricity of 405ka, short period of eccentricity of 100ka and long period of axis slope of 42736a can be used as semi-quantitative basis for the classification of the fourth, fifth and sixth sequences. The Fengcheng Formation can be divided into 2 quaternary sequence cycles, 9 quaternary sequence cycles and 16 quaternary sequence cycles. A long period of eccentricity of 405ka can identify large-scale lake recessional and recessional, and there is a maximum lake flooding surface in the middle of the second Fengcheng Formation, which corresponds to high abundance of organic matter and high hydrocarbon generation potential. The 100ka short period eccentricity can identify the sweet spot layer, and there are 8 sweet spots in the well Maye 1, which is consistent with the results of nuclear magnetic interpretation. The research results provide a semi-quantitative basis for the classification of high-frequency sedimentary cycles and the identification of sweet spots of Fengcheng Formation in Mahu Sag, Junggar Basin.

Keywords: Junggar basin, Fengcheng formation, shale oil, sedimentary cycle, Milankovitch, sweet spot recognition Junggar basin

1 INTRODUCTION

Milankovitch cycles (Ding, 2006) represent the response of paleoclimate variations in sedimentary strata. At the beginning of the 20th century, Milutin Milankovitch proposed that due to the rotation and revolution of the Earth, the parameters of the Earth’s orbit, such as eccentricity, the slope of the Earth’s axis, and the precession, change periodically, which in turn leads to cyclical changes in the Earth’s climate over 10,000 and millions of years (Shi and Liu, 2006). This is still controversial because of the inability to date the Earth accurately geologically. It was not until the 1960s that the cyclic sea level changes corresponding to ice sheet dimensions were found to be consistent with glacier curves calculated by Milankovitch in several coral reef research conducted in various locations, including Hawaii (Mei, 2011). The existence of Milankovitch cycles has been confirmed. The important role of Milankovitch theory in delineating sedimentary cyclones on the scale of several centimeters to several meters was highlighted in 2010 by Mei Mingxiang (Mei, 2010) in the third research advance in stratigraphy. Subsequently, an increasing number of paleoclimate researchers have also corroborated the existence of the Milankovitch Cyclone several times in geological deposits (Raymo and Nisancioglu, 2003; Ding, 2006; Shi et al., 2017). At present, the Tarim Basin (Jia et al., 2018), Songliao Basin (Zhao et al., 2018), Bohai Bay Basin (Xu and Xie, 2012), Ordos Basin (Xie et al., 2016), and Sichuan Basin (Zhang et al., 2013) in China have all achieved good results in dividing the high-frequency stratigraphic sequences by Milankovitch cycles.

The advent of high-quality seismic reflection data in the 1970s led to a real rise in stratigraphy (Sloss, 1963). The current development of stratigraphy based on stratigraphic sequence under the control of tectonic factors can be summarized into four schools: firstly, the classical school represented by Vail et al. (1991); secondly, the diagenetic stratigraphy school represented by Galloway (Galloway, 1989); thirdly, the sea-advancing-sea-retreating stratigraphy school represented by Johnson (Johnson et al., 1985); and fourthly, the high-resolution stratigraphy school represented by Cross (Cross, 1988). The sequence stratigraphy division represented by Vail, Galloway, and Johnson makes it difficult to delineate sequences of less than 1 million years in duration. Van Wagoner and others (Wagoner et al., 1990) first proposed the concept of high-frequency sequence stratigraphy, specifically referring to sedimentary responses generated by sea-level cycles of fourth order and higher with periods of 0.1–0.5 million years. Despite the high-resolution sequence stratigraphy proposed by Cross et al., there is no clear method for defining the base-level cycle, with only a simple three-level division of long-term, medium-term, and short-term base-level cycles. In contrast, the fourth, fifth, and sixth-order sequence boundaries were precisely determined based on glacioeustatic sea-level changes caused by Milankovitch cycles, utilizing the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle (Imbrie et al., 1993; Shi et al., 2019; Berends et al., 2021). With the development of high-resolution stratigraphy, Vail and others (Vail et al., 1977) subdivided the stratigraphy into six classes based on durations according to sea-level change. Subsequently, domestic scholars proposed different division schemes based on the characteristics of terrestrial sedimentation in China. Mei et al. (2000) and others proposed two mechanisms of sea level change in 2010, which are tectonic sea level change with a long period and a slow rate, and glacial sea level change with a high frequency and a high rate. Zheng et al. (2001) and others also in 2010, generalized and summarized the relationship between the structural superposition styles and sedimentary dynamics of the datum cyclothems of several terrestrial basins, such as the Liaohe, western Sichuan, and Baise, and proposed six types of orogenic interfaces with different orogenic features, developmental scales and identifying markers, pointing out the important identifying markers for the interfaces of the corresponding cyclothems and comparing them with the Vail sequential stratigraphic unit.

The fine-grained sedimentary rocks of the Fengcheng Formation in the Mahu Sag of the Junggar Basin exhibit high-frequency sequence stratigraphic characteristics. Conventional sequence stratigraphic research methods often struggle to accurately reflect the true conditions of stratigraphic cycles. The Milankovitch theory provides an effective means for conducting high-precision cycle division and comparison, serving as a powerful supplement to traditional sequence stratigraphy. The Maye 1 well is on the north slope of the Mahu Sag, which is an important exploratory well for oil and gas exploration in the Fengcheng Formation. The continuous core of the Fengcheng Formation obtained from this well spans 348.37 m, with oil and gas shows observed throughout the entire core. A detailed on-site core description revealed the presence of 1,727 small layers, making it difficult to perform cycle division and regional cycle comparison, thus limiting its utility for exploration and production guidance. Therefore, there is an urgent need to establish an effective set of cycle division schemes as a reference for sweet spot identification research.

2 GEOLOGICAL SETTING

The Maye 1 well is in the Mahu Sag, which is the most prolific oil and gas-generating area in the Junggar Basin. It is situated near the Wuxia and Kebai fault zones, which serve as pathways for oil and gas migration (Figure 1). The stratigraphy of the Mahu Sag was deposited on a pre-Carboniferous basement and spans from the Carboniferous to the Quaternary, with a thickness exceeding 10,000 m. The deepest drilled well in the area, Hutan 1, has reached approximately 15,000 m in the Carboniferous system. Due to the early compression and nappe-thrusting of the Hercynian movement, the Permian strata tilt towards the interior of the basin with a steep angle. The Permian strata primarily consist of infill-type sediments and the dip angle becomes gentler for strata above the Triassic.

FIGURE 1 | Structural location map of well Maye 1 in Mahu Sag, Junggar Basin.

The internal stratigraphic relationships within the Permian consist mostly of unconformable contacts, with truncation phenomena observed in the western region. Towards the east, there is the presence of an “apparent downlap” towards the center of the depression, while the eastern strata exhibit thinning and “erosion an upper part and downlap at lower part”. The Fengcheng Formation, along with the overlying and underlying strata, form an independent and integrated sequence stratigraphic unit. The thickness of the stratum is 200–2000 m, and the distribution area is about 5,000 km², which is the most dominant and high-quality alkali lake hydrocarbon source rock of the depression, and the most important material basis for the discovery of the Northwest Marginal Multilayer System Baili Oil Zone.

The stratigraphy of the Mahu Sag is fully developed from the Carboniferous to the Cenozoic, with the Upper Permian locally missing in the tectonically high part of the slope. The Fengcheng Formation in Well Maye 1 exhibits a dual characteristic of both alkaline lake source rocks and fine-grained sedimentary rocks. Currently, widespread distribution of alkaline minerals such as searlesite, shortite, and nahcolite has been discovered. This holds significant importance for the study of paleoclimate, paleohydrology, geochemistry, biology, sedimentation, and structure, among other aspects. The reservoir rocks of the Fengcheng Formation are mainly composed of fine-grained sediments such as silt and clay. The continuity and high-resolution recording of lake-phase fine-grained sedimentation provide valuable information on changes in lake climate, making it an ideal carrier for Milankovitch cycle analysis. Currently, the classification standards, sedimentary environments, and sweet spot prediction of fine-grained sedimentary rocks are hot topics and frontiers in petroleum geology research. The division of high-frequency sequences is the foundation for such studies. Therefore, the sequence stratigraphic study of the Fengcheng Formation in Well Maye 1 holds significant guiding significance for on-site production in the entire Junggar Basin oilfield and petroleum geological research.

3 MILANKOVITCH CYCLE IDENTIFICATION

3.1 Sequence stratigraphic levels and Milankovitch cycles

The Milankovitch cycles exhibit isochronism, providing a semi-quantitative reference for high-frequency sedimentary cycle correlation and comparison. In this paper, the stratigraphic division unit is further refined according to the stratigraphic sequence interface characteristics of the Fengcheng Formation in the Junggar Basin, to achieve the purpose of stratigraphic division and comparison of the Fengcheng Formation shale oils with high temporal precision (Table 1). We divided the sequence of the study area into six levels. The first three sequence interfaces are all tectonic unconformities in seismic profiles. The fourth-order sequence interface is a continuous and strong reflection interface with seismic incongruous changes in logging parameters, which is related to the long Milankovitch period and corresponds to the quasi-sequence set of classical sequence stratigraphy. The fifth order sequence interface is related to the short Milankovitch cycle and corresponds to the quasi-sequence of classical sequence stratigraphy. The sixth level sequence interface is related to the Milankovitch slope or precession period and corresponds to the rhythmic layers of classical sequence stratigraphy.

TABLE 1 | Pore structure data table of the Upper Wuerhe Formation reservoir in the Manan slope.

3.2 Theoretical orbital cycles

Berger and others concluded that there are regular variations in the obliquity and the period of precession in different geohistorical periods and that the ratio between the orbital parameters is fixed (Berger et al., 1992), which was measured and corrected based on satellite measurements (Table 2). The slope cycle and precession cycle have a linear relationship during the geohistory. The cycle time for any period since 2,000 Ma can be obtained by linear interpolation. According to the investigation, the deposition age of the Fengcheng Formation is 283–272 Ma, corresponding to the early Permian. The short period of the earth axis slope is 34,163 a, the long period of the earth axis slope is 42,736 a, the short period of precession is 17,387 a, and the long period of precession is 20,678 a. In the past 250 Ma, the long eccentricity cycle is about 405 ka, and the short eccentricity cycle is about 100 ka. When the Earth is at aphelion, the Earth’s surface receives less insolation; when the Earth is at perihelion, the Earth’s surface receives more insolation; the eccentricity cycle mainly affects the distribution of the insolation by adjusting the amplitude of the precession cycle. The larger the eccentricity, the larger the amplitude of the precession cycle, corresponding to colder winters and hotter summers. The final astronomical period of the Milankovitch Cycle of the Fengcheng Formation was determined from the sedimentary age isochronism method as the ratio of the eccentricity short period, the earth axis slope long period, the earth axis slope short period, the precession long period, and the precession short period are 1.0 : 0.43: 0.34: 0.21: 0.17.

TABLE 2 | Periodic table of precession and slope of historical periods.

3.3 Spectral analysis for extracting Milankovitch cycles

Spectral analysis is to show the stratigraphic rotation information contained in the GR curves through mathematical transformations. The eccentricity cycle, earth axis slope cycle, and precession cycle curves extracted from the well-logging data after filtering are used as the basis for the delineation of high-frequency stratigraphic sequences. Spectral analysis is a complex process that requires repeated comparisons and identifications. Its purpose is to discover frequencies where the wavelength ratios within the analyzed stratigraphic interval are similar or identical to the cyclical period ratios of climate variations during geological periods. Figure 2 shows the results of the spectrum analysis for Well Maye 1. The five peak points, labeled as A, B, C, D, and E, correspond to the eccentricity short period, earth axis slope long period, earth axis slope short period, precession long period, and precession short period, respectively. The corresponding values for the five points in Well Maye 1 are 0.07, 0.18, 0.22, 0.36, and 0.42. Calculating the corresponding sedimentary cyclicity thickness yields 14.29 m, 5.56 m, 4.55 m, 2.78 m, and 2.38 m, respectively. The thickness ratio is approximately 1.0 : 0.39: 0.32 : 0.19: 0.17. The ratios closely approximate the astronomical cycle ratios with an error within 10%, indicating that the fine-grained sedimentation in the Fengcheng Formation of the Mahu Sag exhibits Milankovitch cyclicity.

FIGURE 2 | Spectral analysis of natural gamma curve of well Maye 1 in Mahu Sag, Junggar Basin.

3.4 Wavelet transform for partitioning Milankovitch cycles

Smoothing the whole natural gamma curve (Figure 3, low frequency). The lower part of the Feng 2 section exhibits strong and significant amplitude variations, while the upper part shows weaker and less pronounced amplitude changes. The middle part has lower content and obvious variation from the upper and lower parts, corresponding to a fourth-order sequence unconformity interface. Through Table 1, the three fourth-order stratigraphic interfaces correspond to the Milankovitch eccentricity 405 ka long period, representing a long-term cyclical pattern.

FIGURE 3 | Wavelet transform results of well Maye 1 in Mahu Sag, Junggar Basin.

According to the spectrum analysis, the eccentricity short period and axial tilt long period correspond to frequencies of 0.07 and 0.18, respectively. A higher frequency value indicates a faster amplitude superposition change. The frequency of 0.18 is sufficient to characterize the meter-scale sequence variation features, and a more detailed sequence division does not have practical geological significance. Therefore, the frequencies of 0.07 and 0.18 are chosen to divide the section into five and six-order sequences. Spectrum analysis only reflects the average spectral structure within a specific time (or depth) and cannot extract information about how a particular frequency changes over time. Wavelet analysis is a novel technique in time-scale analysis and multiresolution analysis, which effectively captures the time (or depth) variations in the frequency domain. Wavelet variation pumping of the natural gamma curve of the Maye 1 well was carried out using MATLAB software to present two amplitude curves of medium and high frequency with frequency values of 0.07 and 0.18 (Figure 3, medium and high-frequency). The medium-frequency amplitude curve is an eccentricity 100 ka period, which is equivalent to a fifth-order sequence and represents a medium-term cyclone; the high-frequency amplitude curve is a ground-axis slope 42,736 a long period, which is equivalent to a sixth-order cyclone and represents a short-term cyclone.

Based on the spectral analysis of natural gamma curves and wavelet variations in the Fengcheng Formation of the Maye 1 well, two phases of lake recession-lake advance cyclicity were developed. Two long cycles with an eccentricity of 405 ka, nine short cycles with an eccentricity of 100 ka, and sixteen long cycles with earth axis slopes of 42,736 a were identified. The division of cycles corresponds well to core logging, logging sensitivity curves, and trace element measurement data.

4 APPLICATIONS OF MILANKOVITCH CYCLES

4.1 Comparative analysis of regional cycles

The identification of Milankovitch cycles in the Fengnan 14, Fengnan 1, and Fengnan 4 wells of the Permian Fengcheng Formation of the Mahu Sag, all of which have two long cycles with an eccentricity of 405 ka, reflects that there are two large-scale lake retreat-lake advance cycles in the Fengcheng Formation (Figure 4). Additionally, a prominent maximum lake flooding surface in the middle of the deposition corresponds to the paleoclimate of the warmest and most humid environment. These conditions are conducive to the proliferation of salt-tolerant bacterial algae organisms and provide a favorable environment for the formation of organic matter. The Fengcheng Formation is dominated by lacustrine source rocks with good organic matter types, mainly Ⅱ1 and Ⅱ2.

FIGURE 4 | Regional comparison of Milankovitch cycle of Fengcheng Formation, Mahu Sag.

Petrological characterization shows that longitudinally the rock grains change from coarse to fine and then from fine to coarse. A process of lithologic change dominated by mixed deposition of clastic and volcanic rocks at the base, with a gradual transition to chemosynthetic salt deposition, and dominated by clastic deposition at the top. This process also reflects that there is an important high-order sequence boundary during the period of chemical salt deposition, which corresponds to the identification of Milankovitch eccentricity long-period cycles. It further demonstrates that Earth’s orbital periods have a controlling effect on the rich organic shale sedimentary cycles. Based on the eccentricity of long-period cycles, a unified cyclostratigraphy pattern can be established, which is of great significance for the planar study of sedimentary facies.

4.2 Sweet spot recognition

The Milankovitch cycle of the Fengcheng Formation in the Maye 1 well-identified nine short cycles with an eccentricity of 100 ka (Figure 5), whose cyclic cycles correspond to the process of climate change from warm-wet-dry-cold-warm-wet. The warm-wet environment is favorable for the propagation of fungal and algal organisms, with increased organic matter abundance and high hydrocarbon potential; on the contrary, organic matter abundance is low in the dry-cold environment. By recovering the climate characteristic curve from the cycle changes, assuming that the lowest and highest points of each climate change are the same value, and using the semi-floating-point position for division, eight intervals with high organic matter abundance were identified.

FIGURE 5 | Milankovitch dessert identification of Fengcheng Formation in Well Maye 1, Mahu Sag.

The lithology, porosity, and oil saturation curves were interpreted through nuclear magnetic resonance logging to comprehensively explain eight sweet spots. These sweet spots have strong correspondence with the high-organic-matter intervals identified by the Milankovitch cycles. The correspondence of sweet spots at depths 1 and 8 is slightly poor, primarily due to the coarse-grained sedimentation at the top and bottom of the Fengcheng Formation, which is less influenced by climatic conditions and therefore less suitable. In contrast, the sweet spots identified in the fine-grained sedimentation of intervals two to seven correspond better. The analysis suggests that the Milankovitch cycles reflect the climatic variations in geological deposition due to the change of the earth’s axis orbit cycle. This climate variation is manifested in the sedimentary environment’s temperature and humidity, which directly affect the growth and reproduction of organic organisms, and subsequently, the abundance of organic matter. Since shale oil has the characteristic of being self-generated and self-stored or self-generated and neighboring stored, meaning that the reservoir and hydrocarbon-generating layer are the same layer or closely adjacent layers, the segments with high organic matter abundance exhibit better correspondence with the sweet spots. This also demonstrates that the Milankovitch cycles’ eccentricity 100ka short period can be used to identify sweet spots in the Fengcheng Formation.

5 CONCLUSION

(1) Spectral analysis and wavelet transform analysis of the natural gamma curve of the Well Maye 1 found that the shale formation in the Fengcheng Formation is characterized by Milankovitch’s high-frequency cyclicity. These cycles are controlled by the long period of eccentricity 405 ka, the short period of eccentricity 100 ka, and the long period of earth axis slope 4,2736 a. This provides quantitative evidence for delineating the shale oil minor layer of the Fengcheng Formation in later stages. The eccentricity with a long period of 405 ka, eccentricity with a short period of 100 ka, and obliquity of the Earth’s axis with a long period of 42,736 a are considered as reference curves for the subdivision of high-frequency sequence cycles at the fourth, fifth, and sixth order levels, respectively. The Fengcheng Formation exhibits two long eccentricity cycles, nine short eccentricity cycles, and 16 long earth axis slope cycles.

(2) The eccentricity 405 ka long period has regional applicability and divides the Fengcheng Formation into two phases of lake-retreat-lake-entry cyclones, providing a quantitative basis for establishing a unified stratigraphic grid of cyclones. A short period of 100 ka eccentricity can restore paleoclimate change. By establishing the relationship between paleoclimate and organic matter content, the abundance of organic matter can be predicted, which can indirectly identify desserts. The sweet spot prediction results are consistent with the identification of nuclear magnetic logging.

(3) The Milankovitch cycle is suitable for fine grained sediments affected by climate, but is not suitable for coarse grained sediments and volcanic sediments.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

RW: Writing–original draft. XC: Writing–review and editing. QC: Data curation, Formal analysis, Methodology, Writing–review and editing. XZ: Data curation, Formal analysis, Methodology, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by National Natural Science Foundation of China (Grant number. 42002165).

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Berends, C. J., Köhler, P., Lourens, L. J., and van de Wal, R. S. W. (2021). On the cause of the mid-Pleistocene Transition. Rev. Geophys. 59 (2), e2020RG000727. doi:10.1029/2020RG000727

Berger, A., Loutre, M. F., and Laskar, J. (1992). Stability of the astronomical frequencies over the earth’s history for paleoclimate studies. Sci. New Ser. 255 (5044), 560–566. doi:10.1126/science.255.5044.560

Cross, T. A. (1988). Controls on coal distribution in transgressive-regressive cycles, upper cretaceous, western interior, U.S.A. Special Publications 42, 371–380. doi:10.2110/pec.88.01.0371

Ding, Z. (2006). The Milankovitch theory of pletstocene glacial cycles challenges and chances. Quat. Sci. 26 (5), 710. doi:10.3321/j.issn:1001-7410.2006.05.005

Galloway, W. E. (1989). Genetic Stratigraphic Sequences in Basin Analysis I: Architecture and Genesis of Flooding-Surface Bounded Depositional Units1. AAPG Bull. 73 (2), 125–142. doi:10.1306/703C9AF5-1707-11D7-8645000102C1865D

Imbrie, J., Berger, A., Boyle, E. A., Clemens, S. C., Duffy, A., Howard, W. R., et al. (1993). On the structure and origin of major glaciation cycles 2. The 100, 000-year cycle. Paleoceanography 8 (6), 699–735. doi:10.1029/93pa02751

Jia, D., Tian, J., Lin, X., Yang, G., Feng, W., Zhang, X., et al. (2018). Milankovitch cycles in the Silurian Kepingtage Formation in Shuntuoguole area, Tarim Basin. Oil Gas Geol. 39 (4), 749–758. doi:10.11743/ogg20180412

Johnson, J. G., Klapper, G., and Sandberg, C. A. (1985). Devonian eustatic fluctuations in Euramerica. Geol. Socitery Am. Bull. 96, 567–587. doi:10.1130/0016-7606(1985)96<567:defie>2.0.co;2

Mei, M. (2010). Research on forming mechanism of long-term sequence: The second advance in sequence stratigraphy. J. Palaeogeogr. Chin. Ed. 12 (6), 711–728. doi:10.1017/S0004972710001772

Mei, M. (2011). From vertical stacking pattern of cycles to discerning and division of sequences: The third advance in sequence stratigraphy. J. Palaeogeogr. 13 (1), 37–54. doi:10.19762/j.cnki.dizhixuebao.2022151

Mei, M., Xu, D., and Zhou, H. (2000). Genetic types of meter-scale cyclic sequences and their fabric features of facies-succession. Acta Sedimentol. Sin. 18 (1), 43–62. doi:10.3969/j.issn.1000-0550.2000.01.008

Raymo, M. E., and Nisancioglu, K. H. (2003). The 41 kyr world: Milankovitch's other unsolved mystery. Paleoceanography 18 (1), 1011. doi:10.1029/2002pa000791

Shi, G., and Liu, Y. (2006). Progresses in the Milankovitch theory of earth's climate change. Adv. Earth Sci. 21 (3), 278–285. doi:10.11867/j.issn.1001-8166.2006.03.0278

Shi, J., Jin, Z., Liu, Q., and Huang, Z. (2017). Recognition and Division of High-resolution Sequences based on the Milankovitch Theory: A case study from the Middle Jurassic of Well Ary301 in the South Turgay Basin. Acta Sedimentol. Sin. 35 (3), 436–448. doi:10.14027/j.cnki.cjxb.2017.03.002

Shi, J., Jin, Z., Liu, Q., Huang, Z., and Zhang, R. (2019). Quantitative classification of high-frequency sequences in fine-grained lacustrine sedimentary rocks based on Milankovitch theory. Oil Gas. Geol. 40, 1205–1214. doi:10.11743/ogg20190605

Sloss, L. L. (1963). Sequences in the Cratonic Interior of North America. GSA Bull. 74 (2), 93–114. doi:10.1130/0016-7606(1963)74[93:SITCIO]2.0.CO;2

Vail, P. R., Audemard, F., Bowman, S. A., Eisner, P. N., and Perez-Cruz, C. (1991). “The stratigraphic signatures oftectonics, eustasy and sedimentology – an overview,” in Cycles and events in stratigraphy ed . Editors G. Einsele, W. Ricken, and A. Seilacher (Berlin: Springer-Verlag), 617–659. doi:10.3181/00379727-123-31396

Vail, P. R., Mitchum, R. M., and Thompson, S. (1977). “Seismic Stratigraphy and Global Changes of Sea Level, Part 3: Relative Changes of Sea Level from Coastal Onlap1,” in Seismic stratigraphy — applications to hydrocarbon exploration ed . Editor C. E. Payton ( American Association of Petroleum Geologists). doi:10.1306/M26490C5

Wagoner, J. C. V., Mitchum, R. M., Campion, K. M., and Rahmanian, V. D. (1990). Siliciclastic Sequence Stratigraphy in Well Logs, Cores, and Outcrops: Concepts for High-Resolution Correlation of Time and Facies. Am. Assoc. Petroleum Geol. 7, 7510. doi:10.1306/Mth7510

Xie, H., Yu, B., and Tan, C. (2016). Analyses of Milankovitch cycle and sequence classification for Yanchang Formation in Ordos Basin. Petroleum Geol. Oilfield Dev. Daqing 35 (1), 43–47. doi:10.6038/cjg2022P0784

Xu, W., and Xie, X. (2012). A new method to calculate sedimentary rates based on Milankovitch Cycles: A case study on middle section of 3rd member of Shahejie Formation in well Niu38, Dongying Sag, Bohai Bay Basin. Petroleum Geol. Exp. 34 (2), 207–214. doi:10.11781/sysydz201202207

Zhang, Y., Wang, G., Yu, Z., Zhao, Z., Wang, M., and Sun, Y. (2013). Milankovitch cycles and high-frequency sequences of the Middle Permian Maokou Formation in Sichuan Basin. J. Palaeogeogr. 15 (6), 777–786. doi:10.1002/gj.4130

Zhao, J., Cao, Q., Fu, X., Hang, F., Zhang, F., and Qin, G. (2018). Recovery of denuded strata thickness based on Milankovitch Astronomical Cycles: a case study of Qingshankou Formation in X Oilfield, Songliao Basin. Petroleum Geol. Exp. 40 (2), 260–267. doi:10.11781/sysydz201802260

Zheng, R., Peng, J., and Wu, C. (2001). Grade division of base-level cycles of terrigenous basin and its implications. Acta Sedimentol. Sin. 19 (2), 249–255. doi:10.3969/j.issn.1000-0550.2001.02.015

Conflict of interest: Authors RW, QC, and XZ were employed by PetroChina Xinjiang oilfield Company.
The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Wang, Chen, Chang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 16 May 2024
doi: 10.3389/feart.2024.1399921

Evaluating and study of natural gas injecting in Shunbei-1 block fault-controlled fractured-cavity type reservoir

Yunfeng He¹*, Yaoyu Liu¹, Wen Chen², Linxian Chi¹, Yujun He², Hao Li¹, Yuxuan Sun² and Lixin Wang³

¹Sinopec, Northwest Oilfield Company, No.4 Oil Production Plant, Urumqi, China

²Jiang Su Oilfield Company, No.2 Oil 4 Production Plant, Aksu, China

³Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Bo Liao, China University of Petroleum (East China), China
Muming Wang, University of Calgary, Canada
Pengfei Xie, University of Calgary, Canada

* Correspondence: Yunfeng He, heyf.xbsj@sinopec.com

Received: 12 March 2024
Accepted: 11 April 2024
Published: 16 May 2024

Citation: He Y, Liu Y, Chen W, Chi L, He Y, Li H, Sun Y and Wang L (2024) Evaluating and study of natural gas injecting in Shunbei-1 block fault-controlled fractured-cavity type reservoir. Front. Earth Sci. 12:1399921. doi: 10.3389/feart.2024.1399921

The fault cavern type reservoir in Block 1 of Shunbei area is located in the ultra-deep layer and is classified as a volatile oil reservoir. The reservoir is characterized by very deep burial with an oil/gas ratio between 250 and 500 m³/tone and a saturation pressure range of 28–36 MPa. As of March 2021, the formation pressure has dropped to 30.5 MPa and the pressure retention rate is 35.8%. In order to effectively develop this reservoir, it was decided to use a water injection development method with a planned transition to natural gas injection in March 2022 to further enhance recovery and maintain formation pressure. Through evaluation and research, the pressure distribution, production capacity change and reservoir dynamic response during the water injection development stage were analyzed with respect to the characteristics of fracture-controlled fracture-cave type reservoir in Shunbei-1 block. On this basis, the necessity, feasibility, and expected effects of conversion to natural gas injection are explored, including improvement of displacement efficiency, reduction of fluid output, and enhancement of formation energy replenishment. In addition, this study also carries out in-depth research and prediction on the optimization of natural gas injection parameters, the selection of injection methods and possible problems, aiming to provide scientific basis and technical support for the efficient and sustainable development of this block. In order to smoothly promote the implementation of natural gas injection development, a special reservoir description technique applicable to fault cavern type reservoirs was adopted. Based on this foundation, an in-depth study was conducted on the mechanism of natural gas displacement, so as to optimize the layout of the well network and the setting of injection and extraction parameters. In addition, a set of evaluation system for natural gas injection effect in fault cavern type reservoirs was constructed. By adopting the natural gas injection development method and combining the corresponding supporting technology development and optimization measures, the reservoir development efficiency and oil (gas) production rate have been effectively improved, and the risk of gas flaring has been reduced at the same time. These research results are of great significance for ensuring the comprehensive development and effective utilization of oil and gas resources, and provide strong technical support for future development work.

Keywords: fault-controlled fractured-cavity type, natural gas injecting, gas flooding evaluation, gas leakage, Shunbei fault zone no. 1, deep-ultra-deep oil and gas development

1 INTRODUCTION

Block one in the Shunbei area (Figure 1) possesses a fracture-controlled fracture-cavern type reservoir with a burial depth of more than 7,500 m and an oil column thickness of more than 400 m. The reservoir has a high degree of non-homogeneity and is highly heterogeneous. The fracture and cavern structures within the reservoir are intertwined, resulting in a high degree of inhomogeneity. At the early stage of reservoir development, the elastic energy was huge, and the original static pressure was as high as 85.5 MPa. However, as the development process advances, the pressure drops rapidly and the supplementary injection pressure reaches only 40.2 MPa, making the formation pressure maintain a low level. At present, characteristic difficulties such as the typical water tampering problem have emerged during the water injection process. Therefore, it is particularly important to implement natural gas injection evaluation and research on this reservoir which is deep, complex and has been facing problems such as pressure failure (Yuan, 2018; Ji et al., 2022; Jiawei et al., 2022; Chen et al., 2023).

FIGURE 1 | Location map of study area (A), comprehensive histogram of lithostratigraphy (B).

In order to solve the water runoff problem, it is necessary to select a suitable energy replenishment medium based on a detailed description of the reservoir. Numerical simulations of the oil replacement process using different injection media show that the injection of natural gas can achieve the highest crude oil recovery up to 46.3% (Zhu et al., 2010; Yang et al., 2018; Hou et al., 2022; Xinxing et al., 2023). In view of the rich natural gas resources in the Shunbei oil field area, it was decided to adopt the natural gas injection development method (Tang et al., 2004; Juhua et al., 2008; Ma et al., 2017; Luo et al., 2022). In this process, targeted gas injection strategy design and optimization was implemented for fracture-controlled fracture-cavity type reservoirs, including but not limited to: building accurate geological models to identify favorable gas injection channels (Baozeng et al., 2020; Zhang et al., 2022; CHEN et al., 2024). Formulate reasonable well network layout and injection and extraction parameter programs to avoid or reduce the occurrence of gas flushing; and use advanced monitoring technology to monitor the dynamic effect of gas injection and possible gas transportation behavior in real time, so as to ensure the effective utilization of natural gas and the efficient development of the reservoir (Cong et al., 2014; Liang et al., 2016; Zheng et al., 2022; Hu et al., 2023). In addition, for the complex situations that may occur after natural gas injection, such as uneven pressure distribution and formation injury, we have carried out in-depth research combining theoretical analysis and on-site practice, established a comprehensive evaluation system for natural gas injection, and formulated corresponding countermeasures and technical standards, so as to guarantee the smooth progress and long-term benefits of the natural gas injection development project for the fracture-controlled fracture-cavern-type reservoirs in Shunbei Area Block 1 (Lu and Cai, 2010; YUAN et al., 2023).

This paper, based on an analysis of the mechanisms behind natural gas injection, conducted research on the design and optimization of well patterns and injection-production parameters under natural gas injection conditions. An evaluation of the current stage of natural gas flooding was performed, and preventive policies against gas channeling were formulated. This work provides theoretical and foundational support for the development of natural gas injection in Shunbei Area Block 1.

2 STUDY OF NATURAL GAS INJECTION MECHANISM

In the Shunbei Area Block one reservoir, the original crude oil contains a high content of C2-C6 components, ranging from 11.8% to 19.4% of the total. Additionally, the reservoir has high pressure conditions, which are favorable for the formation of miscible gas flooding. Through laboratory experiments, the miscibility threshold pressure in the Shunbei Area Block one was determined to be 47 MPa, and it was confirmed that the miscible gas flooding in this area follows an evaporative multi-contact miscible flooding mechanism. The multi-contact mechanism significantly reduces the density, viscosity, and interfacial tension of the crude oil, greatly improving its flowability, and increasing the volume coefficient, thereby enhancing elastic energy. Numerical simulations conducted for different injection phase stages in the A1-12 well group indicate that miscible flooding yields better oil recovery results (as shown in Figure 2).

FIGURE 2 | Residual Oil Distribution for Different Injection and Production States in Well Group A1-12 (A). diagram of the minimum mixing pressure for natural gas flooding; (B) residual oil distribution of non-miscible drive in 35MPa; (C) residual oil distribution of near-miscible drive in 45MPa; (D) residual oil distribution of mixed drive in 55MPa; (E) location of wells).

3 INJECTION AND PRODUCTION WELL NETWORK DESIGN

3.1 Injection and production well network design

Shunbei Area one is affected by large-scale strike-slip fracture zones, and develops slit-type carbonate reservoirs, which are strongly controlled by the fracture zones, with weak karst development, and extremely non-homogeneous reservoirs. According to the characteristics of the fractured reservoir in Block one of Shunbei area, four connectivity modes are designed: segmented pulling type along the section, segmented pulling type through the body, segmented panning type along the section and segmented panning type through the body. Meanwhile, three different injection and extraction relationships were established, i.e., high injection and low extraction, low injection and high extraction, and balanced injection and extraction (see Figure 3).

FIGURE 3 | Connectivity mode and injection-production relationship diagram of Shunbei fault-controlled fracture-cavity reservoir.

From the numerical simulation results, it can be observed that in the “low injection and high production” well network, due to the development of high-angle fractures and a significant vertical thickness, the gravitational segregation of injected gas is pronounced. After gas injection, gas channeling is likely to occur easily, leading to a decrease in reservoir energy. Specifically, after 20 days of gas injection, the gas-oil ratio increased by 50%, after 50 days it increased by 100%, and after 90 days it increased by 150% (see Figure 4).

FIGURE 4 | Production gas-oil ratio curves under different injection-production relationships.

In contrast, the “high injection and low production” well network, due to the reduction in the producing interval in oil wells, effectively delays gas channeling (for 50 days). It also fully exploits the effects of gas injection for reservoir re-energizing and gas cap drive (see Figure 5). Specifically, after 70 days of gas injection, the gas-oil ratio increased by 50%, after 100 days it increased by 100%, and after 135 days it increased by 150%.

FIGURE 5 | Pressure curves for different injection-production relationships in well groups.

Therefore, it is preferable to choose the “high injection and low production” injection-production relationship, which results in a slower increase in gas-oil ratio and better reservoir re-energizing effects.

For fractured-dominated fissure-type reservoirs, the development of fractures and the effectiveness of miscible displacement are constrained by factors such as cross-sections, fractures, and reservoir types. When the injection wells are surrounded by predominantly fractured reservoirs and are directly connected to the production wells through fractures or connected through the same cross-section, it is easy to form dominant gas drive channels, leading to the occurrence of gas channeling. In such a connectivity pattern, natural gas drive predominates as the primary mechanism, with miscible displacement playing a secondary role. However, when injection wells are located in areas with developed caves, and production wells are on different cross-sections, natural gas drive is primarily based on miscible displacement. The injection gas extends over a large and uniform area, making gas channeling less likely.

Therefore, it is recommended to preferentially select the connectivity pattern with the development of cave-type reservoirs and injection-production wells on different cross-sections for injection and production operations (see Figure 6). This approach helps optimize reservoir development and enhance uniform gas distribution, reducing the likelihood of gas channeling.

FIGURE 6 | Oil Displacement Effects under Different Connectivity Patterns [(A) Injection well cross the fault and in the same side with the production well; (B) injection well and production well is on different side of the fault].

3.2 Injection and production parameters design

The numerical simulation results of injection and recovery parameter design show that the higher the reservoir pressure is maintained, the higher the cumulative oil production is. However, when the pressure is restored to 130% of the mixed-phase pressure, the production increase effect slows down significantly. Therefore, the optimal pressure maintenance level was determined to be 61 MPa (Figure 7).

FIGURE 7 | Comparison of the effect of gas injection to pressure increase in different formations in the fault-controlled fracture-cavity reservoir in Shunbei one area [(A) Cumulative oil production at different injection times; (B) The volume of free gas at different injection times; (C) Intersection plot of cumulative oil and swept volume].

However, physical simulation experiments simulating changes in oil recovery wells after gas injection through a laboratory setup showed relatively small increases in crude oil recovery after reaching mixed-phase drive. The discrepancy between the numerical simulations and the mixed-phase experimental results is mainly attributed to the consideration of pressure sensitivity factors of the rock. As pressure increases, connectivity and permeability between fractures and pores are enhanced. When designing the injection and extraction parameters, factors such as reservoir pressure, injected gas type and injection rate are comprehensively considered to balance the contradiction between improving oil driving efficiency and preventing gas flushing. By comparing and analyzing different injection and extraction schemes, we select the injection rate and volume that can enhance the formation pressure within the effective range and minimize the occurrence of gas flushing, so as to realize the economic and effective development strategy of natural gas drive. At the same time, in combination with the actual geological conditions, the pressure distribution during the injection phase is finely regulated, and the well network structure is optimized for the characteristics of different well groups, to ensure that the injected gas can be more evenly distributed in the target reservoir, to further reduce the risk of gas flushing and improve the overall efficiency of oil drive.

By selecting different well groups for simulation with varying injection rates, it can be observed that the optimal gas injection rate falls within the range of 90,000 to 150,000 cubic meters per day. When the injection rate exceeds 150,000 cubic meters per day, gas channeling accelerates, adversely affecting the performance (Figure 8).

FIGURE 8 | Comparison of the Effects of Different Injection and Production Ratios and Gas Injection Rates on Different Well Groups [(A) A1-25 well group cumulative oil production curve; (B) A1-25 Well Group Pressure Curve; (C) A1-10 well group cumulative oil production curve; (D) A1-10 Well Group Pressure Curve; (E) A1-12 well group cumulative oil production curve; (F) A1-12 Well Group Pressure Curve).

To assess the oil recovery effect at different stages with varying injection-production ratios, simulations were conducted (Yuanshuai et al., 2008; Gao et al., 2009; Rong et al., 2010) (Table 1). Before the miscible displacement, the injection-production ratio was set at 2:1, resulting in a simulated oil recovery increase of 171,000 tons. After miscible displacement, the ratio was adjusted to 1:1, with a similar simulated increase of 171,000 tons. Therefore, following the principle of ‘one well, one strategy’ and ‘optimal gas drive,’ the initial injection-production ratio can be designed as 2:1, which should transition to 1.2:1 after reaching miscible displacement. This transition should occur gradually while maintaining the reservoir pressure at 61 MPa. Additionally, dynamic optimization adjustments should be made based on wellbore safety, gas drive effectiveness, and gas channeling conditions.

TABLE 1 | Different injection schemes for well group A1-25H.

4 EVALUATION OF NATURAL GAS DRIVE EFFECT

4.1 Evaluation criteria

We have selected eight key indicators to evaluate the effectiveness of natural gas drive in fissure-type reservoirs, for ease of assessment: a. Ineffective Gas Injection: The gas produced beyond the normal gas-oil ratio is considered ineffective gas injection. b. Gas Storage: It is calculated as the gas injection volume minus the ineffective gas injection. c. Gas Storage Ratio: Gas storage divided by the cumulative gas injection volume. d. Sweep Efficiency: The percentage of the reservoir volume (or area) swept by the injected displacement system relative to the total reservoir volume (or area), which is related to reservoir volume/area. The swept area by the displacement system is represented by the effective gas injection volume (gas storage), and the total reservoir volume is the geological reserves of the well group. e. Displacement Efficiency: The ratio of underground crude oil displaced by the injected displacement phase to the crude oil within the displacement range (injected gas volume). f. Enhanced Oil Recovery Degree: It is calculated as the incremental oil production divided by the geological reserves of the well group. g. Enhanced Oil Recovery Rate: Estimated incremental oil production divided by the geological reserves of the well group. h. Gas Drive Utilization Reserves: The gas consumption per unit of pressure increase for the well group divided by the liquid production per unit of pressure decrease for the well group, multiplied by the utilized reserves for the well group (Guo et al., 2004; Tang et al., 2005; Wen et al., 2011).

4.2 Evaluation results

Four different types of typical well groups subjected to natural gas drive were selected for evaluation and comparison. Comparing the injection-production relationships, for the same reservoir with through-reservoir type, the high injection and low production type has a gas storage ratio 1.14 times that of the low injection and high production type. The incremental oil production is 2.6 times higher, displacement efficiency is 1.1 times higher, estimated enhanced recovery rate is 3.7 times higher, and sweep efficiency is 4.7 times higher, and gas channeling is less likely to occur. Comparing the connectivity types, both are low injection and high production types, with through-reservoir and along-the-cross-section types. The former has an incremental oil production 3.6 times higher, and a displacement efficiency of 33.5%, which is 6.1 times higher than the latter. Therefore, overall, the through-reservoir type with high injection and low production exhibits better natural gas drive effectiveness (Figure 9; Table 2).

FIGURE 9 | Comparison of the effects of different injection-production relationships on different well groups at different gas injection rates.

TABLE 2 | Gas injection effect evaluation table for well groups under natural gas drive in Shunbei zone 1.

5 GAS CHANNELING PREVENTION POLICIES AND EFFECTS

5.1 Gas channeling evaluation indicators

Incorporating field trials in fractured control fissure-type oil reservoirs, a 1-main-3-auxiliary set of gas channeling evaluation indicators has been established, with the gas-oil ratio as the primary indicator and natural gas composition, flow pressure gradient deviation, and production changes as auxiliary indicators for the phase division of gas injection well groups. The indicators for the initial stage of gas channeling are as follows: a gas-oil ratio increase of 20%–100%, a C1 content increase of over 5% in produced gas, a decrease of over 45% in C5+ content, and a production decline within the range of 0%–5%. When the gas-oil ratio exceeds 100% or the production decline falls within the range of 5%–12%, it is considered the mid-stage of gas channeling, and when the production decline exceeds 12%, it is deemed to have entered the late stage of gas channeling (Figure 10).

FIGURE 10 | Phase division of gas channeling in shunbei zone 1 Gas injection.

Currently, three low injection-high production wells exhibit abnormal indicators, with one in the late stage of gas channeling, one in the mid-stage, and one in the early stage. Among them, the low injection-high production, along-the-cross-section connected well A1-1H has experienced a 562% increase in gas-oil ratio, a 6.4% increase in C1 content, a 52% decrease in C5+ content, and a 30% production decline, indicating it is in the late stage of gas channeling. The low injection-high production, through-reservoir connected well A1-11 has seen a 114% increase in gas-oil ratio, a 4.9% increase in C1 content, a 22% decrease in C5+ content, and a 4.0% oil production decline, placing it in the mid-stage of gas channeling. The near equal injection-production, along-the-cross-section connected well A1-7H has witnessed a 93.5% increase in gas-oil ratio, a 4.9% increase in C1 content, a −50% decrease in C5+ content, and a slight 3% production increase, indicating it is in the mid-stage of gas channeling. The high injection-low production, through-reservoir connected well A1-15 has shown a current production increase of 50%, with a stable gas-oil ratio and no signs of gas channeling observed. It is evident that in fractured control fissure-type oil reservoirs, prioritizing the construction of a high injection-low production, through-reservoir connected gas injection well network can effectively delay gas channeling and achieve better natural gas drive results (Table 3).

TABLE 3 | Indicators of gas migration in the current 3 wells with gas migration signs.

5.2 Gas migration control measures

For fault-controlled cavity-type oil reservoirs, gas migration management is based on the “well network model,” aiming to “improve recovery rates.” Development strategies are adjusted in stages and differentially according to different pressure types. In the early stage of gas migration, different types of well groups are adjusted differently. For low-injection, high-production well groups that have not reached miscibility, maintain an injection to production ratio of 1.5–2.0 to also accommodate energy supplementation to reach miscibility. For low-injection, high-production well groups with remaining oil at the top, utilize natural gas for its “oil-driving” and “oil-replacing” functions; for those without remaining oil at the top, use natural gas for its “oil-driving” function. In contrast, high-injection, low-production well groups should implement balanced injection and extraction, continuously using natural gas for its “oil-replacing” function. In the mid-stage of gas migration, all should carry out displacement adjustment, maintaining a 1:1 injection to production ratio to sustain production; however, for low-injection, high-production well groups at the top of the reservoir with no remaining oil and not yet reaching miscibility, a larger injection to production ratio should be maintained to achieve miscibility. In the late stage of gas migration, if it is a low-injection, high-production well network, shift the injection and production relationship to high-injection, low-production for natural gas development; if it is already a high-injection, low-production network, then proceed with depletion-type development (refer to Figure 11).

FIGURE 11 | Development strategy diagram for different well network types at different gas appearance stages in fault-controlled fractured cavity-type oil reservoirs.

5.3 The effects of managing gas migration in a specific well group

A1-1H Well Group: This group is a low-injection, high-production well network, interconnected along the fault plane. Early numerical modeling indicated that the primary mechanism of gas drive is gravitational override, with the injected natural gas predominantly advancing along the fault plane.

Gas Migration Management: 21 days after natural gas injection into the A1-26H well, a pressure response was observed in the A1-1H well. 76 days post-injection, the gas-to-oil ratio in the A1-1H well rapidly increased, and the flowing pressure gradually declined, indicating signs of gas appearance. The well group is currently in the late stage of gas migration, with a 1-day response time for injection and production (refer to Figure 12; Table 4).

FIGURE 12 | Plan (A) and cross-sectional views (B) of the A1-1H well group.

TABLE 4 | Statistical table of A1-1H well cluster governance effect.

For the A1-1H well group, several techniques were employed to control gas migration, including intermittent injection, reduced injection, fluid control, increased injection, and fluid control. Overall, fluid control proved to be the most effective. Initially, after intermittent injection, the gas-to-oil ratio continued to rise by 107%, production decreased by 9.5%, formation pressure dropped by −4.3 MPa, the methane (C1) content in the produced gas continuously increased, and the C5+ content consistently decreased. Subsequently, after reduced injection, the gas-to-oil ratio continued to rise by 17.8%, production fell by 11.3%, formation pressure decreased by 1.1 MPa, the methane content still slightly increased, the C5+ content slightly decreased, and the gas storage rate dropped to its lowest at 22.14%. Finally, after fluid control, the results were better: the gas-to-oil ratio decreased by 41%, formation pressure increased by 6.2 MPa, gas storage rate rose to 76.6%, methane content in the produced gas decreased by 1%, and C5+ content increased by 16%. This indicates that injecting natural gas effectively improved the development outcomes (refer to Figure 13).

FIGURE 13 | Gas channeling control effect of well A1-1H.

6 CONCLUSION

The fault-controlled fracture-cavity type reservoirs in the Shunbei Oilfield have achieved the best results in the development using natural gas injection. By rapid initial gas injection, followed by slow gas injection with phase mixing, supplemented by a balanced injection and recovery strategy, the drive process of crude oil was effectively promoted. An evaluation system was also established in the study, indicating that the optimal results are obtained with a high injection-low recovery ratio, good connectivity of the well network in non-single sections, and gas injection through collective reservoir-type wells. In addition, we have developed a gas flurry prediction system and a gas flurry control strategy. Experimental results show that controlling the liquid injection volume is more effective than optimizing the injection period and rate. By targeting different stages and types of well packs with appropriate gas transportation management measures, as shown in the case of A1-1H well pack, we can effectively improve the gas flushing situation, enhance the effect of natural gas drive, and ultimately improve the oil recovery rate and economic benefits of the whole block. This series of research results provide scientific basis and technical guidance for future oil and gas field development under similar geological conditions.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

YNH: Writing–original draft. YL: Writing–review and editing. WC: Writing–review and editing. LC: Data curation, Writing–review and editing. YJH: Data curation, Writing–review and editing. HL: Methodology, Writing–review and editing. YS: Methodology, Writing–review and editing. LW: Writing–review and editing.

FUNDING

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Baozeng, L. I. U., Qi, L., Zongjie, L. I., Jun, L. I. U., Chao, HUANG, Lin, YANG, et al. (2020). Spatial characterization and quantitative description technology for ultra-deep fault-karst reservoirs in the Shunbei area. Acta Pet. Sin. 41 (4), 412–420.

Chen, Y., Zhu, L., and Liu, X. (2023). Feasibility of natural gas miscible flooding in ultra-deep Fault-Controlled reservoirs in shunbei-1 Block. Xinjiang Pet. Geol. 44 (02), 203–209.

Chen, S., Yunfeng, H. E., Wang, L., et al. (2024). Architecture characterization and 3D geological modeling of Ordovician carbonate reservoirs in Shunbei No. 1 fault zone, Tarim Basin. Lithol. Reserv. 36 (2), 124–135.

Cong, T., Peng, X., and Yang, Li (2014). Optimization of waterflooding process for Fault⁃Controlled Fracture⁃Cavity reservoir of ordovician in tahe field. Tarim. Basin 35, 703–707.

Gao, H., Hou, T., and Wu, Y. (2009). Study of EOR by natural gas injection. Drill. Prod. Technol. 32, 25.

Guo, P., Du, Z., and Zhang, M. (2004). Case analysis on hydrocarbon alternative gas miscible flooding in Pubei oil field. J. Southwest Petroleum Inst. 26, 25.

Hou, D., Liu, X., Zheng, W., et al. (2022). Problems and countermeasures of gas-oil ratio rise in gas injection development of Carboniferous oil reservoirs in Oilfield Donghe-1 of Tarim Basin. Petroleum Geol. Oilfield Dev. Daqing 41 (02), 51–58. doi:10.19597/j.issn.1000-3754.202006035

Hu, W., Li, X., Yang, M., et al. (2023). Well pattern optimization for fractured-vuggy carbonate reservoirs in tahe oilfield. Xinjiang Pet. Geol. 44 (4), 429–434.

Ji, B., Zheng, S., and Gu, H. (2022). On the development technology of fractured-vuggy carbonate reservoirs: a case study on Tahe oilfield and Shunbei oil and gas field. Oil Gas Geol. 43 (6), 1459–1465. doi:10.11743/ogg20220614

Jiawei, F. A. N., Ye, YUAN, Shaohua, L. I., et al. (2022). Geology-engineering integrated simulation technology of deep tight oil reservoir in Tarim Basin. Fault-Block Oil Gas Field 29 (2), 194–198.

Juhua, Li, Xiangfang, Li, Jiang, T., et al. (2008). Study on mechanism and features of unstable state displacement of miscible hydrocarbon-gas flooding. Petroleum Geol. oilfield Dev. Da Qing 27, 95.

Liang, CHENG, Zhang, X., Wang, J., et al. (2016). Study on periodic gas injection mechanism of condensate gas reservoirs with high to high-super content of condensate oil in Central Tarim by numerical simulation. J. Xi'an Shiyou Univ. Nat. Sci. Ed. 31 (2), 57–64.

Lu, X., and Cai, Z. (2010). A study of the paleo cavern system in fractured vuggy carbonate reservoirs and oil/gas development-taking the reservoirs in Tahe oilfield as an example. Oil Gas Geol. 31, 22–25.

Luo, Q., Chen, L., Wang, J., et al. (2022). Study on the oil-gas miscibility zone in tarim oilfield. J. Chongqing Univ. Sci. Technol. 24, 42–47.

Ma, Q., Yan, E., Wanfen, Pu, et al. (2017). Experimental study on the natural gas and CO2 injection EOR processes in tuofutai Block of tahe oilfield. Sci. Technol. Eng. 17 (08), 14–20.

Rong, Y., Liu, X., and Yang, M.A discussion on water flooding in the multi⁃well fractured⁃vuggy units of carbonate reservoirs in Tahe oilfield. Oil Gas Geol. , 2010, 31, 28–32.

Tang, Y., Sun, L., Shilun, Li, et al. (2004). On evaluation method of miscible gas-flooding mechanism. Xinjiang Pet. Geol. 25 (4), 414–416.

Tang, Y., Sun, L., Zhou, Y., et al. Mechanism evaluation of condensing/vaporizing miscible flooding with hydrocarbon⁃rich gas injection.Petroleum Explor. Dev. , 2005, 32, 133.

Wen, CHEN, Tang, Y., Liang, T., et al. (2011). Mechanism study of dynamic mixed-phase driving process of supercritical CO2. Drill. Prod. Technol. 34 (03), 77–80+117.

Xinxing, H. E., Huang, Z., Liming, LIAN, et al. (2023). Key technologies for enhancing the recovery of condensate gas reservoirs by gas injection–storage coordination. Nat. Gas. Ind. 43 (1), 86–95.

Yang, L. I., Zhijiang, KANG, Xue, Z., et al. (2018). Theories and practices of carbonate reservoirs development in China. Petroleum Explor. Dev. 45 (4), 669–678.

Yuan, D. (2018). EOR technology and related mechanism research of gas injection to fractured-cavity carbonate reservoir. Beijing: China University of Petroleum.

Yuan, F. Y., Wang, H. P., Yue, P., et al. (2023). Simulation analysis of influencing factors of single well gas injection in fracture-vuggy carbonate reservoir. Unconv. Oil Gas 10 (4), 86.

Yuanshuai, R., Liu, X., and Luo, J. (2008). Pilot study on waterflooding development in multi⁃well fracture⁃cavity units of Tahe oilfield. Oil Drill. Prod. Technol. 30, 83–87.

Zhang, W., Zhang, Y., Duan, T., et al. (2022). Hierarchy modeling of the Ordovician fault-karst carbonate reservoir in Tuopaitai area, Tahe oilfield, Tarim Basin, NW China. Oil&Gas Geol. 43 (1), 207–218. doi:10.11743/ogg20220117

Zheng, S., Kang, Z., Cheng, X., et al. (2022). Waterflooding characteristics and improvement measures of fracture-cavity carbonate reservoirs in Tahe Oilfield. Petroleum Geol. Recovery Effic. 29 (6), 95–104. doi:10.13673/j.cnki.cn37-1359/te.202201022

Zhu, W., Zhou, D., Jilei, F., et al. (2010). Development effect evaluation of horizontal wells in typical oil and gas reservoirs of Tarim Oilfield. Petroleum Explor. Dev. 37 (6), 716–725.

Conflict of interest: Authors YNH, YL, LC, and HL were employed by Sinopec, Northwest Oilfield Company. Authors WC, YJH, and YS were employed by Jiang Su Oilfield Company.
The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 He, Liu, Chen, Chi, He, Li, Sun and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 16 May 2024
doi: 10.3389/feart.2024.1407074

Influence of extensional strike-slip fault systems on hydrocarbon accumulation: case studies from Huanghekoudong and Miaoxi’nan sub-sags

Sheng Fu1,2,³*, Deyu Wang1,², Haifeng Yang⁴, Lijun Song1,²*, Ming Ma3,⁵ and Kaiyu Wang⁶

¹School of Earth Sciences and Engineering, Xi’an Shiyou University, Xi’an, China

²Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi’an Shiyou University, Xi’an, China

³Key Laboratory of Petroleum Resources Research, Lanzhou, China

⁴CNOOC (China) Limited Tianjin branch, Tianjin, China

⁵Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China

⁶Institute of Petroleum Exploration and Development, PetroChina Tarim Oilfield Company, Korla, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Chaojin Lu, University of Miami, United States
Bingshan Ma, Southwest Petroleum University, China
Shiqiang Xia, North China University of Science and Technology, China

* Correspondence: Sheng Fu, xiaofu1241396@163.com; Lijun Song, Ljsong@xsyu.edu.cn

Received: 26 March 2024
Accepted: 11 April 2024
Published: 16 May 2024

Citation: Fu S, Wang D, Yang H, Song L, Ma M and Wang K (2024) Influence of extensional strike-slip fault systems on hydrocarbon accumulation: case studies from Huanghekoudong and Miaoxi’nan sub-sags. Front. Earth Sci. 12:1407074. doi: 10.3389/feart.2024.1407074

Extensional strike-slip basins have unique structural features that influence both sub-sags and traps. Despite previous investigations into the extension-strike-slip fault system, there remains a gap in the comprehensive analysis of its influence on sub-sag formation and trap development. The Huanghekoudong and Miaoxi’nan sub-sags within the Bohai Bay Basin, intersected by the Tan-Lu Fault Zone, offer exemplary cases for examining the interplay between strike-slip and extensional tectonic regimes. The neotectonic strike-slip movements along the Tan-Lu Fault Zone have given rise to various extensional strike-slip overlap zones in these sub-sags, rendering these locales ideal for investigating the dynamics of strike-slip and extensional tectonic processes. Employing three-dimensional seismic data, well logs, and a newly constructed sequence stratigraphic framework, our research delineates the geometric and kinematic phenomena characteristic of the extensional strike-slip fault system, including the dynamics of extensional fault deformation and interaction through time. By scrutinizing the fault system’s vital role in shaping sub-sag evolution and trap genesis, we present a comprehensive model that significantly contributes to our understanding of structural trapping dynamics. This model not only fine-tunes existing trapping models but also offers invaluable insights for future exploration strategies within the Bohai Bay Basin and other similar extensional strike-slip basins worldwide. Our findings highlight the novel and significant implications of the extensional strike-slip fault system in controlling sub-sag and trap features, thus bridging a notable gap in existing geotechnical knowledge.

Keywords: extensional strike-slip fault system, Tan-Lu fault zone, tectonic evolution stage, structural pattern, the Huanghekoudong and Miaoxi’nan sub-sags, hydrocarbon accumulation

1 INTRODUCTION

The concept of the extensional strike-slip fault system delineates a multifaceted fault arrangement arising from the overlay of pronounced late-stage strike-slip movements upon early-stage extensional faults (Li et al., 2020b; Li et al., 2021). This system plays a pivotal role in dictating the tectonic framework of several hydrocarbon-rich sedimentary basins, including the Bohai Bay Basin, Qiongdongnan Basin, Yinggehai Basin, and Liaohe Basin (Ren et al., 2011; Liu et al., 2019; Li et al., 2020a; Li et al., 2023).

These basins exhibit unique structural traits and mechanisms controlling sub-sag formation and trap development. While existing literature has extensively explored the structural aspects of strike-slip faults and their correlation with hydrocarbon occurrences (Tong et al., 2008; Liu et al., 2010; Teng et al., 2016; Ma et el., 2018; Wang et al., 2020; Deng et al., 2022; Deng et al., 2022; Yang et al., 2022), a profound comprehension of the extensional strike-slip architectures and their control mechanisms on sub-sag and trap formations remains elusive.

Focusing on the southern Bohai Sea’s Huanghekoudong (HHKD) sub-sag and MXN (Miaoxi’nan) sub-sag, areas of pronounced oil and gas exploration potential intersected by the Tan-Lu Fault Zone, this study aims to unravel the complexity of superimposed deformation characterized by initial extension followed by strike-slip activity. The presence of abundant and illustrative extensional strike-slip faults renders this area an exemplary site for dissecting the evolution of extensional strike-slip fault systems alongside their implications on depression development and trap genesis. Employing the latest three-dimensional seismic data, augmented by recent drilling, logging, and other analytical outcomes, this research delves into the fault combination characteristics and evolutionary patterns within the strike-slip fault zone, utilizing techniques such as variance slicing. Through this analysis, the study advances our understanding of the control mechanisms exerted by the extensional strike-slip fault system on depression formation, trap configuration, and overall structural trapping paradigms, thereby offering valuable insights for future exploration strategies in the study area and serving as a benchmark for related investigations within extensional strike-slip basins.

2 GEOLOGICAL SETTING

The study area comprises of the HHKD and MXN sub-sags, and the location of the study area is shown in Figure 1C.

2.1 The MXN sub-sag

The MXN sub-sag, nestled within the prolific hydrocarbon terrain of the Bohai Sea area, forms an integral part of the sedimentary tectonic framework of the Bohai Bay Basin (BBB). This Cenozoic basin, stretched along China’s eastern coast, encompasses a vast expanse, estimated at approximately 200,000 km², and is noted for its complex rifted structure shaped through multi-phase tectonic evolutions (Lu et al., 1997; Chi and Zhao, 2000; Hao et al., 2007; 2009; Schlische and Withjack, 2009; Henza et al., 2010; Yin, 2010; Zuo et al., 2011; Xu et al., 2014; Chang et al., 2018; Song et al., 2019; Jin et al., 2020; Ye et al., 2020; Yu et al., 2020) (Figure 1A). Enveloped by ancient uplifts such as Yan Shan, Taihang Shan, Jiaoliao, and Luxi, the BBB is dissected into multiple subbasins and uplifts, including Liaodong Bay, Liaohe, Bozhong, and others, housing significant hydrocarbon reserves (Hao et al., 2009; 2011; Li et al., 2017; Shi et al., 2019; Yu et al., 2020; Zeng et al., 2022).

FIGURE 1 | (A) Tectonic location of the Bohai Bay Basin in China. (B) Tectonic location of the southern sub-sag of the HHKD and MXN sub-sags in the Bohai Bay Basin. (C) Structural unit sketch map of the study area, displaying the present structural units of the HHKD and MXN sub-sags. The figure also includes seismic lines and wells used in this paper.

Residing at the basin’s margin, the MXN sub-sag is a testament to the regional geological complexity, flanked by prominent mountains and demonstrating substantial hydrocarbon accumulations with crude oil and natural gas proven reserves (Jiang et al., 2016; Hou and Hari, 2014). As a Cenozoic sub-sag, it is demarcated by key geological structures, including the Jiaoliao uplift and Bonan Low uplift, and is directly linked to neighboring sub-sags, illustrating a northeastward strip-shaped distribution across 1200 km² (Qi, 2004; Zhou et al., 2010; Zong et al., 2012; Huang et al., 2018) (Figure 1B).

The structural intricacy of the MXN sub-sag is further exemplified by the east branch of the Laizhouwan section within the Tan-Lu Fault Zone (TLFZ), which bisects the sub-sag, creating distinct geological divisions that have played a crucial role in its formation and hydrocarbon potential, including significant oilfields such as Bozhong 36–1 and Penglai 25–6 (Gong et al., 2007; Zhao et al., 2020).

2.2 The HHKD sub-sag

The HHKD sub-sag is located in the eastern Bohai Bay Basin, NE China, sandwiched between the Bonan and Laibei uplifts to the north and south, and connected with the MXN sub-sag to the east (Figure 1B). Two major tectonic systems of east–west extension and northeastward strike–slip structures control the basic tectonic framework of the region (Hsiao et al., 2004; 2010; Liu et al., 2010; Qi and Yang, 2010; Tong et al., 2012; Li et al., 2013; Liu et al., 2015). The structural appearance resembles a graben reworked by slip–strike faults; it is steeper in the south and gentle to the north, higher in the west, and buried deeper in the east (Sun et al., 2017; Liu et al., 2018; Lan and Liu, 2022).

Stratigraphically, the study area hosts both Paleogene and Neogene deposits, with the Paleogene characterized by formations rich in fan- and braided-deltas and lacustrine deposits, while the Neogene features fluvial and deltaic sequences, highlighting the dynamic sedimentary processes that have shaped its geological history (Hou et al., 2001) (Figure 2). The amalgamation of these structural and stratigraphic elements underscores the study area’s significance within the broader context of the BBB’s geological and hydrocarbon framework.

FIGURE 2 | Tectono-stratigraphic log of the HHKD and MXN sub-sags (modified after Sun, 2017) (Abbreviations: N₁₊₂m=Mianghuazhen Formation, N₂mU=Upper member of the Minghuazhen Formation, N₂mL=Lower member of the Minghuazhen Formation, N₁g=Guantao Formation, N₁gU= Upper member of the Guantao Formation, N₁gL= Lower member of the Guantao Formation, E₃d=Dongying Formation, E₃d₁=First member of the Dongying Formation, E₃d₂=Second member of the Dongying Formation, E₃d₃=Third member of the Dongying Formation, E₂₊₃s=Shahejie Formation, E₃s₁=First member of the Shahejie Formation, E₃s₂=Second member of the Shahejie Formation, E₃s₃= Third member of the Shahejie Formation, E₂s₃U=Upper section of the Third member of the Shahejie Formation, E₂s₃M=Middle section of the Third member of the Shahejie Formation, E₂S₃L= Lower section of the Third member of the Shahejie Formation, E₃s₄= Fourth member of the Shahejie Formation; E₂k=Kongdian Formation, E₂k₁=First member of the Kongdian Formation, E₂k₂=Second member of the Kongdian Formation).

3 DATA ACQUISITION AND ANALYTICAL METHODOLOGY

3.1 Acquisition of data

This investigation integrated multidimensional seismic datasets, borehole recordings, and auxiliary geological insights specifically from the HHKD and MXN sub-sags area. A comprehensive spatial coverage was achieved through a high-resolution 3D seismic survey. Detailed lithological interpretations were derived from 15 strategically positioned wells, leveraging an array of logging techniques. The suite of well log data predominantly included gamma-ray (GR) logs, spontaneous potential (SP) logs, acoustic time (AC) logs, among other crucial datasets, providing insights into the primary lithological frameworks within the MXN subregion. The acquisition of both seismic and borehole datasets was facilitated through collaboration with CNOOC (China) Co. Ltd., Tianjin branch.

This method provides thorough geological and spatial knowledge, but it has drawbacks due to seismic resolution limits and the subjective nature of well log interpretation. Seismic data may inaccurately represent stratigraphic features due to complex geology, and well log analysis can be biased by the geologists’ expertise.

3.2 Analytical techniques

Our methodology encompassed a rigorous examination of the spatial and structural dynamics of extensional strike-slip fault systems within the sub-sag. This involved a meticulous interpretation of three-dimensional seismic datasets, including the analysis of sequence boundary variance slices and representative vertical seismic profiles within the established sequence stratigraphic framework. To elucidate the tectono-stratigraphic evolution of the sub-sag, we applied back-stripping analysis. This technique facilitated the construction of balanced cross-sections, enabling a detailed visualization of the basin’s architectural and stratigraphic heterogeneity. Furthermore, our study delved into assessing the impact of extensional strike-slip faulting on the stratigraphic framework, spatial configuration of sub-sags, and the consequent hydrocarbon potential.

The employed analytical methods are robust but have limitations. Seismic data interpretation relies on data quality, which varies and can create uncertainty in fault and boundary analysis. Back-stripping, useful for studying basin evolution, depends on unverifiable assumptions about geological history, which can affect results. The analysis’s success also hinges on the accuracy of seismic and well log data inputs.

4 MAIN FAULT CHARACTERISTICS

The southern Bohai area is characterized by nearly east-west trending extensional faults and NNE trending strike-slip faults, dividing the area into multiple tectonic units. The HHKD and MXN sub-sags are developed with extensional fault systems, strike-slip fault systems, and extensional strike-slip fault systems (Table 1). Due to the nature and activity intensity of the faults, there are significant differences in the structural characteristics of the faults.

TABLE 1 | Elements of the typical faults in eastern sub-sag of the Huanghekou sag and Miaoxinan sub-sag.

4.1 Extensional fault system overview

The extensional fault system in the HHKD and MXN sub-sags features predominantly EW-NW trending faults, exhibiting significant control over sedimentary and structural patterns. These faults display bifurcation and arc shapes, with echelon or feather-like intersections with secondary faults, observed in various combinations (Figure 3). Their listric and shovel-like profiles cut through deep strata, with near-surface “y” and inverted “y” shapes formed by interactions with secondary faults. The system’s activity varies across sections and periods.

FIGURE 3 | Planar distribution of the fault systems in T3 (the interface between E₃s₁ and E₃d) of the HHKD and MXN sub-sags.

4.1.1 East section of the Bonan No. 1 fault (BNF)

The Eastern Section of the Bonan No. One Fault displays EW-NW trending extensional faults with bifurcations and arc shapes. Notably, the eastern section intersects with the EB-TLFZ and extends over 13 km. Fault profiles reveal steep, shovel-like dips in the subsurface and “y” shaped formations near-surface. Survey lines AA’, BB’, and CC’ highlight variations in fault geometry, with angles ranging from 30° to 50° at depth to 75°–85° at shallow levels. Secondary faults create flower structures, particularly near the eastern strike-slip transition zone (Figures 4–6).

FIGURE 4 | Interpreted seismic section AA’, showing the vertical distribution of extensional fault system. (A) Original seismic section AA’, (B) Interpreted section AA’ (see its location in Figure 1).

FIGURE 5 | Interpreted seismic section BB’, showing the vertical distribution of extensional fault system. (A) Original seismic section BB’, (B) Interpreted section BB’ (its location is shown in Figure 1).

FIGURE 6 | Interpreted seismic section CC, showing the vertical distribution of extensional fault system. (A) Original seismic section CC’, (B) Interpreted section CC’ (see its location in Figure 1).

4.1.2 Middle section of the Bonan No. 2 fault

The Middle Section of the Bonan No. 2 Fault characterizes steep shovel-like normal faults in the profile, with deep angles of 25°–35° and shallower angles of 70°–80°. Listric normal faults appear near the eastern end, and secondary faults generate inverted “y” and flower-like structures, forming a fault scarp zone on the northern side (Figures 4–6).

4.1.3 Laibei No. 2 fault

The Laibei No. 2 Fault is marked by steep shovel-like normal faults with deep angles of 25°–40° and shallow angles of 70°–80°. It features continuous main fault sections with developed secondary faults near the surface, forming inverted “y” and flower-like structures (Figures 4–6).

4.2 Strike-slip fault system overview

The eastern branch of the Tan-Lu fault delineates the HHKD and MXN sub-sags, running NNE with notable continuity and variability (Figure 3). Vertical main faults slice through deep strata, with their interactions with secondary faults resulting in complex profiles.

4.2.1 EB-TLFZ sub-branches

The EB-TLFZ delineates the eastern and western borders of the HHKD and MXN sub-sags, stretching approximately 71 km in length and 4 km in width, trending NNE. It comprises mainly the No. 1 and No. 2 sub-branches, exhibiting nearly vertical high-angle listric fault profiles which penetrate deep strata down to the Cenozoic base, showing varying orientations from NWW in the north to SEE in the south, which causes a ribbon effect. This zone is characterized by large-scale secondary fault formations at shallower depths under strong strike-slip stress, presenting various flower-like structures alongside the main faults. Notably, the middle sections of both sub-branches exhibit extensional strike-slip features, significantly influencing the area’s structural integrity (Figures 3, 7, 8).

Seismic analysis across different sections of the EB-TLFZ reveals distinct fault characteristics:

Section JJ’ shows the No. 1 sub-branch with steep shallow and gentler deep angles, and the No. 2 with generally gentler angles but nearly vertical at depth. Secondary faults are notably developed on the No. 1 sub-branch, mainly on the southeast, displaying flower-like structures, while the No. 2 sub-branch has secondary faults on both sides, forming an inverted flower-like structure (Figures 7G, 8G).

FIGURE 7 | Interpreted inline seismic sections showing the vertical distribution of strike-slip and extensional strike-slip fault system. (A) Interpreted seismic section DD’, (B) Interpreted seismic section EE’, (C) Interpreted seismic section FF’, (D) Interpreted seismic section GG’, (E) Interpreted seismic section HH’, (F) Interpreted seismic section II’, (G) Interpreted seismic section JJ’ (see their locations in Fig.1). Notes: F1: No. 1 sub-branch of EB-TLFZ, F2: No. 2 sub-branch of EB-TLFZ.

FIGURE 8 | Geological sections showing the vertical distribution of strike-slip and extensional strike-slip fault system. (A) Geological section DD’, (B) Geological section EE’, (C) Geological section FF’, (D) Geological section GG’, (E) Geological section HH’, (F) Geological section II’, (G) Geological section JJ’ (see their locations in Figure 1).

Section HH’ indicates both sub-branches’ main faults are nearly vertical, with the No. 1 sub-branch displaying developed secondary faults on both sides forming an inverted flower-like structure, and the No. 2 sub-branch having fewer secondary faults, forming a flower-like structure (Figures 7E, 8E).

Section FF’ highlights the No. 1 sub-branch’s nearly vertical main fault cutting down to the Mesozoic base and developing numerous secondary faults under intense strike-slip stress, creating an inverted flower-like structure. The No. 2 sub-branch’s main fault has a dip angle of 70°–80°, with fewer secondary faults forming a semi-flower-like structure at shallow levels (Figures 7C, 8C).

4.2.2 Eastern branch secondary faults

The eastern branch secondary faults, within the strike-slip transition zone of the east, extend northeastward for about 20 km, connecting to the first and second sub-branches of the EB-TLFZ at their southwestern and northeastern ends, respectively. These faults have a continuous presence at depth but are less continuous closer to the surface (Figure 3). Their profiles reveal steep listric shapes with angles between 70° and 80°, and they cut through to the base of the Cenozoic stratum. Adjacent to the main fault, particularly on the northeastern side, is an area of enhanced secondary faulting, which results in a floral structural pattern (Figures 7D, 8D), influenced by strike-slip stress.

4.3 Extensional strike-slip fault system overview

The study area features extensional strike-slip faults primarily within the No. 2 sub-branch of the east branch of the Tan-Lu fault zone, characterized by complex structures akin to inverted or semi-flower patterns superposed on listric and shovel-like normal faults. Seismic section HH’ reveals a concentration of these faults in the same sub-branch, where they exhibit simple patterns in deep layers but complex combinations of derivative faults in shallow layers due to extensional, strike-slip, and compressional interactions. The inline 5200 intersects this sub-branch and shows the main fault with a shallow dip angle of 80°–90° that steepens with depth, maintaining strong continuity. In the shallow portions, secondary faults form flower-like structures with the main fault, while at greater depths, they create “Y” shaped configurations (refer to Figures 7E, 8E).

5 THE STRUCTURAL PATTERN OF THE FAULT SYSTEMS IN HHKD AND MXN SUB-SAGS

The Huanghekou sag and Miaoxi Sag are secondary negative structural units in the southern section of the Bonan area, both controlled by boundary extensional faults that influence the sag sedimentation, with internal extensional fault systems controlling the sag pattern. The Huanghekou sag features a “north-faulted, south-overlapped” fan-shaped structure, while the Miaoxi sag’ sub-sags are variably affected by the Tan-Lu Fault Zone; the northern and eastern sub-sags are mainly reformed by the extensional fault system, and the southern sub-sag is mainly reformed by the east branch strike-slip fault zone of the Tan-Lu Fault, with the northern part being a graben structure and the southern part being a fan-shaped fault sag. The structural pattern of the sags is due to the differentiation of faults (Table 2). The extensional faults of both sags are nearly EW-oriented, and their profile morphologies are mostly listric and shovel-shaped normal faults. Strike-slip faults mainly develop in the central strike-slip transition zone and east branch strike-slip transition zone, with significant morphological differences; the east branch strike-slip fault’s main section is more upright compared to the central branch. The same extensional fault shows morphological differences at different locations, and the combination patterns of secondary faults also vary. Superposed strike-slip faults are primarily distributed in the middle of the central branch strike-slip fault and have similar morphological features on profiles.

TABLE 2 | Typical structural patterns in eastern sub-sag of the Huanghekou sag-Miaoxinan sub-sag.

The Structural Pattern refers to a specific combination of profile morphology and strain mechanism that are closely related (Xu et al., 2014). Based on the analysis of seismic profiles in the southern Bonan area, fault characteristics, and combination patterns, three types of Structural Patterns can be identified: extensional Structural Pattern, strike-slip Structural Pattern, and superposed strike-slip Structural Pattern. Now, the internal Structural Patterns of the HHKD and MXN sub-sags will be analyzed and compared.

5.1 Extensional structural pattern

The extensional structural pattern is a structural combination formed under tensional forces. In the study area, this style is mainly composed of normal faults, forming “Y,” inverted “Y,” and flower-like structures. The extensional faults in the HHKD and MXN sub-sags trend nearly EW (Table 2), with secondary faults arranged in echelon or intersecting the main faults in a feather-like pattern. The HHKD sub-sag has three major extensional faults: the Laibei No. 2 fault, the central section of Bonan No. 2, and the eastern section of Bonan No. 1.

5.2 Strike-slip structural pattern

The strike-slip structural pattern results from compressional or tensional shear stress, leading to a combination of strike-slip faults. The study area’s strike-slip style is mainly characterized by negative flower structures, flower-like structures, and half-flower structures (Table 2). Strike-slip faults mainly distribute along the eastern branch of the Tan-Lu Fault Zone, trending NNE and controlling the boundary of the HHKD sub-sag. The northern part of the eastern strike-slip fault extends to the Miaoxi northern sub-depression, and the southern part to the Laizhou Bay Depression. Negative flower structures have branching faults upward, forming Y-shaped structures, with the main fault appearing as upright or high-angle listric normal faults. The eastern branch No. One strike-slip fault is primarily characterized by negative flower structures, while the eastern branch No. 2 develops a few such structures (Table 2).

Flower-like structures are combinations of a main fault with multiple secondary faults on the profile. Both the eastern branch No. 2 and its secondary faults develop flower-like structures, whereas the eastern branch No. 1 has fewer flower-like structures. In addition, internal structures within the two sub-depressions also develop, mainly in areas with abundant shallow secondary faults, forming flower-like structures in combination with the main faults locally, as Laibei No. 2 and Bonan No. 1 faults. Half-flower structures are combinations with multiple derivative faults on one side of the main fault, with the derivative faults being normal faults. In the eastern branch strike-slip fault zone, local areas of the eastern branch No. 2 fault develop half-flower structures, with the main fault being steep and the overall fault profile being clear (Table 2).

5.3 Extensional strike-slip structural pattern

The overlapping strike-slip structural pattern is a combination of faults formed under both extensional and strike-slip tectonic deformation. Overlapping strike-slip structures are later strike-slip tectonic movements superimposed on early extensional faults, manifesting as flower structures and multi-level “Y”-shaped combination styles. The overlapping strike-slip area is distributed in the central part of the eastern branch strike-slip fault zone between the HHKD and MXN sub-sags, mainly superimposing strike-slip movements on listric and listric normal faults (Table 2).

All three structural patterns are developed in the study area. Among them, the extensional structural pattern is more widespread, mainly distributed along the edges and interiors of the two sub-depressions, trending nearly EW, with long extensions; strike-slip and overlapping strike-slip structural patterns mainly develop along the eastern branch strike-slip fault zone on the edge of the HHKD sub-sag, trending NNE and extending long. The main extensional structures in the HHKD and MXN sub-sags are listric and listric normal faults, with shallow secondary faults forming “Y,” inverted “Y,” and flower-like structures in combination with the main faults; the main strike-slip faults on the profile appear as nearly upright listric faults, combined with shallow secondary faults to form negative flower, half-flower, and flower-like structures; overlapping strike-slip develops in the overlapping area of extension and strike-slip, mainly distributed in the central part of the eastern branch strike-slip transition zone, with extensional structures on both sides being more developed, leading to overlapping, mainly characterized by strike-slip movements superimposing on listric and listric normal faults in the strike-slip zone (Table 2).

6 DISCUSSION

6.1 Tectono-stratigraphic evolution of the study area

Throughout the faulting phase, the boundary faults of the MXN sub-sag were subject to intense activation, markedly influencing sedimentary processes and leading to distinct segmentation within the sub-sags. Initial formation of the MXN sub-sag was observed during the E₂k depositional phase, signified by the rise of the eastern secondary sub-sag. Sedimentary accumulation predominantly occurred within the southern and eastern secondary sub-sags, with sedimentary focus centralized in the eastern section. The E₂k stratigraphy was shaped by three critical faults, notably with the northwest fault acting as the eastern segmental offshoot of the EB-TLFZ (Figure 9).

FIGURE 9 | Balanced section KK’ illustrating the stratigraphic evolution characteristics of the southern secondary sub-sag of the MXN sub-sag (the location of seismic section KK’ are shown in Figure 1).

In the E₂s₄ depositional phase, expansion of the MXN sub-sag was notable, especially within its eastern secondary section. The sedimentary focus gradually migrated northwestward, remaining within the eastern secondary sub-sag. This phase was predominantly governed by a syndepositional fault, with the eastern secondary branch of the EB-TLFZ continuing its evolution and the commencement of its western secondary branch. Additionally, seven distinct faults were instrumental in shaping the development of the eastern secondary sub-sag, marking the initial formation of the southern secondary sub-sag with the sedimentary focus retained in the eastern section (Figure 9).

During the E₂s₃ phase, development of the eastern secondary sub-sag halted, while the southern secondary sub-sag’s evolution proceeded. The period was characterized by the continued evolution of two major strike-slip faults and the influence of seven syndepositional normal faults along with the west secondary branch of the EB-TLFZ, steering the development direction. The sedimentary focus shifted northwestward (Figure 9).

The E₃s₁₋₂ phase saw the entire region undergoing stable deposition, with the southern secondary sub-sag undergoing adjustments due to two major strike-slip and several normal faults, which also modulated other secondary sub-sags (Figure 9).

Transitioning from the faulting to the depression phase, boundary faults maintained a significant control over sediment deposition, leading to a stabilization of regional depositional thickness and a trend towards uniformity (Figure 9).

In the E₃d phase, the southern secondary sub-sag experienced rapid development and substantial N₁g strata deposition, with a slight southeastward migration of the sedimentary focus. The deposition was primarily controlled by one syndepositional fault alongside two major strike-slip faults (Figure 9).

Post E₃d deposition saw the southeast sag undergoing uplift and erosion, with E₂s₄, E₃s₁₋₂, and N₁d strata facing differential erosion - E₃s₁₋₂ experiencing the most severe erosion, followed by E₂s₄, and N₁d experiencing the least (Figure 9).

During the depression phase, Neogene deposition was recorded across all secondary sub-sags, with the sedimentary focus in the northern secondary sub-sag. The N₁g phase featured the thickest strata centrally and in the northwest, tapering towards the southeast, governed by the continued evolution of two major strike-slip faults and several normal faults, which directed sediment deposition in the southern secondary sub-sag. The northern secondary sub-sag was influenced by a singular normal fault (Figure 9).

The N₂mL phase witnessed uniform deposition across the region, modulated by seven normal faults, with the eastern secondary branch of the EB-TLFZ directing the southern secondary sub-sag.

Presently, post- N₂mL deposition is characterized by its thickness centrally and in the northwest, thinning towards the southeast, controlled by two major strike-slip faults and adjusted by several normal faults in the eastern secondary sub-sag and other secondary sub-sags. In essence, the MXN sub-sag has undergone a progressive migration of the depositional and subsiding centers westward and northward (Figure 9).

6.2 The control of extensional strike-slip fault system on sub-sag development

During the E₂k depositional period, NW-oriented initial oblique extension faults were formed, and the strike-slip faults at this stage exist latently in the basement. During this period, the east, and north secondary sub-sag of the MXN sub-sag were formed. The east secondary sub-sag is mainly controlled by the oblique extension and the pre-existing basement strike-slip fault of the East sub-branch, showing a spindle-shaped distribution; while the north secondary sub-sag is controlled by the EW-extension fault (Figure 10A).

FIGURE 10 | The overlap map of the strata isopach map and the planar distribution of fault systems, indicating the control of fault systems on the sub-sag development (A) overlap map of the E₂k isopach map and the planar distribution of fault systems in T7 (the top surface of E₂k), (B) overlap map of the E₂s₄ isopach map and the planar distribution of fault systems in T6 (the top surface of E₂s₄).

During the E₂s₄ depositional period, with the increase of extension displacement, the NE-oriented oblique extension faults continued to develop. At the same time, the East sub-branch strike-slip fault became significantly active under the action of this stress field, showing a right-lateral stepover fault combination mode. It generally bends in an arc shape from south to north, and the end of the segmented fault in the north is accompanied by near EW-oriented secondary extension faults. The west sub-branch strike-slip fault was still latent and inactive during this period. Therefore, in addition to the NE-oblique growth fault controlling the sporadic depression of the hanging wall, in the right-lateral strike-slip deformation of the southern bend of the East sub-branch strike-slip fault, a tensional release type negative unit was formed, that is, a “spindle-shaped” depression was formed. And in the joint part of the two faults in the northern part of the East sub-branch strike-slip fault, controlled by the secondary tensional stress field accompanied by strike-slip shear displacement, a “L-shaped” depression was formed, that is, the North secondary sub-sag of MXN (Figure 10B).

During the E₂s₃ depositional period, the range of fault activity expanded, including the intense activity of the west sub-branch strike-slip fault. But there are still active segments, specifically manifested in the western part of the end of the segmented activity of the strike-slip fault in the HHKD sub-sag, which is accompanied by several relatively large near EW-extension faults, thereby controlling the formation of the “L-shaped” HHKD sub-sag (Figure 11A).

FIGURE 11 | The overlap map of the strata isopach map and the planar distribution of fault systems, indicating the control of fault systems on the sub-sag development (A) overlap map of the E₂s₃ isopach map and the planar distribution of fault systems in T5 (the top surface of E₂s₃), (B) overlap map of the E₃s₁₋₂ isopach map and the planar distribution of fault systems in T3 (the top surface of E₃s₁₋₂).

During the depositional period from E₃s₁-E₃s₂ to E₃d, the activity of the strike-slip fault dominated. The East and West sub-branch strike-slip faults penetrated the entire area of the MXN sub-sag and even a larger area of the Bohai Sea. The West sub-branch strike-slip fault showed a complete through-type combination, and the East sub-branch strike-slip fault had a higher degree of connection, extended in a through-type and retained the bifurcated fault traces left by the previous left-step combination mode. Therefore, during this period, the “diamond-shaped” depression, namely, the south secondary sub-sag, was mainly formed in the release bending parts of the two strike-slip faults in the middle and south, similar to the strike-slip fault bend basin morphology (Figures 11B, 12).

FIGURE 12 | The overlap map of the strata isopach map and the planar distribution of fault systems, indicating the control of fault systems on the sub-sag development. Overlap map of the E₃d isopach map and the planar distribution of fault systems in T2 (the top surface of E₃d).

Looking at the transformation relationship of different evolution stages of strike-slip faults and oblique extension faults and their control on the characteristics of the depressions formed, in the early stage of basin evolution, that is, the E₂k -E₂s₄ stage and E₂s₃ stage, mainly formed branch “L-shaped” sub-sag and depressurization bending “spindle-shaped” sub-sag, the depression is mainly controlled by the segmentation expansion mechanism of the strike-slip fault, and the scale of the depression is relatively large. In the middle period of basin evolution, that is, E₃s₁, E₃s₂, and E₃d stages, the main formation is depressurization “diamond-shaped” depression, and the scale of the depression is also relatively large (Figure 13).

FIGURE 13 | Interaction relationship between strike-slip and extensional fault at different stages and model diagram of sub-sag types in the eastern sub-sag Huanghekou sag and MXN sub-sag. (A) E₂k deposition period, concealed strike-slip (B) E₃s₄ deposition period, segmented strike-slip faulting with terminal extension (C) E₂s₃ deposition period, segmented penetrative strike-slip faulting with terminal extension (D) E₃s₁₋₂ and E₃d deposition period, monolithic penetrative strike-slip faulting (E) N₁mL deposition period, conversion of concealed strike-slip to penetrative strike-slip faulting.

6.3 The control of extensional strike-slip fault system on the formation and distribution of the traps

Since the Cenozoic era, the southwestern depression of the temple has been profoundly transformed by the forces of early extensional fault stretching, numerous phases of tectonic uplift, and late-stage strike-slip faulting. It has developed a series of strike-slip faults atop a foundation of pre-existing faults from the Paleogene period, resulting in a collection of blocks with compressional and torsional characteristics, alongside groups of fault-related folds and reverse faulted anticlines (Figure 14). Influenced by the differing features of the eastern and western branches of the strike-slip faults, the genesis and types of structural closures within various tectonic zones exhibit considerable variation.

FIGURE 14 | The overlap map of the traps distribution and the planar distribution of fault systems. (A) The overlap map of the traps distribution and the planar distribution of fault systems in top surface of E₃s₁, indicating the control of fault systems on the trap development in deep layer. (B) The overlap map of the traps distribution and the planar distribution of fault systems in top surface of N₁g, indicating the control of fault systems on the trap development in deep layer. (the distribution of traps was revised from Zhao et al., 2020).

Notably, the eastern branch of the strike-slip fault zone is distinguished by a fan-shaped arrangement of faults, with clear signs of early extensional and late compressional-torsional deformations. The strike-slip faults, with their curved segments and “right-lateral left-stepping” overlap zones, have given rise to a series of significant faulted anticlines and faulted block closure groups. For example, the Longkou 31-C structural zone in the southern part of the depression is developed against the backdrop of the southward extension of the Jiao-Liao uplift to the secondary depression, situated in the dual overlap zone of the strike-slip faults. Late-stage strike-slip adjusting faults and configurations have led to the formation of inherited faulted anticline closures at both deep and shallow levels (Figure 14).

The Longkou 31-B structure, located within the depression area, falls within the strike-slip pressure transition zone, forming a “right-lateral left-stepping” block closure adjacent to the eastern branch’s strike-slip fault. The Penglai 25-A structural zone in the north of the eastern branch of the strike-slip fault lies within the overlap zone of the fault, where the Paleogene has experienced multiple episodes of compression, uplift, and erosion. Since the initiation of the new tectonic movement, intense clockwise compressional strike-slip actions have facilitated the emergence of large faulted anticline closures in the area’s shallow layers (Figure 14).

The eastern and western sides of the western branch of the strike-slip fault display pronounced differences in bedding and closure characteristics. For instance, the Penglai 31 structural zone on the western side of the strike-slip fault comprises a block closure group formed through the combined effects of the strike-slip fault and its adjusting faults, presenting a “toothbrush-shaped” configuration. The Penglai 25 west structural zone in the northern part of the strike-slip fault features Paleogene structures with compressional and torsional properties, forming a collection of fault blocks and faulted anticline closures. Additionally, the deep-layer development above the central structural ridge in the southern depression is controlled by the Longkou 31-A block and faulted anticline closures, influenced by early (strike-slip nature) faults. This structural zone is adjacent to the center of the depression, characterized by a thick overlying layer and excellent preservation conditions (Figure 14).

6.4 The relationship between the faults systems and hydrocarbon accumulation

In the Bohai Bay Basin, the fault system is quite complex, making faults the main pathways for oil and gas migration. For a fault to be a favorable source of oil, its active periods must coincide with the hydrocarbon expulsion periods. Otherwise, it is considered a poor oil source fault, such as the nearly east-west trending extensional faults like Bonan No. 1 and Laibei No. 2 faults and the eastern branch strike-slip faults, which are good oil source faults. Most oil and gas reservoirs in the study area were formed when large faults provided pathways for early hydrocarbon source rocks to accumulate within traps, forming large oil and gas fields.

Since faults serve as channels for hydrocarbon movement, their effectiveness in aiding oil and gas accumulation depends on the timing of their activity and hydrocarbon expulsion. Extensional faults create structural traps for hydrocarbons due to the stretching of the earth’s crust, and their formation periods are critical for hydrocarbon migration and trapping. Strike-slip faults allow for horizontal strata displacement and enhance vertical hydrocarbon migration to traps, with secondary faults formed by this movement also acting as migration pathways. Extensional Strike-slip Faults facilitate hydrocarbon migration but require suitable reservoir-cap rock combinations to effectively trap hydrocarbons. In summary, the control of various fault systems on hydrocarbon migration and trapping is a critical factor in the development of oil and gas reservoirs in the Bohai Bay Basin.

6.4.1 The relationship between extensional faults and hydrocarbon accumulation

The relationship between extensional faults and hydrocarbons is exemplified by the BZ34- 9–1 well on the south side of the HHKD sub-sag. The Lai Bei No. 2 is a controlling fault for the sag in the southern sub-sag of the Huanghekou sag. During the early rift stage, thick hydrocarbon source rocks were rapidly deposited, and the E₂s₃ and E₃s₁- E₃s₂ formations provided hydrocarbons for the internal oil and gas reservoirs of the sag. Drilling data show that due to factors such as depositional environment, diagenesis, and sediment composition, good reservoirs developed in the E₃s, E₃d, N₁g, and N₁₊₂m, which can store oil and gas. The main traps in this area are block and anticlinal traps formed by the upper-derived faults of the Lai Bei 2 fault. The BZ34-9 structure has developed a good set of reservoir-cap combinations: braided river delta front sheet sand bodies and lacustrine mudstone of the E₃s₂- E₃s₁ Formations; the same for the E₃d₂L down to the E₃d₃L; the N₁g; and shallow delta sand bodies and thick lacustrine mudstones of the lower part of the N₁₊₂m. During the N₁₊₂m, the E₂s₃ and E₃s₁- E₃s₂ source rocks entered the hydrocarbon expulsion period. During this period, extensional faults developed inheritedly, strike-slip movement was significant, and secondary faults developed, facilitating the connection between source rocks and reservoirs, opening pathways for oil and gas migration. Some hydrocarbons accumulated in situ in the E₂s₃, while the rest migrated stepwise to the E₃s₂, E₃s₁ Formations, E₃d, N₁g, and N₁₊₂m. From the N₁g to the present, the Huanghekou sag and the MXN sub-sag have been in a stage of weak extension and strong strike-slip. Some secondary faults provide favorable channels for the continuous migration of oil and gas, forming oil and gas reservoirs in the shallow N₁₊₂m and N₁g. The BZ34-9 structure is located at a structurally high position and is a long-term favorable migration area (Figure 15A).

FIGURE 15 | Hydrocarbon accumulation model map. (A) Map of extensional fault-controlled hydrocarbon accumulation model. (B) Map of strike-slip fault-controlled hydrocarbon accumulation model. (C) Map of extensional strike-slip fault-controlled hydrocarbon accumulation model.

6.4.2 The relationship between strike-slip faults and hydrocarbon accumulation

The relationship between strike-slip faults and hydrocarbons is exemplified by the well PL31-3s-1 in the HHKD sub-sag and the western part of the MXN sub-sag, which lies on the eastern branch of the Tan-Lu strike-slip fault zone. Strike-slip and secondary faults are well-developed here, providing vertical migration channels for hydrocarbons and trapping them in shallow block and (semi-) anticlinal traps. Well PL31-3s-1 is a typical oil and gas field in the study area, utilizing the strike-slip fault system for hydrocarbon transport. The main source rocks of the E₂s₃ and E₃s₂-E₃s₁ Formations provide good hydrocarbon sources, with depositional environments such as braided river deltas, meandering river deltas, and lacustrine sediments serving as good reservoirs. The main strike-slip fault cuts through the Cenozoic strata, extending upwards to the Quaternary, as a continuously active fault, providing favorable channels for vertical hydrocarbon migration; shallow secondary faults develop negative flower structures, providing channels for small-scale hydrocarbon accumulation. Late Cenozoic fault activity was weak, and the good sealing properties of the eastern branch can act as effective controlling faults, blocking hydrocarbon migration. Overall, PL31-3S has good source-reservoir-cap combinations, making it a favorable oil and gas area, with the N₁₊₂m and N₁g as key levels (Figure 15B).

6.4.3 The relationship between extensional strike-slip faults and hydrocarbon accumulation

The relationship between extensional strike-slip faults and hydrocarbons is exemplified by the extensional strike-slip fault (No.2 sub-branch of EB-TLFZ) on the Inline 5280 seismic line through the eastern branch of the strike-slip fault zone. The line crosses the middle part of the eastern branch strike-slip fault zone, which has high slip intensity, with the HHKD sub-sag to the west and the MXN sub-sag to the east. In the profile, the extensional strike-slip structure of the No. 2 sub-branch of EB-TLFZ is developed, providing a good channel for hydrocarbon migration. Early on, the No. 2 sub-branch of EB-TLFZ was an extensional fault, cutting the lower basement and providing a pathway for the hydrocarbons of the E₃s₂. During the Cenozoic, strong early extensional activity overlapped with strong strike-slip motion during the E₃d depositional period, with secondary faults not only providing pathways for hydrocarbons but also associated structural traps. However, the upper reservoir-cap conditions are poor, so oil and gas reservoirs are mostly formed in the middle to deep parts (Figure 15C).

Superimposed strike-slip faults controlled the migration and accumulation of hydrocarbons in two phases. During the early Eocene, the E₃s’s third and E₃s₁-E₃s₂ formations were deposited, providing two sets of high-quality source rocks. The source rocks deposited during the late Eocene and Oligocene did not enter thermal evolution stage; the hydrocarbon expulsion period occurred during the E₃d and Neogene periods. The enhanced fault activity during the E₃d provided good conditions for hydrocarbon migration.

During the depositional period of the E₂k-E₂s₄, the sag-controlling faults and the Tan-Lu fault zone were active, in a strong rift stage. During the deposition of the E₂s₃, the main faults had inherited extensional activities, with depositional environments dominated by wide semi-deep to deep lake facies, and well-developed source rocks. In the late stage of the E₃s₁ Formation, the main faults continued to develop, inheriting the semi-deep to deep lake depositional environments and depositing source rocks of the E₃s₁-E₃s₂ Formations, providing a fundamental guarantee for the later transportation of hydrocarbons. During the E₃d depositional period, the eastern branch of the Tan-Lu strike-slip fault became active, with derived faults starting to develop. On the plane, secondary faults are arranged in echelon or intersect with the main faults in a feather-like pattern, while on the profile, they appear as negative flower structures superimposed on early extensional faults. The development of derived faults forms channels for the later transportation of hydrocarbons and block-type traps.

At the end of the E₃d to the N₁g, the source rocks reached the hydrocarbon generation threshold and began to expel hydrocarbons. During this period, extensional fault activity was weak, but strike-slip activity was still strong, with secondary faults developing on the plane and obvious negative flower structures on the profile. The fault activity and the expulsion period of the source rocks coincided, allowing for the vertical and horizontal migration of hydrocarbons through faults and sand bodies, forming block and anticlinal traps. The continuous activity of the faults still provided migration channels for hydrocarbons, with sand bodies of the E₃s₂ serving as lateral migration channels, continuously accumulating hydrocarbons for early oil and gas reservoirs.

6.5 Comparison with similar global basins

6.5.1 Comparison of hydrocarbon accumulation models

(1) Jeanne d’Arc Basin, Canada

The basin is characterized by a combination of extensional and strike-slip faulting which has facilitated the creation of multiple hydrocarbon traps. By comparing the Jeanne d'Arc Basin with the HHKD and MXN sub-sags, the authors can discuss the implications of different types of traps and reservoir qualities influenced by similar tectonic regimes.

(2) Pannonian Basin, Central Europe

The Pannonian Basin is known for its extensional and strike-slip faulting which created accommodation space for hydrocarbon-bearing sediments. Comparing the stratigraphic sequences and the timing of hydrocarbon generation could provide insights into the petroleum system’s efficiency in different tectonic settings.

6.5.2 Comparison of fault dynamics and sedimentation

(1) East African Rift System:

The East African Rift is a contemporary analog for extensional tectonics with strike-slip components. It provides a live model for observing how rift dynamics influence sedimentation and trap formation. Comparing the sedimentary environments can shed light on the role of syn-tectonic sedimentation in hydrocarbon basin evolution.

(2) Dead Sea Transform, Middle East:

The Dead Sea Transform is a prominent strike-slip fault system that has facilitated the formation of pull-apart basins with significant hydrocarbon prospects. The sediment fill and structural traps along the transform can be compared with the HHKD and MXN sub-sags to evaluate the effects of strike-slip faulting on basin morphology and hydrocarbon potential.

7 CONCLUSION

The HHKD and MXN sub-sags’ extensional faults are predominantly east-west oriented, delineating the structural framework of these depressions.

The main strike-slip faults, trending nearly NNE, significantly influence the basins’ depositional centers.

Key faults (Bonan No. 1, Bonan No. 2, and Laibei No. 2) within the HHKD sub-sag are notably parallel with an east-west orientation.

(1) Main faults characteristics:

① The HHKD and MXN sub-sags feature various fault configurations such as linear, arcuate, en echelon, and feather-like.

② Fault geometries include listric, planar, “Y,” inverted “Y,” multi-tiered “Y,” and flower-like patterns.

③ Strike-slip faults show continuity at depth and form negative and semi-flower structures near the surface.

(2) Tectono-stratigraphic evolution:

① A westward and northward migration of the depocenters and subsidence axes is evident throughout the basins’ evolutionary history.

② The area’s stratigraphic architecture is primarily influenced by extensional and strike-slip fault interactions.

③ Sub-branch faults near the EB-TLFZ significantly impact sedimentary distribution.

(3) Controls of extensional strike-slip fault on sub-sags development:

① During the E₂k period, “C-shaped” depressions developed due to small-scale oblique extension.

② The E₂s₄-E₂s₃ phase was dominated by “L-shaped” and “spindle-shaped” troughs, with segmented movements along the EB-TLFZ’s sub-branches.

③ The E₃s₂₋₁ to E₃d period saw “diamond-shaped” depressions formed as a result of intense strike-slip fault activity, particularly around the EB-TLFZ’s sub-branches.

(4) Hydrocarbon exploration implications:

① The area exhibits complex fault interactions, including extensional, strike-slip, and combined dynamics, forming distinct geological traps.

② A series of large fault blocks, faulted anticlinal closures, and overlapping zones create valuable targets for hydrocarbon exploration.

③ Three hydrocarbon accumulation models are proposed: extensional fault-controlled, strike-slip fault-controlled, and combined extensional strike-slip fault-controlled models.

(5) Future research directions or unanswered questions:

① Quantitative analysis of fault slip would improve our understanding of fault evolution and its effects on basin deformation and hydrocarbon migration.

② Employing 3D basin modelling could simulate the basin’s tectono-stratigraphic history, shedding light on potential hydrocarbon reservoirs.

③ An integrated hydrocarbon system analysis is essential for evaluating hydrocarbon potential and identifying prime exploration sites.

④ Research should also assess the effect of sub-branch faults on reservoir quality, particularly how they impact porosity and permeability.

⑤ Lastly, it's important to study how climate change affects sedimentary processes and the stratigraphy of hydrocarbon basins.

DATA AVAILABILITY STATEMENT

The data analyzed in this study is subject to the following licenses/restrictions: the corporation and Country’s law limits the data. Requests to access these datasets should be directed to xiaofu1241396@163.com.

AUTHOR CONTRIBUTIONS

SF: Conceptualization, Methodology, Writing–original draft. DW: Investigation, Writing–review and editing. HY: Investigation, Resources, Writing–review and editing. LS: Conceptualization, Writing–review and editing. MM: Investigation, Methodology, Writing–review and editing. KW: Investigation, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The paper was supported By and Open Fund (SZDKFJJ20231008) of Key Laboratory of Petroleum Resources Research, Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences; and Natural Science Basic Research Program of Shaanxi (2023-JC-QN-0287).

ACKNOWLEDGMENTS

Special thanks to the experts in CNOOC (China) Limited Tianjin branch for their help and support in this paper. We also extend our gratitude to all the editors and reviewers for their constructive suggestions and comments, contributing to the improvement of our paper.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Chang, J., Qiu, N., Zhao, X., Shen, F., Liu, N., and Xu, W. (2018). Mesozoic and Cenozoic tectono-thermal reconstruction of the western Bohai Bay Basin (East China) with implications for hydrocarbon generation and migration. J. Asian Earth Sci. 160, 380–395. doi:10.1016/j.jseaes.2017.09.008

Chi, Y. L., and Zhao, W. Z. (2000). Strike-slip deformation during the cenozoic and its influence on hydrocarbon accumulation in the Bohai Bay Basin. Acta Pet. Sin. 21 (2), 14. (in Chinese). doi:10.3321/j.issn:0253-2697.2000.02.003

Deng, S., Zhao, R., Kong, Q., Li, Y., and Li, B. (2022). Two distinct strike-slip fault networks in the Shunbei area and its surroundings, Tarim Basin: hydrocarbon accumulation, distribution, and controlling factors. AAPG Bull. 106 (1), 77–102. doi:10.1306/07202119113

Gong, Z. S., Cai, D. S., and Zhang, G. C. (2007). Dominating action of Tanlu Fault on hydrocarbon accumulation in eastern Bohai Sea area. Acta Pet. Sin. 28 (4), 1–10. doi:10.3321/j.issn:0253-2697.2007.04.001

Hao, F., Zhou, X., Zhu, Y., Bao, X., and Yang, Y. (2009). Charging of the Neogene Penglai 19-3 field, Bohai Bay Basin, China: oil accumulation in a young trap in an active fault zone. AAPG Bull. 93 (2), 155–179. doi:10.1306/09080808092

Hao, F., Zhou, X. H., Zhu, Y. M., and Yang, Y. Y. (2011). Lacustrine source rock deposition in response to co-evolution of environments and organisms controlled by tectonic subsidence and climate, Bohai Bay Basin, China. China Org. geochem. 42 (4), 323–339. doi:10.1016/j.orggeochem.2011.01.010

Hao, F., Zou, H. Y., Gong, Z. S., and Deng, Y. H. (2007). Petroleum migration and accumulation in the Bozhong sub-basin, Bohai Bay basin, China: significance of preferential petroleum migration pathways (PPMP) for the formation of large oilfields in lacustrine fault basins. Mar. Petrol. Geol. 24 (1), 1–13. doi:10.1016/j.marpetgeo.2006.10.007

Henza, A. A., Withjack, M. O., and Schlische, R. W. (2010). Normal-fault development during two phases of noncoaxial extension: an experimental study. J. Struct. Geol. 32 (11), 1656–1667. doi:10.1016/j.jsg.2009.07.007

Hou, G., and Hari, K. R. (2014). Mesozoic-Cenozoic extension of the Bohai Sea: contribution to the destruction of north China craton. Front. Earth Sci. 8, 202–215. doi:10.1007/s11707-014-0413-3

Hou, G. T., Qian, X. L., and Cai, D. S. (2001). The tectonic evolution of Bohai Basin in mesozoic and cenozoic time. Acta Sci. Nat. Univ. Pekin. 37 (6), 845–851. (In Chinese with English Abstract). doi:10.3321/j.issn:0479-8023.2001.06.016

Hsiao, L. Y., Graham, S. A., and Tilander, N. (2004). Seismic reflection imaging of a major strike-slip fault zone in a rift system: Paleogene structure and evolution of the Tan-Lu fault system, Liaodong Bay, Bohai, offshore China. AAPG Bull. 88, 71–97. doi:10.1306/4b1ab41b-fec1-4493-8f923371c08d166a

Hsiao, L. Y., Graham, S. A., and Tilander, N. (2010). Stratigraphy and sedimentation in a rift basin modified by synchronous strike-slip deformation: southern Xialiao basin, Bohai, offshore China. Basin Res. 22 (1), 61–78. doi:10.1111/j.1365-2117.2009.00449.x

Huang, Y., Guan, D. Y., Zhao, G. X., Zhu, Y. C., and Ren, Y. P. (2018). Geological characteristics of tight oil in the Shahejie Formation in northeast subsag of Laizhou Bay Sag and its formation condition. Petroleum Geol. Recovery Effic. 25 (1), 22–29. (in Chinese with English abstract). doi:10.13673/j.cnki.cn37-1359/te.2018.01.004

Jiang, F. J., Pang, X. Q., Bai, J., Zhou, X. H., Li, J. P., and Guo, Y. H. (2016). Comprehensive assessment of source rocks in the Bohai Sea area, eastern China. AAPG Bull. 100 (6), 969–1002. doi:10.1306/02101613092

Jin, F., Huang, J., Pu, X., Ma, C., Fu, L., Leng, C., et al. (2020). Characteristics of the Cretaceous magmatism in Huanghua depression and their relationships with hydrocarbon enrichment. J. Earth Sci. 31, 1273–1292. doi:10.1007/s12583-020-1308-8

Lan, X., and Liu, H. (2022). The geochemical characteristics of the Paleogene lacustrine source rock and Cenozoic oil in the eastern Huanghekou Sag, Bohai Bay Basin, China: an oil–source rock correlation. J. Petrol. Sci. Eng. 214, 110434. doi:10.1016/j.petrol.2022.110434

Li, K., Dong, R., Xu, G. S., Liang, Q., and Yang, H. F. (2021). Development and evolutionary model of the Cenozoic strike-slip-extensional composite fault system in Laizhouwan Sag. J. Chengdu Univ. Technol. Sci. Technol. Ed. 48 (1), 11. (In Chinese with English Abstract). doi:10.3969/j.issn.1671-9727.2021.01.02

Li, F. Y., Yang, H. C., Yang, D. S., Guo, S., Sun, R., Ji, M., et al. (2020a). The extension-strike-slip composite fault belts in central Changchang Sag of Qiongdongnan Basin and their significance for oil and gas exploration. Mar. Oil Gas Geol. 25 (3), 6. (In Chinese with English Abstract). doi:10.3969/j.issn.1672-9854.2020.03.008

Li, S. W., Huang, Y. L., Feng, Y. H., Guo, Y. M., Zhang, B., and Wang, P. J. (2020b). Cenozoic extensional-strike-slip tectonics and fracture characteristics in the eastern part of the Liaohe Basin. Chin. J. Geophys. 63 (2), 612–626.

Li, S. Z., Suo, Y. H., Santosh, M., Dai, L. M., Liu, X., Yu, S., et al. (2013). Mesozoic to cenozoic intracontinental deformation and dynamics of the north China craton. Geol. J. 48 (5), 543–560. doi:10.1002/gj.2500

Li, Y., Pu, R., Zhang, G., and Fan, X. (2023). Differences of polygonal faults with irregularly polygonal geometries: a case study from the changchang sag of Qiongdongnan Basin, northern south China sea. J. Ocean Univ. China 22 (1), 119–135. doi:10.1007/s11802-023-5285-2

Li, Z., Zuo, Y., Qiu, N., and Gao, J. (2017). Meso–cenozoic lithospheric thermal structure in the Bohai Bay Basin, eastern north China craton. Geosci. Front. 8 (5), 977–987. doi:10.1016/j.gsf.2016.09.003

Liu, C. C., Dai, F. G., Yang, J., and Yang, K. C., 2010. Seismic interpretation of Eogene-Neogene geological structures and tectonic styles in the sea area of Bohai Bay basin. Chin. Geol. 37 (6), 1545–1558. (in Chinese with English abstract). doi:10.3969/j.issn.1000-3657.2010.06.001

Liu, H., Xia, Q. L., Somerville, I. D., Wang, Y., Zhou, X. H., Niu, C. M., et al. (2015). Paleogene of the Huanghekou Sag in the Bohai Bay Basin, NE China: deposition-erosion response to a slope break system of rift lacustrine basins. Geol. J. 50 (1), 71–92. doi:10.1002/gj.2527

Liu, H., Xia, Q. L., and Zhou, X. H. (2018). Geologic-seismic models, prediction of shallow-water lacustrine delta sandbody and hydrocarbon potential in the Late Miocene, Huanghekou Sag, Bohai Bay Basin, northern China. J. Palaeogeogr. 7 (1), 66–87. doi:10.1016/j.jop.2017.11.001

Liu, L., Chen, H., Wang, J., Zhong, Y., Du, X., Gan, X., et al. (2019). Geomorphological evolution and sediment dispersal processes in strike-slip and extensional composite basins: a case study in the Liaodong Bay Depression, Bohai Bay Basin, China. China. Mar. Petrol. Geol. 110, 73–90. doi:10.1016/j.marpetgeo.2019.07.023

Lu, K. Z., Qi, J. F., and Dai, J. S. (1997). Tectonic model of cenozoic petroliferous basin Bohai Bay Province. Beijing: Geological Publishing House. (in Chinese).

Ma, D. B., Wang, Z. C., Duan, S. F., Gao, J. R., Jiang, Q. C., Jiang, H., et al. (2018). Strike-slip faults and their significance for hydrocarbon accumulation in Gaoshiti–Moxi area, Sichuan Basin, SW China. Petroleum Explor. Dev. 45 (5), 851–861. doi:10.1016/s1876-3804(18)30088-0

Qi, J. F. (2004). Two tectonic systems in the Cenozoic Bohai Bay basin and their genetic interpretation. Geol. China 31 (01), 15–22. (in Chinese with English abstract).

Qi, J. F., and Yang, Q. (2010). Cenozoic structural deformation and dynamic processes of the Bohai Bay basin province, China. China. Mar. Petrol. Geol. 27 (4), 757–771. doi:10.1016/j.marpetgeo.2009.08.012

Ren, J. Y., Lei, C., Wang, S., Li, T., and Zhang, X. L. (2011). Tectonic stratigraphic framework of the Yinggehai-Qiongdongnan Basins and its implication for tectonics province division in South China Sea. Chin. J. Geophys. 54 (6), 1124–1137. doi:10.3969/j.issn.0001-5733.2011.12.028

Schlische, R. W., and Withjack, M. O. (2009). Origin of fault domains and fault-domain boundaries (transfer zones and accommodation zones) in extensional provinces: result of random nucleation and self-organized fault growth. J. Struct. Geol. 31 (9), 910–925. doi:10.1016/j.jsg.2008.09.005

Shi, H. S., Wang, Q. B., Wang, J., Liu, X. J., Feng, C., Hao, Y. W., et al. (2019). Discovery and exploration significance of large condensate gas fields in BZ19-6 structure in deep Bozhong sag. China Petrol. Explor 24 (1), 36–45. (in Chinese with English abstract). doi:10.3969/j.issn.1672-7703.2019.01.005

Song, G., Wang, H., Xu, M., Xu, J., and Sang, G. (2019). Sedimentary facies, sequence stratigraphic patterns in pre-cenozoic inland compressional basin: example from early yanshanian succession of eastern yihezhuang Salient, jiyang depression, Bohai Bay Basin, China. J. Earth Sci. 30, 194–205. doi:10.1007/s12583-018-0867-4

Sun, G. Y. (2017). Study on hydrocarbon source rocks and resource evaluation in the Miaoxinan sag. Beijing: China University of Geosciences. (Master thesis in Chinese with English Abstract).

Sun, Z., Zhu, H., Xu, C., Yang, X., Du, X., Wang, Q., et al. 2017. Pore fluid evolution influenced by volcanic activities and related diagenetic processes in a rift basin: evidence from the Paleogene medium-deep reservoirs of Huanghekou Sag, Bohai Bay Basin, China. Geofluids , 2017.

Teng, C., Hao, F., Zou, H., Zhou, X., and Xu, C. (2016). Tan-Lu fault system and its significance in oil accumulation in the central Liaodong Bay subbasin, Bohai Bay Basin, China. AAPG Bull. 100 (2), 289–314. doi:10.1306/10221515011

Tong, H., Yu, F., and Geng, C. (2008). Characteristics and evolution of strike-slip tectonics of the Liaohe western sag, Bohai Bay Basin. Petroleum Sci. 5, 223–229. doi:10.1007/s12182-008-0034-0

Tong, K. J., Zhao, C. M., Lu, Z. B., Zhang, Y. C., Zheng, H., Xu, S. N., et al. (2012). Reservoir evaluation and fracture chracterization of the metamorphic buried hill reservoir in Bohai Bay Basin. Petrol. explor. Dev. 39 (1), 62–69. doi:10.1016/s1876-3804(12)60015-9

Wang, Z., Gao, Z., Fan, T., Shang, Y., Qi, L., and Yun, L. (2020). Structural characterization and hydrocarbon prediction for the SB5M strike-slip fault zone in the Shuntuo Low Uplift, Tarim Basin. Mar. Petrol. Geol. 117, 104418. doi:10.1016/j.marpetgeo.2020.104418

Xu, J. Y., Ben-Avraham, Z., Kelty, T., and Yu, H. S. (2014). Origin of marginal basins of the NW Pacific and their plate tectonic reconstructions. Earth-Sci. Rev. 130, 154–196. doi:10.1016/j.earscirev.2013.10.002

Yang, G., Yin, H., Gan, J., Wang, W., Zhu, J., Jia, D., et al. (2022). Explaining structural difference between the eastern and western zones of the Qiongdongnan Basin, northern South China Sea: insights from scaled physical models. Tectonics 41 (2), e2021TC006899. doi:10.1029/2021tc006899

Ye, Z. Q., Zhu, H. T., Du, X. F., Yang, X. H., Yao, T. T., and Li, S. (2020). Sedimentary characteristics and model of mixed siliciclastic-carbonates of member 1 of Paleogene Shahejie formation, huanghekou sag, Bohai Bay Basin. Earth Sci. 45 (10), 3731–3745. (in Chinese with English abstract). doi:10.3799/dqkx.2020.224

Yin, A. (2010). Cenozoic tectonic evolution of Asia: a preliminary synthesis. Tectonophysics 488, 293–325. doi:10.1016/j.tecto.2009.06.002

Yu, Y., Zhou, X., Xu, C., Wu, K., Lv, D., Liu, Y., et al. (2020). Architecture and evolution of the Cenozoic offshore Bohai Bay basin, eastern China. J. Asian Earth Sci. 192, 104272. doi:10.1016/j.jseaes.2020.104272

Zeng, Z., Zhu, H., Zeng, H., Yang, X., and Xu, C. (2022). Seismic sedimentology analysis of fluvial-deltaic systems in a complex strike-slip fault zone, Bohai Bay Basin, China: implications for reservoir prediction. J. Petrol. Sci. Eng. 208, 109290. doi:10.1016/j.petrol.2021.109290

Zhao, Y., Yang, H. F., Huang, Z., Zhao, D. J., and Wang, H. (2020). Strike-slip structural characteristics and its controlling effect on hydrocarbon accumulation in Miaoxinan Sag, Bohai Sea. Petroleum Geol. Recovery Effic. 27 (4), 35–44. (in Chinese with English abstract). doi:10.13673/j.cnki.cn37-1359/te.2020.04.004

Zhou, X. H., Yu, Y. X., Tang, L. J., Lv, D. Y., and Wang, Y. B. (2010). Cenozoic offshore basin architecture and division of structural elements in Bohai sea. China Offshore Oil Gas 22 (5), 285–289. (in Chinese with English abstract).

Zong, Y., Liang, J. S., and Liu, L. F. (2012). Structural characteristics of Miaoxi sag. Lithol. Reserv. 24 (1), 36–39. (in Chinese with English abstract). doi:10.3969/j.issn.1673-8926.2012.01.007

Zuo, Y., Qiu, N., Zhang, Y., Li, C., Li, J., Guo, Y., et al. (2011). Geothermal regime and hydrocarbon kitchen evolution of the offshore Bohai Bay Basin, North China. AAPG Bull. 95 (5), 749–769. doi:10.1306/09271010079

GLOSSARY

Conflict of interest: Author HY was employed by the CNOOC (China) Limited Tianjin branch. Author KW was employed by the PetroChina Tarim Oilfield Company.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Fu, Wang, Yang, Song, Ma and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 20 May 2024
doi: 10.3389/feart.2024.1411126

Utilizing logs to identify complex lithology of tight marl reservoir in the Leikoupo Formation 32 Submember (T2l32) of the Sichuan Basin, China

Rui Yuan¹, Zhiwei Wu¹*, Yongguang Xin², Hao Zhang², Saijun Wu³ and Siyue Yang¹

¹School of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei, China

²Sichuan Basin Research Center, PetroChina Research Institute of Petroleum Exploration and Development, Chengdu, Sichuan, China

³PetroChina Research Institute of Petroleum Exploration and Development, Beijing, China

Edited by:
Yongqiang Chen, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia

Reviewed by:
Zhiyang Li, Texas A&M International University, United States
Feng Wu, Southwest Petroleum University, China
Tao Nian, Xi’an Shiyou University, China

* Correspondence: Zhiwei Wu, 2022710367@yangtzeu.edu.cn

Received: 02 April 2024
Accepted: 02 May 2024
Published: 20 May 2024

Citation: Yuan R, Wu Z, Xin Y, Zhang H, Wu S and Yang S (2024) Utilizing logs to identify complex lithology of tight marl reservoir in the Leikoupo Formation 32 Submember (T2l32) of the Sichuan Basin, China. Front. Earth Sci. 12:1411126. doi: 10.3389/feart.2024.1411126

Recent exploration has revealed that the Middle Triassic Leikoupo Formation 3² Submember (T₂l₃²) in the Sichuan Basin contains unconventional marl reservoirs with significant natural gas potential. Due to limited cores, old wells, and conventional logs, however, the lithological understanding of T₂l₃² is incomplete and relies solely on inaccurate mud logs. This lack of lithological foundation challenges geology and petroleum research. To identify complex lithology, this paper presents a double-hierarchical workflow to identify seven types of lithology using logs. The first order distinguishes salt, anhydrite, and marl, while the second order further subdivides marl into anhydrite marl, argillaceous limestone, shaly limestone, and limy shale. Different rocks’ logging response characteristics are summarized based on quantity-limited cores and micro-resistivity imaging logs. Lithological identification of 2D and 3D plots is established using sensitive GR, DEN, and RT. Corresponding identification standards are built in two hierarchies. According to these standards, the lithology of T₂l₃² is identified in a total of 119 wells. Finally, the lithological characteristics of vertical, horizontal, and plane are discussed in the research area. The research results may aid in comprehending the entire lithological characteristics of the complex marl reservoir in T₂l₃² of the Sichuan Basin. It would help the exploration potential of petroleum systems in turn.

Keywords: logs, complex lithology, tight marl, Leikoupo Formation, Sichuan Basin

1 INTRODUCTION

The identification of lithology is the foundation of petroleum resource exploration and development. Drilling cores provide direct evidence of subsurface strata. However, they are costly and time-consuming. Mud logs offer initial information in drilling formation, but their lithological data is rough and inaccurate. These lithological results are unsuitable for detailed descriptions of oil and gas reservoirs. Various well logs record multiple physical properties of stratigraphy within high resolution, which are the widespread data in each open hole. The identification of lithology using logs is the most effective and direct method for recognizing subsurface rocks of formation (Kumar et al., 2018; Ramos and Neves, 2019; Saporetti et al., 2019).

Carbonate is one kind of significant hydrocarbon reservoir with complex lithology, including limestone, dolomite, anhydrite, salt, and other rocks (Paul and Stephen, 2006; Ben et al., 2019). Carbonate reservoirs are essential natural gas producers in China (Chen et al., 2022). Dozens of carbonate gas fields have been built in the Sinian, Cambrian, and Permian of the Sichuan Basin of China (Ma et al., 2008; Tan et al., 2011; Wang L. et al., 2020; Liao et al., 2020; Tan et al., 2021). Specifically, the Middle Triassic Leikoupo Formation 3² Submember (T₂l₃²) in the central Sichuan Basin has yielded 3 × 10⁵ m³ gas and 47.04 m³ oil per day in oil tests of CT1 Well in 2020 (Wang et al., 2023). It is a breakthrough in the marl intervals of T₂l₃², which kicked off the hydrocarbon exploration in this formation (Shen et al., 2008; Zhou et al., 2010; Xin et al., 2013a; Xin et al., 2013b; Lü et al., 2013; Tian et al., 2018; Wang X. et al., 2020; Tian et al., 2020; Tian et al., 2021; Zhang et al., 2021; Xin et al., 2022). Marl is a type of complex carbonate that is often a mixture of mud and limestone (Balumi et al., 2022; Yin et al., 2023). Marl reservoir exhibits poor physical properties, with porosity ranging from 2% to 8% and permeabilities of (0.001–1) × 10⁻³ μm² in T₂l₃². Due to limited cores, imprecise mud logs, and old well data, obtaining accurate geological information would be difficult in the T₂l₃² formation. In this paper, a hierarchical lithological identification method from logs is formed to solve the lithological problem of tight marl reservoir in T₂l₃² effectively. Log response characteristics of different rocks are summarized on the quantity-limited cores and micro-resistivity imaging log. Corresponding 2D and 3D lithological plots are established. Finally, the lithological characteristics of T₂l₃² are discussed.

2 GEOLOGICAL SETTING

2.1 Tectonic and depositional background

Yangtze Para-platform is a significant platform for the Chinese mainland. The Sichuan Basin is in the northwestern Yangtze Para-platform as a secondary tectonic unit. For the early tectonic uplift in the Indo-China Movement, the Sichuan Basin was enclosed by palaeo-highs and palaeo-lands in the Yangtze Cratonic Block. In the Middle Triassic Leikoupo Stage, therefore, the Sichuan Basin was a part of the Yangtze Craton Basin. Sichuan Basin adjoined Yunnan-Tibet Palaeo-ocean in the west through Longmen Mountain Island-chain; adjoined Qinling Palaeo-ocean and Northern Qinling Palaeo-land in the north by Michang and Daba Mountain Uplift; and was surrounded by Kangdian Palaeo-land in the southwest and Jiangnan Palaeo-land in the east. In the Sichuan Basin, Chuanxi, Chuanzhong, and Chuandong Depressions, and Kaijing and Luzhou Uplifts (Figure 1A) (Tian et al., 2021). This palaeogeographic framework of the Sichuan Basin in the Leikoupo Stage results in the depositional environment of an evaporated-restricted platform. Mixed lagoon and flat were widely distributed in the T₂l₃² of the Sichuan Basin. In the central basin, depositional facies were mainly developed in marly and anhydrite lagoon and marly flat, which is the dominant research region of this paper (Figure 1B) (Tian et al., 2021).

FIGURE 1 | Tectonic of the Sichuan Basin and depositional background of T₂l₃². (A) Tectonic background of the Sichuan Basin (modified from Tian et al., 2021). (B) Depositional background of T₂l₃² in the Sichuan Basin (modified from Tian et al., 2021).

2.2 Stratigraphic background

According to the stratigraphy in the Sichuan Basin, the Middle Triassic Leikoupo Formation is divided into five members, named Lei-1 (T₂l₁), Lei-2 (T₂l₂), Lei-3 (T₂l₃), Lei-4 (T₂l₄), and Lei-5 (T₂l₅) upwards (Figure 2A). Within a thickness of 200 m, the Lei-3 Member is further subdivided into three submembers, termed 3¹ (T₂l₃¹), 3² (T₂l₃²), and 3³ (T₂l₃³) upwards. T₂l₃² is approximately 100 m thick and is the main gas pay of the T₂l₃. Based on current geological understanding, T₂l₃² is composed of six dominant lithologies: salt, anhydrite, anhydrite marl, argillaceous limestone, shaly limestone, and limy shale (Figure 2B). Oilfield petrologists consider that hydrocarbon would be generated in the limy shale and enriched in the shaly limestone; the salt and anhydrite are cover rocks. Therefore, the whole T₂l₃² is considered as a “self-generation and self-storage” complex unconventional marl gas reservoir (Yang et al., 2022; Zhang et al., 2023).

FIGURE 2 | Stratigraphic characteristics of the T₂l₃² in the central Sichuan Basin. (A) Lithological characteristics of the Leikoupo Formation. (B) Generalized open-hole stratigraphic T₂l₃² of CT1 Well. Conventional logs: CAL-borehole diameter (in), GR-natural gamma ray (API), AC-acoustic time (μs/ft), DEN-compensated bulk density (g/cm³), CNL-compensated neutron (decimal), RT-deep investigation resistivity (Ω·m), RXO-shallow investigation resistivity (Ω·m). (C) Core description and photos in CT1 Well.

3 DATA AND METHODS

3.1 Database

In the central Sichuan Basin, more than one hundred wells have been drilled in T₂l₃². Almost all wells have been run by conventional logs, including borehole diameter (CAL, in), natural gamma ray (GR, API), deep resistivity (RT, Ω·m), shallow resistivity (RXO, Ω·m), acoustic time (AC, μs/ft), compensated neutron (CNL, decimal) and bulk density (DEN, g/cm³) (Figure 2B). 119 wells are involved in this paper. Although cuttings while drilling were recorded to understand the lithology of formation, this information is in large error. Therefore, coring has become an important criterion for lithological identification. For the long drilling time and high coring cost, just two coring runs (8.65 m) and one coring run (8.4 m) were operated in CT1 and JY1 Well respectively (Figure 2C). Meanwhile, micro-resistivity imaging logs were generated in JY1 Well. These ultrahigh-resolution images cover the shortage of cores and play an important role in describing lithology and fabric of formation finely (Zhou, 2008; Xu et al., 2009; Yan et al., 2011; Folkestad et al., 2012; Brekke et al., 2017; Luo et al., 2018; Zhang et al., 2018; Yuan et al., 2020).

3.2 Methods

The method of using well logs to identify lithology of complex unconventional marl reservoir in T₂l₃² of the Sichuan Basin requires knowledge of primary carbonate and comprises five stages in this article. The first stage is the pre-processing of multiple data. All logging curves involved are standardized to eliminate systematic errors between logging tools (Figure 3). Depth of cores and borehole electrical images are corrected to the depth of logging curves in CT1 and JY1 Well. High-resolution 256-grayscale electrical conductivity images are generated in JY1 Well. The second stage makes the applicable lithological identification hierarchical workflow. In the first-order, three categories of rocks are distinguished: salt, anhydrite, and marl. In the second-order, the marl is further subdivided into four types of lithology: anhydrite marl, argillaceous limestone, shaly limestone, and limy shale (Figure 4). The third stage summarizes the logging response characteristics of the above three categories and four types of lithologies. The lithological evidence comes from mud logs, cores, and micro-resistivity images. The fourth stage produces several 2D and 3D lithological plots hierarchically from logs. The fifth stage applies plots to identify lithology in boreholes and discusses the vertical, horizontal, and plane lithological characteristics of T₂l₃² in the Sichuan Basin.

FIGURE 3 | Standardization of logging curves in some wells. (A) Pre-standardization of GR. (B) Post-standardization of GR. (C) Pre-standardization of DEN. (D) Post-standardization of DEN. (E) Pre-standardization of RT. (F) Post-standardization of RT.

FIGURE 4 | Hierarchical workflow of lithological identification in T₂l₃².

4 RESULTS

4.1 Logging response characteristics of different lithologies

Salt, anhydrite, and marl are the first-order lithologies in T₂l₃². Precipitated in evaporating sea and lake water, salt is a rock of pure chemical genesis. It consists mainly of halite, sylvine, gypsum, anhydrite, and so on minerals. Due to these chemical substances, salt is usually within the physical properties of high solubility, low radioactivity, low density, and non-conductivity (Wang et al., 2016; Zhang et al., 2022). It results in high CAL, low GR, low DEN, and high RT logging values in salt intervals (Shen et al., 2015; Xu et al., 2023). Similarly, with salt in a depositional environment, anhydrite is generated in highly concentrated water. In logging, anhydrite rocks have low GR, high DEN, and high RT normally (Carrasquilla and Caetano, 2021; Meng et al., 2022). Marl is a kind of transitional rock between carbonate and shale rock. Different from salt and gypsum, marl is characterized by medium-high GR, medium-low DEN, and medium-low RT in logging responses (Figure 5A) (Afsar et al., 2014; Cherif et al., 2021).

FIGURE 5 | Logging response characteristics of different lithologies. (A) Logging response characteristics of first-order lithologies in GS1 Well. (B) Logging response characteristics of second-order lithologies in JY1 Well.

In second-order lithologies, anhydrite marl, argillaceous limestone, shaly limestone, and limy shale are finer lithologies in marl. Anhydrite marl is a type of mixture of anhydrite and marl. Marl is the primary material and anhydrite is the secondary mineral. The logging responses of anhydrite marl are between anhydrite and marl. In the micro-resistivity imaging logs, the anhydrite marl is blurry and massive. Argillaceous limestone, shaly limestone, and limy shale are mixed with calcareous and clay minerals. The content of clay minerals increases and that of calcareous minerals gradually decreases in these three lithologies. Theoretically, limestone has low radioactivity and low electrical conductivity. In contrast, clay is high in both of these characteristics. Therefore, these three fine lithologies would be carefully distinguished during logs. Limy shale has the highest GR and lowest RT; argillaceous limestone has the lowest GR and highest RT; logging responses of shaly limestone are intermediate between the above two lithologies. In the micro-resistivity imaging logs, argillaceous limestone, shaly limestone, and limy shale are alternated by horizontal dark and light stripes. Centimeter-thickness dark stripes indicate limy shale, while light bands indicate argillaceous limestone. Extreme thin dark and light stripes interbedding intervals imply shalylimestone (Figure 5B).

4.2 Lithological identification plots

Based on the logging response characteristics of different order lithologies, 2D and 3D lithological plots are produced, involving sensitive GR, DEN, and RT. Four plots are effective in distinguishing the first-order lithologies of salt, anhydrite, and marl. In the GR-DEN plot, salt is in the low GR and low DEN zone; anhydrite is in the low GR and high DEN zone (Figure 6A). In the RT-GR plot, marl is in the medium-high GR and low RT zone; salt and anhydrite are in low GR and high RT zone (Figure 6B). In the RT-DEN plot, marl is in medium DEN and low RT zone; salt is in low DEN and high RT zone; anhydrite is in high DEN and high RT zone (Figure 6C). These three lithologies are easily distinguished in the 3D GR-DEN-RT plot (Figure 6D). The corresponding identification standard would be: salt GR<35 API, DEN<2.5 g/cm³, RT>3,000 Ω·m; anhydrite GR<35 API, DEN>2.7 g/cm³, RT>800 Ω·m; marl GR>30 API, RT<2000 Ω·m, DEN 2.5–2.8 g/cm³.

FIGURE 6 | Lithological identification plots for the first-order lithologies of salt, anhydrite, and marl. Lithology and logging data derived from coring intervals of CT1 and JY1 Well. (A) 2D GR-DEN plot. (B) 2D RT-GR plot; (C) 2D RT-DEN plot; (D) 3D GR-DEN-RT plot.

The GR-RT plate is also used to distinguish the second-order lithologies of anhydrite marl, argillaceous limestone, shaly limestone, and limy shale in marl. Anhydrite marl is in the lowest GR and highest RT zone; limy shale is in the highest GR and lowest RT zone; argillaceous limestone is in low-medium GR and medium-high RT zone; shaly limestone is in medium GR and medium RT zone (Figure 7). The corresponding identification standard would be: anhydrite marl GR<35 API, RT 800–2000 Ω·m; argillaceous limestone GR 30–50 API, RT 200–2000 Ω·m; shaly limestone GR 40–60 API, RT 30–300 Ω·m; limy shale GR>45 API, RT<80 Ω·m. Finally, the logging lithological identification criteria for the T₂l₃² complex marl reservoir in the central Sichuan Basin are summarized in Table 1.

FIGURE 7 | Lithological identification GR-RT plot for the second-order lithologies of anhydrite marl, argillaceous limestone, shaly limestone, and limy shale in marl. Lithology and logging data derived from coring intervals of JY1 Well.

TABLE 1 | Lithological identification standard of logs for complex marl reservoir of the T₂l₃² in the central Sichuan Basin.

4.3 Compare identification results with cores

To check the availability of the lithological identification standard, identifying results are compared with core lithology in CT1 Well. In the coring interval, 3,560–3,568.65 m, upper lithology is argillaceous limestone and lower lithology is shaly limestone in coring description. In the lithological identification result, the interval of 3,561.8–3,562.54 m is in higher GR than up and down, and lithology is recognized as shaly limestone Figure 8. Coincidence rate between coring identifying and lithology is about 91%. Using the proposed method, centimeter-scale lithology would be acquired. In addition, the marl reservoir in T₂l₃² is very tight, within 2%–8% porosity. Water is hard to store in this tight marl reservoir. Gas and oil are usually in low saturation. Therefore, the logging responses are principally rooted in the rock framework. Different amount of pore fluids affects the GR, DEN, and RT finitely.

FIGURE 8 | Compare identification results with cores in CT1 Well.

5 DISCUSSION

5.1 Lithological characteristics in single well

Based on the above lithological identification standard for complex marl reservoir in T₂l₃², lithology is identified in a total of 119 wells. In the vertical formation, the lithological characteristics of T₂l₃² are obvious. Taking LG47 Well as an example, the lithological identification result is shown in Figure 9. The thickness of T₂l₃² is 213 m. In the mud log lithology, both anhydrite and marl are large sets of thick layers and no salt rock. In the lithological identification result, two salt layers, about 12 m thick, cover the upper formation. They are the significant cap rocks. Four main anhydrite layers, about 8 m thick, are developed in the middle formation. They are the interlayers between the marl reservoirs. Marl layers are thin and alternating. The number of layers, minimum thickness, average thickness, and maximum thickness of anhydrite marl is 8, 0.77 m, 1.58 m, and 3.23 m respectively. The number of layers, minimum thickness, average thickness, and maximum thickness of argillaceous limestone is 31, 0.5 m, 1.9 m, and 6.33 m respectively. The number of layers, minimum thickness, average thickness, and maximum thickness of shaly limestone is 33, 0.45 m, 1.67 m, and 4.98 m respectively. The number of layers, minimum thickness, average thickness, and maximum thickness of limy shale are 20, 0.35 m, 0.82 m, and 1.8 m respectively (Figure 10A).

FIGURE 9 | Lithological identification results of LG47 Well.

FIGURE 10 | Quantitative thickness statistics of different lithologies. (A) Layer number, single layer minimum thickness, average thickness, and maximum thickness of different lithologies. Numbers in the brackets are layer numbers. (B) Total thickness percentage of different lithologies.

In the entire T₂l₃² formation, the percentage of salt thickness is 16.1%; the percentage of anhydrite thickness is 16.9%; the percentage of anhydrite marl thickness is 5.9%; the percentage of argillaceous limestone thickness is 27.6%; the percentage of shaly limestone thickness is 25.8%; the percentage of limy shale thickness is 7.7% (Figure 10B). These quantitative parameters reveal the characteristics of the different lithologies. As reservoir rocks, the individual thicknesses of the shaly limestone and argillaceous limestone are medium, and the number of layers is large. The total thicknesses of these two lithologies are the greatest. As cap and interlayer rocks, the individual layer thicknesses of salt and anhydrite are large, but the number of layers is small. The total thickness of these two lithologies is the second. As source rocks, the single-layer thickness of limy shale is the smallest, generally not exceeding 1 m, and the total thickness is the smallest.

5.2 Lithological characteristics in horizontal formation

During the deposition of the Middle Triassic Leikoupo Formation, uplifts were developed at the edge of the basin. The saline seawater was evaporated in the restricted platform in the arid paleoclimate. From the center to the margin of the Sichuan Basin, the depositional facies are anhydrite-salt lagoon, marly lagoon, lagoon margin of marly flat, dolomitic flat, and marly flat (Figure 1B).

Based on the lithological identification results in individual boreholes, the lithological characteristics in the horizontal formation are interpreted in different depositional facies (Figure 11). The salt, as regional cap rock, is the thickest in the central anhydrite-salt lagoon and thins towards the basin margin. The total thickness percentage of salt is reduced from the central to the marginal in the basin. The number of layers and the total thickness of anhydrite decrease towards the margin of the basin as well. Thin interbedded argillaceous limestone, shaly limestone, and limy shale are separated by anhydrite. The number of marl layers in the central basin is greater than in the marginal basin; The monolayer thickness and total percentage of marl thickness in the central basin are less than in the marginal basin. Gases are well trapped in these thin marls.

FIGURE 11 | Lithological characteristics in the horizontal formation of T₂l₃². The location of the wells is marked in Figure 1B.

5.3 Lithological characteristics of the region

Based on lithological identification results in 119 wells, thickness contour plans of each lithology are generated in the survey region. The first-order lithologies are shown in Figure 12. Anhydrite is developed in the center with NE-SW, and the maximum thickness is in the northeast. The anhydrite cap rock is region-wide (Figure 12A). Salt is just dominated in the southeast, with no development in the northwestern (Figure 12B). The distribution of the marl is relatively wide and developed throughout the area, mainly concentrated in the center NE-SW region (Figure 12C). The different marl lithologies are shown in Figure 13. Regional features of the preponderant reservoir, interlayer, and source rock are clear. As an unconventional reservoir, the thickness of anhydrite marl, argillaceous limestone, shaly limestone, and limy shale are crucial factors for further geological understanding. These lithological characteristics of the region would offer an important reference for subsequent exploration and development of natural gas.

FIGURE 12 | First-order lithological thickness plane spread of T₂l₃². (A) Thickness plane spread of anhydrite. (B) Thickness plane spread of salt. (C) Thickness plane spread of marl.

FIGURE 13 | Lithological thickness plane spread of marl in T₂l₃². (A) Thickness plane spread of anhydrite marl. (B) Thickness plane spread of argillaceous limestone. (C) Thickness plane spread of shaly limestone. (D) Thickness plane spread of limy shale.

6 CONCLUSION

(1) Lithological identification of 2D and 3D plots is established for the complex marl reservoir in T₂l₃² of the Sichuan Basin. Identification standard of the first-order lithology is: salt GR<35 API, DEN<2.5 g/cm³, RT>3,000 Ω·m; anhydrite GR<35 API, DEN>2.7 g/cm³, RT>800 Ω·m; marl GR>30 API, RT<2000 Ω·m, DEN 2.5–2.8 g/cm³. Identification standard of the fine lithology in marl is: anhydrite marl GR<35 API, RT 800–2000 Ω·m; argillaceous limestone GR 30–50 API, RT 200–2000 Ω·m; shaly limestone GR 40–60 API, RT 30–300 Ω·m; limy shale GR>45 API, RT<80 Ω·m.

(2) In the coring interval, the coincidence rate of identifying lithological results is larger than 90%. As reservoir rocks, the individual thicknesses of the shaly limestone and argillaceous limestone are medium, and the number of layers is large. As cap and interlayer rocks, the individual layer thicknesses of salt and anhydrite are large, but the number of layers is small. As source rock, the single-layer thickness of limy shale is the smallest, generally not exceeding 1 m.

(3) The anhydrite cap rock is region-wide, while salt is just dominated in the southeastern, with no development in the northwestern. The distribution of the marl is relatively wide and developed throughout the area, mainly concentrated in the center NE-SW region. The plane thickness of anhydrite marl, argillaceous limestone, shaly limestone, and limy shale have practical significance for follow-up exploration in T₂l₃² of the central Sichuan Basin.

DATA AVAILABILITY STATEMENT

The data analyzed in this study is subject to the following licenses/restrictions: The dataset not available. Requests to access these datasets should be directed to ZW, 2022710367@yangtzeu.edu.cn.

AUTHOR CONTRIBUTIONS

RY: Writing–original draft, Writing–review and editing. ZW: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Visualization, Writing–original draft. YX: Resources, Visualization, Writing–review and editing. HZ: Data curation, Formal Analysis, Writing–review and editing. SW: Methodology, Writing–review and editing. SY: Formal Analysis, Writing–original draft.

FUNDING

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

ACKNOWLEDGMENTS

The reviewers are gratefully acknowledged for constructive comments that substantially improved the quality of this manuscript. Also, we appreciate the editor’s suggestions to revise this manuscript.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Afsar, F., Westphal, H., and Philipp, S. L. (2014). How facies and diagenesis affect fracturing of limestone beds and reservoir permeability in limestone–marl alternations. Mar. Pet. Geol. 57, 418–432. doi:10.1016/j.marpetgeo.2014.05.024

Balumi, W. B., Elghonimy, R. S., Sonnenberg, S., and Puckette, J. (2022). Chemostratigraphy of unconventional shale reservoirs: a case study of the Niobrara Formation within the Denver-Julesburg Basin. Mar. Pet. Geol. 146, 105957. doi:10.1016/j.marpetgeo.2022.105957

Ben, C. N., Khemiri, F., Soussi, M., Latil, J. L., Robert, E., and Belhajtaher, I. (2019). Aptian-Lower Albian Serdj carbonate platform of the Tunisian Atlas: development, demise and petroleum implication. Mar. Pet. Geol. 101, 566–591. doi:10.1016/j.marpetgeo.2018.10.036

Brekke, H., MacEachern, J. A., Roenitz, T., and Dashtgard, S. E. (2017). The use of microresistivity image logs for facies interpretations: an example in point-bar deposits of the McMurray Formation, Alberta, Canada. AAPG Bull. 101 (5), 655–682. doi:10.1306/08241616014

Carrasquilla, A., and Caetano, L. (2021). Proportional integral derivative controller used to simulate the mineral concentration and fluid saturations from geological data and well logs in the Namorado reservoir, Southeastern Brazil. J. South Am. Earth Sci. 111 (Nov), 103455–103455.10. doi:10.1016/j.jsames.2021.103455

Chen, J. X., Guo, Z. L., He, Z. L., Tao, Z., Zhu, H. H., Luo, T., et al. (2022). Maturity assessment of solid bitumen in the Sinian carbonate reservoirs of the eastern and central Sichuan Basin, China: Application for hydrocarbon generation modelling. Geol. J. 57 (11), 4662–4681. doi:10.1002/gj.4564

Cherif, A., Naimi, M. N., and Belaid, M. (2021). Deep-sea trace fossils and depositional model from the lower Miocene Tiaret marl formation (northwestern Algeria). J. Afr. Earth Sci. 175 (Mar), 104115–104115.16. doi:10.1016/j.jafrearsci.2021.104115

Folkestad, A., Veselovsky, Z., and Roberts, P. (2012). Utilising borehole image logs to interpret delta to estuarine system: a case study of the subsurface Lower Jurassic Cook Formation in the Norwegian northern North Sea. Mar. Pet. Geol. 29 (1), 255–275. doi:10.1016/j.marpetgeo.2011.07.008

Kumar, M., Dasgupta, R., Singha, D. K., and Singh, N. P. (2018). Petrophysical evaluation of well log data and rock physics modeling for characterization of Eocene reservoir in Chandmari oil field of Assam-Arakan Basin India. J. Pet. Explor Prod. Technol. 8 (2), 323–340. doi:10.1007/s13202-017-0373-8

Liao, Z. H., Wu, M. N., Chen, X. F., and Zou, H. Y. (2020). Fracture mechanical properties of carbonate and evaporite caprocks in Sichuan Basin, China with implications for reservoir seal integrity. Mar. Pet. Geol. 119, 104468–104471. doi:10.1016/j.marpetgeo.2020.104468

Lü, Y. Z., Ni, C., Zhang, J. Y., Gu, M. F., Sun, Q. F., Liu, Z. S., et al. (2013). Favorable sedimentary facies zones and lithofacies palaeogeography of middle Triassic Leikoupo Formation in Sichuan Basin. Mar. Ori Pet. Geol. 18 (1), 26–32. doi:10.3969/j.issn.1672-9854.2013.01.004

Luo, X. P., Pang, X., Su, D. X., Lu, H., Zhang, N., and Wang, G. (2018). Recognition of complicated sandy conglomerate reservoir based on micro-resistivity imaging logging: a case study of Baikouquan Formation in western slope of Mahu Sag, Junggar Basin. Xinjiang Petrol Geol. 39 (3), 345–351. doi:10.7657/XJPG20180313

Ma, Y. S., Zhang, Y. C., Guo, T. L., Zhu, G., Cai, X., and Li, M. (2008). Petroleum geology of the Puguang sour gas field in the Sichuan Basin, SW China. Mar. Pet. Geol. 25 (4/5), 357–370. doi:10.1016/j.marpetgeo.2008.01.010

Meng, Q. Q., Li, J. Z., Liu, W. H., Fu, Q., Wang, X. F., and Wang, J. (2022). Simulation study on the effect of anhydrite-salt content on hydrocarbon generation in mature stage shale. Spe Oil Gas Reserv 29 (5), 113–118. doi:10.3969/j.issn.1006-6535.2022.05.016

Paul, H. N., and Stephen, N. E. (2006). Sandstone vs carbonate petroleum reservoirs: a global perspective on porosity-depth and porosity-permeability relationships: Reply. AAPG Bull. 90 (5), 811–813. doi:10.1306/11070505163

Ramos, L. D. M., and Neves, A. A. J. (2019). Lithology identification on well logs by fuzzy inference. J. Pet. Sci. Eng. 180, 357–368. doi:10.1016/j.petrol.2019.05.044

Saporetti, C. M., Da Fonseca, L. G., and Pereira, E. (2019). A lithology identification approach based on machine learning with evolutionary parameter tuning. IEEE Geosci. Remote Sens. Lett. 16 (12), 1819–1823. doi:10.1109/LGRS.2019.2911473

Shen, A. J., Zhou, J. G., Xin, Y. G., and Luo, X. Y. (2008). Origin of Triassic Leikoupo dolostone reservoirs in Sichuan Basin. Mar. Ori Petro Geol. 13 (4), 19–28. doi:10.3969/j.issn.1672-9854.2008.04.004

Shen, J. J., Chen, B., Wang, C. L., Chang, J. J., Zhou, X. F., Guan, X. Q., et al. (2015). Sedimentary characteristics and control factors of anhydrite-salt rocks in the Paleogene Xingouzui formation in Jiangling Depression, Jianghan basin. J. Palaeogeogr. (China Ed. 17 (2), 265–274. doi:10.7605/gdlxb.2015.02.022

Tan, L., Liu, H., Tang, Q. S., Li, F., Tang, Y. Z., Liang, F., et al. (2021). Application of seismic geomorphology to carbonate rocks: a case study of the Cambrian Longwangmiao formation in the Gaoshiti-Moxi area, Sichuan Basin, China. Mar. Pet. Geol. 126, 104919–104921. doi:10.1016/j.marpetgeo.2021.104919

Tan, X. C., Li, L., Liu, H., Luo, B., Zhou, Y., Wu, J., et al. (2011). General depositional features of the carbonate platform gas reservoir of the Lower Triassic Jialingjiang Formation in the Sichuan Basin of southwest China: Moxi gas field of the central basin. Carbonates Evaporites 26 (4), 339–350. doi:10.1007/s13146-011-0070-5

Tian, H., Feng, Q. F., Xin, Y. G., and Zhang, H. (2020). Well logging petrophysical experiment analysis of dolomite reservoir in Leikoupo Formation in Zhongba Area. Well Logging Techno 44 (5), 438–442. doi:10.16489/j.issn.1004-1338.2020.05.003

Tian, H., Tang, S., Zhang, J. Y., Xin, Y. G., Wang, X., and Li, Z. W. (2018). Characteristics and formation conditions of carbonate reservoir in Leikoupo Formation of western Sichuan Basin. Nat. Gas. Geosci. 29 (11), 1585–1594. doi:10.11764/ji.ssn.1672-1926.2018.08.010

Tian, H., Wang, G. W., Duan, S. F., Xin, Y. G., and Zhang, H. (2021). Reservoir characteristics and exploration target of the middle Triassic Leikoupo Formation in Sichuan Basin. China Pet. Explor 26 (5), 60–73. doi:10.3969/j.issn.1672-7703.2021.05.006

Wang, L., He, Y. M., Peng, X., Deng, H., Liu, Y. C., and Xu, W. (2020). Pore structure characteristics of an ultradeep carbonate gas reservoir and their effects on gas storage and percolation capacities in the Deng IV member, Gaoshiti-Moxi Area, Sichuan Basin, SW China. Mar. Pet. Geol. 111, 44–65. doi:10.1016/j.marpetgeo.2019.08.012

Wang, X., Xin, Y. G., Tian, H., Zhu, M., Zhang, H., and Li, W. Z. (2020). Research progress on sedimentation and reservoir of Leikoupo Formation of middle Triassic in Sichuan Basin. Mar. Ori Pet. Geol. 25 (3), 210–222. doi:10.3969/j.issn.1672-9854.2020.03.003

Wang, Y. X., Zhou, W., Guo, R., Fu, M. Y., Shen, Z. M., Zhao, L. M., et al. (2016). Characteristics and origin of high porosity and low permeability carbonate reservoirs in the Sa’di Formation, Halfaya Oil Field, Iraq. Pet Geol Exp. 38 (2), 224–230. doi:10.11781/sysydz201602224

Wang, Z. C., Xin, Y. G., Xie, W. R., Wen, L., Zhang, H., Xie, Z. Y., et al. (2023). Petroleum geology of marl in Triassic Leikoupo Formation in central Sichuan Basin and discovery significance of Chongtan1 well. Pet. Explor Dev. 50 (5), 950–961. doi:10.11698/PED.20220715

Xin, Y. G., Wen, L., Zhang, H., Tian, H., Wang, X., Sun, H. F., et al. (2022). Study on reservoir characteristics and exploration field of the middle Triassic Leikoupo Formation in Sichuan Basin. China Pet. Explor 27 (4), 91–102. doi:10.3969/j.issn.1672-7703.2022.04.007

Xin, Y. G., Zheng, X. P., Zhou, J. G., Ni, C., Gu, M. F., Gong, Q. S., et al. (2013a). Characteristics and distribution of reservoirs in the Lei-3³ of the Leikoupo Formation in the western central Sichuan Basin. Nat. Gas. Ind. 33 (3), 5–9. doi:10.3787/j.issn.1000-0976.2013.03.002

Xin, Y. G., Zhou, J. G., Ni, C., Gu, M. F., Gong, Q. S., Dong, Y., et al. (2013b). Sedimentary facies features and favorable lithofacies distribution of Middle Triassic Leikoupo barriered carbonate platform in Sichuan Basin. Mar. Ori Pet. Geol. 18 (2), 1–7. doi:10.3969/j.issn.1672-9854.2013.02.001

Xu, C. M., Cronin, T. P., McGinness, T. E., and Steer, B. (2009). Middle Atokan sediment gravity flows in the Red Oak field, Arkoma Basin, Oklahoma: a sedimentary analysis using electrical borehole images and wireline logs. AAPG Bull. 93 (1), 1–29. doi:10.1306/09030808054

Xu, Z. P., Chen, S. P., Luo, C. M., Yang, G., Xu, S. D., Hu, F. J., et al. (2023). Distribution and sealing capacity evaluation of anhydrite-salt rocks in the Middle Cambrian in Tarim Basin. China Pet. Explor 28 (5), 54–67. doi:10.3969/j.issn.1672-7703.2023.05.005

Yan, J. P., Cai, J. G., Zhao, H. M., Li, Z. Z., and Xu, G. H. (2011). Application of electrical image logging in the study of sedimentary characteristics in sandy conglomerates. Petrol Explor Devel. 38 (4), 445–451.

Yang, Y., Xie, J. R., Zhang, J. Y., Wen, L., Zhao, L. Z., Zhang, H., et al. (2022). Characteristics and exploration potential of unconventional middle Triassic Lei3² reservoirs in the central Sichuan Basin. Nat. Gas. Ind. 42 (12), 12–22. doi:10.3787/j.issn.1000-0976.2022.12.002

Yin, S. J., Wen, Z., Jin, X. Y., Yan, W. L., Wang, C. Y., Shi, J. B., et al. (2023). Characteristics and logging characterization method of unconventional marl reservoir: a case study of the Member 1 of Maokou Formation in Hechuan-Tongnan Area, Sichuan Basin. Acta Pet. Sin. 44 (7), 1105–1117. doi:10.7623/syxb202307007

Yuan, R., Yang, B., Pan, C. F., Guo, X. G., Huang, L. L., He, W. J., et al. (2020). Conglomerate petrology characterization using high-definition borehole electrical images in the Upper Urho Formation at well JL42, Zhongguai Uplift, Junggar Basin, China. Interpretation 8 (3), SL137–SL150. doi:10.1190/INT-2019-0243.1

Zhang, H., Xin, Y. G., and Tian, H. (2021). Gas⁃bearing property prediction of Leikoupo Formation in the northwest Sichuan Basin based on the theory of two⁃phase media. Geophy Geoche Explor 45 (6), 1386–1393. doi:10.11720/wtyht.2021.1340

Zhang, J., Nie, X., Xiao, S. Y., Zhang, C., and Zhang, Z. (2018). Generating porosity spectrum of carbonate reservoirs using ultrasonic imaging log. Acta geophys. 66 (2), 191–201. doi:10.1007/s11600-018-0134-1

Zhang, J. Y., Xin, Y. G., Zhang, H., Tian, H., Zhu, X. J., and Chen, W. (2023). A new unconventional gas reservoir type: source-reservoir integrated carbonate gas reservoir from evaporated lagoon facies in Lei₃² sub-member in Central Sichuan Basin. Nat. Gas. Geosci. 34 (1), 23–34. doi:10.11764/j.issn.1672-1926.2022.10.012

Zhang, Q. Y., Shi, W. X., Liu, X., Hui, G. J., and Yuan, C. Y. (2022). Application of hyperspectral scanning in mineral composition analysis of carbonate rocks. Rock Min. Anal. 41 (5), 815–825. doi:10.15898/j.cnki.11-2131/td.202112100200

Zhou, J. G., Xin, Y. G., Gu, M. F., Zhang, J. Y., Hao, Y., Li, G. J., et al. (2010). Direction of gas exploration in the middle Triassic Leikoupo Formation of the Sichuan Basin. Nat. Gas. Ind. 30 (12), 16–19+121. doi:10.3787/j.issn.1000-0976.2010.12.004

Zhou, L. X. (2008). Application of Fullbore formation Microimager (FMI) to study of glutenite sedimentary structures in Jiyang Depression. Xinjiang Petrol Geol. 29 (5), 654–656.

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Yuan, Wu, Xin, Zhang, Wu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 11 June 2024
doi: 10.3389/feart.2024.1401026

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Wei Wei, Yanlin Shao*, Zhonggui Hu, Qing Wang, Fan Deng, Yu Huang and Kunpeng Zhao

School of Geosciences, Yangtze University, Wuhan, China

Edited by:
Wenguang Wang, Northeast Petroleum University, China

Reviewed by:
Wang Lihui, Chinese Academy of Sciences (CAS), China
Huan Li, Eötvös Loránd Research Network, Hungary
Guanzhou Chen, Wuhan University, China

* Correspondence: Yanlin Shao, 500171@yangtzeu.edu.cn

Received: 14 March 2024
Accepted: 17 May 2024
Published: 11 June 2024

Citation: Wei W, Shao Y, Hu Z, Wang Q, Deng F, Huang Y and Zhao K (2024) Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning. Front. Earth Sci. 12:1401026. doi: 10.3389/feart.2024.1401026

Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R² value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration.

Keywords: hyperspectral, dolomite content, hybrid feature selection, machine learning, outcrop

1 INTRODUCTION

Carbonate rocks are significant reservoir rocks that account for approximately 50% of petroleum reserves worldwide (Gaffey, 1987). Dolomite, one of the key mineral components in carbonate rocks, is an important indicator for assessing reservoir quality, understanding the Earth’s historic climate, and current climate changes. Traditional methods for analyzing dolomite content, which include X-ray diffraction (XRD), scanning electron microscopy (SEM), differential thermal analysis (DTA), thin-section analysis and staining, are primarily laboratory based. These methods are costly, time-consuming, and not suitable for real-time field applications. By contrast, hyperspectral techniques, effectively utilize the spectral differences between minerals for mineralogical analysis (van der Meer et al., 2012; Hecker et al., 2019). Thus, hyperspectral approaches have advantages in terms of speed, efficiency, cost, and non-destructiveness (Zaini et al., 2014; Hamedianfar et al., 2023). Commonly used hyperspectral devices for outcrop studies include ground-based hyperspectral imagers and field spectrometers, such as analytical spectral devices (ASD). ASD spectrometers are particularly favored for their portability and fine spectral resolution, which make them suitable for field analysis of rock mineral content. The unique advantages of hyperspectral analysis are related to the ability to provide abundant information. However, this also presents significant challenges for subsequent data processing and analysis (Rasti et al., 2020; Deepa et al., 2023).

Methods for estimating mineral content based on hyperspectral data can be primarily categorized into two groups: spectral knowledge-based and machine learning-based approaches. Spectral knowledge-based methods focus on extracting a limited but highly representative set of feature bands according to specific rules derived from the spectral characteristics of rocks or minerals. Statistical methods such as linear regression are then used for estimation. These hyperspectral techniques involve selecting mineral diagnostic bands, constructing features such as band ratio, mineral indices through various algebraic operations (Haest et al., 2012; Seo et al., 2023), and analyzing local waveform features including depth, width, symmetry, and area (Hebert, 2019; Kurz et al., 2022; Tan et al., 2023). These spectral knowledge-based methods effectively reduce the dimensionality of hyperspectral data and have clear physical and chemical significance (Kurz et al., 2012; Okyay et al., 2016). However, spectral knowledge-based methods depend significantly on substantial expert knowledge, particularly when constructing complex features like mineral spectral indices. A lack of expert knowledge could result in the omission of critical features, thereby adversely affecting the precision of the outcomes. In addition, these methods are typically used in conjunction with linear regression models, resulting in suboptimal performance when addressing nonlinear issues.

An alternative approach involves the integration of machine learning with hyperspectral data. Machine learning-based methods involve two key steps: feature selection/extraction and machine learning modeling. During feature selection/extraction, feature selection techniques preserve the physical characteristics of the original data and provide strong interpretability, making them popular for reducing the dimensionality of the hyperspectral data analysis. Feature selection techniques mainly include filter, wrapper, and embedded approaches. Filter methods select bands based on specific measurement criteria, such as correlation, information divergence, entropy, and mutual information (Zhou et al., 2022; Tan et al., 2023). Wrapper techniques select features by exploring various feature subsets and evaluating model performance; this process is independent of the model training. Embedded approaches are integrated with certain machine learning algorithms, including random forest (RF) and support vector machine (SVM), often have inherent evaluation metrics to select optimal features (Thomas and Gupta, 2020). Due to their consideration of training samples and feature interdependencies, embedded approaches typically yield superior model performance compared with other feature selection methods (Kumar, 2014; Thomas and Gupta, 2020). Algorithms frequently used in the modeling stage include partial least squares regression (PLSR) (Knox et al., 2015), ensemble learning (Carranza, 2015; Tan et al., 2020; Lin et al., 2022), SVM (Iglesias, 2020; Chatterjee et al., 2022), artificial neural networks (ANN) (Saikia et al., 2020), and deep learning (Zhang T. et al., 2023). Nonlinear models are widely regarded to outperform linear models in a variety of instances (Zhou et al., 2021; Sim et al., 2023). However, the significant potential of deep learning in geochemical analysis is currently hindered by limitations in data quantity (He et al., 2022; Dawson et al., 2023). The benefits of machine learning-based techniques are their powerful data processing and analytical capabilities, impressive analytical speed, independence from expert knowledge, and proficiency in addressing nonlinear challenges (Rodriguez-Galiano et al., 2018; Shirmard et al., 2022). Nonetheless, it is important to note that the efficacy of machine learning is highly correlated with the quality of the input features (Jia et al., 2013; Zhang Y. et al., 2023).

In this study, we propose a scheme for estimating dolomite content in carbonate rocks based on a combination of spectral knowledge and machine learning. This proposed method combines the advantages of spectral knowledge and machine learning to significantly improve the quality of the input data used in machine learning models. Moreover, our method preserves the advantages of machine learning-based approaches, allowing to effectively address nonlinear problems. The main contributions of this study are:

(1) Providing an efficient mechanism for hyperspectral feature selection. The hybrid method for feature selection is characterized by high accuracy, robust interpretability, and low computational cost

(2) Developing a scheme for estimating mineral content based on ASD spectral data. The scheme described in this article covers the entire process from data preprocessing to feature selection and modeling. This method offers a practical and efficient approach for on-site mineral content estimation.

2 DATA AND METHODS

2.1 Data

2.1.1 Study area

The study area lies in the eastern and northwestern regions of the Sichuan Basin, a substantial oil and gas basin in southwestern China. This basin is positioned to the northwest of the Yangtze platform. The stratigraphy of the Sichuan Basin is both comprehensive and diverse. From the Sinian to the Middle Triassic, the primary depositional environment was marine and dominated by carbonate rocks, By contrast, from the Upper Triassic to the Quaternary, terrestrial deposition primarily involved clastic rocks. This study primarily focuses on the marine sediments from the Sinian to the Triassic, which predominantly comprise carbonate rocks. To ensure sample representativeness, nine outcrops were selected as sampling sites, the specific locations of the sampling sites are depicted in Figure 1.

FIGURE 1 | Study area, nine outcrops as our sampling sites (map source provided by ArcGIS online).

2.1.2 Data acquisition

Data acquisition for this study comprised two tasks: fieldwork and laboratory work. Fieldwork encompassed three procedures: selecting the sampling locations, collecting hyperspectral data, and gathering rock samples. To enhance the applicability of the model in predicting dolomite content in field outcrops, the hyperspectral data of the training samples were collected on-site in the field. The mineral content of the training samples, which serve as labels for the training data, is ascertained through chemical analysis conducted in the laboratory. The detailed data acquisition process is as follows:.

(1) Selecting field sampling locations. Due to the complex surface conditions of the outcrops (e.g., varying degrees of weathering, smoothness, and the practicality of measuring rock samples), selecting sampling points requires careful consideration. Areas with minimal weathering and relatively smooth, flat surfaces were prioritized for sampling (Figure 2A).

(2) Collecting hyperspectral data. Hyperspectral data were collected using the Analytical Spectral Devices (ASD) FieldSpec3 Hi-Res spectrometer equipped with a contact probe and an integrated light source to minimize the effects of stray light. During field data collection, the spectrometer contact probe was aligned vertically with respect to the sampling point on the outcrop surface, as shown in Figure 2B. The spectral range was between 350 and 2,500 nm, and the spectral resolutions were 3 nm at 700 nm, 10 nm at 1,400 nm, and 10 nm at 2,100 nm. Each sampling point was measured three times, with each comprising an average of 10 scans. All data were subsequently averaged to obtain the final spectrum for that rock sample. A white reference panel was used for calibration every 5 min during the sampling process (Asadzadeh and Souza Filho, 2016).

(3) Gathering rock samples. Following the collection of field hyperspectral data, rock samples were chiseled from the sampling sites using a hammer (Figure 2C). The rock samples were then packed in bags and transported back to the laboratory for analysis (Figure 2D).

(4) Laboratory analysis: XRD analysis was conducted to determine the mineral compositions of the rock samples. The rock samples were ground into powders and analyzed using a SmartLab X-ray diffractometer.

FIGURE 2 | (A) One sample site (B) Field hyperspectral data collection (C) Rock sample collection location (D) One rock sample.

In this study, 206 carbonate rock samples were collected, and detailed mineral content data were obtained for each sample. The collected data were divided into two parts: one part was used for training the model, and the other part was used to validate the model and assess its accuracy.

2.2 Methods

2.2.1 Preprocessing

In this study, the data preprocessing involved outlier analysis and continuum removal. During the acquisition of field hyperspectral data, outliers may arise due to a variety of factors including mishandling, inadequate proximity of the instrument probe to the outcrop surface, and sensor malfunctions. These outliers have the potential to result in overfitting or underfitting by the machine learning algorithms, affecting the efficacy of the model. For outlier analysis, the mean spectrum was calculated for each band ±2 standard deviations for all samples. If 80% of the sample spectral value fell outside of this range, the sample was considered an outlier and removed.

After outlier deletion, continuum removal was executed to suppress the effect of background noise and accentuate subtle spectral variations (Clark and Roush, 1984; Baugh et al., 1998). The continuum removal method applied in this study was based on the approach proposed by Clark and Roush (1984). In addition to enhancing differences, the continuum removal process also normalized the data, facilitating comparison between different spectral curves.

2.2.2 Feature selection based on the mineral spectral knowledge (MSK)

MSK-based feature selection relies on mineral diagnostic bands. The diagnostic bands of dolomite in the near-infrared region are primarily attributed to the energy produced by the vibrations of atoms deviating from their equilibrium positions within the carbonate molecular group (Clark et al., 1990). Specifically, these bands are produced by overtones or combinations of the three fundamental vibration modes of icons (Hunt, 1977): (symmetric C–O stretching), (asymmetric C–O stretching) and (in-plane bending). Table 1 lists the diagnostic bands of carbonate rocks in the near-infrared region. Different regions exhibit diverse rock and mineral compositions; thus, a unified standard has not yet been established for the exact intervals of feature bands (Hunt, 1977; Gaffey, 1987; Zaini et al., 2014).

TABLE 1 | Diagnostic bands of carbonate rocks in near-infrared region.

Building on previous research and considering the spectral characteristics of samples from the study area, we employ a boxplot methodology to ascertain the range of feature bands for carbonate rock minerals. The specific steps are outlined below:

(1) Determination of the approximate positions of diagnostic bands. Considering the characteristics of the samples, we determined the approximate positions of the absorption bands specific to dolomite minerals in this region.

(2) Location of absorption valleys. The absorption valleys near the diagnostic bands determined in the preceding step were identified.

(3) Extraction of shoulder bands. The left and right shoulder wavelengths for each absorption valley (λs1 and λs2, respectively) were extracted (see Figure 3).

(4) Definition of interval boundaries. The boundaries of intervals were established via the boxplot analysis of shoulder wavelengths (Tukey, 1977). We establish the first quartile (Q1) of the left shoulder band and the third quartile (Q3) of the right shoulder band as the boundary bands for each interval. Here, Q1 represents the first quartile, indicating that 25% of the data are below this value, and Q3 represents the third quartile, indicating that 75% of the data set are below this threshold. This analytical method is designed to capture prevalent patterns across most samples, effectively minimizing the effect of outliers.

FIGURE 3 | Local waveform features, λm: the wavelength of the absorption valley’s bottom, λs1: left shoulder wavelength, λs2: right shoulder wavelength.

2.2.3 Feature selection based on random forest feature importance (RFFIM)

Feature selection based on RFFIM employs an RF algorithm to rank and select input features according to the criteria. In classification tasks, criteria such as Gini impurity or information gain are typically employed, whereas variance reduction is used in regression problems. Since this study involved a regression problem, we chose variance reduction as the evaluation criterion (Breiman, 2001; Verikas et al., 2011). The specific steps were as follows:

(1) Construction of decision trees. Assume a total of p training samples, each with features, random sampling was used to select training samples from the p training samples to construct a set of n decision trees, denoted as , ,…… .

(2) Contribution of each feature to variance reduction. The contribution of each feature to variance reduction caused by the split at each node j in each decision tree was calculated by Eq. 1:

where is the variance of the target variable in the parent node before the split, and represents the weighted variance after the split.

(3) Total contribution to variance reduction. The total contribution to variance reduction for each feature for all nodes j across all decision trees was determined by Eq. 2:

(4) Quantification of feature importance. The importance of feature was quantified as its total contribution to variance reduction, expressed as a proportion of the aggregate contribution from all features as Eq. 3:

2.2.4 Support vector regression (SVR)

SVR is a regression analysis method derived from the principles of SVM. The foundational concept of SVR is to seek an optimal balance between model accuracy and generalizability (Smola and Schölkopf, 2004; Iglesias, 2020). The primary aim of SVR is to identify a smooth model function that minimizes the discrepancy between the estimated and actual values. The linear form of this function is given by Eq. 4:

where represents the estimated value for the i-th sample, is the weight vector, is the input feature vector of the i-th sample with dimensions corresponding to the number of feature bands, and is the bias.

A central aspect of SVR is its focus on a subset of data points known as support vectors, which are determined by the ε-insensitive zone. This zone allows small estimation errors to be ignored while considering only significant deviations in model accuracy. In addition, SVR introduces slack variables (slack variables) to allow larger estimation errors (Bennett and Mangasaian, 1992). The objective function, The objective function is given as Eq. 5:

where is a regularization parameter that balances the model’s complexity and error tolerance, and represents the number of samples. The first part of the objective function aims to minimize the complexity of the model and prevent overfitting, while the second part penalizes significant estimation error.

In this study, a linear SVR was employed due to the limited number of available samples.

2.2.5 MSK-RFFIM-SVR scheme

In this study, a MSK-RFFIM-SVR scheme was developed for estimating dolomite content. In this scheme, MSK-RFFIM is used for feature selection in hyperspectral data, while the SVR algorithm is employed for inversion modeling. The workflow of this approach is illustrated in Figure 4, and the specific implementation steps were as follows:

(1) Preprocessing. The hyperspectral data were preprocessed via outlier deletion and continuum removal.

(2) Preliminary feature selection based on the MSK. After analyzing the spectral characteristics of dolomite minerals, box plots were employed to identify the spectral feature regions of the samples, thereby facilitating the preliminary screening of the original hyperspectral bands.

(3) Secondary feature selection based on the RFFIM. An RF algorithm was used to rank the importance of the preliminarily screened features, and the top 50 bands were selected.

(4) Modeling. The top 50 bands selected in the preceding step were used as input variables for the machine learning model to estimate the dolomite content. The results were evaluated to ensure the model’s effectiveness.

FIGURE 4 | The workflow of dolomite content estimation.

3 EXPERIMENTAL RESULTS

In this study, we have developed the MSK-RFFIM-SVM inversion scheme for estimating dolomite content and verified its effectiveness in comparison to other input variables and modeling methods. The input variables considered in the comparison ranged from Input V1 to Input V8, defined as follows: Input V1 retained all continuum-removed bands; Input V2 selected features based on the MSK; Input V3 utilized the top 50 features selected by RFFIM; Input V4 was based on our MSK-RFFIM method; Input V5 was the top 50 features selected based on correlation coefficients; Input V6 and Input V7 were obtained by transforming hyperspectral data using principal component analysis (PCA) and Kernel PCA (KPCA), respectively, and selecting the top 20 components for each; and Input V8 employs feature selection based on the local waveform characteristics, including the wavelength and reflectance of the bottom of the absorption valley’s, the double-shoulder wavelengths, and the width, height, area, and symmetry of each absorption valley. The detailed extraction methods can be found in Hecker et al. (2019). Table 2 presents detailed information on these eight input variables. The machine learning algorithms included in the comparison were PLSR, RF, SVM and ANN (multilayer perceptron). All algorithms in this experiment were implemented using the Python programming language.

TABLE 2 | Description of eight input variables.

3.1 Model parameters and evaluation metrics

To select the best model hyperparameters, we aimed to minimize the number of hyperparameters requiring tuning, thereby reducing model complexity, and enhancing its generalizability. In this study, the ‘GridSearchCV’ method from the ‘sklearn’ library was employed to determine the optimal hyperparameters of the model. ‘GridSearchCV’ integrates grid search with cross-validation to identify the most accurate parameters within a specified range, traversing all potential combinations. Based on the hyperparameter tuning range presented in Table 3, ‘GridSearchCV’ employed a k-fold cross-validation approach in which the training set was partitioned into k non-overlapping subsets. In each iteration, k−1 subsets were selected for the training set, and the remaining subset served as the validation set for subsequent testing of the trained model. After computing the average score over k iterations, the hyperparameter combination with the highest mean score was selected as the optimal choice. In this study, k was set to 5.

TABLE 3 | Hyperparameters and optimization range.

The evaluation metrics in this study were the coefficient of determination (R²) and root mean square error (RMSE), as shown in Eqs 6, 7:

where denotes the estimation value, denotes the mean measured value, m represents the number of samples for evaluation. The value of R² ranges from −1 to 1, with values closer to 1 indicating higher estimated accuracy. RMSE is a commonly used accuracy metric; a larger RMSE value indicates a greater disparity between the estimated and actual values, thus signifying poorer accuracy.

3.2 Feature intervals based on the MSK

After outlier removal, 203 samples were retained for subsequent work. Based on previous studies, the dolomite mineral exhibits seven significant features in the near-infrared range (see Section 2.2.2). However, based on the spectral range of our instrument, features around 2,500–2,550 nm were not included, and the characteristics near 2,248 and 1740 nm were extremely weak, bordering on negligible. Therefore, we excluded those three features. Consequently, the characteristic spectral features of dolomite minerals in the study area were primarily distributed around 1900, 2000, 2,160, and 2,350 nm (see Figure 5). Subsequently, we located the corresponding absorption valleys at these four wavelengths and extracted the left and right shoulder wavelengths for boxplot analysis. As depicted in Figure 6. The final feature intervals were determined as 1839–1870,1950–2009, 2,110–2,183 and 2,133–2,386 nm with a total of 416 bands.

FIGURE 5 | ASD spectrum of carbonate rock samples with different dolomite contents; solid line denotes the original spectrum; dashed line describes the continuum-removed spectrum; black vertical lines represented four diagnostic bands; the percentages in the legend indicate the content of dolomite of samples.

FIGURE 6 | Box diagrams for left and right shoulder wavelengths of four absorption valleys of samples. (A–D) respectively represent the absorption valleys near 1,900 nm, 2,000 nm, 2,160 nm, and 2,350 nm.

3.3 Modeling evaluation and analysis

In total, 203 samples were used in the modeling process. These samples were randomly divided into a test set and a training set, with 80% (162 samples) designated as training samples and 20% (41 samples) as testing samples.

Table 4 and Figure 7 show the results of the accuracy evaluation for dolomite estimation based on eight features extracted from the continuum-removed data and four machine learning models. In Table 4, our method, along with the optimal and average accuracy values, is highlighted in bold font.

TABLE 4 | Accuracy assessment of dolomite content estimation based on continuum-removal data.

FIGURE 7 | Accuracy assessment of dolomite content estimation based on continuum-removed data, (A): Test R² assessment (B): RMSE assessment.

First, regarding input variables, significant disparities in performance were observed among the eight input variables across the four machine learning models (Table 4; Figure 7). For the PLSR model, the highest accuracy was achieved when using Input V7, while the lowest accuracy was observed with Input V3. Conversely, for the RF model, the highest and lowest accuracies were, respectively, observed with Input V8 and Input V7. The results obtained using the SVR and ANN models were consistent: Input V4 yielded the highest accuracy, whereas Input V3 resulted in the lowest accuracy. The average test R²and RMSE values were calculated for all input features and are presented in the two rightmost columns of Table 4. The average Test R²values for the eight input variables decreased in the following order: Input V4 > Input V5 > Input V2 > Input V8 > Input V7 > Input V6 > Input V1 > Input V3. The results indicate that Input V4 achieved the best overall performance among all input features.

Next, comparing the different machine learning models, the four models exhibited different performances when combined with different input variables. When selecting Input V1, Input V3, and Input V8, the RF model demonstrated the highest accuracy, while the ANN model had the lowest accuracy. With Input V2, the SVR model displayed the highest accuracy, whereas the ANN model had the lowest. For Input V4 and Input V5, the SVR model achieved the highest accuracy, and the PLSR model result in the lowest accuracy. With Input V6 and Input V7, the PLSR model achieved the highest accuracy, while RF results in the lowest. Although the test R² value of PLSR was not always the lowest among the four models, its RMSE value was significantly higher than those of the other three models, as shown in Figures 7A, B, indicating that PLSR was not suitable for estimating dolomite mineral content in this study. Furthermore, the average test R² value for all input variables was calculated across the four models (see Table 4), resulting in the following order in average test R² accuracy: SVR > ANN > RF > PLSR. The results suggest that among the tested models, SVR achieved best average accuracy and thus the best overall performance.

In summary, by combining the good feature selection performance of MSK-RFFIM with the overall accuracy of SVR, the integrated MSK-RFFIM-SVR model demonstrated exceptional efficacy for estimating the dolomite content in carbonate rocks. Figure 8 shows a detailed comparison of the estimation accuracies for each input variable when integrated with the SVR model. The MSK-RFFIM-SVR approach proposed in this study achieved a test R² of 0.917 and an RMSE of just 0.122, superior to other methods.

FIGURE 8 | Comparison of the estimated results of all input variables based on continuum-removed data and SVM algorithm with the XRD data of the test samples, (A–H) corresponds to Input V1- Input V8 respectively.

4 DISCUSSION

4.1 Comparison of original data and continuum-removed data

The purpose of continuum removal is to enhance information while simultaneously eliminating background noise. A correlation analysis was conducted to compare the dolomite content based on both the original and continuum-removed hyperspectral data (Figure 9). As shown in Figure 9A, the original hyperspectral data was influenced by environmental factors and background noise and showed relatively low variance in reflectance. Consequently, the correlation coefficients between the reflectance of all bands and the dolomite content were relatively uniform. However, the correlations between continuum-removed data and the dolomite content exhibited more pronounced variations (Figure 9B). Therefore, continuum removal improved the quality of the hyperspectral data and facilitated subsequent feature selection and modeling.

FIGURE 9 | (A,B) are correlation analysis of original data and continuum-removed data with the dolomite content respectively, OB: original hyperspectral data, CR: continuum-removed data.

4.2 Feature selection

The hybrid MSK-RFFIM feature selection method proposed in this paper leverage the strengths of both constituent methods. As shown in Table 4, the MSK-RFFIM method resulted in enhanced accuracy compared with either the MSK or RFFIM method alone. Previous studies have also indicated that hybrid feature selection methods can enhance the overall effectiveness of feature selection (Chen et al., 2023; Wang et al., 2023). The MSK-RFFIM method was further analyzed using the top 20 bands from Input V3, Input V4, and Input V5 (refer to Table 5). Some selected bands of InputV3 were situated below 1800 nm; the reflectance of these bands is likely related to the water molecules present within the minerals. Similarly, for Input V5, a significant number of the first 20 features included bands above 2,400 nm. Given that the detection limit of the employed equipment was 2,500 nm, the reflectance spectra in the proximal wavelength range (2,450–2,500 nm) tend to exhibit a sawtooth pattern, suggesting that they may be unreliable due to noise or intrinsic equipment limitations. Therefore, using the MSK method as a preliminary screening tool effectively eliminated irrelevant features, thereby reducing interference in subsequent models. Further comparison between the MSK and MSK-RFFIM appoaches (as seen in Table 4; Figure 8, Input V2 and Input V4) indicated a notable improvement in the average test R² for MSK-RFFIM (0.833 compared with 0.789 for MSK). This improvement is likely attributed to the ability of the RF algorithm to eliminate unimportant features and reduce the effect of redundant features (Genuer, 2010).

TABLE 5 | The Top 20 features of input variables and their ranking index scores.

4.3 Model sensitivity

In addition to accuracy, we also considered the sensitivity of the model hyperparameters. High sensitivity to model hyperparameters complicates the tuning of machine learning algorithms; minor adjustments can markedly affect outcomes, potentially undermining the generalization ability of the model. Here, we conducted a sensitivity analysis of model hyperparameters based on the optimal approach. The hyperparameter sensitivity of the model was evaluated based in terms of the estimation accuracy of different hyperparameter combinations with the four machine learning algorithms.

In Figure 10, the vertical axis represents the ‘mean_test_score’ while the horizontal axis represents the different hyperparameter combinations for each machine learning algorithm. The ‘mean_test_score’ represents the average accuracy from a 5-fold cross-validation on training samples using the ‘GridSearchCV’ algorithm. For PLSR and SVM, optimization focused on a single hyperparameter: ‘n_components’ for PLSR and ‘C′ for SVM (Figures 10A, C). By contrast, RF and ANN required adjustments across multiple hyperparameters; the hyperparameter tuning ranges for each are detailed in Table 3. Consequently, RF and ANN possessed a total of 84 hyperparameter combinations, which form the horizontal axis (Figures 10B, D). Figure 10 illustrates that both the PLSR and RF models exhibited minimal sensitivity to hyperparameter variations. The SVM model showed unstable performance when the ‘C′ value was low, but stabilized when the ‘C′ hyperparameter reached a certain critical value (Wang et al., 2019). Conversely, the ANN model exhibited the highest sensitivity to the hyperparameters, consistent with previous finding (Rodriguez-Galiano et al., 2015).

FIGURE 10 | Sensitivity analysis of model hyperparameters. (A–D) represent the effects of hyperparameter variations in PLSR, RF, SVR, and ANN on accuracy.

In summary, PLSR was not favorable due to its significant error. The RF model stood out for its stability and simplicity in parameter tuning. However, the accuracy of the RF model was lower compared with other non-linear models. The ANN algorithm achieved higher accuracy, but demonstrated notable sensitivity to the model parameters, complicating hyperparameter optimization. Overall, the SVR model achieved the optimal balance among accuracy, parameter count, and parameter sensitivity. It mainly optimizes the ‘C′ parameter, with performance plateauing beyond a certain threshold. Therefore, the experimental results suggest that the SVR algorithm is the optimal choice.

4.4 Limitations and future work

The primary focus of this study was carbonate rocks, other rock types were not considered. Furthermore, during hyperspectral sampling, sampling points with relatively fresh surfaces were specifically chosen, while severely weathered areas intentionally avoiding to prevent the known effect of weathering on spectral data (Shin et al., 2019). Future work will involve samples taken from weathered surfaces and comparative analysis of the accuracy of different features and modeling algorithms between weathered and fresh surfaces.

5 CONCLUSION

This paper presents the MSK-RFFIM-SVR scheme for estimating mineral content based on field-collected hyperspectral data. In this scheme, following the preprocessing of the hyperspectral data, a hybrid MSK-RFFIM approach is used for feature selection. The SVR algorithm is then used to inversely estimate the dolomite content. Compared with conventional machine learning methods, this scheme more effectively selects key features of the target mineral, significantly enhancing model accuracy. Our estimation results for dolomite samples produced a test R² value of 0.91 and an RMSE of 0.122. The proposed approach is especially advantageous for field outcrop exploration and can provide high-precision quantitative data to support field geologists in identifying distribution of outcrop minerals, understanding the reservoir characteristics, and detecting changes in ancient environments. The carbonate rock samples in this study mainly consisted of dolomite and calcite. Future research will explore more complex mineral compositions and surface environmental conditions in carbonate rock outcrops.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

AUTHOR CONTRIBUTIONS

WW: Investigation, Methodology, Validation, Writing–original draft, Writing–review and editing. YS: Conceptualization, Investigation, Validation, Writing–review and editing. ZH: Methodology, Writing–review and editing. QW: Writing–review and editing. FD: Writing–review and editing. YH: Data curation, Writing–review and editing. KZ: Data curation, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by CNPC (China National Petroleum Corporation) Technology Project., grant number 2021DJ0402.

ACKNOWLEDGMENTS

Thanks to the Research Institute of Petroleum Exploration and Development, CNPC for providing the funding for this project. Special appreciation to Qihong Zeng, Youyan Zhang and Zhiguo Ma from the Research Institute of Petroleum Exploration and Development, CNPC for their assistance in this project.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Asadzadeh, S., and Souza Filho, C. R. (2016). A review on spectral processing methods for geological remote sensing. Int. J. Appl. Earth Observation Geoinformation 47, 69–90. doi:10.1016/j.jag.2015.12.004

Baugh, W. M., Kruse, F. A., and Atkinson, W. W. (1998). Quantitative geochemical mapping of ammonium minerals in the Southern Cedar Mountains, Nevada, using the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 65 (3), 292–308. doi:10.1016/S0034-4257(98)00039-X

Bennett, K. P., and Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization methods and software 1 (1), 23–24. doi:10.1080/10556789208805504

Breiman, L. (2001). Random forests. Mach. Leaning 45, 5–32. doi:10.1023/A:1010933404324

Carranza, E. J. M., and Laborte, A. G. (2015). Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Comput. Geosciences 74 (10), 60–70. doi:10.1016/j.cageo.2014.10.004

Chatterjee, S., Mastalerz, M., Drobniak, A., and Karacan, C. Ö. (2022). Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA. Int. J. Coal Geol. 259, 104054. doi:10.1016/j.coal.2022.104054

Chen, L., Sui, X., Liu, R., Chen, H., Li, Y., Zhang, X., et al. (2023). Mapping alteration minerals using ZY-1 02D hyperspectral remote sensing data in coalbed methane enrichment areas. Remote Sens. 15 (14), 3590. doi:10.3390/rs15143590

Clark, R. N., King, T. V., Klejwa, M., Swayze, G. A., and Vergo, N. (1990). High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. 95 (B8), 12653–12680. doi:10.1029/JB095iB08p12653

Clark, R. N., and Roush, T. L. (1984). Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J. Geophys. Res. Solid Earth 89 (B7), 6329–6340. doi:10.1029/JB089iB07p06329

Dawson, H. L., Dubrule, O., and John, C. M. (2023). Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification. Comput. Geosciences 171, 105284. doi:10.1016/j.cageo.2022.105284

Deepa, C., Shetty, A., and Narasimhadhan, A. V. (2023). Performance evaluation of dimensionality reduction techniques on hyperspectral data for mineral exploration. Earth Sci. Inf. 16 (1), 25–36. doi:10.1007/s12145-023-00956-2

Gaffey, S. J. (1987). Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 um): Anhydrous carbonate minerals. J. Geophys. Res. 92 (B2), 1429–1440. doi:10.1029/JB092iB02p01429

Genuer, R., Poggi, J. M., and Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognit. Lett. 31 (3), 2225–2236. doi:10.1016/j.patrec.2010.03.014

Haest, M., Cudahy, T., Laukamp, C., and Gregory, S. (2012). Quantitative mineralogy from infrared spectroscopic data. I. Validation of mineral abundance and composition scripts at the Rocklea Channel iron deposit in western Australia. Econ. Geol. 107 (2), 209–228. doi:10.2113/econgeo.107.2.209

Hamedianfar, A., Laakso, K., Middleton, M., Törmänen, T., Köykkä, J., and Torppa, J. (2023). Leveraging high-resolution long-wave infrared hyperspectral laboratory imaging data for mineral identification using machine learning methods. Remote Sens. 15 (19), 4806. doi:10.3390/rs15194806

He, Y., Zhou, Y., Wen, T., Zhang, S., Huang, F., Zou, X., et al. (2022). A review of machine learning in geochemistry and cosmochemistry: method improvements and applications. Appl. Geochem. 140, 105273. doi:10.1016/j.apgeochem.2022.105273

Hebert, B., Baron, F., Robin, V., Lelievre, K., Dacheux, N., Szenknect, S., et al. (2019). Quantification of coffinite (USiO4) in roll-front uranium deposits using visible to near infrared (Vis-NIR) portable field spectroscopy. J. Geochem. Explor. 199, 53–59. doi:10.1016/j.gexplo.2019.01.003

Hecker, C., van Ruitenbeek, F. J., van der Werff, H. M., Bakker, W. H., Hewson, R. D., and van der Meer, F. D. (2019). Spectral absorption feature analysis for finding ore: a tutorial on using the method in geological remote sensing. IEEE Geoscience Remote Sens. Mag. 7 (2), 51–71. doi:10.1109/MGRS.2019.2899193

Hunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 42 (3), 501–513. doi:10.1190/1.1440721

Iglesias, C., Antunes, I., Albuquerque, M., Martínez, J., and Taboada, J. (2020). Predicting ore content throughout a machine learning procedure – an Sn-W enrichment case study. J. Geochem. Explor. 208, 106405. doi:10.1016/j.gexplo.2019.106405

Jia, X., Kuo, B. C., and Crawford, M. M. (2013). Feature mining for hyperspectral image classification. Proc. IEEE 101 (3), 676–697. doi:10.1109/JPROC.2012.2229082

Knox, N. M., Grunwald, S., McDowell, M. L., Bruland, G. L., Myers, D. B., and Harris, W. G. (2015). Modelling soil carbon fractions with visible near-infrared (VNIR) and mid-infrared (MIR) spectroscopy. Geoderma 239–240, 229–239. doi:10.1016/j.geoderma.2014.10.019

Kumar, V. (2014). Feature selection: a literature review. Smart Comput. Rev. 4 (3), 211–229. doi:10.6029/smartcr.2014.03.007

Kurz, T. H., Buckley, S. J., and Howell, J. A. (2012). Close-range hyperspectral imaging for geological field Studies: workflow and methods. Int. J. Remote Sens. 34 (5), 1798–1822. doi:10.1080/01431161.2012.727039

Kurz, T. H., Miguel, G. S., Dubucq, D., Kenter, J., Miegebielle, V., and Buckley, S. J. (2022). Quantitative mapping of dolomitization using close-range hyperspectral imaging: kimmeridgian carbonate ramp, Alacón, NE Spain. Geosphere 18 (2), 780–799. doi:10.1130/GES02312.1

Lin, N., Liu, H., Li, G., Wu, M., Li, D., Jiang, R., et al. (2022). Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image. Open Geosci. 14 (1), 1444–1465. doi:10.1515/geo-2022-0436

Okyay, Ü., Khan, S., Lakshmikantha, M., and Sarmiento, S. (2016). Ground-based hyperspectral image analysis of the lower Mississippian (Osagean) Reeds Spring Formation rocks in southwestern Missouri. Remote Sens. 8 (12), 1018. doi:10.3390/rs8121018

Rasouli, B. M., and Tangestani, M. H. (2020). A new band ratio approach for discriminating calcite and dolomite by ASTER imagery in arid and semiarid regions. Natural Resources Research 29 (5), 2949–2965. doi:10.1007/s11053-020-09648-w

Rasti, B., Hong, D., Hang, R., Ghamisi, P., Kang, X., Chanussot, J., et al. (2020). Feature extraction for hyperspectral imagery: the evolution from shallow to deep: overview and toolbox. IEEE Geoscience Remote Sens. Mag. 8 (4), 60–88. doi:10.1109/MGRS.2020.2979764

Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. J. O. G. R. (2015). Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol. Rev. 71, 804–818. doi:10.1016/j.oregeorev.2015.01.001

Rodriguez-Galiano, V. F., Luque-Espinar, J. A., Chica-Olmo, M., and Mendes, M. P. (2018). Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods. Sci. Total Environ. 624, 661–672. doi:10.1016/j.scitotenv.2017.12.152

Saikia, P., Baruah, R. D., Singh, S. K., and Chaudhuri, P. K. (2020). Artificial neural networks in the domain of reservoir characterization: a review from shallow to deep models. Comput. Geosciences 135, 104357. doi:10.1016/j.cageo.2019.104357

Seo, J., Yu, J., and Wang, L. (2023). Indicator spectral bands and logistic models for detecting diesel and gasoline polluted soils based on close-range hyperspectral image data. IEEE Trans. Geoscience Remote Sens. 61, 1–13. doi:10.1109/TGRS.2023.3264967

Shin, J. H., Yu, J., Wang, L., Kim, J., Koh, S. M., and Kim, S. O. (2019). Spectral responses of heavy metal contaminated soils in the vicinity of a hydrothermal ore deposit: a case study of Boksu Mine, South Korea. IEEE Trans. Geoscience Remote Sens. 57 (6), 4092–4106. doi:10.1109/TGRS.2018.2889748

Shirmard, H., Farahbakhsh, E., Muller, R. D., and Chandra, R. (2022). A review of machine learning in processing remote sensing data for mineral exploration. Remote Sens. Environ. 268, 112750. doi:10.1016/j.rse.2021.112750

Sim, J., Dixit, Y., Mcgoverin, C., Oey, I., Frew, R., Reis, M. M., et al. (2023). Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification. Food Res. Int. 174, 113518. doi:10.1016/j.foodres.2023.113518

Smola, A. J., and Schölkopf, B. (2004). A tutorial on support vector regression. Statistics Comput. 14 (3), 199–222. doi:10.1023/B:STCO.0000035301.49549.88

Tan, K., Chen, L., Wang, H., Liu, Z., Ding, J., and Wang, X. (2023). Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics. J. Environ. Manag. 347, 119196. doi:10.1016/j.jenvman.2023.119196

Tan, K., Wang, H., Chen, L., Du, Q., Du, P., and Pan, C. (2020). Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987. doi:10.1016/j.jhazmat.2019.120987

Thomas, R. N., and Gupta, R. (2020). “Feature selection techniques and its importance in machine learning: a survey,” in 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), (Bhopal, India, 22-23 February 2020) ( IEEE), 1–6.

Tukey, J. W. (1977). Exploratory data analysis. MA: Addison-Wesley.

van der Meer, F. D., van der Werff, H. M., van Ruitenbeek, F. J., Hecker, C. A., Bakker, W. H., Noomen, M. F., et al. (2012). Multi- and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Observation Geoinformation 14 (1), 112–128. doi:10.1016/j.jag.2011.08.002

Verikas, A., Gelzinis, A., and Bacauskiene, M. (2011). Mining data with random forests: a survey and results of new tests. Pattern Recognit. 44 (2), 330–349. doi:10.1016/j.patcog.2010.08.011

Wang, S., Chen, Y., Wang, M., and Li, J. (2019). Performance comparison of machine learning algorithms for estimating the soil salinity of salt-affected soil using field spectral data. Remote Sens. 11 (22), 2605. doi:10.3390/rs11222605

Wang, Y., Niu, R., Hao, M., Lin, G., Xiao, Y., Zhang, H., et al. (2023). A method for heavy metal estimation in mining regions based on SMA-PCC-RF and reflectance spectroscopy. Ecol. Indic. 154, 110476. doi:10.1016/j.ecolind.2023.110476

Zaini, N., van der Meer, F., and van der Werff, H. (2014). Determination of carbonate rock chemistry using laboratory-based hyperspectral imagery. Remote Sens. 6 (5), 4149–4172. doi:10.3390/rs6054149

Zhang, T., Fu, Q., Tian, R., Zhang, Y., and Sun, Z. (2023a). A spectrum contextual self-attention deep learning network for hyperspectral inversion of soil metals. Ecol. Indic. 152, 110351. doi:10.1016/j.ecolind.2023.110351

Zhang, Y., Wei, L., Lu, Q., Zhong, Y., Yuan, Z., Wang, Z., et al. (2023b). Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images. Environ. Pollut. 320, 120962. doi:10.1016/j.envpol.2022.120962

Zhou, M., Zou, B., Tu, Y., Feng, H., He, C., Ma, X., et al. (2022). Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area. Geocarto Int. 37 (26), 13248–13267. doi:10.1080/10106049.2022.2076921

Zhou, W., Yang, H., Xie, L., Li, H., Huang, L., Zhao, Y., et al. (2021). Hyperspectral inversion of soil heavy metals in Three-River source region based on random forest model. Catena 202, 105222. doi:10.1016/j.catena.2021.105222

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors declare that this study received funding from CNPC. The funder had the following involvement in the study: study design, data collection and analysis, decision to publish, and preparation of the manuscript.

Copyright © 2024 Wei, Shao, Hu, Wang, Deng, Huang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 13 June 2024
doi: 10.3389/feart.2024.1404178

Paleoenvironment and shale gas potential of the Carboniferous Dawuba and the Cambrian Niutitang shales in the Upper Yangtze Platform, South China

Ting Wang1,2,³, Zhengjian Xu4,5,6,⁷*, Kun Yuan2,3,⁸, Xunlian Wang¹ and Mingna Ge2,3,⁸

¹School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, China

²Oil and Gas Survey, China Geological Survey, Ministry of Natural Resources, Beijing, China

³The Key Laboratory of Unconventional Oil and Gas, China Geological Survey, Beijing, China

⁴School of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing, China

⁵Chongqing Institute of Geology and Mineral Resources, Chongqing, China

⁶Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education, Wuhan, China

⁷Key Laboratory of Sedimentary Basin and Oil and Gas Resources, Ministry of Natural Resources, Chengdu, China

⁸School of Energy Resources, China University of Geosciences (Beijing), Beijing, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Ruiliang Guo, Xi’an Shiyou University, China
Ximeng Wang, Texas A&M University, United States
Tao Hu, China University of Petroleum, China

* Correspondence: Zhengjian Xu, xumou08@sina.com

Received: 20 March 2024
Accepted: 30 April 2024
Published: 13 June 2024

Citation: Wang T, Xu Z, Yuan K, Wang X and Ge M (2024) Paleoenvironment and shale gas potential of the Carboniferous Dawuba and the Cambrian Niutitang shales in the Upper Yangtze Platform, South China. Front. Earth Sci. 12:1404178. doi: 10.3389/feart.2024.1404178

Marine shale gas has emerged as a prominent unconventional petroleum resource in recent years, known for its abundant reserves and energy potential. Based on the database of geochemical, mineralogy and physical lab measurements, this study investigates the paleoenvironmental conditions and shale gas potential of the Carboniferous Dawuba and Cambrian Niutitang shales in the Upper Yangtze Platform, South China. Analysis of the paleoclimate and water conditions reveals that the Dawuba shale was deposited under a warm and arid climate with reducing conditions that favored organic matter (OM) preservation, transitioning towards marine conditions with increasing salinity. The Niutitang shale experienced a cooler, arid climate with prevalent saltwater and reducing conditions, also conducive to OM preservation. Both formations have reached the post-mature stage, displaying good to excellent source rock potential. The Dawuba shales are characterized by Type II₂ kerogens, while the Niutitang shales predominantly contain Type I kerogens, indicating high gas generation potentials for both. The formations are composed of mixed and argillaceous shales, exhibiting ultra-low porosity and permeability but featuring development of dissolution pores, OM pores, and micro-fractures essential for gas storage. Comparative analysis shows the Dawuba shales have superior BET-specific surface areas, total pore volumes, and average pore diameters than the Niutitang shales. However, gas contents in both formations are relatively low, underscoring the necessity for further research on shale gas preservation conditions. The Qiannan Depression in Guizhou, particularly the Shangyuan and Zongdi areas of the Dawuba Formation, are identified as promising regions for shale gas exploration due to favorable geological characteristics. This study highlights the significant shale gas potential in the Upper Yangtze Platform and calls for focused research to optimize exploration and extraction efforts.

Keywords: paleoenvironment, Carboniferous Dawuba Formation, Cambrian Niutitang Formation, Upper Yangtze Platform, shale gas potential

1 INTRODUCTION

Mudrocks, encompassing sedimentary rocks such as shale, claystone, mudstone, and siltstone, are characterized by particles smaller than 62.5 μm and constitute more than 50% of sedimentary rock types (Milliken, 2014; Wang X. X. et al., 2019; Xu et al., 2019; Xu Z. J. et al., 2022; Xu Z. J. et al., 2022; Hu et al., 2022; Wang and Cheng, 2023; Wang et al., 2023). While “shale” is sometimes narrowly defined to describe visibly laminated, fissile varieties of sedimentary rock, this research adopts a broader application of the term. Here, “shale” refers to the entire class of fine-grained layered sedimentary rocks, and the term is used interchangeably with “mudrock” where appropriate (Boggs, 2006). Notably, shale represents a significant portion of the Earth’s depositional record, accounting for approximately two-thirds, and covers a quarter of the continental landmass (Garrels and Mackenzie, 1969; Blatt, 1982; Jin et al., 2014).

Shale gas, the leading unconventional petroleum resource, is generated and stored in fine-grained shale, which serves as both the source rock and reservoir in the gas shale system. Shale gas is primarily found in the absorbed phase on clay and organic matter (OM) surfaces, the free phase within pore spaces and fractures, and in low abundance as the dissolved phase in residual hydrocarbons or water, and produced by horizontal drilling and multi-stage hydraulic fracturing (Curtis, 2002; Jarvie et al., 2007). In China, organic-rich shales have been identified in diverse sedimentary environments, including marine (the Silurian Longmaxi Formation shale in the Sichuan Basin), lacustrine (the Cretaceous Qingshankou Formation shale in the Songliao Basin), and transitional environments (the Permian Shanxi Formation shale in the Ordos Basin) (Zou et al., 2022).

Since the year 2000, global shale gas production has seen a steady increase, reaching 840×109 m3 in 2022 and comprising 20.9% of the world’s natural gas production. The United States, benefiting from advancements in hydraulic fracturing technology, reported a significant contribution of 795×109 m3 to its natural gas production in 2022, representing 73% of the national total. Meanwhile, shale gas production in China, commencing in 2012, has reached over 25×109 m3 by 2023, accounting for 10.7% of its natural gas output. Noteworthy industrial breakthroughs in shale gas exploration and development within China have predominantly occurred in the marine shales of the Ordovician Wufeng and the Silurian Longmaxi Formations in Weiyuan, Zhaotong, Jiaoshiba, and Fuling areas in Sichuan Basin (Li et al., 2015; Wu et al., 2016; Yuan et al., 2018; Zhang, 2019; Ge et al., 2020; Wang, 2020; Yang et al., 2021; Ge et al., 2024).

As shale gas development and production progress in the Sichuan Basin, it is crucial to prioritize the selection of new target layers and favorable zones. In 2023, Well Z201, tapped into the Cambrian Qiongzhusi Formation in the Southern Sichuan Basin, demonstrated the productivity potential of the region, with a single-well shale gas output reaching 73.88×104 m³ per day. The Qiongzhusi shale is geologically equivalent to the Niutitang shale found in the Chongqing and Guizhou areas. Field investigations and drilling activities have indicated that the Cambrian Niutitang Formation and the Carboniferous Dawuba Formation, hereafter referred to as Niutitang and Dawuba shales, in Guizhou and Chongqing are promising strategic exploration zones for marine shale gas on the periphery of the Sichuan Basin (Li et al., 2015; Wu et al., 2016; Yuan et al., 2018; Zhang, 2019; Ge et al., 2020; Wang, 2020; Yang et al., 2021; Ge et al., 2024). These formations feature thick shale layers, up to 200 m, making them primary targets for further exploration (Li et al., 2015; Wu et al., 2016; Yuan et al., 2018; Zhang, 2019; Ge et al., 2020; Wang, 2020; Yang et al., 2021; Ge et al., 2024). Additionally, these shales are characterized by relatively high OM abundance and thermal maturity, which are critical factors for successful shale gas extraction. However, the geological settings in Guizhou and Chongqing are more complex compared to the central Sichuan Basin. These regions have experienced multi-stage tectonic evolution, prolonged periods of uplifting, the presence of numerous faults, and higher levels of OM thermal maturity (Li et al., 2015; Wu et al., 2016; Yuan et al., 2018; Zhang, 2019; Ge et al., 2020; Wang, 2020; Yang et al., 2021; Ge et al., 2024). Such complexity poses significant challenges for shale gas exploration. Consequently, there is a pressing need for more comprehensive studies focusing on source rocks and reservoir characteristics to better understand and exploit these resources effectively.

This research aims to analyze the shales within the Cambrian Niutitang Formation and Carboniferous Dawuba Formation in Guizhou and Chongqing, situated in the Upper Yangtze Platform. The study’s objectives include reconstructing the paleoenvironments, evaluating source rock characteristics, shale reservoir properties, and gas content to offer insights into the shale gas potential of the Cambrian and Carboniferous periods in the Upper Yangtze Platform. These findings are intended to guide unconventional hydrocarbon exploration efforts in analogous regions.

2 GEOLOGICAL SETTINGS

The Upper Yangtze Platform, situated in the western part of the Yangtze Platform in South China, encompasses a vast area including eastern Sichuan, the Chongqing area, the majority of Guizhou, western parts of Hubei and Hunan, and northern Yunnan province (Tan et al., 2013) (Figure 1). Notably, southeastern Chongqing, geographically positioned within the eastern Sichuan Basin, plays a pivotal role in the Upper Yangtze plate due to its location in the transitional zone between the Central Sichuan Uplift and the Central Guizhou Uplift (Li et al., 2015; Wu et al., 2016; Zhang, 2019; Wang, 2020). Further south, the Qiannan Depression in South Guizhou Province is encircled by significant geological features: the Guiyang-Zhenyuan fault zone and the Qianzhong uplift to the northwest, the Tongren-Sandu fault zone and the Xuefengshan uplift to the east and southeast, and the Ziyun-Luodian fault zone to the southwest (Wu et al., 2012; Lu, 2019).

FIGURE 1 | Location of the Yangtze block in SW China (A). Locations of studied wells in Chongqing and Guizhou (B). (The figure (B) was modified after Wang et al. (2016)).

During the Ediacaran-Cambrian transition, the Yangtze Block, bordered by two narrow margin-slope zones along the northern and south-southwest flanks (Chen et al., 2009), experienced significant geological changes influenced by global continental rearrangements. Specifically, these changes included the rifting of Laurentia from western Gondwana (Meert, 2003) and the simultaneous amalgamation of Australia with East Antarctica and eastern Gondwana, which coincided with the collision between East and West Gondwana (Kirschvink, 1992; Meert, 2003). These tectonic events indirectly influenced sedimentation patterns on the Yangtze Platform (Goldberg et al., 2007). At this time, much of the Yangtze Block was covered by extensive carbonate platforms. These platforms were subsequently submerged by a deep muddy shelf system at the onset of the Early Cambrian, a result of a global marine transgression known as the “Niutitang Event” (Steiner et al., 2001). This significant marine transgression during the early Cambrian period led to the widespread deposition of thick black shales in restricted marine environments across the region (Li et al., 2015; Wu et al., 2016; Zhang, 2019; Wang, 2020). The Upper Yangtze Platform functioned as a pericontinental marine shelf during this period. It was bordered by a paleocontinent to the west and featured a gradient of depositional environments extending southeastward. This gradient included a marine platform, a restricted marine basin, a former island arc system, and a deep marine setting (Tan et al., 2014; Tan et al., 2015). The early Cambrian shales of the Niutitang Formation (also known as the Qiongzhusi Formation shales or Jiulaodong Formation shales in the southwestern part of the Sichuan Basin) consisted of lower siliceous successions and upper black shale successions. These formations were extensively deposited around the northern and southern flanks of the Yangtze Platform, establishing the Lower Cambrian black shales as one of the principal source rocks and shale gas reservoirs on the Upper Yangtze Platform.

The Early Carboniferous was a pivotal period marked by the transition from the mid-Paleozoic greenhouse climate to the Late Paleozoic Ice Age. This interval featured two short glaciation events during the mid-Tournaisian and Visean stages, which were consistent with frequent global sea-level changes (Kaiser et al., 2011; Lakin et al., 2016; Liu et al., 2019; Qie et al., 2019). Concurrently, the Carboniferous Period was characterized by intense global tectonic activity, notably including strong rifting in the Qiannan Depression of the South China (Wang X. D. et al., 2019; Wang Q. F. et al., 2020). During the late Middle Devonian to early Late Devonian, the extent of marine transgression was at its peak, leading to the formation of a chert–siliceous mudstone assemblage (Wang et al., 2006). This transgressive phase slightly receded in the early Early Carboniferous but resumed in the middle Early Carboniferous. This subsequent transgression resulted in the formation of a parallel unconformity within the Datangian strata across various ages, leading to the deposition of a distinctive shale–chert–limestone sedimentary assemblage (Guizhou Geological Survey Institute, 2017). The Lower Carboniferous Dawuba Formation, primarily located in the Luodian, Wangmo, and Ziyun areas of Guizhou, is a notable geological unit from this period. It consists of Datangian slope-basin facies shales interbedded with chert and limestone, stratigraphically positioned above the Muhua Formation limestone and below the limestone of the Nandan Formations (Guizhou Geological Survey Institute, 2017). Characterized by a thickness of approximately 150–250 m, the Dawuba Formation includes shale, silty shale, siltstone, and sandstone, indicative of deposition in an offshore delta (Yuan et al., 2018; Lu, 2019). This formation is further divided stratigraphically into four members, from the first to the fourth member, arranged sequentially from the bottom to the top. Exploration efforts, including the drilling of three exploratory wells, have established the Dawuba Formation as a significant target for shale gas exploration in the Qiannan Depression of the South China (Yuan et al., 2018). Notably, the shales within this formation are identified as the primary source rocks for gas, distinguished by their high OM abundance and thermal maturity. Additionally, these shales also serve as direct cap rocks for the gas reservoirs, enhancing their significance in hydrocarbon exploration.

3 DATA AND METHODS

3.1 Samples and data

The collection and analysis of Dawuba Shale samples were comprehensively conducted from Well QZY1, encompassing a variety of tests to assess the shale’s characteristics. A structured approach was applied in sample collection and analysis, with a total of 45 samples designated for major element analysis and 35 samples for trace element analysis. To understand the organic composition, 25 samples underwent organic petrographic characterization, and 26 samples were analyzed for vitrinite reflectance (Ro%). The total organic carbon (TOC) content was determined from 33 samples. For mineralogical composition, 60 samples were subjected to X-ray diffraction (XRD) analysis. Additionally, the study included physical property assessments on six core plug samples and gas content analysis on 16 samples. Preparation of samples varied according to the analysis requirements, with polished sections being prepared for organic petrology and vitrinite reflectance analysis, while other samples were processed into powders for various chemical analyses.

In the case of the Niutitang shales within the Guizhou area, samples and data were obtained from Well HD1, complemented by research findings from Ge et al. (2020, 2024) (Ge et al., 2020; Ge et al., 2024)and Yang et al. (2021) (Yang et al., 2021). For the Niutitang shales in the Chongqing area, data were sourced from studies by Li et al. (2015) (Li et al., 2015), Wu et al. (2016) (Wu et al., 2016), Zhang (2019) (Zhang, 2019), and Wang (2020) (Wang, 2020). This diverse dataset, combining direct sample analysis with insights from recent studies, provides a robust foundation for evaluating the geochemical, petrographic, and gas potential of these shale formations.

3.2 Methods

No unexpected or unusually high safety hazards were encountered during the testing.

3.2.1 Methods for major and trace element analysis

The preparation of samples for major and trace element analysis involved a meticulous process. Initially, the samples were pulverized to a fine consistency of about 200 mesh. Subsequently, they were dried at 105 °C for 4 h to eliminate moisture, and then allowed to cool to room temperature, approximately 23°C, in a desiccator to prevent moisture reabsorption. For determining the concentrations of major elements, X-ray Fluorescence Spectrometry (XRF) was employed. The preparation of fused glass beads, essential for the XRF analysis, adhered to the procedure outlined by Couture et al. (1993) (Couture et al., 1993). Additionally, the loss on ignition (LOI), which quantifies the amount of material lost upon heating, was measured gravimetrically at a temperature of about 1,100°C. The detection of trace element concentrations was facilitated by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) following the dissolution of the solid samples. This step was guided by the protocols of the Chinese National Standard GB/T14506.30-2010, utilizing a screw-top Teflon bomb with an aqueous HF-HNO3 mixture for dissolution (Jenner et al., 1990).

3.2.2 Methods for source rock characterization

The characterization of source rocks commenced with the preparation of polished sections for organic petrological analysis. Kerogen, the insoluble OM in sedimentary rocks, was extracted through a meticulous process that involved the successive removal of soluble OM, mineral matter, and water from the shale samples (Durand et al., 1980; Suleimenova et al., 2014). The isolated kerogen was then thinly sliced and mounted for microscopic examination. The composition of macerals within the kerogen was assessed using a Leica MPV Compact II microscope, capable of both reflected white and fluorescent light imaging. Additionally, vitrinite reflectance (Ro%), a key indicator of thermal maturity, was measured with the same microscope model, equipped with a micro-photometer and an oil immersion lens (Zeng et al., 2013; Ayinla et al., 2017). Calibration of these measurements was achieved using a standard reflectance value of 1.78% for the GGG standard (gallium-gadolinium-garnet), with results captured via DISKUS software (Ayinla et al., 2017).

For the determination of Total Organic Carbon (TOC), the samples underwent grinding to a particle size of less than 200 mesh and were then pre-treated with a 5% HCl solution at 80°C to eliminate carbonates (Espitalie et al., 1977; Tissot and Welte, 1984; Peters et al., 1994). The TOC content was quantified using a LECO elemental analyzer, providing a comprehensive overview of the organic carbon present within the samples.

3.2.3 Methods for reservoir bed characterization

The characterization of reservoir beds in the Dawuba and Niutitang shales involved a series of meticulously designed experiments to assess their mineral composition, porosity, permeability, pore morphology, and gas content.

3.3 Mineral composition analysis

For mineral content and clay composition, X-ray diffraction analysis (XRD) was conducted on crushed samples (200 mesh). Utilizing a Bruker D8 Advance XRD system with Cu-Kα radiation at 40 kV voltage and 40 mA current, this analysis semi-quantified the mineral constituents of the shales.

3.4 Porosity and permeability measurements

Porosity was determined using the CMS-300 instrument on core plugs at a pressure of 200 psi, providing insights into the storage capacity of the shales. Air permeability was assessed with a pulse-decay permeameter at a mean gas pressure of 435 psi (3 MPa), offering information on the ease with which gases can flow through the shale matrix.

3.5 Pore morphology characterization

Scanning electron microscopy (SEM) was employed to examine the pore morphology on freshly broken surfaces of the shales. Samples were coated with a gold/palladium (Au/Pd) alloy and observed under a Tescan Vega 3 LM system. The SEM operated at a high vacuum with 20 kV acceleration voltages and electric detectors, achieving a maximum resolution of 3 nm.

3.6 Pore characterization through low-pressure N2-physisorption

Low-pressure N2-physisorption tests were performed using a Micromeritics ASAP 2020 apparatus to characterize the pores within the shales. Crushed samples (60–80 mesh) were outgassed overnight at 110°C under vacuum to eliminate volatile substances and moisture. The N2 adsorption/desorption isotherms were then recorded at 77 K. Analysis of the adsorption data employed non-local density functional theory (NLDFT) and multipoint Brunauer–Emmett–Teller (BET) analyses for specific surface area (SSA), along with Barrett–Joyner–Halenda (BJH) analysis for pore size distribution (PSD), as detailed by Sing (1995), Lowell et al. (2004), and Barrett et al. (1951) (Barrett et al., 1951; Sing, 1995; Lowell et al., 2004). The pore-size distribution was illustrated using the dV/dlog(D) versus D plot for N2 adsorption, which effectively highlights the volume distribution of pores.

3.7 Shale gas collection

The collection of shale gas utilized a gas gathering system based on the water gas displacing method, using a saturated salt solution as the displacing medium. Pressure-core samples were immediately placed in an air collector upon extraction from the drilling borehole. The gas collected during the unheated stage was categorized as desorbed gas. After reaching a plateau in desorbed gas collection, the system was heated to gather residual gas. The quantification of lost gas was not included in this study.

4 DEPOSITIONAL ENVIRONMENTS OF SHALE LAYERS

The characterization of depositional environments in shale layers involves analyzing the composition and concentration of major and trace elements within the samples. The major elements, presented as oxides, include SiO2, Al2O3, and CaO, which dominate the samples. In contrast, Fe2O3, MgO, K2O, and SO3 are found in relatively lower abundances (Table 1). Additionally, trace elements such as sodium oxide (Na2O), manganese oxide (MnO), titanium dioxide (TiO2), vanadium pentoxide (V2O5), chromium oxide (Cr2O3), barium oxide (BaO), and phosphorus pentoxide (P2O5) are present but in concentrations below 1%. Notably, the silica (SiO2) content in the samples varies significantly, ranging from 2.86% to 69.31%, with an average of 45.98%. This average is lower than the typical shale content of 58.9% reported by Wedepohl (1971) (Wedepohl, 1971). The SiO2/Al2O3 ratio further highlights this variance, ranging from 2.38 to 14.68, with an average of 3.71, slightly higher than the average for typical shale (AS, 3.53). A comparative analysis reveals that the Dawuba shales are enriched in elements such as Be, Cr, Ni, Mo, Ba, and U, yet exhibit depletion in Li, Cu, and Zn when compared to AS, with the remaining trace elements showing similar concentrations to AS (Table 2).

TABLE 1 | Relative content of the major elements of the Dawuba shales in Guizhou, SW China (Data from Well QZY1).

TABLE 2 | Contents of the trace elements of the Dawuba shales in Guizhou, SW China (Data from Well QZY1).

4.1 Paleoclimate

The composition of major and trace elements, alongside their ratios, serves as a robust proxy for understanding the depositional environments of detrital sediments. These proxies offer valuable insights into the paleoclimate, water restriction, redox conditions, and paleoproductivity of the depositional period (Jones and Manning, 1994; Lin et al., 2008; Zeng et al., 2015; Guo et al., 2020). Specifically, the Ga/Rb and Sr/Cu ratios are instrumental in reconstructing paleoclimate conditions. Higher Ga/Rb ratios suggest warmer temperatures and reduced aridity (Lerman and Gat, 1989; Bai et al., 2015), whereas Sr/Cu ratios provide an indication of humidity levels, with values below 10.0 pointing to humid conditions and those above 10.0 suggesting aridity (Adegoke et al., 2014; Sarki Yandoka et al., 2015). Analysis of the Dawuba shales in Guizhou, as depicted in Figures 2, 3A, indicates that they were primarily deposited under conditions that were both warm and arid.

FIGURE 2 | Correlations between Sr/Cu and Ga/Rb showing the paleoclimate during the Carboniferous Dawuba shale deposition in Guizhou, SW China.

FIGURE 3 | Depth profile of elemental ratios [(A) for the Carboniferous Dawuba Formation in Guizhou, Data from Well QZY1; (B) for the Cambrian Niutitang Formation in Chongqing, Data from Well YK1 collected from Li et al. (2015) (Li et al., 2015); (C) for the Cambrian Niutitang Formation in Guizhou, Data from Well TX1 collected from Yang et al. (2021) (Yang et al., 2021)].

According to the data collected from Li et al. (2015) (Li et al., 2015) and Yang et al. (2021) (Yang et al., 2021), the Niutitang shales from the Chongqing area exhibit Sr/Cu ratios ranging from 1.99 to 17.41, with an average of 6.70, and Ga/Rb ratios between 0.15 and 0.22, averaging at 0.18 (Figure 3B). Similarly, the Niutitang shales from the Guizhou area display Sr/Cu ratios from 1.37 to 25.64, with an average of 4.29 (Figure 3C). These results suggest the prevalence of cooler paleo-conditions during the Cambrian Niutitang deposition.

4.2 Paleosalinity

Understanding paleosalinity, which is a key factor in reconstructing past marine environments, can be approached through the analysis of specific elemental ratios and content. The content of molybdenum (Mo) and its ratio to total organic carbon (TOC) are particularly informative. Mo tends to accumulate under anoxic reducing conditions, making its concentration and the Mo/TOC ratio valuable for assessing the degree of water restriction (Algeo and Lyons, 2022). Analysis of the Mo-TOC relationships in the Dawuba shale in Guizhou, particularly within the first member of the Dawuba Formation, suggests sedimentation occurred under conditions of restricted water flow, indicative of an enclosed environment.

Another significant indicator of paleosalinity is the (MgO/Al2O3) × 100 ratio. Higher values within this ratio are indicative of increased salinity levels (Wang X. F. et al., 2020). The classification of these ratios into <1, 1–10, and >10 correspond to fresh, transitional, and saltwater conditions, respectively. Analysis of the Dawuba shale in Guizhou, as illustrated in Figure 3A, reveals that the depositional environment predominantly ranged from transitional to saltwater. This suggests that during the deposition of the Dawuba shale, the environment transitioned to or was primarily marine.

According to the data collected from Li et al. (2015) (Li et al., 2015) and Yang et al. (2021) (Yang et al., 2021), the (MgO/Al2O3) × 100 ratios in the Niutitang shales from the Chongqing area demonstrate a range from 8.74 to 79.49, with an average of 18.72 (Figure 3B). This indicates a predominance of saltwater conditions. Furthermore, the Sr/Ba ratio, another proxy for inferring paleosalinity where higher ratios suggest greater salinity (Meng et al., 2012), supports this conclusion. In the Niutitang shales of the Guizhou area, Sr/Ba ratios range from 1.80 to 9.22, with an average of 4.03 (Figure 3C), reinforcing the interpretation that the water conditions were saltwater during the Cambrian Niutitang deposition.

4.3 Paleoredox conditions

The assessment of paleoredox conditions in sedimentary environments is crucial for understanding the preservation potential of OM and the historical oxygenation state of the water column. Trace elements such as vanadium (V), chromium (Cr), nickel (Ni), cobalt (Co), and uranium (U) serve as vital indicators of these conditions (Jones and Manning, 1994; Lin et al., 2008). Specifically, the concentrations of V and Ni in organic-rich sediments are highly sensitive to the prevailing redox conditions, providing insights into the oxygenation levels in various sedimentary settings (López et al., 1995; Mongenot et al., 1996). The ratio of V/(V + Ni) is particularly informative: values above 0.84 are indicative of euxinic conditions, ratios between 0.54 and 0.82 suggest anoxic environments, and those ranging from 0.46 to 0.60 point to dysoxic conditions (Hatch and Leventhal, 1992; López et al., 1995; Wang X. F. et al., 2020).

It is important to note that the enrichment of certain trace elements, such as Cr and Co, which are associated with detrital components, is less influenced by the redox state of the environment (Ross and Bustin, 2006). Thus, elevated V/Cr and Ni/Co ratios are indicative of relatively anoxic conditions. Conventionally, V/Cr ratios below two signal oxic environments, those between two and 4.25 imply dysoxic conditions, and values above 4.25 suggest anoxic conditions (Jones and Manning, 1994). Similarly, Ni/Co ratios below five denote oxic conditions, those between five and seven indicate dysoxic environments, and ratios above seven point to anoxic conditions (Jones and Manning, 1994). According to these criteria, the Dawuba shale in Guizhou, as depicted in Figure 3A, is characterized by reducing conditions, which are conducive to the preservation of OM during deposition.

According to the data collected from Li et al. (2015) (Li et al., 2015), in the Niutitang shales of the Chongqing area, V/Cr ratios range from 1.22 to 30.77, with an average of 7.94, and Ni/Co ratios vary from 2.71 to 36.80, with an average of 10.60 (Figure 3B). Additionally, the V/(V+Ni) ratios range from 0.64 to 0.98, with an average of 0.80 (Figure 3B). These figures collectively indicate that the Niutitang shales were deposited under reducing conditions.

Similarly, according to the data collected from Yang et al. (2021) (Yang et al., 2021), for the Niutitang shales in the Guizhou area, V/(V+Ni) ratios range from 0.58 to 0.95, with an average of 0.76, and Ni/Co ratios span from 3.38 to 20.92, with an average of 9.67 (Figure 3C). The rare earth element (REE) index Ceanom is another proxy for redox conditions, where values exceeding −0.1 suggest cerium enrichment, a positive anomaly, and thus a reducing environment (Wright et al., 1987; Zhang et al., 2017). The Ceanom values for these shales range from −0.225 to 0.006, with an average of −0.062 (Figure 3C), further supporting the inference of reducing conditions during the Niutitang deposition in Guizhou.

4.4 Impacts of paleoenvironments on source rocks and reservoir characteristics

As demonstrated in the cases of the Niutitang and Dawuba shales, the prevailing paleoenvironments during their deposition significantly influenced their sedimentary characteristics. In both the Chongqing and Guizhou areas, the Niutitang shale was deposited under a cooler paleoclimate, saltwater conditions, and reducing environments, consistent with a deep muddy shelf in the Early Cambrian period in the Upper Yangtze region. These conditions are corroborated by previous studies (Steiner et al., 2001). In contrast, the Dawuba shale in the Guizhou area was deposited under a warm and arid paleoclimate with transitional to marine conditions, also under reducing conditions. This aligns with the sedimentary characteristics of slope-basin facies in the Early Carboniferous period in the Upper Yangtze region, as supported by other studies (Guizhou Geological Survey Institute, 2017).

Both the Carboniferous Dawuba and Cambrian Niutitang shales experienced predominantly reducing water conditions, crucial for the preservation of organic matter (OM). Reducing environments, characterized by low oxygen levels, inhibit OM degradation by aerobic bacteria, thus enhancing its preservation. The warm and arid conditions during the Dawuba shale deposition, coupled with reducing conditions, facilitated the accumulation and preservation of OM. Conversely, the cooler and arid conditions during the Niutitang shale deposition, along with the reducing conditions, supported OM preservation. Additionally, varying paleosalinity levels influenced the type and quantity of organic matter preserved. For instance, the Dawuba shale saw a transition from transitional to predominantly saline water environments, indicating a shift towards more marine conditions conducive to OM preservation due to factors like higher primary productivity and reduced clastic dilution. On the other hand, the Niutitang shale was deposited under stable saltwater conditions, promoting a consistent marine environment favorable for OM preservation.

Furthermore, the distinct paleoclimatic conditions under which the Dawuba and Niutitang shales were deposited significantly influenced their mineralogical compositions, impacting reservoir characteristics such as brittleness—a key feature for hydraulic fracturing. The paleoclimate affected the type and deposition of minerals, with warmer, arid conditions leading to more clay deposition and cooler, arid conditions favoring the deposition of brittle minerals like quartz and carbonates. Paleosalinity also influenced the chemical precipitation and types of minerals deposited. Meanwhile, paleoredox conditions impacted the preservation of organic matter, which indirectly influences the type and paleoproductivity of organic matter and its interplay with the mineral matrix within the shale. The warm, arid climate with transitional to saline water conditions during the Dawuba shale deposition resulted in a mineralogy richer in clay, which typically confers ductility rather than brittleness. Conversely, the cooler, arid climate with stable saltwater conditions during the Niutitang shale deposition led to a mineralogy richer in brittle minerals, reflecting variations in chemical precipitation and sediment supply controlled by the paleoclimate and paleosalinity.

5 EVALUATION OF THE SHALE SOURCE ROCKS

5.1 Thermal maturity

The thermal maturity of OM in sedimentary rocks is a critical factor in hydrocarbon generation, reflecting the extent of physical and chemical transformations under the influence of heat, pressure, microbial activity, subsidence, and time post-deposition (Ma et al., 2019). Parameters such as vitrinite reflectance (Ro%) and maximum pyrolysis peak temperature (Tmax) serve as reliable indicators of the thermal maturity of source rocks (Tissot and Welte, 1984; Bordenave et al., 1993).

In the Dawuba shales of Guizhou area, Ro% values range from 2.85% to 3.46%, with an average of 3.14%, as presented in Table 3 and illustrated in Figure 4, collectively suggesting that the Dawuba shales have attained a post-mature stage of thermal maturity.

TABLE 3 | Vitrinite reflectance (RO %) data of the Dawuba shales in Guizhou, SW China (Data from Well QZY1).

FIGURE 4 | Correlations of Ro vs depth indicating the thermal maturity for the Dawuba shales in Guizhou, China.

For the Niutitang shales, according to the data collected from Wang (2020) (Wang, 2020) and Ge et al. (2020) (Ge et al., 2020), thermal maturity differs between locations. In the Chongqing area, Ro values span from 1.55% to 3.56%, with an average of 2.68%, whereas in the Guizhou area, they range from 1.43% to 2.81%, averaging at 2.37%. These findings indicate that the Niutitang shales in both areas have also reached the post-mature stage, suggesting significant hydrocarbon generation potential.

5.2 Organic matter abundance

The TOC content is a primary measure of OM abundance in shale source rocks. For the Dawuba shales in Guizhou, TOC values predominantly range from 0.63 to 4.25 wt%, with an average of 1.78 wt% (Table 4). According to the source rock evaluation criteria by Peters and Cassa (1994) and Tissot and Welte (1984) (Tissot and Welte, 1984; Peters et al., 1994), these values classify the Dawuba shales in Guizhou as having fair to excellent source rock potential.

TABLE 4 | TOC data of the Dawuba shale in Guizhou, SW China (Data from Well QZY1).

TABLE 5 | Relative content of the macerals of organic matters of the Dawuba shales in Guizhou, SW China (Data from Well QZY1).

According to the data collected from Wang (2020) (Wang, 2020) and Ge et al. (2020) (Ge et al., 2020), the Niutitang shales in the Chongqing area display TOC values between 0.16 wt% and 9.62 wt%, averaging at 3.43 wt%. Similarly, in the Guizhou area, TOC values range from 0.60 wt% to 8.89 wt%, with an average of 3.55 wt%. These ranges suggest that the Niutitang shales possess predominantly good to excellent source rock potential, aligning with the criteria for evaluating source rocks.

5.3 Organic matter types

Given that the type of OM significantly influences the properties, composition, and quantity of hydrocarbon generation (Peters et al., 1994), identifying the kerogen type within potential source rocks is crucial. The maceral compositions, namely, liptinite, vitrinite, and inertinite, serve as the basis for this classification. In the context of China, liptinite is further categorized into sapropelinite and exinite, with sapropelinite encompassing lginate and sapropelic amorphous material, and exinite including various forms like cutinite, suberinite, sporinite, resinite, exsudatinite, chlorophyllinite, liptodetrinite, and bituminite. The proportions of liptinite, vitrinite, and inertinite within the Dawuba shales in Guizhou are detailed in Table 5. Specifically, in the shales, liptinites, which are primarily composed of liptodetrinite, exhibit a range between 71% and 92%, with an average of 82.6%. Vitrinite, characterized by its distinct outlines, varies from 5% to 22%, averaging at 13.3%, as depicted in Figure 5. Inertinite, mainly consisting of fusinite, ranges from 1% to 8%, with an average of 4.1%.

The kerogen type is ascertainable through the TI index, as defined by Mukhopadhyay et al. (1995) (Mukhopadhyay et al., 1995) and Hakimi et al. (2011) (Hakimi et al., 2011): (TI = (100 × sapropelinite+50 × exinite-50 × vitrinite-75 × inertinite)/100). TI indexes are interpreted as follows: greater than 80 for Type I, between 80 and 40 for Type II1, between 40 and 0 for Type II2, and less than 0 for Type III kerogens.

The Dawuba shales in Guizhou predominantly exhibit TI indexes ranging from 0 to 40, indicative of Type II2 kerogens, as summarized in Table 5. Studies by Wang (2020) (Wang, 2020) and Ge et al. (2020) (Ge et al., 2020) suggest that the kerogen types in the Niutitang shales, specifically within the Chongqing and Guizhou areas, are predominantly Type I, with some instances of Type II1. This conclusion is supported by the observed maceral compositions (Ge et al., 2020; Wang, 2020).

Consequently, the source rocks of the Niutitang shales in both the Chongqing and Guizhou areas are characterized as post-mature, with predominantly good to excellent generation potential, and primarily contain Type I kerogens. Concurrently, the source rocks of the Dawuba shales in the Guizhou area are also post-mature but exhibit a range of fair to excellent generation potential, and contain Type II2 kerogens. These characteristics indicate that both the Niutitang and Dawuba shales possess significant gas generation potential.

6 SHALE RESERVOIR CHARACTERISTICS

6.1 Mineralogy characteristics

The mineralogical composition of the Dawuba shales in Guizhou is characterized by a significant presence of clay minerals, ranging from 4.0% to 62.0%, with an average of 44.3%, and quartz, ranging from 15.0% to 68.0%, with an average of 33.2%. Other constituents include carbonates (0%–54.0%, Avg.=15.7%), pyrite (0%–13.0%, Avg.=4.75%), and feldspar (0%–11.0%, Avg.=2.02%) (Figure 6A; Table 6). The clay minerals are predominantly illite (Avg.=74.7%), followed by mixed-layer illite-smectite (I/S, Avg.=16.3%) and chlorite (Avg.=7.4%) (Figure 6B; Table 6). This composition suggests that, compared to the Chang-7 lacustrine shale in the Ordos Basin and the Longmaxi marine shale in South China, the Dawuba shales in Guizhou have a lower content of brittle minerals (such as quartz, feldspar, and carbonates) and a higher proportion of clays.

FIGURE 5 | Photomicrographs of identified vitrinite in the Dawuba shales in Guizhou area under oil-immersed reflected light. (A) for sample depth of 2770 m, (B) for sampledepth of 2834 m, (C) for sample depth of 2914 m, and (D) for sample depth of 2924 m.

FIGURE 6 | Mineral compositions of the Dawuba shales in Guizhou, SW China [(A) is for the whole rock XRD analysis, (B) is for the clay minerals XRD analysis].

TABLE 6 | XRD data of the Dawuba shale in Guizhou, SW China (Data from Well QZY1).

According to the data collected from Ge et al. (2020) (Ge et al., 2020), the Niutitang shales in the Guizhou area display a relatively high content of brittle minerals, with overall ranges from 37% to 68%, and an average of 51%. Quartz content varies from 28.0% to 53.0%, averaging 37.8%, while clay mineral content ranges from 18.0% to 58.0%, with an average of 38.3% (Figure 7A). Carbonate minerals are also present, albeit with few pyrites. The dominant clay mineral is the I/S mixed layer, which ranges from 47.0% to 64.0%, averaging 56.1% (Figure 7B), indicating strong adsorption capacity in the Niutitang Formation shale of Well HD1. Illite and a small amount of chlorite are also present, with montmorillonite absent, suggesting its complete conversion to illite under high-to over-mature conditions.

FIGURE 7 | Mineral compositions of the Niutitang shales in Guizhou, SW China [(A) is for the whole rock XRD analysis, (B) is for the clay minerals XRD analysis; part of data was collected from Ge et al. (2020)].

Shale classification into calcareous, siliceous, argillaceous, and mixed types is based on the relative contents of calcareous, siliceous, and argillaceous minerals, with a classification threshold set at greater than 50% (Dong et al., 2016). In the Guizhou area, both the Dawuba and Niutitang shales exhibit similar classifications. Specifically, the Dawuba shales, which are richer in clay minerals, are predominantly classified as argillaceous (31.7%) and mixed (61.7%). Conversely, the Niutitang shales, which contain higher amounts of brittle minerals, are primarily mixed shale (66.7%) and argillaceous shale (26.7%). These compositional differences have significant implications for shale gas exploration. The Niutitang shales, being rich in brittle minerals, tend to preserve more solid hosted (meso and macro) porosity compared to the more clay-rich Dawuba shales of similar maturity. This characteristic makes the Niutitang shales more amenable to hydraulic fracturing, a key technique used in shale gas extraction, due to the presence of more brittle minerals which facilitate the fracturing process.

6.2 Physical properties

The physical properties of shale, specifically porosity and permeability, are critical in evaluating its potential as a hydrocarbon reservoir. For the Dawuba shale samples in Guizhou, data presented in Table 7 indicate that porosity values range from 0.88% to 5.92%, averaging 3.45%. Permeability values vary from 0.0009 to 0.0441 millidarcies (mD), with an average of 0.0098 mD (Table 7). These values suggest that the Dawuba shales generally exhibit low porosity and permeability. However, a notable exception is observed within the first member of the formation, where both porosity and permeability show relatively higher values. This improvement can be attributed to the presence of various pore types, including inorganic and organic pores, as well as bedding fractures. These features collectively enhance the formation’s potential as an effective hydrocarbon reservoir by facilitating fluid flow.

TABLE 7 | Porosity and permeability of the Dawuba shale in Guizhou, SW China.

According to the study conducted by Ge et al. (2020), the Niutitang shales from Well HD-1 in the Guizhou area were characterized by their porosity and permeability values. The porosity of these shales ranged from 0.2% to 1.8%, with an average value of 0.51%. Similarly, permeability values were found to range between 0.0001 mD and 0.01 mD, with an average value of 0.001 mD. These measurements categorize the reservoir as having ultra-low porosity and ultra-low permeability.

6.3 Pore structure characteristics

The pore structure of shale significantly influences its capacity to store and transmit hydrocarbons. In the Dawuba shales of Guizhou, pore types identified under Field Emission Scanning Electron Microscopy (FE-SEM) primarily include inter-particle pores (Inter-P), intra-particle pores (Intra-P), and OM pores, as illustrated in Figure 8. Intra-P pores are mainly inter-crystalline within pyrite and clay minerals and dissolution pores within calcite and quartz or along particle rims. Inter-P pores, situated between mineral grains, are polygonal, elongated, or slit-like and exhibit good connectivity. OM pores are predominantly spongy, with bubble-like OM pores being uniformly and independently distributed within the OM.

FIGURE 8 | FE-SEM images showing the types of pores: (A) dissolution pores within calcite, (B) dissolution pores within quartz, (C) dissolution pores and micro-fracture within dolomite, (D) dissolution pores and micro-fracture within calcite, (E) intergranular pores and micro-fracture between clay minerals, (F) intragranular pores within pyrite, (G) OM pores, (H) OM pores, (I) OM pores.

According to a study by Ge et al. (2024), the Niutitang shales in the Guizhou area feature micro-storage spaces dominated by matrix pores and fractures, including diagenetic fractures, layered clay mineral interlayer pores, and pyrite intercrystalline pores. Additionally, interstitial organic pores are present, featuring a size range of 10–150 nm. The pyrite observed is often in the form of strawberry-shaped or aggregate shapes, frequently coexisting with OM. Furthermore, a minor presence of residual pores and intragranular pores has also been noted (Ma et al., 2015).

Isothermal adsorption curves, which often form a hysteresis loop separating from the desorption curve within a specific pressure range, are influenced by the relative pressure of capillary condensation and evaporation, varying with pore morphology. By analyzing the shapes of hysteresis loops, it is possible to speculate about the morphology of the pore shape (Sing, 1995). In this study, the shapes of the N2 adsorption and desorption isotherms displayed minimal differences, as shown in Figures 9, 10.

FIGURE 9 | N2 isotherms of the Dawuba shale in Guizhou, SW China (Data from Well QZY1; (A) for the sample depth of 2708 m, (B) for the sample depth of 2828 m, (C) for the sample depth of 2883.7 m, (D) for the sample depth of 2951.75 m).

FIGURE 10 | N2 isotherms of the Niutitang shale in Guizhou, SW China (Data from Well HD1).

N2 adsorption was employed to quantify the mesopore and macropore structures in both Dawuba and Niutitang shales in Guizhou. The N2 adsorption-desorption isotherms showed similar trends across all shale samples, indicating a broad adsorption range. The volumes of adsorption at P/P0 < 0.05 and near P/P0 = 1.0 suggest the presence of microporosity and macroporosity, respectively (Clarkson et al., 2013). As P/P0 increases to 0.5, a hysteresis loop appears between the adsorption and desorption branches, indicating the presence of mesopores (Kuila and Prasad, 2013). According to the IUPAC, the N2 adsorption isotherms follow the Type IV classification, and the hysteresis loops exhibit an intermediate type between types H2 and H3, with hysteresis loops suggesting inkbottle and slit pore shapes, although SEM analyses confirm the polymorphic nature of shale pore shapes.

Accurate characterization of the nanoscale pore system in shales requires the selection of appropriate methods for determining low-pressure gas adsorption. In this study, the DFT model was used to calculate the specific surface area (SSA) and pore volume (PV) of micropores, while the NLDFT model was applied to determine the SSA and PV of mesopores and macropores, based on the applicable conditions and principles of these methods (Sing, 1995; Clarkson et al., 2013; Kuila and Prasad, 2013).

As detailed in Table 8, the Dawuba shales in Guizhou exhibited BET-specific surface areas ranging from 8.126 to 13.937 m²/g, with an average of 11.918 m²/g. The total pore volumes ranged from 12.03×10⁻³ to 32.29 ×10⁻³ cm³/g, averaging 18.03×10⁻³ cm³/g. The average pore diameters varied from 4.194 to 9.267 nm, with a mean of 5.968 nm. The pore size distribution (PSD), plotted as dV/dlog(D) versus D curves using the BJH model on the adsorption branches, showed one sample with a bimodal curve and three samples with unimodal curves, indicating a predominance of micropores and mesopores, as depicted in Figure 11.

TABLE 8 | Porosity and permeability of the Dawuba shale in Guizhou, SW China.

FIGURE 11 | dV/dlog(D) versus D curves showing the pore size distribution of the Dawuba shales in Guizhou, based on the N2 adsorption data [(A) for the sample depth of 2708 m, (B) for the sample depth of 2828 m, (C) for the sample depth of 2883.7 m, (D) for the sample depth of 2951.75 m].

Conversely, the Niutitang shales in Guizhou showed BET-specific surface areas ranging from 0.178 to 7.874 m²/g, with an average of 4.691 m²/g. The total pore volumes varied from 0.634×10⁻³ to 6.52×10⁻³ cm³/g, averaging 2.63×10⁻³ cm³/g. The average pore diameters ranged from 2.46 to 16.81 nm, with a mean of 5.27 nm. The PSD, plotted using the BJH model on the adsorption branches, indicated a predominance of micropores and mesopores, as shown in Figure 12.

FIGURE 12 | dV/dlog(D) versus D curves showing the pore size distribution of the Niutitang shales in Guizhou, based on the N2 adsorption data (Data from Well HD1).

Consequently, the pore types observed in the Niutitang and Dawuba shales in Guizhou exhibit similarities, including the prevalence of inter-particle pores (Inter-P), intra-particle pores (Intra-P), organic matter pores (OM-P), and micro-fractures. The low-pressure N2 physisorption analyses reveal a predominance of micropores and mesopores in both the Niutitang and Dawuba shales in Guizhou.

TABLE 9 | Gas contents of the Dawuba shale in Guizhou, SW China.

6.4 Gas contents

The gas content measurements for Dawuba shales in the Guizhou area, presented in Table 9 and illustrated in Figure 13A, reveal a range in desorbed gas content from 0.07 m³/t to 1.28 m³/t, averaging at 0.46 m³/t. The residual gas contents vary from 0.09 m³/t to 0.44 m³/t, with an overall mean of 0.27 m³/t. Notably, some samples exhibited no detectable levels of residual gas. Consequently, the total gas contents in Dawuba shales range from a minimum of 0.07 m³/t to a maximum of 1.68 m³/t, with an average of 0.59 m³/t.

FIGURE 13 | Gas contents of the Dawuba shales and the Niutitang shales in Guizhou, SW China [(A) for the Dawuba shales, (B) for the Niutitang shales; the figure (B) was modified after Ge et al. (2020)].

According to the data collected from Ge et al. (2020), the Niutitang shales in the Guizhou area, as depicted in Figure 13B, demonstrate a different gas content profile. The desorbed gas content for Niutitang shales ranges from 0.02 m³/t to 0.66 m³/t, with an average of 0.19 m³/t. The overall total shale gas content for Niutitang shales varies between 0.09 m³/t and 1.31 m³/t, averaging at 0.42 m³/t. These differences in gas content between the Dawuba and Niutitang shales can be attributed to variations in physical properties and preservation conditions, with the Dawuba shales generally exhibiting higher gas content levels.

7 PREDICTING FAVORABLE ZONES FOR SHALE GAS

Research and practical experiences have demonstrated that the enrichment and high productivity of marine shale gas are closely linked to specific geological, engineering, and economic characteristics. These ideal conditions for shale gas extraction are often summarized as “sweet, crisp, and good,” encapsulating the essence of “good gas-bearing property” from a geological perspectivity, “good fracturing ability” from an engineering standpoint, and “good economic efficiency” in terms of financial benefits (Dong et al., 2016; Wang et al., 2018). Focusing on the Wufeng-Longmaxi Formations across shale gas fields such as Changning, Weiyuan, and Fuling in the Sichuan Basin, prior studies have pinpointed the critical parameters for identifying shale gas-rich sections. These parameters include the “four highs” (high Total Organic Carbon (TOC) value, high gas content, high porosity, and high formation pressure) and the “two developments” (well-developed shale bedding/lamination and natural micro-fractures) (Wang et al., 2012; Wang et al., 2013; Zhou et al., 2019). The evaluation criteria used to predict favorable exploration zones are detailed in Table 10.

TABLE 10 | Geological reference standards for favorable areas of a non-typical marine shale gas in South China.

Taking the Carboniferous Dawuba Formation shale in the South Guizhou Depression as a case study, this formation is characterized by its rich OM, favorable OM types, and substantial storage capacity. Previous studies have shown that hydrocarbon generation in the Dawuba shales occurred in the Late Carboniferous to Early Permian period (Chen et al., 2007). Gas generation began in the Middle to Late Permian, and hydrocarbon expulsion and migration occurred before the Middle Triassic (the Indosinian movement around 205 Ma) (Chen et al., 2007). However, the marine shale layer in southern China has undergone multiple stages of tectonic evolution (the Hercynian, Indosinian, Yanshanian, and Himalayan movements) and prolonged uplift and erosion, which have significantly influenced the target Carboniferous series. Therefore, it is imperative to consider the effects of shale self-sealing and fractures on gas preservation, as well as the impact of regional folding and denudation.

To accurately predict and optimize the most favorable areas for shale gas within the Dawuba Formation, this study utilizes a comprehensive information overlay approach. This method integrates geological benchmarks (Wang et al., 2021) for identifying favorable conditions in non-typical marine shales of southern China (Table 10), alongside parameters that reflect shale development conditions, source rock geochemical attributes, reservoir properties, and preservation conditions.

Data from the China Geological Survey highlight several advantageous geological features of the Carboniferous Dawuba Formation shale in the Qiannan Depression. These features include considerable thickness, widely distribution, moderate burial depth, effective OM preservation during deposition, high TOC content, predominance of type II2 kerogen, a range from high maturity to over maturity of OM, a high content of brittle minerals within the reservoir, relatively high shale gas content, and a well-developed overlying direct cap rock. In the research, we selectively focus on the TOC content, Ro values, burial depth, and shale thickness. Collectively, this comprehensive analysis identifies the Carboniferous Dawuba Formation in the Shangyuan and Zongdi areas of the Qiannan Depression as promising exploration zones for shale gas, as illustrated in Figure 14.

FIGURE 14 | Prediction map of favorable exploration zones for shale gas in the Carboniferous Dawuba Formation of the Qiannan Depression, China [The location of figure (B) is depicted in figure (A), and the location of figure (C) is depicted within figure (B)].

8 CONCLUSION

(1) Paleoclimate and Water Conditions: In the Upper Yangtze region, the deposition of the Carboniferous Dawuba shale took place under a warm and arid paleoclimate, with predominantly reducing water conditions that facilitated the preservation of OM. The salinity levels during this period suggested a transitional phase towards marine conditions, characterized by a shift from transitional to predominantly saline water environments. Conversely, during the Cambrian Niutitang deposition, the paleoclimate was cooler and arid, with saltwater conditions prevailing. Similar to the Dawuba shale, reducing conditions were predominant during the Niutitang deposition, which were conducive to OM preservation.

(2) Source Rock Potential and Organic Matter Types: The Carboniferous Dawuba shales have advanced to the post-mature stage, indicating their significant gas generation potential in ancient times, primarily due to the presence of Type II2 kerogens. Similarly, the Cambrian Niutitang shales have also reached the post-mature stage, demonstrating good to excellent gas generation potential. The dominant presence of Type I kerogens in these shales further underscores their high gas generation potential.

(3) Shale Composition, Porosity, and Gas Content: Both the Carboniferous Dawuba and Cambrian Niutitang shales are characterized by a composition of mixed shale and argillaceous shale. These formations exhibit ultra-low porosity and permeability, yet they feature the development of dissolution pores, OM pores, and micro-fractures, which are crucial for gas storage. The Dawuba and Niutitang shales primarily contain micropores and mesopores. Comparative analysis reveals that the Dawuba shales possess superior BET-specific surface areas, total pore volumes, and average pore diameters than the Niutitang shales. However, the gas contents in both shale formations are relatively low, with average values of 0.59 m³/t for Dawuba and 0.42 m³/t for Niutitang shales. This observation highlights the need for further research to explore the conditions that favor shale gas preservation.

(4) Prospective Exploration Areas: In Guizhou, the Qiannan Depression hosts the Carboniferous Dawuba Formation, particularly within the Shangyuan and Zongdi areas, which has emerged as promising regions for shale gas exploration. These areas exhibit favorable geological characteristics, notably high total organic carbon (TOC) and vitrinite reflectance (Ro) values, alongside moderate burial depths and shale thicknesses. These features collectively enhance the potential for significant shale gas reserves.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

TW: Conceptualization, Methodology, Resources, Writing–review and editing. ZX: Conceptualization, Funding acquisition, Methodology, Writing–original draft. KY: Data curation, Methodology, Resources, Writing–review and editing. XW: Conceptualization, Supervision, Writing–review and editing, Resources. MG: Writing–review and editing, Data curation, Formal Analysis, Resources.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The Natural Science Foundation of Chongqing [No. cstc2021jcyj-msxmX0733], the China Geological Survey Projects of Strategic Selection of Upper Paleozoic Shale Gas in the Yunnan-Guizhou-Guangxi Region [No. DD20160196], the Shale Gas Survey in the Guizhong-Nanpanjiang Area [No. DD20190088], the Science and Technology Innovation Project of Oil and Gas Survey, China Geological Survey [No. 2023YC08], the Open Fund of Key Laboratory of Sedimentary Basins and Oil and Gas Resources, Ministry of Natural Resources [No. cdcgs2022006, cdcgs2023009], the Science and Technology Research Program of Chongqing Municipal Education Commission [No. KJQN202101530], the China Postdoctoral Science Foundation [No. 2023MD734109], the Open Fund of Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education (No. TPR-2023-06), and the College Students’ Innovative Entrepreneurial Training Plan Program of CQUST [No. 2023151]. Their support has been instrumental in the successful completion of this research project.

ACKNOWLEDGMENTS

The authors would like to express their sincere gratitude to the Oil and Gas Survey, China Geological Survey for providing the necessary data and granting permission to publish the results of this study.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Adegoke, A. K., Abdullah, W. H., Hakimi, M. H., and Sarki Yandoka, B. M. (2014). Geochemical characterisation of Fika Formation in the Chad (Bornu) Basin, northeastern Nigeria: implications for depositional environment and tectonic setting. Appl. Geochem. 43, 1–12. doi:10.1016/j.apgeochem.2014.01.008

Algeo, T. J., and Lyons, T. W. (2022). Mo-total organic carbon covariation in modern anoxic marine environments: implications for analysis of paleoredox and paleohydrographic conditions. Paleoceanography 21, PA1016. doi:10.1029/2004pa001112

Ayinla, H. A., Wan, H. A., Makeen, Y. M., Abubabar, M., Jauro, A., Mohammed, B., et al. (2017). Petrographic and geochemical characterization of the Upper Cretaceous coal and mudstones of Gombe Formation, Gongola sub-basin, northern Benue trough Nigeria: implication for organic matter preservation, paleodepositional environment and tectonic settings. Int. J. Coal Geol. 180, 67–82. doi:10.1016/j.coal.2017.06.010

Bai, Y., Liu, Z., Sun, P., Liu, R., Hu, X., Zhao, H., et al. (2015). Rare earth and major element geochemistry of Eocene fine-grained sediments in oil shale- and coal bearing layers of the Meihe Basin, northeast China. J. Asian Earth Sci. 97, 89–101. doi:10.1016/j.jseaes.2014.10.008

Barrett, E. P., Joyner, L. G., and Halenda, P. P. (1951). The determination of pore volume and area distributions in porous substances. I. Computations from nitrogen isotherms. J. Am. Chem. Soc. 73 (1), 373–380. doi:10.1021/ja01145a126

Blatt, H. (1982). Sedimentary petrology. Oxford: Freeman and Company, Ltd.,

Boggs, S. (2006). Principles of sedimentology and stratigraphy. London: Pearson Prentice Hall.

Bordenave, M. L., Espitalié, L., Leplat, P., Oudin, J. L., and Vandenbroucke, M. (1993). “Screening techniques for source rock evaluation,” in Applied petroleum Geochemistry ed . Editor M. L. Bordenave (Paris: Editions Technip).

Chen, D., Wang, J., Qing, H., Yan, D., and Li, R. (2009). Hydrothermal venting activities in the Early Cambrian, south China: petrological, geochronological and stable isotopic constraints. Chem. Geol. 258, 168–181. doi:10.1016/j.chemgeo.2008.10.016

Chen, Y. Z., Liu, L. F., Cai, X. Y., and Wang, Y. (2007). An analysis of hydrocarbon reservoiring conditions of Late Paleozoic marine strata in South China. Nat. Gas. Ind. 27 (10), 20–23.

Clarkson, C. R., Solano, N., Bustin, R. M., Bustin, A. M. M., Chalmers, G. R. L., He, L., et al. (2013). Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel 103, 606–616. doi:10.1016/j.fuel.2012.06.119

Couture, R. A., Smith, M. S., and Dymek, R. F. (1993). X-ray fluorescence analysis of silicate rocks using fused glass discs and a side-window Rh source tube: accuracy, precision and reproducibility. Chem. Geol. 110 (4), 315–328. doi:10.1016/0009-2541(93)90326-e

Curtis, J. B. (2002). Fractured shale-gas systems. AAPG Bull. 86, 1921–1938. doi:10.1306/61eeddbe-173e-11d7-8645000102c1865d

Dong, D. Z., Wang, Y. M., Huang, X. N., Zhang, C. C., Guan, Q. Z., Huang, J. L., et al. (2016). Discussion about geological characteristics, resource evaluation methods and its key parameters of shale gas in China. Nat. Gas. Geosci. 27 (9), 1583–1601.

Durand, B., and Nicaise, G. (1980). “Procedures for kerogen isolation,” in kerogen: insoluble organic matter from sedimentary rocks ed . Editor B. Durand (Paris: Editions Technip).

Espitalie, J., LaPorte, J. L., Madec, M., Marquis, F., LePlat, P., Paulet, J., et al. (1977). Méthode rapide de caractérisation des roches mètres, de leur potentiel pétrolier et de leur degré d'évolution. Oil Gas. Sci. Technol. 32, 23–42. doi:10.2516/ogst:1977002

Garrels, R. M., and Mackenzie, F. T. (1969). Sedimentary rock types: relative proportions as a function of geological time. Science 163 (3867), 570–571. doi:10.1126/science.163.3867.570

Ge, M. N., Bao, S. J., Shi, D. S., Ma, Y., Zhang, J. C., Zhang, Q., et al. (2024). Multi-scale characterization of shale pore structure of Niutitang Formation in southeastern Guizhou and its influence on shale gas enrichment. Geol. China 51 (01), 57–72.

Ge, M. N., Chen, K., Chen, L. X., Wang, C., and Bao, S. J. (2020). The influence factors of gas-bearing and geological characteristics of Niutitang Formation shale in the southern margin of Xuefeng Mountain ancient uplift: a case of Well Huangdi 1. China Geol. 3, 533–544. doi:10.31035/cg2020072

Goldberg, T., Strauss, H., Guo, Q., and Liu, C. (2007). Reconstructing marine redox conditions for the Early Cambrian Yangtze Platform: evidence from biogenic sulphur and organic carbon isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 254, 175–193. doi:10.1016/j.palaeo.2007.03.015

Guizhou Geological Survey Institute (2017). Regional geology of China, Guizhou province. Beijing: Geological Publishing House, 1153.

Guo, R. L., Zhao, Y. D., Wang, W. B., Hu, X. Y., Zhou, X. P., Hao, L. W., et al. (2020). Application of rare-earth elements and comparison to molecular markers in oil-source correlation of tight oil: a case study of Chang 7 of the Upper Triassic Yanchang Formation in Longdong area, Ordos Basin, China. ACS Omega 5, 22140–22156. doi:10.1021/acsomega.0c02233

Hakimi, M. H., Abdullah, W. H., and Shalaby, M. R. (2011). Organic geochemical characteristics of crude oils from the Masila Basin, eastern Yemen. Org. Geochem. 42, 465–476. doi:10.1016/j.orggeochem.2011.03.015

Hatch, J. R., and Leventhal, J. S. (1992). Relationship between inferred redox potential of the depositional environment and geochemistry of the Upper Pennsylvanian (Missourian) Stark Shale Member of the Dennis Limestone, Wabaunsee County, Kansas, U.S.A. Chem. Geol. 99, 65–82. doi:10.1016/0009-2541(92)90031-y

Hu, T., Pang, X. Q., Xu, T. W., Li, C. R., Jiang, S., Wang, Q., et al. (2022). Identifying the key source rocks in heterogeneous saline lacustrine shales: Paleogene shales in the Dongpu depression, Bohai Bay Basin, eastern China. AAPG Bull. 106 (6), 1325–1356. doi:10.1306/01202218109

Jarvie, D. M., Hill, R. J., Ruble, T. E., and Pollastro, R. M. (2007). Unconventional shale-gas systems: the Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment. AAPG Bull. 91, 475–499. doi:10.1306/12190606068

Jenner, G. A., Longerich, H. P., Jackson, S. E., and Fryer, B. J. (1990). ICP-MS-A powerful tool for high-precision trace-element analysis in earth sciences: evidence from analysis of selected U.S.G.S. reference samples. Chem. Geol. 83, 133–148. doi:10.1016/0009-2541(90)90145-w

Jin, L. X., Ogrinc, N., Yesavage, T., Hasenmueller, E. A., Ma, L., Sullivan, P. L., et al. (2014). The CO2 consumption potential during gray shale weathering: insights from the evolution of carbon isotopes in the Susquehanna Shale Hills critical zone observatory. Geochim. Cosmochim. Acta 142, 260–280. doi:10.1016/j.gca.2014.07.006

Jones, B., and Manning, D. A. C. (1994). Comparison of geochemical indices used for the interpretation of palaeoredox conditions in ancient mudstones. Chem. Geol. 111, 111–129. doi:10.1016/0009-2541(94)90085-x

Kaiser, S. I., Becker, R. T., Steuber, T., and Aboussalam, S. Z. (2011). Climate-controlled mass extinctions, facies, and sea-level changes around the Devonian–Carboniferous boundary in the eastern Anti-Atlas (SE Morocco). Palaeogeogr. Palaeoclimatol. Palaeoecol. 310, 340–364. doi:10.1016/j.palaeo.2011.07.026

Kirschvink, J. L. (1992). A paleogeographic model for vendian and cambrian time, the proterozoic biosphere: a multidisciplinary study. New York: Cambridge University Press, 567–581.

Kuila, U., and Prasad, M. (2013). Specific surface area and pore-size distribution in clays and shales. Geophys Prospect 61 (2), 341–362. doi:10.1111/1365-2478.12028

Lakin, J. A., Marshall, J. E. A., Troth, I., and Harding, I. C. (2016). “Greenhouse to icehouse: a biostratigraphic review of latest Devonian–Mississippian glaciations and their global effects,” in Devonian climate, sea level and evolutionary events ed . Editors R. T. Becker, P. K¨onigshof, and C. E. Brett (London: Special Publications. Geological Society), 439–464.

Lerman, A. D. I., and Gat, J. (1989). Physics and chemistry of lakes. Berlin: Springer-Verlag.

Li, Y. F., Fan, T. L., Zhang, J. C., Zhang, J. P., Wei, X. J., Hu, X. L., et al. (2015). Geochemical changes in the Early Cambrian interval of the Yangtze Platform, South China: Implications for hydrothermal influences and paleocean redox conditions. J. Asian Earth Sci. 109, 100–123. doi:10.1016/j.jseaes.2015.05.003

Lin, Z. J., Chen, D. F., and Liu, Q. (2008). Geochemical indices for redox conditions of marine sediments. Bull. China Soc. Mineral. Petrol. Geochem. 27 (1), 72–80.

Liu, J. S., Algeo, T. J., Qie, W. K., and Saltzman, M. R. (2019). Intensified oceanic circulation during early Carboniferous cooling events: evidence from carbon and nitrogen isotopes. Paleogeogr. Paleoclimatol. Paleoecol. 531, 108962. doi:10.1016/j.palaeo.2018.10.021

López, L., Lo Monaco, S., Galarraga, F., Lira, A., and Cruz, C. (1995). V/Ni ratio in maltene and asphaltene fractions of crude oils from the west Venezuelan basin: correlation studies. Chem. Geol. 119, 255–262. doi:10.1016/0009-2541(94)00100-m

Lowell, S., Shields, J. E., Thomas, M. A., and Thommes, M. (2004). Characterization of porous solids and powders: surface area, pore size and density. Berlin, Germany: Springer.

Lu, S. F. (2019). Study on shale gas accumulation rules and exploration area of Dawuba Formation of Carboniferous in Guizhou Province. Guiyang, China: Guizhou University.

Ma, F. H., Pan, J. L., Ma, R. Y., Zhang, Y., and Ma, X. J. (2019). Division of immature mud-shale organic type of Madongshan Formation in Liupanshan Basin. Nat. Gas. Geosci. 30, 1370–1377.

Ma, Y., Zhong, N. N., Cheng, L. J., Pan, Z. J., Li, H. Y., Xie, Q. M., et al. (2015). Pore structure of two organic-rich shales in southeastern Chongqing area: Insight from Focused lon Beam Scanning Electron Microscope (FIB- EM). Petroleum Geol. Exp. 37 (1), 109–116.

Meert, J. G. (2003). A synopsis of events related to the assembly of eastern Gondwana. Tectonophysics 362, 1–40. doi:10.1016/s0040-1951(02)00629-7

Meng, Q. T., Liu, Z. J., Bruch, A. A., Liu, R., and Hu, F. (2012). Palaeoclimatic evolution during Eocene and its influence on oil shale mineralisation, Fushun basin, China. J. Asian Earth Sci. 45, 95–105. doi:10.1016/j.jseaes.2011.09.021

Milliken, K. A. (2014). A Compositional Classification For Grain Assemblages In Fine-Grained Sediments and Sedimentary Rocks. J. Sediment. Res. 84 (12), 1185–1199. doi:10.2110/jsr.2014.92

Mongenot, T., Tribovillard, N., Desprairies, A., Lallier-Verg`es, E., and Laggoun-Defarge, F. (1996). Trace elements as palaeoenvironmental markers in strongly mature hydrocarbon source rocks: the Cretaceous La Luna Formation of Venezuela. Sediment. Geol. 103, 23–37. doi:10.1016/0037-0738(95)00078-x

Mukhopadhyay, P. K., Wade, J. A., and Kruge, M. A. (1995). Organic facies and maturation of Jurassic/Cretaceous rocks, and possible oil source rock correlation based on pyrolysis of asphaltenes, Scotian Basin, Canada. Org. Geochem. 22, 85–104. doi:10.1016/0146-6380(94)00061-1

Peters, K. E., and Cassa, M. R. (1994). “Applied source rock geochemistry,” in The petroleum system-from source to trap ed . Editors L. B. Magoon, L. B. Magoon, and W. G. Dow (Tulsa, OK: AAPG Memoir), 93–120.

Qie, W. K., Algeo, T. J., Luo, G. M., and Herrmann, A. (2019). Global events of the late Paleozoic (early Devonian to Middle Permian): a review. Palaeogeogr. Palaeoclimatol. Palaeoecol. 531, 109259. doi:10.1016/j.palaeo.2019.109259

Ross, D. J. K., and Bustin, R. M. (2006). Sediment Geochemistry of the Lower Jurassic Gordondale Member, Northeastern British Columbia. Bull. Can. Petroleum Geol. 54 (4), 337–365. doi:10.2113/gscpgbull.54.4.337

Sarki Yandoka, B. M., Abdullah, W. H., Abubakar, M. B., Hakimi, M. H., and Adegoke, A. K. (2015). Geochemical characterisation of Early Cretaceous lacustrine sediments of Bima Formation, Yola Sub-basin, Northern Benue Trough, NE Nigeria: Organic matter input, preservation, paleoenvironment and palaeoclimatic conditions. Mar. Petrol. Geol. 61, 82–94. doi:10.1016/j.marpetgeo.2014.12.010

Sing, K. S. (1995). Physisorption of nitrogen by porous materials. Mater 2 (1), 5–8. doi:10.1007/bf00486564

Steiner, M., Wallis, E., Erdtmann, B. D., Zhao, Y. L., and Yang, R. D. (2001). Submarine hydrothermal exhalative ore layers in black shales from South China and associated fossils – insights into a Lower Cambrian facies and bio-evolution. Palaeogeogr. Palaeoclimatol. Palaeoecol. 169, 165–191. doi:10.1016/s0031-0182(01)00208-5

Suleimenova, A., Bake, K. D., Ozkan, A., Valenza, J. J., Kleinberg, R. L., Burnham, A. K., et al. (2014). Acid demineralization with critical point drying: a method for kerogen isolation that preserves microstructure. Fuel 135, 492–497. doi:10.1016/j.fuel.2014.07.005

Tan, J., Horsfield, B., Fink, R., Krooss, B., Schulz, H., Rybacki, E., et al. (2014). Shale gas potential of the major marine shale formations in the Upper Yangtze Platform, South China, part III: mineralogical, lithofacial, petrophysical, and rock mechanical properties. Energy fuels. 28, 2322–2342. doi:10.1021/ef4022703

Tan, J., Horsfield, B., Mahlstedt, N., Zhang, J., Boreham, C., Hippler, D., et al. (2015). Natural gas potential of Neoproterozoic and lower Palaeozoic marine shales in the Upper Yangtze Platform, South China: geological and organic geochemical characterization. Int. Geol. Rev. 57 (3), 305–326. doi:10.1080/00206814.2015.1004200

Tan, J., Horsfield, B., Mahlstedt, N., Zhang, J., Diprimio, R., Vu, T., et al. (2013). Physical properties of petroleum formed during maturation of Lower Cambrian shale in the upper Yangtze Platform, South China, as inferred from PhaseKinetics modelling. Mar. Petrol. Geol. 48, 47–56. doi:10.1016/j.marpetgeo.2013.07.013

Tissot, B. P., and Welte, D. H. (1984). Petroleum Formation and occurrence. Berlin: Springer-Verlag.

Wang, J. Z., Li, X. G., Xu, Z. J., Xin, Y. L., Li, Z., and Li, Y. W. (2021). Shale Gas Accumulation Conditions and Favorable-zone prediction in the Lower Carboniferous Luzhai Formation in Donglan Area of the Nanpanjiang Depression, China. Earth Sci. 46 (5), 1814–1828.

Wang, L. (2020). Enrichment conditions and recoverable resources of shale gas of the focus layers in southeast Chongqing. Beijing, China: China University of Geosciences Beijing.

Wang, P.F., Jiang, Z.X., Chen, L., Yin, L.S., Li, Z., Zhang, C., et al. (2016). Pore structure characterization for the Longmaxi and Niutitang shales in the Upper Yangtze Platform, South China: Evidence from focused ion beam-He ion microscopy, nano-computerized tomography and gas adsorption analysis. Mar. Pet. Geol . 77, 1323–1337.

Wang, P. F., Jiang, Z. X., Han, B., Lv, P., Jin, C., Zhang, K., et al. (2018). Reservoir Geological Parameters for Efficient Exploration and Development of Lower Cambrian Niutitang Formation Shale Gas in South China. Acta Pet. Sin. 39 (2), 152–162.

Wang, Q. F., Yang, L., Xu, X. J., Santosh, M., Wang, Y. N., Wang, T. Y., et al. (2020a). Multi-stage tectonics and metallogeny associated with Phanerozoic evolution of the South China Block: a holistic perspective from the Youjiang Basin. Earth-Sci. Rev. 211, 103405. doi:10.1016/j.earscirev.2020.103405

Wang, S. J., Yang, T., Zhang, G. S., Li, D. H., and Chen, X. M. (2012). Shale gas enrichment factors and the selection and evaluation of the core area. Strategic Study CAE 14 (6), 94–100.

Wang, S. Q., Wang, S. Y., Man, L., Dong, D. Z., and Wang, Y. M. (2013). Appraisal method and key parameters for screening shale gas play. J. Chengdu Univ. Technol. Sci. Technol. Ed. 40 (6), 609–620.

Wang, S. Y., Zhang, H., Wang, T. H., Wang, C. H., Peng, C. L., Hu, R. F., et al. (2006). Filling and evolution of the late Paleozoic Shuicheng-Ziyun aulacogen in western Guizhou, China. Geol. Bull. China 25, 402–407. (in Chinese with English abstract).

Wang, X. D., Hu, K. Y., Qie, W. K., Sheng, Q. Y., Chen, B., Lin, W., et al. (2019b). Carboniferous integrative stratigraphy and timescale of China. Sci. China Earth Sci. 62, 135–153. doi:10.1007/s11430-017-9253-7

Wang, X. F., Peng, J., Yu, L. D., and Xu, T. Y. (2020b). A review of the research methods of paleosalinity geochemistry in continental strata. Sichuan Geol. J. 40 (2), 301–308.

Wang, X. X., Hou, J. G., Li, S. H., Dou, L. X., Song, S. H., Kang, Q. Q., et al. (2019a). Insight into the nanoscale pore structure of organic-rich shales in the Bakken Formation, USA. J. Petroleum Sci. Eng. 176, 312–320.

Wang, Y., and Cheng, H. F. (2023). Advances in microscopic pore structure characterization of fine-grained mudrocks. Energy fuels. 37 (3), 1495–1510. doi:10.1021/acs.energyfuels.2c03144

Wang, Y., Cheng, H. F., Hu, Q. H., Jia, L. B., Wang, X. M., Gao, S. S., et al. (2023). Adsorption of methane onto mudstones under supercritical conditions: Mechanisms, physical properties and thermodynamic parameters. Petrol. Sci. 20 (1), 34–47. doi:10.1016/j.petsci.2022.08.017

Wedepohl, K. H. (1971). Environmental influences on the chemical composition of shales and clays. Phys. Chem. Earth 8, 307–333. doi:10.1016/0079-1946(71)90020-6

Wright, J., Schrader, H., and Holser, W. T. (1987). Paleoredox variations in ancient oceans recorded by rare earth elements in fossil apatite. Geochem Cosmochim. Acta 51 (3), 631–644. doi:10.1016/0016-7037(87)90075-5

Wu, C. J., Tuo, J. C., Zhang, M. F., Sun, L. N., Yu, Q., and Liu, Y. (2016). Sedimentary and residual gas geochemical characteristics of the Lower Cambrian organic-rich shales in Southeastern Chongqing, China. Mar. Petroleum Geol. 75, 140–150. doi:10.1016/j.marpetgeo.2016.04.013

Wu, G. Y., Wang, W. F., and Chi, H. X. (2012). Basin evolution and later reformation of marine sediments in southern Guizhou Depression and neighboring areas. J. Palaeogeograhy 14 (4), 507–521.

Xu, Z. J., Li, X. G., Li, J., Xue, Y. L., Jiang, S., Liu, L. F., et al. (2022b). Characteristics of Source Rocks and Genetic Origins of Natural Gas in Deep Formations, Gudian Depression, Songliao Basin, NE China. ACS Earth Space Chem. 6 (7), 1750–1771. doi:10.1021/acsearthspacechem.2c00065

Xu, Z. J., Liu, L. F., Liu, B. J. M., Wang, T. G., Zhang, Z. H., Wu, K. J., et al. (2019). Geochemical characteristics of the Triassic Chang 7 lacustrine source rocks, Ordos Basin, China: Implications for paleoenvironment, petroleum potential and tight oil occurrence. J. Asian Earth Sci. 178, 112–138. doi:10.1016/j.jseaes.2018.03.005

Xu, Z. J., Wang, Y., Jiang, S., Fang, C., Liu, L. F., Wu, K. J., et al. (2022a). Impact of input, preservation and dilution on organic matter enrichment in lacustrine rift basin: A case study of lacustrine shale in Dehui Depression of Songliao Basin, NE China. Mar. Petr. Geol. 135, 105386. doi:10.1016/j.marpetgeo.2021.105386

Yang, X., Lei, Y., Zhang, J. C., Chen, S. J., Chen, L. Q., Zhong, K. S., et al. (2021). Depositional environment of shales and enrichment of organic matters of the Lower Cambrian Niutitang Formation in the Upper Yangtze Region. Nat. Gas. Ind. B 8, 666–679. doi:10.1016/j.ngib.2021.11.001

Yuan, K., Wang, K. Y., Gong, S. H., Qin, Y. L., Lu, S. F., Chen, R., et al. (2018). Shale Gas Enrichment Features and Impacting Factors in Carboniferous Dawuba Formation, Southern Guizhou Area. Coal Geol. China 30, 28–34.

Zeng, S. Q., Wang, J., Fu, X. G., Chen, W. B., Feng, X. L., Wang, D., et al. (2015). Geochemical characteristics, redox conditions, and organic matter accumulation of marine oil shale from the Changliang Mountain area, northern Tibet, China. China. Mar. Petrol. Geol. 64, 203–221. doi:10.1016/j.marpetgeo.2015.02.031

Zeng, Y. H., Fu, X. G., Zeng, S. Q., and Du, G. (2013). Upper Triassic potential source rocks in the Qiangtang Basin, Tibet: organic geochemical characteristics. J. Pet. Geol. 36, 237–255. doi:10.1111/jpg.12554

Zhang, L. T. (2019). Geochemical characteristics and geological significance of organic-rich shale in Chongqing area. Beijing, China: China University of Geosciences Beijing.

Zhang, Y. Y., He, Z. L., Gao, B., and Liu, Z. B. (2017). Sedimentary environment of the Lower Cambrian organic-rich shale and its influence on organic content in the Upper Yangtze. Pet. Geol. Exp. 39 (2), 154–161.

Zhou, W., Jiang, Z. X., Qiu, H. Y., Jin, X. C., Wang, R. H., Cen, W. P., et al. (2019). Shale Gas Accumulation Conditions and Prediction of Favorable Areas for the Lower Carboniferous Luzhai Formation in Guizhong Depression. Acta Pet. Sin. 40 (7), 798–812.

Zou, C. N., Zhu, R. K., Dong, D. Z., and Wu, S. T. (2022). Scientific and technological progress, development strategy and policy suggestion regarding shale oil and gas. Acta Pet. Sin. 43 (12), 1675–1686.

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Wang, Xu, Yuan, Wang and Ge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 20 June 2024
doi: 10.3389/feart.2024.1418087

Experimental and numerical studies on CO2 injectivity in low permeability oil reservoirs

Fankun Meng1,2,³, Lin Cao1,²*, Yuhui Zhou1,2,³, Botao Liu⁴, Chengyue Wen1,² and Jia Liu1,²

¹Key Laboratory of Drilling and Production Engineering for Oil and Gas, Wuhan, China

²School of Petroleum Engineering, Yangtze University, Wuhan, China

³Western Research Institute, Yangtze University, Karamay, China

⁴School of Computer Science, Yangtze University, Jingzhou, China

Edited by:
Wenguang Wang, Northeast Petroleum University, China

Reviewed by:
Guoqiang Xing, Changzhou University, China
Mingxian Wang, Xi’an Shiyou University, China
Lin Du, University of Alberta, Canada

* Correspondence: Lin Cao, shenglizhiguang123@163.com

Received: 16 April 2024
Accepted: 03 June 2024
Published: 20 June 2024

Citation: Meng F, Cao L, Zhou Y, Liu B, Wen C and Liu J (2024) Experimental and numerical studies on CO2 injectivity in low permeability oil reservoirs. Front. Earth Sci. 12:1418087. doi: 10.3389/feart.2024.1418087

Introduction: Contrary to the traditional recognition that CO₂ has large injectivity (the ratio of CO₂ injection volume to pressure drop) in low permeability oil reservoirs to keep the formation pressure at a high level, the CO₂ injection rate usually cannot attain the set value. It is essential to study the factors that influence CO₂ injectivity and propose the optimal strategies to improve the CO₂ injectivity.

Methods: Therefore, in this study, several core samples collected from low permeability oil reservoirs are used to experimentally investigate the influences of CO₂ injection rate, formation permeability, pressure and water saturation on CO₂ injectivity, and the corresponding pressure drop, oil and gas production are examined. To determine the primary factor that influences the CO₂ injectivity, orthogonal experimental design (ODE) and numerical simulations are utilized. In addition, to improve CO₂ injectivity, the techniques of mini-fracturing and radial perforation are presented, and the threshold values for these two parameters are determined.

Result and discussion: The results demonstrate that according to the magnitude of the extent that influences CO₂ injectivity, the rank for the above factors is CO₂ injection rate, reservoir pressure, formation water saturation and permeability. The oil recovery is mainly influenced by CO₂ injection rate and formation permeability, and the influences of reservoir pressure and water saturation on oil recovery are smaller. The threshold values for radial perforation and fracture half-length are 25 m and 50m, respectively, which can provide some guidance for the strategies that should be taken to improve the CO₂ injectivity and recover more oil from low permeability oil reservoirs.

Keywords: CO₂ injectivity, low permeability oil reservoir, CO₂ flooding, experimental study, numerical simulation

1 INTRODUCTION

Plenty of laboratory experiments and field pilot tests show that CO₂ flooding can enhance oil recovery significantly. According to the magnitude of formation pressure and minimum miscible pressure (MMP), there are two typical flooding types, miscible flooding and immiscible flooding (Dong et al., 2019). In the condition of miscible flooding, CO₂ can mix with the oil phase easily, which reduces the oil viscosity and density greatly, and the displacement efficiency is much higher than the immiscible flooding (Liu et al., 2020; Wang et al., 2020). In addition, the effects of CO₂ viscous fingering and gas override are weakened largely (Singh, 2018). Therefore, to improve CO₂ sweep efficiency and oil recovery, CO₂ miscible flooding should be utilized preferentially. To keep the condition of miscible flooding, the formation pressure should be larger than the MMP, and the amount of injected CO₂ should be increased as large as possible. However, in some low permeability oil reservoirs, the CO₂ injection rate cannot attain the set value, which causes the rapid decline of formation pressure, and the status gradually changes from miscible to immiscible flooding. Hence, it is significant to study the primary factors that influence the CO₂ injectivity, and to present the optimal strategies that improve the CO₂ injectivity.

As there are complex fluid distribution patterns, more researches focused on the abnormality of CO₂ injectivity during the water-alternating-gas/CO₂ (WAG) process. Experimentally, the effect of some primary features, such as relative permeability, water saturation, WAG ratio and CO₂ slug size, on CO₂ and bring injectivity during WAG flooding were studied and analyzed (Prieditis et al., 1991; Kamath et al., 1998; Shen et al., 2010; Yang et al., 2015). As the laboratory experiments are complicated and time-consuming, analytical or numerical methods are always used to evaluate the WAG injectivity. Some simple analytical models were developed to analyze the reasons that result in the differences of laboratory- and filed-observed CO₂ tertiary injectivity (Christman and Gorell, 1990; Meng et al., 2018; Meng et al., 2020). Nevertheless, for analytical models, it is difficult to consider some complex effects, such as phase behavior, dispersive mixing, gravity, viscous instability and crossflow. Therefore, numerical simulators are widely utilized to study the impact of these factors on WAG injectivity (Jr et al., 1992; Jr et al., 1992; Faisal et al., 2009; Qiao et al., 2016). For the factors that affect WAG injectivity (John and Rogers, 2001), summarized that wettability, chemical effects, entrapment, relative permeability, saturation effects, interfacial tension (IFT), formation heterogeneity, anisotropy, and stratification have significant impacts on the injectivity.

In analogy to the abundant studies on the injectivity during WAG flooding, the evaluation of CO₂ injectivity during CO₂ sequestration has been researched comprehensively. In terms of the factors that affect CO₂ injectivity (Sokama-Neuyam et al., 2017), found that the fines mobilization can seriously impair CO₂ injectivity in sandstone cores, which is more important than salt precipitation. In addition, the effects of SO₂ as an impurity, irreducible water saturation in a near-well region, non-Darcy flow, phase miscibility and gas compressibility on CO₂ injectivity in sandstone saline aquifer also have been investigated (Mijic et al., 2014; Raza et al., 2015; Wang et al., 2016; Parka et al., 2019). To select the suitable sites for CO₂ storage, and determine the CO₂ injectivity and storage capacity for these sites, some reactive transport models, which consider salt precipitation, and CO₂-water-rock geochemical reactions, were established properly (Xie et al., 2016; Dai et al., 2017; Cui et al., 2018). However, during the CO₂ injection process, several CO₂ storage reservoirs exhibit insufficient formation properties to support commercial-scale injection. Therefore, the application of hydraulic fracturing to enhance CO₂ injectivity was explored, which could significantly increase the CO₂ injection rate and reduce the well bottom-hole pressure (Raziperchikolaee et al., 2013; Huerta et al., 2020; Jung et al., 2020).

According to the above comprehensive reviews about CO₂ injectivity, we can see that the factors that cause the abnormality of CO₂ and water injectivity during WAG flooding have been investigated sufficiently. However, few studies pay attention to the evaluation of CO₂ injectivity during CO₂ flooding in low permeability oil reservoirs. Furthermore, specific measures to improve CO₂ injectivity have not been presented. Although the method of hydraulic fracturing has been implemented to enhance CO₂ injectivity during CO₂ sequestration, there are many differences between CO₂ flooding in low permeability oil reservoirs and CO₂ storage in saline aquifers. Hence, firstly, this paper studies the principal factors that influence CO₂ injectivity during CO₂ flooding in low permeability oil reservoirs through an experimental approach. Then, with the numerical simulation and ODE methods, the primary factors that affect CO₂ injectivity are ranked. Finally, the threshold values for two measures, mini-fracturing and radial perforation are determined, which could provide some guidance for the practical operations.

2 EXPERIMENTAL

2.1 Materials

The core, oil and brine samples are collected from a low permeability oil reservoir in the Shengli oil field. The measured size, porosity and permeability for eight cores are shown in Table 1. In addition, the pore volume (PV) of cores is also calculated. As can be seen, for all of the cores, the permeability is lower than 10 mD, whereas the porosity is almost the same.

TABLE 1 | Measured size, porosity and permeability for collected cores.

For oil samples, through the PVT experiments, the viscosity and density for live oil are measured to be 2.46 mPas and 0.7914 g/cm³ at the initial reservoir pressure 43.6 MPa and the reservoir temperature 126°C. The measured bubble-point pressure is 10.2 MPa, and the oil-gas ratio and formation volume factor are 37.6 and 1.144, respectively. With the slim-tube displacement tests, the MMP of the oil sample utilized in experiments is 28.9 MPa under reservoir temperature. The detailed compositional analysis for the oil sample is shown in Table 2. It can be seen that the oil samples mainly consist of light or medium components, particularly for C₁, which accounts for 14.77%, while the mole fraction of heavy component C₁₁₊ is only 27.19%. The measured molecular weight for C₁₁₊ is 313 g/mol.

TABLE 2 | Components of oil sample with compositional analysis.

For formation brine, the total salinity is 62428 mg/L, and the concentrations for Chloride, Sodium/Potassium are 37764 mg/L and 20617 mg/L. Then, according to the measured results, the salinity of synthetic brine for core experiments could be prepared.

2.2 Experimental setup

Figure 1 shows a schematic of the laboratory experimental setup utilized in this study. In this experimental set-up, there are five subsystems, which include the pump system, storage system, displacement system, monitoring system and temperature control system. For the fluid pump system, on the one hand, the fluids of oil, synthetic water and CO₂ are injected into the core sample under constant injection rate with a syringe pump, on the other hand, the nitrogen with high pressure is used to support the overburden pressure to the core holder. The back pressure at the outlet is set to be greater than the critical pressure of CO₂ (7.39 MPa), which can make the CO₂ keep in a supercritical state. In the monitoring system, the CO₂ injection pressure, the produced oil and gas, are measured with pressure gauge, liquid collector, and gas flow meter, respectively. With the temperature control system, the experiment temperature is maintained at reservoir temperature.

FIGURE 1 | Schematic of the experimental setup for CO₂ flooding.

2.3 Experimental procedure

The factors that affect CO₂ injectivity could be classified into geological features and production parameters. In this experimental study, the effects of four primary factors on CO₂ injectivity and oil recovery, which are CO₂ injection rate, formation pressure, water saturation, and formation permeability, are investigated. Some scholars had also studied these four parameters. The presence of residual natural gas saturation, in the short term, would decrease the CO2 injectivity considerably. However, with a permeability dependent rate, the situation would improve as more natural gas is recovered (Saeedi and Rezaee, 2012). In addition, it would be wised to evaluate injectivity experimentally or numerically by considering the residual constraints (fraction of water, remaining gas, and condensate (oil phase)) as it varies with the injection rates, formation pressure (Raza A., et al., 2017; Lzgec O., et al., 2008). However, to further investigate the effect of these four parameters on CO₂ injectivity, four scenarios are designed reasonably, and the detailed information of used cores in each scenario is listed in Table 3. The introduction for each scenario is shown as follows.

(1) Scenario #1. CO₂ is injected into the two core samples with similar porosity, permeability and connate water saturation at rates of 0.1 mL/min and 0.05 mL/min.

(2) Scenario #2. The production pressure is set to be 20 MPa and 10 MPa for two core samples with similar porosity, permeability and connate water saturation.

(3) Scenario #3. Two core samples with similar porosity and permeability, whose connate water saturation are 0.42 and 0.69, are selected and used to conduct CO₂ flooding.

(4) Scenario #4. Two core samples with similar porosity and connate water saturation, whose formation permeability are 2.5 mD and 0.63 mD, are employed to conduct CO₂ flooding.

TABLE 3 | Core sample specifications and designed parameters for four scenarios.

The experimental procedure is as follows.

Step 1: to simulate the oil reservoir under the initial condition, the core samples should be evacuated with a vacuum pump before the implementation of CO₂ flooding.

Step 2: synthetic water is injected into the cores at different rates. Subsequently, the core samples are flooded with live oil at a constant rate until there is no water production. Thus, the irreducible water saturation (connate water saturation) and original oil in place (OOIP) for each core sample can be acquired, which is shown in Table 3.

Step 3: The production pressure is set to be 20 MPa, which makes it identical to the current formation pressure.

Step 4: During the CO₂ flooding process for these four scenarios, the pressure drop, gas production and oil production are measured. Then, the oil recovery and CO₂ injectivity are calculated. In this study, CO₂ injectivity is defined as the ratio of CO₂ injection rate to a measured pressure drop. Once the CO2 flooding is terminated, by reducing the backpressure in a stepwise manner, the system pressure could be reduced to the atmospheric pressure.

3 EXPERIMENTAL RESULTS AND DISCUSSION

3.1 CO₂ injection rate (scenario #1)

Scenario #1 evaluates the effects of CO₂ injection rate on CO₂ injectivity. For different CO₂ injection rates, the oil recovery and cumulative gas production versus injected volume are shown in Figure 2, and the pressure drop and CO₂ injectivity versus injected volume are shown in Figure 3. As can be seen from these two figures, in the beginning, the CO₂ is compressed and injected into the core sample, in this stage, the injection pressure drop increase, and there is no oil production. It is obvious that the ultimate oil recovery and pressure drop increase with injection rate, while the gas breakthrough time and CO₂ injectivity in the later period decrease with it. The reasons for this phenomenon can be attributed to the fact that a larger CO₂ injection rate can greatly increase formation pressure, which increases the miscibility between CO₂ and oil and reduces the moving velocity of the CO₂ displacement front. Due to the better miscibility under a high CO₂ injection rate (0.1 cc/min), thus the swept efficiency of CO₂ and oil recovery are improved, and after CO₂ breakthrough the CO₂ injectivity is much lower than the injectivity under a small CO₂ injection rate (0.05 cc/min).

FIGURE 2 | Measured oil recovery, cumulative gas production versus CO₂ injected volume.

FIGURE 3 | Measured injection pressure drop, calculated CO₂ injectivity versus CO₂ injected volume.

3.2 Formation pressure (scenario #2)

The influences of formation pressure on CO₂ injectivity are studied in scenario #2. Figure 4 and Figure 5 show the oil recovery and cumulative gas production versus injected volume, the pressure drop and CO₂ injectivity versus injected volume, respectively. Since a larger formation pressure can increase the miscibility of CO₂ and oil, then as shown in Figure 4, the growth rate of cumulative gas production for this case is less than the case with lower formation pressure, and the ultimate oil recovery is higher. As the CO₂ injection rate is the same for both cases, the gas breakthrough time, maximum pressure drop and injectivity are almost identical. Nevertheless, with the decline of pressure drop, the CO₂ injectivity increases gradually. Since on the condition of large formation pressure, the mixing of CO₂ and oil is sufficient, thus the rising rate of CO₂ injectivity is less than the case with lower formation pressure.

FIGURE 4 | Measured oil recovery, cumulative gas production versus CO₂ injected volume.

FIGURE 5 | Measured injection pressure drop, calculated CO₂ injectivity versus CO₂ injected volume.

3.3 Formation water saturation (scenario #3)

Under the different water saturation conditions, Figure 6 and Figure 7 plot the measured oil recovery, cumulative gas production versus CO₂ injected volume and measured pressure drop, calculated CO₂ injectivity versus CO₂ injected volume, respectively. As shown in these two figures, for the case with higher water saturation, the maximum values of cumulative gas production and pressure drop are significantly less than the case with lower saturation, which also causes the CO₂ injectivity for the former case to be larger than the latter case. Through the comprehensive analysis for the above phenomenon, it can be speculated that with high water saturation, CO₂ flooding is similar to carbonated water or WAG displacement, thus CO₂ mobility is greatly reduced, and CO₂ swept efficiency is improved. In addition, influenced by the capillary trapping effect for cases with high water saturation, a large amount of CO₂ is trapped in the porous media, which results in the decline of gas production. Since the mobility of oil is much lower than the water, the CO₂ injectivity in the formation with low water saturation is less than the formation with high water saturation.

FIGURE 6 | Measured oil recovery, cumulative gas production versus CO₂ injected volume.

FIGURE 7 | Measured injection pressure drop, calculated CO₂ injectivity versus CO₂ injected volume.

3.4 Formation permeability (scenario #4)

For core samples with different permeability, Figure 8 shows the oil recovery, gas production versus CO₂ injected volume, and Figure 9 shows the injection pressure drop, CO₂ injectivity versus CO₂ injected volume. As the porous space for formation with lower permeability is mainly consisted of micro and nanopores, the CO₂ moving velocity in these pores is slower, and a large number of CO₂ is trapped in the micropores, which causes that the rising rate of oil recovery and gas production is lower, as shown in Figure 2. To obtain a higher oil recovery, more CO₂ is needed for tight formation in practice. Furthermore, impacted by the large portion of micro and nanopores, the CO₂ injectivity for tight formation is significantly less than the formation with larger permeability.

FIGURE 8 | Measured oil recovery, cumulative gas production versus CO₂ injected volume.

FIGURE 9 | Measured injection pressure drop, calculated CO₂ injectivity versus CO₂ injected volume.

4 NUMERICAL SIMULATION

As shown in the above parts, the factors that influence the CO₂ injectivity have been studied through experiments; however, due to the complexity and time-consuming of experiments, it is difficult to evaluate the influence of these factors on CO₂ injectivity quantitatively. Therefore, the combinations of numerical simulation and OED methods are used to solve the mentioned problems.

4.1 Basic model

The oil reservoir of interest is located in Bohai Bay Basin, Shengli oilfield, which has 19 production wells and 11 injection wells in total. As can be seen from Figure 10A that there are many faults in this reservoir, which belongs to a complex fault-block oil formation. Currently, CO₂ flooding is only implemented in the main body of this reservoir, and the well group is a classical five-point pattern. To evaluate the CO₂ injectivity of injection wells and the production performance of production wells directly, one of the well groups is selected, and the detailed information about formation and production parameters is shown in Table 4. The fluid compositional components are shown in Table 2. With Eclipse compositional simulator, a grid system of 70 × 70 × 5 is developed and represented the target well group, which results in a grid block size of 5 × 5 × 2.12 m, as shown in Figure 10B.

FIGURE 10 | (A) Well locations in the reservoir of interest and (B) the presented model in this paper.

TABLE 4 | Formation and production parameters for target well group.

Initially, the depleted development is adopted, and after 5 years of depleted production, CO2 flooding is used to enhance oil recovery in the low permeability reservoir. The relative permeability curves for oil/gas phases and oil/water phases used in this study are shown in Figure 11, which are acquired from the laboratory experiments. The measurement process of the relative permeability curve for oil/water phases and oil/gas phases is as follows:

FIGURE 11 | Relative permeability curves for (A) oil-water phase and (B) oil-gas phase.

The oil and water are injected into the core at a certain flow rate at the same time, which creates a pressure difference at both ends of the core. When the oil/water flow rate are stable, the oil/water saturations in the core do not change. According to Darcy’s law, the permeabilities of the oil/water phases at a certain saturation are calculated. By changing the oil/water flow ratio, the permeabilities of the oil/water phases under different saturations can be calculated, and then the relative permeability curve for oil/water phases can be drawn. The same is true for the relative permeability curve of gas and oil.

4.2 Orthogonal experimental design (OED)

To evaluate the influences of four factors (CO₂ injection rate, formation pressure, water saturation, and permeability) mentioned in the experimental study quantitatively, the OED method is employed. According to a practical variation range for injection rate, formation pressure, reservoir water saturation, and rock permeability, the levels for these parameters are designed reasonably, which are shown in Table 5.

TABLE 5 | Design of levels for four primary influential factors.

Since there are four factors, thus orthogonal array L₉ (3⁴) is used to design the schemes, which is shown in Table 6. For each scheme, through the application of the numerical simulator, the production performances for the reservoir and wells can be obtained. The other essential parameters during the simulation can be found in Section 4.1. In Table 6, the CO₂ injectivity is defined as the ratio of total CO₂ injection volume to formation pressure drop. The used pressure drop is the difference between the formation average pressure in the last moment and the value before CO₂ flooding.

TABLE 6 | Orthogonal design schemes and simulation results.

According to the numerical simulation results, the mean value for each level can be calculated, which is expressed as K₁, K₂and K₃in Table 7. In addition, the range for each level, R, can also be obtained.

It can be seen from Table 7 that the range R for different CO₂ injection rate is the largest (342.1 t/MPa), while for formation permeability it is the lowest (107.5 t/MPa), which indicates that CO₂ injectivity is primarily influenced by CO₂ injection rate, and the formation permeability has few effects on CO₂ injectivity. The formation water saturation and permeability have moderate impacts on CO₂ injectivity. The results are identical to experimental observations. For instance, through the comparisons of Figures 3, 5, 7, and 9, we can see that the difference of maximum CO₂ injectivity for different injection rates is largest, which is 7.15 cc/(hMPa). Whereas the difference of maximum injectivity for different permeability is 0.07 cc/(hMPa), which is lower than the other factors. For different formation pressure and water saturation, the differences of maximum injectivity are 0.76 cc/(hMPa) and 0.25 cc/(hMPa), respectively, which are intermediate between the injection rate and formation permeability. Therefore, through the combination of OED results and experimental observations, we can conclude that the rank for the influential factors of CO₂ injectivity is CO₂ injection rate, reservoir pressure, formation pressure, and permeability.

TABLE 7 | Analysis for the simulation results of designed schemes.

4.3 CO₂ injectivity improvement strategies

As mentioned above, the CO₂ injection rate is the primary factor that affects CO₂ injectivity, therefore, reasonable measures that can improve the CO₂ injection rate should be presented. In this study, two measures are proposed, which are radial perforation and mini-fracturing. The locations of radial perforation wellbore and mini-fractures are shown in Figure 12. To simulate the mini-fractures properly, Local Grid Refinement (LGR) technology is utilized, and the refined grids conform to the logarithmic distribution pattern.

FIGURE 12 | Numerical simulation models for (A) radial perforation and (B) mini-fracturing measures.

On the one hand, the employment of radial perforation and mini-fracturing can enhance the CO₂ injectivity, on the other hand, the implementation of radial perforation and mini-fracturing can result in the appearance of gas channeling in advance. To obtain large CO₂ injectivity and avoid the early gas breakthrough simultaneously, the lengths for radial perforation and mini-fracturing should be optimized. The parameters that are used have been listed in Section 4.1.

4.3.1 Radial perforation

Figure 13 plots the relationship curve between radial perforation length and CO₂ injectivity. The method to calculate CO₂ injectivity has been stated in Section 4.2. It can be seen from this figure that when the radial perforation length is less than 25 m, the CO₂ injectivity increases with the radial perforation length; when it is larger than 25 m, the CO₂ injectivity for different radial perforation lengths is nearly the same, which demonstrates that the threshold value for radial perforation length is 25 m. The reasons for this phenomenon may be attributed to the fact that on the condition of identical injection volume for different radial perforation lengths, when it is lower than one certain value, the formation energy cannot be supplemented in time. However, when it is larger than this critical value, the formation energy can be supplemented, and the rise of radial perforation length has few effects on the CO₂ injectivity.

FIGURE 13 | Relationship between radial perforation length and CO₂ injectivity.

4.3.2 Mini-fracturing

The relationship between mini-fracture half-length and CO₂ injectivity is shown in Figure 14. As can be seen from this figure, in the beginning, the CO₂ injectivity increases with min-fracture half-length drastically. Nevertheless, when the half-length is larger than 50 m, the increasing rate of CO₂ injectivity decline obviously. Therefore, it can be concluded that the threshold value for min-fracture half-length is 50 m in this study. In addition, it can be seen that the relationship curves in Figure 13 and Figure 14 are similar, which indicates that the reasons for interpreting the variation rule are also the same as each other.

FIGURE 14 | Relationship between mini-fracture half-length and CO₂ injectivity.

5 CONCLUSION

This paper studies the factors that affect CO₂ injectivity and oil recovery through the experiments. With the numerical simulation and OED method, the rank for primary factors that influence CO₂ injectivity is given, and the optimal radial perforation length and mini-fracture half-length are presented. Some highlights of this work include the following.

(1) CO₂ injection has the largest impact on CO₂ injectivity, while the formation permeability has the lowest influence on CO₂ injectivity, and the formation saturation and pressure are intermediate between the CO₂ injection and formation permeability.

(2) For formation with large water saturation, the injectivity and displacement efficiency is higher, while the gas production is lower, which indicates that it is more suitable for the implementation of CO₂ flooding and sequestration.

(3) On the condition of large formation pressure, due to the sufficient solution and diffusivity of oil and CO₂, the gas production is less, and the CO₂ injectivity increases gradually, which indicates that the earlier CO₂ flooding is implemented, the better production performance can be obtained.

(4) To enhance CO₂ injectivity, the measures of radial perforation and mini-fracturing can be taken, and the threshold values for the perforation length and the fracture half-length are 25 m and 50 m, respectively.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

AUTHOR CONTRIBUTIONS

FM: Writing–review and editing. LC: Writing–original draft. YZ: Resources, Project administration, Software, Writing–review and editing. BL: Writing–original draft, Resources, Supervision. CW: Conceptualization, Data curation, Formal Analysis, Writing–original draft. JL: Investigation, Methodology, Software, Validation, Writing–review and editing.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work is supported by the National Natural Science Foundation of China (Grant No. 52104018, 52274030).

ACKNOWLEDGMENTS

The authors would like to appreciate reviewers and editors whose critical comments were helpful in preparing this article.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Christman, P. G., and Gorell, S. B. (1990). Comparison of laboratory-and field-observed CO₂ tertiary injectivity. J. Petroleum Technol. 42 (2), 226–233. doi:10.2118/17335-pa

Cui, G., Wang, Y., Rui, Z., Chen, B., Ren, S., and Zhang, L. (2018). Assessing the combined influence of fluid-rock interactions on reservoir properties and injectivity during CO₂ storage in saline aquifers. Energy 155, 281–296. doi:10.1016/j.energy.2018.05.024

Dai, Z., Zhang, Y., Stauffer, P., Xiao, T., Zhang, M., Ampomah, W., et al. (2017). Injectivity evaluation for offshore CO₂ sequestration in marine sediments. Energy Procedia 114, 2921–2932. doi:10.1016/j.egypro.2017.03.1420

Dong, P., Liao, X., Chen, Z., and Chu, H. (2019). An improved method for predicting CO₂ minimum miscibility pressure based on artificial neural network. Adv. Geo-Energy Res. 3 (4), 355–364. doi:10.26804/ager.2019.04.02

Faisal, A., Bisdom, K., Zhumabek, B., Mojaddam Zadeh, A., and Rossen, W. R. (2009). “Injectivity and grativity segregation in WAG and SWAG enhanced oil recovery,” in Paper presented at SPE annual technical conference and exhibition , 4–7.

Huerta, N. J., Cantrell, K. J., White, S. K., and Brown, C. F. (2020). Hydraulic fracturing to enhance injectivity and storage capacity of CO₂ storage reservoirs: benefits and risks. Int. J. Greenh. Gas Control 100 (1013105), 103105–103112. doi:10.1016/j.ijggc.2020.103105

Izgec, O., Demiral, B., Bertin, H., and Akin, S. (2008). CO₂ injection into saline carbonate aquifer formations I: laboratory investigation. Transp. Porous Media 72 (1), 1–24. doi:10.1007/s11242-007-9132-5

JohnRogers, D. R. B. G., and Grigg, R. B. (2001). A literature analysis of the WAG injectivity abnormalities in the CO₂ process. SPE Reserv. Eval. Eng. 4 (5), 375–386. doi:10.2118/73830-pa

Jr, M. K. R., Cheng, C. T., Varnon, J. E., Pope, G. A., and Sepehrnoori, K. (1992). “Interpretation of a CO₂ WAG injectivity test in the San Andres formation using a compositional simulator,” in Paper presented at SPE/DOE eighth symposium on enhanced oil recovery . Tulsa, Oklahoma, USA.

Jr, M. K. R., Pope, G. A., and Sepehrnoori, K. (1992). “Analysis of tertiaryinjectivity of carbon dioxide,” in Paper presented at SPE permian basin oil and gas recovery conference midland , 18–20.

Jung, H., Espinoza, D. N., and Hosseini, S. A. (2020). Wellbore injectivity response to step-rate CO₂ injection: coupled thermo-poro-elastic analysis in a vertically heterogeneous formation. Int. J. Greenh. Gas Control 102, 103156. doi:10.1016/j.ijggc.2020.103156

Kamath, J., Nakagawa, F. M., Boyer, R. E., and Edwards, K. A. (1998). “Laboratory investigation of infectivity losses during WAG in West Texas Dolomites,” in Paper presented at SPE permian badn oil and gas recovery cofderence, midland,Texas, USA , 22–27.

Liu, H., Zhu, Z., Patrick, W., Liu, J., Lei, H., and Zhang, L. (2020). Numerical visualization of supercritical CO₂ displacement in pore-scale porous and fractured media saturated with water. Adv. Geo-Energy Res. 4 (4), 419–434. doi:10.46690/ager.2020.04.07

Meng, F., Su, Y., Hao, Y., Li, Y., and Tong, G. (2018). Injectivity of CO₂ WAG in low permeability oil reservoirs based on B-L equation. J. China Univ. Petroleum 42 (4), 91–99. doi:10.3969/j.issn.1673-5005.2018.04.011

Meng, F., Su, Y., Wang, W., Lei, Q., and He, D. (2020). Semi-analytical evaluation for water-alternating-CO₂ injectivity in tight oil reservoirs. Int. J. Oil Gas Coal Technol. 24 (1), 62–84. doi:10.1504/ijogct.2020.106704

Mijic, A., LaForce, T. C., and Muggeridge, A. H. (2014). CO₂ injectivity in saline aquifers: the impact of non-Darcy flow, phase miscibility, and gas compressibility. Water Resour. Res. 50 (5), 4163–4185. doi:10.1002/2013wr014893

Parka, Y.-C., Kimb, S., Leec, J. H., and Shinn, Y. J. (2019). Effect of reducing irreducible water saturation in a near-well region on CO₂ injectivity and storage capacity. Int. J. Greenh. Gas Control 86, 134–145. doi:10.1016/j.ijggc.2019.04.014

Prieditis, J., Wolle, C. R., and Notz, P. K. (1991). “A laboratory and field injectivity study CO₂ WAG in the San Andres formation of West Texas,” in Paper presented at SPE 66rd annual technical conference and exhibition, Dallas, TX, USA .

Qiao, C., Li, L., Johns, R. T., and Xu, J. (2016). Compositional modeling of dissolution-induced Injectivity alteration during CO₂ flooding in carbonate reservoirs. SPE J. 21 (03), 0809–0826. doi:10.2118/170930-pa

Raza, A., Gholami, R., Rezaee, R., Bing, C. H., Nagarajan, R., and Hamid, M. A. (2017). Preliminary assessment of CO₂ injectivity in carbonate storage sites. Petroleum 3 (1), 144–154. doi:10.1016/j.petlm.2016.11.008

Raza, A., Rezaee, R., Gholami, R., Rasouli, V., Bing, C. H., Nagarajan, R., et al. (2015). Injectivity and quantification of capillary trapping for CO₂ storage: a review of influencing parameters. J. Nat. Gas Sci. Eng. 26, 510–517. doi:10.1016/j.jngse.2015.06.046

Raziperchikolaee, S., Alvarado, V., and Yin, S. (2013). Effect of hydraulic fracturing on long-term storage of CO₂ in stimulated saline aquifers. Appl. Energy 102, 1091–1104. doi:10.1016/j.apenergy.2012.06.043

Saeedi, A., and Rezaee, R. (2012). Effect of residual natural gas saturation on multiphase flow behaviour during CO₂ geo-sequestration in depleted natural gas reservoirs. J. Petroleum Sci. Eng. 82-83, 17–26. doi:10.1016/j.petrol.2011.12.012

Shen, P., Chen, X., and Qin, J. (2010). Pressure characteristics in CO₂ flooding experiments. Petroleum Explor. Dev. 37 (2), 211–215. doi:10.1016/s1876-3804(10)60026-2

Singh, H. (2018). Impact of four different CO₂ injection schemes on extent of reservoir pressure and saturation. Adv. Geo-Energy Res. 2 (3), 305–318. doi:10.26804/ager.2018.03.08

Sokama-Neuyam, Y. A., Ginting, P. U. R., Timilsina, B., and Ursin, J. R. (2017). The impact of fines mobilization on CO₂ injectivity: an experimental study. Int. J. Greenh. Gas Control 65, 195–202. doi:10.1016/j.ijggc.2017.08.019

Wang, W., Meng, F., Su, Y., Hou, L., Geng, X., Hao, Y., et al. (2020). A simplified capillary bundle model for CO₂-alternating-water Iinjection using an equivalent resistance method. Geofluids 2020, 1–14. doi:10.1155/2020/8836287

Wang, Z., Wang, J., Lan, C., Ko, V., Ryan, D., and Wigston, A. (2016). A study on the impact of SO₂ on CO₂ injectivity for CO₂ storage in a Canadian saline aquifer. Appl. Energy 184, 329–336. doi:10.1016/j.apenergy.2016.09.067

Xie, J., Zhang, K., Li, C., and Wang, Y. (2016). Preliminary study on the CO₂ injectivity and storage capacity of low-permeability saline aquifers at Chenjiacun site in the Ordos Basin. Int. J. Greenh. Gas Control 52, 215–230. doi:10.1016/j.ijggc.2016.07.016

Yang, D., Song, C., Zhang, J., Ji, Y., and Gao, J. (2015). Performance evaluation of injectivity for water-alternating-CO₂ processes in tight oil formations. Fuel 139, 292–300. doi:10.1016/j.fuel.2014.08.033

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Meng, Cao, Zhou, Liu, Wen and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 26 June 2024
doi: 10.3389/feart.2024.1395384

The differential collapse–filling evolution process of the paleo-underground river in the Northern Uplift of the Tarim Basin

Hong-Qi Dong1,², Jia-Peng Liang²*, Qing-Yu Zhang², Jing-Rui Li², Jun-Wei Wan¹, Guo-Quan Nie² and Yu-Lu Li¹

¹School of Environmental Studies, China University of Geosciences, Wuhan, China

²Institute of Karst Geology, Chinese Academy of Geological Sciences/Karst Dynamics Laboratory, MLR and GZAR, Guilin, China

Edited by:
Xixin Wang, Yangtze University, China

Reviewed by:
Keji Yang, Hebei GEO University, China
Sheng Fu, Xi’an Shiyou University, China

* Correspondence: Jia-Peng Liang, liangjiapeng@mail.cgs.gov.cn

Received: 03 March 2024
Accepted: 26 March 2024
Published: 26 June 2024

Citation: Dong H-Q, Liang J-P, Zhang Q-Y, Li J-R, Wan J-W, Nie G-Q and Li Y-L (2024) The differential collapse–filling evolution process of the paleo-underground river in the Northern Uplift of the Tarim Basin. Front. Earth Sci. 12:1395384. doi: 10.3389/feart.2024.1395384

The characteristics and stages of collapse–filling in paleo-underground rivers vary in recharge–runoff–discharge zones, constraining the associated fracture-caved reservoirs in carbonate strata. This paper comprehensively uses core, fluid inclusion, and carbon–oxygen isotope to probe the evolution process and migration of collapse–filling in paleo-underground rivers in the Northern Uplift of the Tarim Basin. The results show that 1) more than three stages of collapse–filling were identified in recharge–runoff–discharge zones, and a four-stage differential collapse–filling process was proposed to summarize the evolution of paleo-underground rivers. 2) The collapse–filling process varies spatiotemporally in the recharge, runoff, and discharge zones. Hydrodynamic strength and filling capacity migrate gradually from the recharge zone to the discharge zone. 3) Collapse–filling mechanisms, including gravity, suffusion, and suction–erosion mechanisms, also vary along with the collapse–filling evolution process of paleo-underground rivers. The research provided a new insight to recognize and interpret the differential planar distribution and vertical filling of the paleo-underground river system, which can be further applied to investigate the fracture-caved karst reservoirs.

Keywords: collapse–filling evolution, paleo-underground river, mechanism, Ordovician carbonate rock, Tarim Basin

1 INTRODUCTION

Carbonate rocks can form pores, caves, and fractures at different scales under karstification, which are important storage spaces for oil, natural gas, groundwater, and other resources (James et al., 1988; Yuan, 1993). After multi-stage karstification and continuous transformation, the paleo-underground river became one of the important reservoir spaces that commonly existed in the karst system. However, this reservoir is easily neglected due to its difficulty and complexity in identifying and defining its architecture underground (Loucks et al., 2004; Zhang et al., 2018; Zhang et al., 2020; Zhang et al., 2021; Dan et al., 2023; Wang et al., 2023; Zhang et al., 2023). During the evolution process of paleo-underground river channel formation and the subsequent multi-stage karst reconstruction, the collapse–filling phenomenon could be clearly observed. Generally, a large number of chemical precipitation, water flow transportation, collapse deposition, and organisms consist of the fillings under physical and chemical interaction (Chen et al., 2023). These fillings change the original reservoir space and can be taken as excellent samples for studying the karst reservoir architecture, evolution process, and formation mechanism (Shi et al., 2015).

Fracture-cave fillings are significant samples for studying the collapse–filling process and further probing karst reservoir formation and transformation process (Liu et al., 2009; Dan et al., 2012; Sun et al., 2013; Qiang et al., 2015). Geochemical analysis of fillings is the common method used to distinguish karst stages, such as the analysis of trace elements in sand–mud filling (Liu and Cai, 2009), isotopic analysis of cave macrocrystalline calcite (Liu et al., 2008), strontium isotope (Zhang et al., 2005; Liu et al., 2006; Liu et al., 2007; Zhang and Cai, 2007), carbon and oxygen isotopes (Winter and Knauth, 1992; Chen, 1994; Wang and Al-Aasm, 2002; Xia and Tang, 2004; Xia et al., 2006; Qian et al., 2008; Ainsaar et al., 2010; Wu et al., 2010; Li et al., 2011; Meng et al., 2011; Xia et al., 2011; Haeri-Ardakani et al., 2013), aqueous inclusion homogenization temperature (Xia and Tang, 2004; Xia et al., 2006; Wu et al., 2010; Xia et al., 2011; Li et al., 2012; Li et al., 2013), and autogenous illite K–Ar dating (Xu et al., 2012). Combined with the analysis of regional tectonic evolution and associated quantitative parameters (Xu et al., 2012; Zhang, 2020), a comprehensive application of a variety of geochemical methods (Zhang et al., 2012; Wang et al., 2017; Lyu et al., 2020) was also used to distinguish collapse–filling stages. However, there is still a lack of suitable methods to classify karst stages in detail by integrating core, isotope, and other means. Moreover, detailed research studies on dissecting karst stages and evolutionary differences of different zones in a unified karst system are rare.

The karst products are characterized by complicated mechanisms and architecture that are difficult to precisely identify underground. More than 70% of the reservoir space of large karst caves is filled with sedimentary mud, sand, collapse breccia, and other materials, which are controlled by complicated filling mechanisms and influencing factors (Xu et al., 2010; Zou et al., 2016; Xu et al., 2020). Loucks et al. (2004) proposed the evolutionary history model of a single karst cave and the collapse model of combined caves and believed that karst caves developed from the near-surface subsurface flow zone were then transformed in the seepage zone, and finally underwent multiple stages and effects such as mechanical compaction and gravity collapse to form karst caves during burial. Studies on many sedimentary basins have proven that the development of paleo-karst was influenced by many factors, such as tectonic movement, weathering and denudation, and burial (Kang et al., 2014). In different stages and zones during the karst evolution process, it is worth considering whether the factors and formation mechanism changed temporally and spatially along with the karst evolution process (Li et al., 2021).

In the northern Tarim Basin, a large paleo-karst cave system was well observed in the mid-Ordovician carbonate rocks under the seismic attributes, and the paleo-underground river channels were widely identified in the karst system (Zhang et al., 2020; Hu, 2022; Lyu et al., 2022). Many scholars explored and analyzed the identification (Zhang et al., 2012), architecture type (Xu et al., 2010; Liu et al., 2021), filling type and distribution rule, filling stage, formation mechanism, reservoir architecture, and prediction of karst caves in the Tahe area (Zou et al., 2016; Sun et al., 2017; Zhang et al., 2017; Xu et al., 2020), but they did not take the formation and evolution of karst caves as a complete karst system to explore the internal relations and differences. Therefore, the fillings of paleo-underground rivers in the weathering crust karst area of the Tahe oilfield were taken as samples in this paper. Based on the observation and description of drilling cores, carbon and oxygen isotope tests, inclusion analysis, logging data analysis, and seismic data interpretation, we depict the filling architecture, analyze the stages of collapse–filling, and discuss the differential evolution process and associated filling mechanism of typical paleo-underground rivers.

2 GEOLOGICAL SETTING

The Tarim Basin covers an area of 560,000 km², and it is the largest oil- and gas-bearing basin in China; it is located between the northern Tianshan Mountain and the southern Kunlun–Altun Mountains (Figure 1A; Jia, 1997). The basin contains the Late Neoproterozoic–Quaternary sediments covering Archean–Early Neoproterozoic crystalline basement with a thickness of more than 10 km (Figure 1B; Wu et al., 2010). The study area is located on the southern slope of the Northern Uplift of the Tarim Basin.

FIGURE 1 | Simplified map of the tectonic units (A) and comprehensive stratigraphic column (B) in the northern Tarim Basin.

The Ordovician strata in the study area can be divided into the Penglaiba (O₁p), Yingshan (O₁y), Yijianfang (O₂y), Chalbac (O₃q), Lianglitage (O₃l), and Sangtamu (O₃s) formations from the bottom to top. The Middle–Lower Ordovician strata in the area are composed of a set of carbonate rocks formed in the platform facies. The Yingshan Formation is widely distributed in the area and is one of the main formations where karst is widely observed in the study area. The lithology is mainly gray, light gray, and brown–gray thick-bedded micrite, along with algal clot pelletic micrite containing algal sand and gravel calcarenite, and the lithology is dense and easy to dissolve. The strata, therefore, provide a good material foundation for later karstification and associated karst reservoir, which has been proven as a high-quality karst reservoir in the Tahe oilfield (Qi and Yun, 2010; Zhang et al., 2012; Hu et al., 2014; Kang, 2014).

The study area has undergone multiple stages of uplift and associated karstification, and the fracture-cave filling of Ordovician carbonate rocks is characterized by multiple stages and styles (Jin et al., 2015). Specifically, the Ordovician carbonate rocks in this area commonly experienced four stages of exposure and weathering karst processes (Yu et al., 2011; Chen et al., 2016). 1) After the period of the Yijianfang Formation, the study area was uplifted and denudated, with strong weathering and karstification. 2) After the period of the Lianglitage Formation, the study area was exposed again, with the characteristic of structural high in the north and low in the south. 3) Before the Silurian period, some strata were denuded, and several formations such as Yijianfang and Yingshan formations were directly exposed to the surface, forming a pre-Silurian buried hill. 4) In the early Hercynian period, the orogenic belt was strongly folded, and a local unconformity between the Middle–Lower Ordovician and the overlying strata was formed.

After multi-stage dissolution and transformation, a series of highly heterogeneous karst reservoir spaces such as paleo-underground rivers, large karst caves, and dissolution caves have been formed in the Yijianfang and Yingshan formations (Xia et al., 2011; Lyu et al., 2020). In the study area, the thickness of affected strata of buried hill karst ranges up to 200 m, and a large-scale karst cave system is formed. The karst cave fillings contain mud, carbonate breccia, and some chemical deposits such as peat and stalactites. During the reconstruction process of dissolution, collapse, and filling, the structural characteristics in different parts of the paleo-underground river are quite different (Zhang, 1985; Liu and Cai, 2009).

3 DATA AND METHODOLOGY

3.1 Methodology for karst cave architecture analysis

A solutional cave is a macroscopic void in a rock created and shaped by moving aggressive fluids, and morphology is the fundamental attribute of the cave that reflects its origin and evolution. Thus, morphogenetic analysis of the caves aims to reconstruct the geological controls of the speleogenesis and parameters of speleogenetic agents (fluids) (Klimchouk et al., 2016). Important attributes of fluids that determine various aspects of cave morphology are their aggregate states and various modes of movement and aggressiveness (Lauritzen and Lundberg, 2000). These attributes differ between various hydrodynamic zones, types of flow systems, and components of the latter. The relationships between morphologic features (also termed forms, speleomorphs, and speleogens) and speleogenetic agents were established, although not always unequivocally, from generalizations based on field observations and physical and numerical models (Bögli, 1980; Ford and Williams, 1989; Lauritzen and Lundberg, 2000; Palmer, 2007; Klimchouk et al., 2016).

Figure 2 was mapped using the above methodology. The RMS amplitude space ranging from 40 to 120 ms (confidential data) under the bottom of the Middle Ordovician Yijianfang Formation and paleo-topography after the period of the Middle–Lower Ordovician Yingshan Formation was comprehensively applied to constrain and describe the paleo-channels in the Yingshan Formation.

FIGURE 2 | Typical paleo-underground river channel on the seismic profile (T₇⁴, top of Yijianfang Formation; the profile location is shown as a red line in Figure 3; the red line denotes fault, the black line denotes the bottom boundary, and the blue line denotes the well.).

3.2 Geochemical method for cave filling analysis

3.2.1 Carbon and oxygen isotopes

All of the 22 samples were collected in the paleo-underground river in the Yingshan Formation in the Upper–Middle Ordovician strata. The collected samples were subjected to grinding, drying, acidification, and other pretreatments, and then tested for carbon and oxygen isotope composition. The test work was completed by the Karst Geological Resources and Environmental Supervision and Inspection Center of the Ministry of Nature Resources. A stable isotope mass spectrometer (Item no.: MAT253, Thermo Fisher Scientific, Waltham, MA, American) was used to test δ¹³C and δ¹⁸O. The test precision was 0.1‰. The ambient temperature and relative humidity were 24.3°C and 40%, respectively.

3.2.2 Fluid inclusion of calcite

All samples were calcite in fracture caves from one underground river system developed in the Yingshan Formation in the Upper–Middle Ordovician strata. The homogenization temperature (Th) and ice temperature (Tm) data involved were completed at the Karst Geological Resources Environmental Monitoring Center, Ministry of Natural Resources. The instrument used was a Linkam hot and cold table, model THMS600. The salinity (wt% NaCl equivalent) of aqueous inclusions was calculated based on Tm values.

4 RESULTS

4.1 Distribution and architecture of the paleo-underground river

The study area mainly shows the structural characteristics that north is higher than south, which belongs to the karst platform-peak cluster depression landform in the karst geomorphology, and the more detailed division belongs to the karst gentle slope land-peak cluster ridge valley landform (Figure 3). Through the interpretation of the paleo-geomorphological map, it was found that the surface and underground river channels in the study area were obviously developed, the surface and underground river channels in the study area were further characterized, and the development characteristics of the channels were defined, in addition to the transformation positions of the surface and underground river. The surface runoff was mainly in the NE and NW directions, which is consistent with the development direction of strike–slip faults, but some transverse river systems also developed, which were related to the local geomorphology. From north to south, surface runoff gradually converged southward from each tributary into the karst lake. Surface runoff entered the underground river system mainly through depressions, sinkholes, and voltamps. In the whole study area, the development of the underground river channels had obvious zonal characteristics. With well T416 as the boundary, the underground river channels mainly developed in the NNW direction in the east, while the underground river channels differed from north to south in the west, which showed that the channels mainly developed in the NNW direction and nearly in the EW direction in the north, and the overall trend changes to the NE direction, which is more consistent with the development position of the NE direction fault in the south.

FIGURE 3 | Paleo-karst topography of the paleo-underground river in the Tahe oilfield.

On the seismic profile, the top Yingshan Formation gradually decreased from north to south, with the paleo-underground channels characterized by strong amplitude within the Yingshan Formation, which could be identified using seismic data. However, from north to south, there were significant differences observed in the seismic characteristics of the paleo-underground channels in the recharge–runoff–discharge zones. In the recharge zone, the paleo-underground channel mainly was characterized by weak-to-moderate continuity seismic reflection events, locally strong amplitude, and more chaotic reflections on the seismic profile. In the runoff area, paleo-underground channels mainly exhibited strong continuity seismic reflection events, with strong amplitude in continuous sections and local scattered reflections. In the discharge area, the overall characteristics of the paleo-underground channel showed weakened amplitude and limited scale, but seismic reflection events were relatively continuous. Strong amplitude reflection could be only observed at a certain depth in local areas. The strong amplitude reflection area was discontinuous and appeared as “beading” vertically.

4.2 Multiphase collapse–filling architecture of the paleo-underground river

The paleo-underground channels showed significant seismic geological differences in different recharge–runoff–discharge zones in a whole karst water system, including the recharge, runoff, and discharge zones. This study selected and probed three typical wells in different karst areas to analyze the architecture of the paleo-underground channels, with a focus on dissecting the collapse–filling architecture.

4.2.1 Paleo-underground river channel architecture in the recharge zone

The initial part of the paleo-underground channel belonged to the recharge area of the karst water system; the channel ranged from 1 to 22.6 m, and the hall-type karst cave was locally developed. Well TK483 was observed to be a typical well located in the recharge area (Figure 3), belonging to the karst platform-peak cluster and trough valley of karst geomorphology, with depression and sinkhole existing. Well TK483 was located in the conversion area where both surface and underground rivers developed. From the results of core observation (Figure 4), the well entered the Yingshan Formation at 5526.0 m; drilling encountered a karst cave with a height of 19.8 m at the well depth of 5535.0–5554.8 m. A karst cave with a height of 20 m could be observed at the well interval of 5678.0–5698.0 m. Specifically, at the depth of 5665.0–5676.0 m, there was a fractured zone at the top of the cave. The karst body was mainly composed of dissolution fractures and pores, which were partially filled with calcium mud (Figure 4A), resulting in severe drilling losses. Since entering the cave, the resistivity curve shows a downward trend and slightly increases in some areas. The cave appeared to be characterized by a multi-layer filling architecture from top to bottom. The upper part of the cave (5678.0–5682.0 m) was partially filled with calcareous mudstone (Figure 4B), with a porosity greater than 5%. The middle part of the cave (5682.0–5684.0 m) was filled with calcium mud-cemented collapsed breccia, with a porosity greater than 5%. The lower part of the karst cave (5684.0–5686.0 m) was partially filled with calcareous mudstone, with a porosity greater than 5%. The bottom of the karst cave (5686.0–5692.0 m) was fully filled with karst breccia and calcareous mudstone (Figure 4C), with a porosity of 2%–2.5%. The fractured zone at the bottom of the cave (5692.0–5698.0 m) was filled with calcium mud, with a porosity of 2%–2.5%, and a slight decrease in the resistivity curve could be observed in this interval.

FIGURE 4 | Comprehensive column of well T483 illustrating multiphase filling features of the paleo-underground river in the recharge zone (well location is shown in Figure 3). (A) mud fillings of fracture in the top of cave; (B) mud fillings in the middle of cave; (C) argillaceous cement breccia in the bottom of cave.

4.2.2 Paleo-underground river channel architecture in the runoff zone

Well T403 was located in the runoff area of the karst sub-system (Figure 3), with a channel size of 1.5–20 m. This well was also developed in the west wall of the NW-striking strike–slip fault. There were numerous fractures and fractured rocks observed in this area, and the landform was the karst platform-to-peak cluster depression landform.

Karstification was mainly observed in the Yingshan Formation (Figure 5). Well T403 entered the Yingshan Formation at a depth of 5405.0 m. The depth of 5487.4-5554.4 m was th karst cave, while the depth of 5488.0-5488.8 m, 5503.9-5507.5 m and 5538.9-5542.4 m were developed by dissolution fractures and pores, which were filled with calcium and mud. The resistivity curve sharply decreased from the beginning of the karst section and showed obvious fluctuations. The 5488.0–5488.8 m section was filled with gray–green calcareous mudstone (Figure 5A), and the top dissolution fractures were filled with calcite without breccia. The 5503.9–5507.5 m section was filled with gray–green calcium mud, containing limestone breccia (Figure 5B), which was distributed in a suspended state. The upper part of the 5538.8–5542.6 m section was fully filled with sand and mud (Figure 5C), while the lower part was filled with karst breccia and cemented with mud, sand, and quartz sandstone (Figures 5D,E). The degree of breccia roundness and sorting was observed to be moderate, and the arrangement of the breccia showed the direction of paleo-curbation.

FIGURE 5 | Comprehensive column of well T403 illustrating filling features of the paleo-underground river in the runoff zone (well location is shown in Figure 3). (A) mud fillings of fracture in the top of cave; (B) argillaceous cement brecciain of the fracture in the top of cave; (C) mud fillings in the top of cave; (D) argillaceous cement big breccia in the middle of cave; (E) argillaceous cement small breccia in the bottom of cave.

4.2.3 Paleo-underground river channel architecture in the discharge zone

Well T615 was located in the discharge area of the karst sub-system (Figure 3), which belongs to the top of the steep slope of the peak cluster karst hilly depression. Well T615 was located at the edge of the valley, near the development of a sinkhole. The location of the well was found to belong to the outflow section of the underground river, which is the outflow type channel with a size of 2–49 m.

Karstification was mainly observed in the Yingshan Formation (Figure 6). Well T615 entered the Yingshan Formation at a depth of 5520.9 m. The karst cave developed at a depth of 5520.9–5560.0 m, with a height of 39.1 m, and was observed to be located in the runoff zone. The well had the following logging response characteristics at a depth of 5520.9–5560.0 m: the natural gamma value increased abnormally from 10 API to 65 API; the resistivity was abnormally low, as low as 10 Ω; the karst cave was fully filled with sandstone and gray–green calcareous mudstone, with parallel bedding and small–medium cross-bedding. There were three layers of limestone breccia observed at the bottom, middle–upper, and top of the karst cave. The top limestone breccia had the largest volume and poor roundness and sorting, while the bottom limestone breccia had a smaller volume and better roundness and sorting.

FIGURE 6 | Comprehensive column of well T615 illustrating filling features of the paleo-underground river in the discharge zone (well location is shown in Figure 3). (A) argillaceous cement brecciain of the fracture in the top of cave; (B) argillaceous cement brecciain in the top of cave; (C) sandy fillings in the top of cave; (D). argillaceous cement big breccia in the middle of cave.

At a depth of 5520.9–5527.6 m the top part of the karst cave, near the weathering crust, was observed. The karst development degree was higher, mainly developing high-angle fractures, filled with gray–green calcareous mudstone, and a small amount of breccia with poor rounding and poor sorting (Figure 6A). The well section, at a depth of5533.4–5560.6 m, was composed of arenaceous micrite and cave filling material. The karst cave height was found to be approximately 19 m, and the lithology of the top of the karst cave was mainly light gray arenaceous micrite bedrock, developed high-angle fractures, filled with gray–green calcareous mud and limestone breccia with poor rounding and poor sorting (Figure 6B). The cave was fully filled with fine sandstone and gray–green calcareous mud, with parallel bedding, and small–medium cross-bedding could be seen (Figure 6C). This karst cave was a sandstone reservoir with an oil leaching phenomenon. A series of limestone breccia was developed in the middle–upper part of the karst cave, and the roundness and sorting of the limestone breccia were better than those in the top part. The bottom of the cave was mainly composed of gray micrite and cave fillings, and high-angle fractures were developed (Figure 6D). The fractures were filled with gray–green calcareous mud and breccia with poor sorting and rounding.

4.3 Geochemical characteristics of collapse–fillings of the paleo-underground river

4.3.1 Characteristics of carbon and oxygen isotopes

The carbon and oxygen isotope values of 22 samples were tested (Table 1). δ¹³C values in the recharge area ranged from −3.83‰ to −0.66‰, with an average value of −1.51‰, and δ¹⁸O values ranged from −12.34‰ to −7.5‰, with an average value of −8.63‰. In the runoff zone, δ¹³C values ranged from −4.41‰ to −0.97‰, with an average value of −2.26‰, and δ¹⁸O values ranged from −10.56‰ to −7.11‰, with an average value of −8.8‰. In the discharge zone, δ¹³C values ranged from -5.62‰ to −0.12‰, with an average value of −2.94‰, and δ¹⁸O values ranged from −12.43‰ to −7.11‰, with an average value of −9.51‰. The δ¹³C and δ¹⁸O values of fillings in different zones are different, reflecting the filling characteristics in different filling environments.

TABLE 1 | Test results of δ¹³C and δ¹⁸O (PDB standard).

According to the carbon–oxygen isotope intersection chart (Figure 7), the filling stages were divided into the following four stages: in the first stage, the δ¹³C values ranged from −4.72‰ to −0.12‰, and the δ¹⁸O values ranged from −10.86‰ to −7.11‰. In the second stage, the δ¹³C values ranged from −3.83‰ to −3.07‰, and the δ¹⁸O values ranged from −12.43‰ to −12.34‰. In the third stage, the δ¹³C values ranged from −4.41‰ to −2.45‰, and the δ¹⁸O values ranged from −10.81‰ to −9.14‰. The δ¹³C and δ¹⁸O values of the fourth phase are 5.62‰ and −8.14‰, respectively.

FIGURE 7 | Carbon–oxygen isotope crossplot of karst fracture-cave fillings and bedrock (RCZ, recharge zone; RFZ, runoff zone; DCZ, discharge zone).

4.3.2 Characteristics of fluid inclusion

A total of 203 gas–liquid two-phase fluid inclusions were tested using fractured-cave calcite samples obtained from four typical wells (T417, T403, S65, and T615) for inclusion homogenization temperature and ice temperature tests (Table 2). The results showed that the inclusions were mainly distributed in zonation and cluster distribution (Figure 8). The lowest homogenization temperature was 78°C and the highest was 155°C, and no low-temperature inclusions below 70°C were detected. Some calcite contained single-liquid phase inclusions, and calcite inclusions were formed at low temperatures in the reaction. From the homogenization temperature distribution histogram of inclusions (Figure 9A), it could be seen that fluid inclusions were mainly distributed in two temperature segments of 85°C–100°C and 115°C–130°C.

TABLE 2 | Th and distribution characteristics of inclusions in different zones.

FIGURE 8 | Micro-photographs of fluid inclusions from cores in the Tahe oilfield. (A) T417-4-6/29, 5507 m, calcite filled in dissolution fracture, zonal distribution along the healing fractures of calcite minerals; (B) T417-6-24/55,5563.3 m, calcite filled in fracture, zonal distribution along the healing fractures of calcite minerals; (C) T615-8-4/33, 5521.6 m, calcite filled in the karst cave, clustered distribution in calcite minerals; (D) T615-8-4/33, 5521.6 m, calcite filled in the karst cave, clustered distribution in calcite minerals; (E) T615-6-26/40, 5564 m, calcite filled in fracture, zonal distribution along the healing fractures of calcite minerals, T615-6-26/40, 5564.0 m. (F) T615-6-26/40, 5564.53 m, calcite filled in fracture, clustered distribution along the in calcite minerals.

FIGURE 9 | Diagram showing the frequency of homogenization temperature in recharge–runoff–discharge zones in the Tahe oilfield. (A) Total sample of four wells; (B) 90 samples in well T417; (C) 37 samples in well T403; (D) 60 samples in well S65; (E) 16 samples in well T615.

In the recharge zone, taking well T417 as an example, 90 saline inclusions were statistically analyzed, and the inclusions were mainly distributed in zonation, elliptical and rectangular in shape, with sizes ranging from 2 μm to 20 μm, and homogenization temperature ranging from 78°C to 134°C. There were at least two peaks in the frequency histogram (Figure 9B), with 45 inclusions with the highest frequency ranging from 85°C to 100°C, followed by 19 inclusions at 115°C–130°C.

In the runoff zone, 37 inclusions from well T403 and 60 inclusions from well S65 were analyzed, respectively. The inclusions of well T403 were distributed in zonation and free form, and the morphology was mainly rectangular and polygonal, followed by diamond shapes, nearly oval shapes, and a small number of irregular shapes. The size of inclusions was observed to be between 3 and 35 μm, and the homogenization temperature was between 95°C and 155°C. In the frequency histogram (Figure 9C), there were at least two peaks, with the highest frequency in the temperature range of 130°C–145°C, with up to 11 inclusions, and the second in the temperature range of 145°C–160°C, with 10 inclusions. The inclusions were mainly distributed in free and small zonation, with rectangular followed by oval shapes. The size of inclusions ranged from 3 to 30 μm. The homogenization temperature span between 80°C and 140°C in the frequency histogram (Figure 9D) between 85°C and 100°C, and there were 36 samples in total.

In the discharge zone, taking well T615 as an example, the inclusions were mainly distributed in zonation, followed by clusters, with irregular shapes and oval shapes. The size of inclusions ranged from 3 μm to 74 μm, the homogenization temperature span ranged from 80°C to 107°C, and there was a peak of eight inclusions ranging from 85°C to 100°C (Figure 9E).

5 DISCUSSION

5.1 Multiphase collapse–filling process in the paleo-underground river

Generally speaking, the core is one of the most intuitive pieces of evidence for understanding the basic architecture of paleo-underground channels. A series of test analyses based on core samples can further distinguish the formation environment and filling stage of the paleo-underground channels (Dan et al., 2023; Zhang et al., 2020; Zhang et al., 2021; Zhang et al., 2023; Wang et al., 2023; Zhang et al., 2018; Chen et al., 2023; Shi et al., 2015). Some scholars have analyzed the filling architecture and formation stage of the paleo-underground channels in the Tahe area according to their characteristics (Zhang et al., 2018; Zhang et al., 2020; Zhang et al., 2021; Chen et al., 2023; Zhang et al., 2023), but the differences in filling architecture of different recharge–runoff–discharge zones and the relationship between karst stages have been ignored. In this paper, the core filling characteristics and structural differences of three typical wells in three recharge–runoff–discharge zones were analyzed, and the analysis of fluid inclusion and carbon–oxygen isotope was carried out to comprehensively explore the internal relations and differences between different recharge–runoff–discharge zones, providing hydrogeological basis for the study of the distribution law and development degree of karst reservoirs and providing evidence for the study of karst fracture-cave architecture and filling characteristics of the karst fracture-cave system.

5.1.1 Collapse–filling characteristics from core analysis

Through detailed dissections of fracture-cave filling characteristics from the core and results of logging curves, different collapse–filling architectures in different recharge–runoff–discharge zones can be obviously observed, and the collapse–filling stages in different zones are also different.

In the recharge zone, taking well T483 as an example, a karst cave was observed in the well section at a depth of 5678.0–5698.0 m with a height of 20 m. According to the difference in fill lithology and architecture, we believe that at least three stages of collapse–filling process existed in this cave. The first stage was mainly characterized by large karst breccia and calcareous cementation, and the bottom of the karst breccia was covered with mud upward (Figure 4C), which was a combined result of the top collapse and calcareous filling. The calcareous material was the product of accumulation by runoff. During the second stage, smaller karst breccias and calcareous cementation filled above the early fillings. The third stage was characterized by calc-argillaceous cementation in the fractures of the top of the cave, which was the result of seepage filling in the later burial process.

Well T403 is a good example to study the collapse–filling process in the runoff zone. Core data confirmed that a 67-m high karst cave could be observed in the 5487.4–5554.4 m well section, with at least four stages of collapse–filling occurring inside the cave. The most prominent feature during the first stage was the calcium mud-cemented karst cave breccia, located at a depth of 5549.0–5554.4 m, at the bottom of the cave. This is the residual breccia of the underground river formation process, with a particle size relatively small (Figure 5E). The representative products of the second stage were karst cave breccia and siltstone cemented by calcium and mud (Figure 5D), where the breccia was larger than in the previous stage. It was formed during the burial process of the underground river and included the collapse materials of the cave ceiling and the runoff sand–mud fillings cemented by calcium. In addition, local bedding could be observed, which was formed during the burial process of the underground river. Thicker sediments were deposited in this stage, and they were probably formed by the superposition of more than one stage of collapse and filling. The third stage was characterized by smaller karst breccias and calcium mud fillings, which filled the branch cavities above the main cave (Figure 5B). During the fourth stage, gray–green and brown calcium mud cement filled above the previous fillings in the cave (Figure 5A), with the filling part belonging to the fracture zone at the top of the cave at a depth of 5487.4–5510.0 m and formed by groundwater infiltration and filling during the burial process.

Well T615 is the example chosen to illustrate the filling features in the discharge area. In this well, a karst cave with a height of 39.1 m was observed. The fillings are characterized by complex lithology and filling style inside the cave and include the karst breccia, sandy mud, calcareous mud, calcite, and other rocks. According to the filling characteristics of the lithological differences, we believe that the karst cave underwent at least five stages of the collapse–filling process. During the first stage, gray micrite filled the karst cave, and high-angle fractures filled with gray–green calcareous clay and breccia also developed (Figure 6D), which may be related to the common strike–slip faults in the study area. During the second stage, sandstone and gray–green calcareous fillings were deposited, with features such as parallel bedding and small–medium-sized cross-bedding, which were products carried by runoff. The argillaceous layer with horizontal bedding filled here may be another stage of filling caused by hydrodynamic or provenance changes. The fillings in the third stage were the mixed collapsed breccia, sand, and mud (Figure 6C). The fillings in the fourth stage were gray–green calcareous clay and a small amount of karst breccia (Figure 6B), and the pores diffused along the fractures in the top of the cave were also filled with calcareous clay and breccia (Figure 6A), which may have been filled at the same time and may have been filled by materials carried by runoff or vertical seepage. In the fifth stage, the fractures in the top cave were filled in, and the calcite veins developed in the well may be the chemical filling formed at a later stage.

5.1.2 Collapse–filling characteristics from geochemical analysis

Three typical wells were selected from three different zones, such as recharge, runoff, and discharge zones, and the filling material in the paleo-underground river channels was sampled, with fluid inclusion and carbon–oxygen isotope tests carried out using these samples. The core characteristics analyzed above confirmed the different characteristics of multi-stage filling in different zones. The filling materials of different rock types were selected for analysis, and their geochemical characteristics were subsequently analyzed. The fluid inclusion temperature measurement results and carbon–oxygen isotopes also suggested that the study area underwent multiphase collapse–filling stages with differential features in different recharge–runoff–discharge zones. It reflected the formation of paleo-karst filling in different karst dynamic systems, which was also supported by other researchers (Cai et al., 2009; Liu and Cai, 2009; Qian et al., 2009; Chen et al., 2023).

In well T417 of the recharge area, three types of fluid inclusions, zonation, small cluster, and free form, can be captured under three different karst dynamic systems. These represent three different karst environments, which may indicate at least three stages of filling processes in the karst cave; however, it was difficult to determine the sequence of formation periods for each type of inclusion. In addition, the formation time of the three types of fluid inclusions may be close and could not be separated by homogenization temperature. Two peaks of homogenization temperature tested from fluid inclusions represent at least two important stages of collapse–filling (Figure 9B). However, there are still other temperatures distributed on either side of the peaks that represent at least three or more stages of filling, and have a coinciding association with various types of inclusion forms. Well S65 is also an important well in the recharge area, where inclusion distribution is dominated by free-form and small-cluster inclusions, with the most significant peak (85°C–100°C) consistent with well T417.

Well T403 is located in the runoff area. The fluid inclusions in the well are mainly distributed in the cluster and free-form styles, and the homogenization temperature of the fluid inclusions shows two peaks, i.e., 100°C–115°C and 130°C–145°C, indicating at least two stages of collapse and filling process in the cave (Figure 9C). However, compared with samples from the recharge area, the peak value of homogenization temperature here is relatively higher, indicating that the filling period of the inclusions is lagging behind that of the recharge area. In addition, the maximum peak of homogenization temperature in the runoff area is 130°C–145°C, suggesting the filling period of the stronger filling in the runoff area may be later in terms of geological history. It can be seen from the morphology and distribution characteristics of inclusions in the runoff area that most of the inclusions were formed under the conditions of slow flow alternations and relatively stable change of solution concentration and had the environmental characteristics of buried paleo-karstification, which was the main reason for the occurrence of high-temperature inclusions in the area and also reflected that the main body filling period was late (Xia and Tang, 2004).

In the discharge zone, the fluid inclusions observed in well T615 are mainly distributed in clusters and zonations. Although a few samples were tested in this area compared with the other zones, the peak values of the homogenization temperature, between 85°C and 100°C, are in good agreement with both the recharge area and the runoff area (Figure 9E), indicating that the whole karst system underwent a relatively uniform collapse–filling process at the same stage as the recharge and runoff areas, that is, a contemporaneous or early diagenetic rock environment. The fluid at this stage is atmospheric fresh water, the homogenization temperature of fluid inclusions in calcite of this origin is low, and the oxygen isotope is more negative. However, in the discharge zone, there were large quantities of fillings observed with large volume differences and a certain cycle rhythm, which showed that there are hydrodynamic conditions at different stages and degrees, and there are also strong hydrodynamic conditions. This could be the reason why fewer inclusion samples were captured (Xia and Tang, 2004). In addition, Chen et al. (2023) also pointed out that multi-stage collapse filling existed in well T615, especially in the developmental burial stage.

The carbon–oxygen isotope results indicate that there were at least four stages of filling that could be observed in this area, of which the first stage (I in Figure 7) indicated the contemporaneous or early diagenetic rock environment. All samples from the three wells in the recharge–runoff–discharge zones confirmed that contemporaneous filling was generally developed in the three zones. The second stage samples (II in Figure 7) suggest an early-buried karst environment, which is supported by two data points from the recharge and discharge zones. Due to the small number of samples, it cannot be excluded that the runoff area also developed the same filling process at the same time, and this stage may also be the second filling process that generally develops. A late-buried karst environment dominated in the third stage (III in Figure 7), and only well samples in the runoff and recharge areas supported this stage, proving that filling began to migrate to the lower part of the runoff. During the fourth stage (IV in Figure 7), only well samples in recharge zones supported a deep-buried environment, suggesting the basic law of filling process migration from the recharge area to runoff and the recharge area to a certain extent.

5.1.3 Collapse–filling stages and evolution in different recharge–runoff–discharge zones

On the basis of the above analysis, the paleo-underground river channels were proven to have experienced at least three stages of the collapse–filling process, which is supported by the filling characteristics of karst caves in the core, inclusion characteristics, and carbon–oxygen isotope results. The collapse–filling process shows obvious differences in the recharge, runoff, and recharge zones (Figure 10). In other words, the paleo-underground river channel system underwent multiple stages of the collapse–filling process, among which collapse–filling in different karst zones follows the evolution law of inheritance, development, and migration.

FIGURE 10 | Comparison of differential collapse–filling stages in typical single wells in recharge–runoff–discharge zones (well locations are shown in Figure 3).

Although the collapse–filling stages are divided according to the characteristics of the core, fluid inclusions, and carbon–oxygen isotopes, the stages divided by different schemes are different. The stages divided according to the geochemical scheme are relatively simple and can be detected when the geological environment changes significantly. It cannot be excluded that the multiple filling processes occurred in a relatively stable karst environment in a short period of time. In addition, the matching relationship between geochemical analysis results and geological historical stages is relatively rough and limited by samples; therefore, some stages may not be fully detected. According to the stage division of core filling characteristics, the formation stage can be easily determined by changes in lithology and lithofacies, but the filling caused by changes in the karst environment is difficult to accurately identify by cores. It is relatively scientific to comprehensively analyze the change in the collapse–filling environment using the geochemical analysis method and the architectural change in the collapse–filling from cores to determine the stage. In addition, discussing the filling characteristics from different zones provides more effective evidence support for comparing the differences and migration patterns in recharge, runoff, and discharge zones.

Based on core observation and geochemical analysis, it is concluded that at least four stages of the collapse-filling process occurred in the study area. In the first stage of the underground river collapse–filling, a mixture of collapsed breccia and sandy mud was formed uniformly at the bottom of the cave (Figure 5), and similar values of the carbon–oxygen isotope in the three wells support this and have the characteristics of a large negative degree of oxygen isotope, suggesting a relatively uniformed syngenetic or early diagenetic rock environment (Figure 6). Jin et al. (2015) also obtained carbon and oxygen isotope values belonging to this stage from cave calcite. The relatively uniform first peak (85°C–100°C) of the homogenization temperature may be a response to the first stage. The first peak of fluid inclusion homogenization temperature (85°C–100°C) reflects the first filling stage, which is consistent with the filling stage indicated by carbon and oxygen isotopes.

The mixed fillings, including karst breccia and mud, all exist at the bottom of the single hole in the three wells in the recharge, runoff, and discharge zones. Similar filling characteristics probably formed in the first uniform stage. Some caves in the core were observed to be filled with sand. All of the fillings were formed by the combination of karst collapse and the sand–mud filling carried by the runoff during the runoff process of the paleo-underground river and belong to the first-stage fillings of the underground river. The S80 well that Jin et al. (2015) dissected had similar characteristics. By using the U–Pb dating of calcite, Wei et al. (2023) obtained the age of ∼466 Ma, which basically represents the environment of the early diagenzoic stage and has a time matching with the underground river filling in the first stage. However, the filling characteristics differ in the recharge, runoff, and discharge zones. The karst breccia in the recharge area is larger, gradually decreases in the runoff zone, and is smaller and more dispersed in the discharge area, which suggests that the main body of filling is mainly concentrated in the recharge area, and the filling material and collapse effect became weaker and weaker from the recharge to the runoff zone and the distal discharge zone.

A relatively unified karst breccia and sand-argillic mixed filling existed in the second stage of the paleo-underground river, which is supported by the relatively consistent karst breccia observed in three wells. The carbon–oxygen isotope data indicated an early buried karst environment, and the second peak of fluid inclusion homogenization temperature (115°C–130°C) may be the response of this stage. However, filling differences also existed among the recharge, runoff, and discharge zones. Karst breccias decrease from the bottom to the middle part of the cave, while the opposite characteristics exist in the runoff zone, suggesting that the hydraulic power was relatively weakened in the recharge area and relatively strengthened in the runoff area, that is, the main body of the filling was transferred from the recharge zone to the runoff zone. In addition, the migration of the peak homogenization temperature also suggests that the strong filling stage in the runoff area is slightly later than that of the recharge area, which is consistent with the above analysis.

The third-stage filling was formed in a deep burial environment, and the position of the third-stage carbon–oxygen isotopes indicated that collapse–filling processes were mainly developed in runoff and discharge zones. Unlike the formation of relatively uniform collapse breccia and sand-argillaceous mixed sediments in the first and second stages, the paleo-underground river in the third stage may be filled with mud in the fractures in the top of the cave by vertical percolation in the recharge area, accompanied by collapse and sand-argillaceous filling of the top of the cave in the runoff and discharge zones.

The fourth-stage fillings were deposited in a deep-buried karst environment, and samples of carbon–oxygen isotopes indicated that fillings only existed in the discharge zone. The filling characteristics in the three zones were extremely inconsistent. In the discharge area, cave collapse and sand-argillaceous fillings could still be developed, and in the runoff area, mud filled in the fractures of the top of the cave; in the recharge area, fluid activities in the fractures dominated, and large-scale fillings were no longer developed. In the third and fourth stages, it is not excluded that small- and medium-scale fluid activities deposited in fractures and captured the fluid inclusions, and the inclusions with high homogenization temperatures probably formed in the recharge and runoff zones (Jin et al., 2015).

5.2 Mechanism change during the collapse–filling evolution process of the paleo-underground river

5.2.1 Collapse–filling styles of the paleo-underground river

The collapse of the karst cave is closely related to the scale, style, and structural condition of the surrounding rocks. Combining modern karst theory with core fill lithology and filling style, it is concluded that there are three important ways of collapse in the karst cave within the paleo-underground river in the Tahe area.

The first type is gravity-induced collapse (Figure 11A), which refers to the collapse process or phenomenon caused by the structural fractures of the surrounding rock and the gravity effect caused by its gravity on the top of a karst fracture-cave system, resulting in peeling layer by layer or overall collapse. Gravity-induced collapse is one of the most important modes of karst fracture caves, such as funnels (sinkholes), shafts, and other fracture caves located in depressions, which are often formed by this gravity-induced collapse.

FIGURE 11 | Different models illustrating different collapse–filling mechanisms in the Tahe oilfield. (A) model of gravity-induced collapse in karst caves; (B) model of suffosion collapse in karst caves; (C) model of suction erosion collapse in karst caves.

The second type is suffusion-induced collapse (Figure 11B), which refers to the process and phenomenon of mechanical suffusion in the surrounding rocks of the karst fracture-cave system, which causes the surrounding rocks to be damaged and collapse when the hydraulic gradient is large enough under the action of groundwater seepage. This kind of collapse mode is generally located in karst fractures and caves in strong runoff zones and is a common collapse mode of karst caves in karst depressions, valleys, and low-lying areas of karst plain. This kind of collapse is not only affected by seepage but also affected by load loss and the gravity effect of the surrounding rocks.

The third type is suction-induced collapse (Figure 11C), which refers to the process and phenomenon of collapse caused by the failure of surrounding rocks when the negative pressure state is formed in the karst fracture and cave system with good sealing. This kind of collapse is affected not only by the absorption effect but also by the structural fractures and the gravity effect of the surrounding rocks.

5.2.2 Collapse–filling mechanism evolution of the paleo-underground river

Based on the collapse–filling characteristics of the karst cave, formation stages, and evolution processes of the paleo-underground river channel from the comprehensive analysis of core observation and geochemical data in the Tahe area, the study examined the styles and architectures of collapse–filling migration from the proximal recharge zone to the distal discharge zone during the evolution process. Therefore, the collapse–filling mechanisms probably changed during the different evolution stages.

In the early diagenetic environment or syngenetic stage of the karst evolution stage (Figure 12A), a unified mixed filling of karst breccia and calcareous clay was formed in the three zones, which is supported in the core observation in three wells of three zones. Meanwhile, the breccia was sharp-edged, and the breccia is more inclined to breccia deposition by collapse, which is the filling product formed in the early stage of karst caves formation and expansion. Due to the continuous runoff and groundwater percolation corrosion of underground rivers, the scale of karst caves gradually increases. In addition to the sand and mud carried by runoff and the residual breccia of karst caves, the cave filling is also filled by collapse breccia and the subsequent post-burial calcareous sediments. During this stage, the collapse and filling mechanism is mainly gravity-induced collapse with weak hydrodynamic force and other mechanisms in local areas.

FIGURE 12 | Collapse and filling evolution model of the paleo-underground river during different stages. (A) the initial stage of collapse-filling; (B) The expanded stage of collapse-filling; (C) The climax stage of fillings migration; (D) The final full-filled stage.

In the early buried karst stage (Figure 12B), i.e., the second larger stage of collapse–filling, the recharge area (e.g., TK483) is still dominated by the mixed filling of the karst breccia and calcareous mud. The runoff area (e.g., T403) is characterized by a large amount of breccia, calcareous clay, and a small amount of sandy fill, and very thick sandy and shale fillings that existed in the discharge area (e.g., T615). These different phenomena suggest that the collapse–filling mechanism differs among the three zones. The mechanism in the recharge area is still dominated by gravity-induced collapse. A small amount of sand in the runoff area suggests a slightly enhanced hydrodynamic force, further indicating that the collapse–filling was primarily gravity-induced and local suffusion-induced mechanisms. A large amount of sand and mud filling in the discharge area confirms a strong hydrodynamic condition, and the suffusion-induced collapse–filling mechanism dominates, while the gravity-induced and suction-induced mechanisms locally influence the collapse–filling process.

In the climax filling karst stage (Figure 12C), the recharge area is dominated by calcareous filling, which is influenced by percolation; in the runoff area, the mixed filling of karst breccia and calcareous filling is dominated by gravity-induced collapse; in the discharge area, the strong hydraulic power carries the sandy and calcareous filling combined with karst breccia, which is controlled by the suffusion-induced mechanism or suction–erosion mechanism, and the gravity-induced action is also involved.

In the deep-buried karst stage (Figure 12D), only local fluid activity influenced by seepage is developed in the recharge zone and runoff zone. In the discharge area, there is a mixed filling of karst breccia and calcareous mud, which is the product of a gravity-induced mechanism.

Therefore, the collapse mechanism of the paleo-underground river varies temporally and spatially along with the collapse–filling evolution process of the paleo-underground river, which confirms the transferability of the evolution of collapse mechanisms. The gravity-induced mechanism plays a long and wide leading role in the early collapse–filling stage of the paleo-underground river, as well as in the collapse–filling–shrinkage stages in the three zones. In the filling expanded and mature stages of the paleo-underground river, the suffusion-induced mechanism mainly influences the filling architecture in the runoff and discharge zones but is locally influenced by the gravity-induced mechanism.

6 CONCLUSION

Integration of core, fluid inclusion, and carbon–oxygen isotopic data from the Tahe area leads to the following conclusions:

(1) The paleo-underground river channel underwent a four-stage differential collapse–filling process in different recharge–runoff–discharge zones. The first collapse–filling stage was characterized by filling with breccia and calcium–mud material developed at the bottom of karst caves in the whole karst system. The widespread filling consisting of breccia and calcium–mud material was the main feature in the second stage, and local sand materials were infilled in the runoff zone and discharge zone. In the third stage, fewer fillings, breccia, calcium–mud material fillings, and sand materials were the main fillings in the recharge zone, runoff zone, and discharge zones, respectively. In the fourth stage, breccia and calcium–mud materials were only infilled in the discharge zone.

(2) Hydrodynamic strength and filling capacity vary spatiotemporally in the recharge zone, runoff zone, and discharge zone during the collapse–filling process. The hydrodynamic force and filling capacity dominate in the recharge zone during the initial stage, migrate to the runoff zone during the later stage, and concentrate in the discharge zone in the last stage.

(3) The collapse–filling mechanism varies along with the collapse–filling evolution process of the paleo-underground river. The gravity-induced mechanism dominates in the initial and late stages of the whole paleo-underground river system and dominates all the time in the recharge zone. The suffusion-induced mechanism dominates in the mature stage in runoff and discharge zones, while the suction–erosion mechanism is locally involved.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

H-QD: data curation, methodology, writing–original draft, and writing–review and editing. J-PL: data curation and writing–review and editing. Q-YZ: Data curation, Writing–original draft. J-RL: Methodology and Writing–review and editing. J-WW: Methodology, Writing–review and editing. G-QN: Data curation and Writing–original draft. Y-LL: Resources, Writing–original draft.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Basic Research Funds of the Chinese Academy of Geological Sciences (JKYQN202367), the Guangxi Key Research and Development Plan Project (AB23026062), and the China Geological Survey Project (DD20221658).

ACKNOWLEDGMENTS

The authors thank the editor, Xixin Wang, and two professional reviewers for their constructive comments and efforts. They appreciate Northwest Oilfield Branch of SINOPEC for data support. They thank Lujuan Li for data and graphics preparation and disposal.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, or the reviewers. Any product that may be evaluated in this article, or any claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Ainsaar, L., Kaljo, D., Martma, T., Meidla, T., Männik, P., Nõlvak, J., et al. (2010). Middle and upper Ordovician carbon isotope chemostratigraphy in Baltoscandia: acorrelation standard and clues to environmental history. Palaeogeogr. Palaeoclimatol. Palaeoecol. 294 (3), 189–201. doi:10.1016/j.palaeo.2010.01.003

Bögli, A. (1980). Karst hydrology and physical speleology. Berlin: Springer-Verlag.

Cai, C. F., Li, K. K., and Li, B. (2009). Geochemical characteristics and origins of fracture-and vug-fillings of the Ordovician in Tahe oilfield,Tarim basin. Acta Petrol. Sin. 25 (10), 2399–2404.

Chen, H. H., Wu, Y., and Zhu, H. T. (2016). Eogenetic karstification and reservoir formation model of the Middle-Lower Ordovician in the northeast slope of Tazhong Uplift (in Chinese with English abstract), Tarim Basin. Acta pet. sin. 37, 1231–1246.

Chen, R. K. (1994). Application of stable oxygen and carbon isotope in the research of carbonate disgenetic environment. Acta Sedimentol. Sin. 4, 11–21.

Chen, X., Liu, W. H., and Bao, D. (2023). Ordovician palaeokarst caves in the Tahe oilfield:Burial age of cave fills and its implication for hydrocarbon reservoirs. Earth Sci. Front. 30 (4), 65–66. doi:10.12336/biomatertransl.2023.02.002

Dan, G. J., Zhou, C. G., and Liu, Y. H. (2023). Seismic characteristics of the paleo-underground river system in Ordovician carbonate paleo-buried hills in the western Lungu area. Geophys. Geochem. Explor. 47 (2), 290–299. doi:10.11720/wtyht.2023.1052

Dan, Y., Liang, B., and Cao, J. W. (2012). Geochemical features and environmental signifcances of deposits in Ordovician karstic fractures and caves, Lunnan area, Tarim Basin. Petrol Geol. Exp. 34 (6), 623–628.

Ford, D. C., and Williams, P. W. (1989). Karst geomorphology and hydrology. London: Unwin Hyman.

Haeri-Ardakani, O., Al-Aasm, I. S., and Coniglio, M. (2013). Petrologic and geochemical attributes of fracture-related dolomitization in Ordovician carbonates and their spatial distribution in southwestern Ontario, Canada. Mar. Petroleum Geol. 43 (3), 409–422. doi:10.1016/j.marpetgeo.2012.12.006

Hu, W. G. (2022). Paleokarst fracture-vug types and their reconstruction in buried hill area,Tahe oilfield,Tarim Basin. Oil Gas Geol. 43 (1), 43–53. doi:10.11743/ogg20220104

Hu, X. Y., Quan, L. S., and Qi, D. S. (2014). Cavern filling characteristics of fracture-vuggy carbonate reservoir in Tahe oilfield. Special Oil Gas Reservoirs 21 (1), 18–21. doi:10.3969/j.issn.1006-6535.2014.01.004

James, N. P., and Choquette, P. W.(1988). Paleokarst. New York: Springer-Verlag.

Jia, C. Z. (1997). Tectonic Characteristics and Petroleum, Tarim Basin. China: Geological Publishing House, 29–261 (in Chinese).

Jin, Q., Kang, X., and Tian, F. (2015). Genesis of chemical fillings in fracture-caves in paleo-karst runoff zone in Ordovician and their distributions in Tahe oilfield, Tarim Basin. Acta Pet. Sin. 36 (7), 791–798. doi:10.7623/syxb201507003

Kang, X. (2014). Filling of the fracture-cavity space in ordovician karstreservoirs of the Tahe oilfield and its reservoirs petrophysics. Master Degree Thesis. China: China University of Petroleum EastChina.

Kang, Z. H., Lu, X. B., and Tang, X. R. (2014). Recognition and seismic reflection structure of ordovician paleocave collapse in Tahe oilfield. Xinjiang Geol. 32 (4), 540–545. doi:10.3969/j.issn.1000-8845.2014.04.025

Klimchouk, A., Auler, A. S., Bezerra, F. H. R., Cazarin, C. L., Balsamo, F., and Dublyansky, Y. (2016). Hypogenic origin, geologic controls and functional organization of a giant cave system in precambrian carbonates, Brazil. Geomorphology 253 (15), 385–405. doi:10.1016/j.geomorph.2015.11.002

Lauritzen, S. E., and Lundberg, J. (2000). “Solutional and erosional morphology of caves,” in Speleogenesis: evolution of karst aquifers ed . Editors A. Klimchouk, A. Palmer, D. Ford, and W. Dreybrodt (Huntsville, Al: National Speleological Society), 408–426.

Li, K. P., Chen, H. H., and Feng, Y. (2012). Characteristics of homogenization temperatures of fluid inclusions and application in deeply buried carbonate rocks. Nat. Gas. Geosci. 23 (4), 756–763.

Li, P., Ren, J. Y., and Yang, H. Z. (2011). Hydrothermal activities in the fault system and their effects on carbonate rocks in the bachu area. Geotect. Metallogenia 35 (3), 378–385. doi:10.3969/j.issn.1001-1552.2011.03.007

Li, X. H., Kang, L. I. J., and Zhang, Y. W. (2021). Characteristics identification and formation of ordovician karst collapse reservoir structure in Tahe oilfield. Geoscience 35 (6), 1830–1843. doi:10.19657/j.geoscience.1000-8527.2021.187

Li, Z. H., Qiu, L. W., and Sun, B. Q. (2013). Characteristics of fluid inclusion and charging events of natural gas in Permian Jiamuhe Formation of Zhongguai area, Junggar Basin. Nat. Gas. Geosci. 24 (5), 931–939.

Liu, C. G., Li, G. R., and Zhang, Y. W. (2007). Application of strontium isotope to the study of paleokarst——an case from ordovician in the Tahe oilfield, Tarim Basin. Acta Geol. Sin. 81 (10), 1398–1406.

Liu, C. G., Zhang, Y., and Lu, H. T. (2008). Genesis and evolution of gigantic calcites in paleokarstic caves of middle-lower ordovician in Tahe oilfield. Geol. Sci. Technol. Inf. 27 (4), 33–38.

Liu, C. Y., Wu, M. B., and Gong, G. (2006). Caledonian karstification of Ordovician carbonates in the Tahe oilfield, northern Tarim basin, Northwest China, and its petroleum geological significance. Geol. Bull. CHINA 25 (9), 1128–1134. doi:10.3969/j.issn.1671-2552.2006.09.025

Liu, J. J., Mao, C., and Wei, H. H. (2021). Ordovician fracture-cavity filling sequence and its logging responses in Tahe Oilfield. Xinjiang Pet. Geol. 42 (1), 46–52. doi:10.7657/XJPG20210106

Liu, X. F., and Cai, Z. X. (2009). Element geochemistry characteristics of karstic cave deposit in Tahe Oilfield and its environmental significance. Geol. Sci. Technol. Inf. 28 (3), 53–57. doi:10.3969/j.issn.1000-7849.2009.03.009

Liu, X. F., Cai, Z. X., Yun, L., and Xu, W. (2009). Element geochemistry characteristic response for karstification in the Tahe Oilfield. Petroleum Geol. Exp. 31 (3), 270–274.

Loucks, R. G., Mescher, P. K., and Mcmechan, G. A. (2004). Three-dimensional architecture of a coalesced, collapsed-paleocave system in the Lower Ordovician Ellenburger Group, central Texas. AAPG Bull. 88 (5), 545–564. doi:10.1306/12220303072

Lyu, X., Wang, Y., and Yang, D. (2020). Characterization of paleo-karst reservoir and faulted karst reservoir in Tahe Oilfield, Tarim Basin, China. Adv. Geo-Energy Res. 4 (3), 339–348. doi:10.46690/ager.2020.03.11

Lyu, X. R., Wu, X. W., and Sun, J. F. (2022). Physical simulation and distribution prediction of karst cave collapsing in deep carbonate reservoirs. Oil Gas Geol. 43 (6), 1505–1514. doi:10.11743/ogg20220619

Meng, Q. Q., Zhu, D. Y., and Jie, Q. L. (2011). Controlling factors for deep fluid activity and prediction of positive effect area in middle Tarim and Bachu regions. Petroleum Geol. Exp. 33 (6), 597–601. doi:10.3969/j.issn.1001-6112.2011.06.008

Palmer, A. N. (2007). Cave geology. Dayton, Ohio: Cave Books.

Qi, L. X., and Yun, L. (2010). Development characteristics and main controlling factors of the Ordovician carbonate karst in Tahe oilfield. Oil Gas Geol. 31 (1), 1–12.

Qian, Y. X., Chen, Q. L., and Chen, Y. (2009). Mineralogical and geochemical identification for diagenetic settings of paleo-caves and fractures-filling and vugs calcites in carbonate:Taking wells S79 and S85 for example. ACTA SEDIMENTOL. SIN. 27 (6), 1027–1032.

Qian, Y. X., He, Z. L., and Zou, Y. R. (2008). The meteoric diagenesis of the Upper Ordovician carbonate rocks occurred insyn-sedimentary karstification in the No. 1 belt of Northwestern Tazhong, Tarim basin-taking the Well Shur-2 as an example. Earth Sci. Front. 15 (2), 59–66.

Qiang, J., Fei, T., and Xinbian, L. (2015). Characteristics of collapse breccias filling in caves of runoff zone in the Ordovician karst in Tahe oilfield,Tarim Basin. Oil Gas Geol. (5), 729–735. doi:10.11743/ogg20150503

Shi, S. Y., Hu, S. Y., and Liu, W. (2015). Distinguishing paleokarst period by integrating carbon-oxygen isotopes and fluid inclusion characteristics. Nat. Gas. Geosci. 26 (2), 208–217. doi:10.11764/j.issn.1672-1926.2015.02.0208

Sun, M. S., Liu, C. Y., and Yang, Y. (2017). The forward modeling of fracture and cave carbonate reservoirs of the Yingshan Formation in Tazhong area, Tarim Basin. Earth Sci. Front. 24 (5), 339–349. doi:10.13745/j.esf.yx.2016-11-48

Sun, S., Zhao, W. Z., Zhang, B. M., Liu, J., Zhang, J., and Shan, X. (2013). Observation and implication of the paleo-cave sediments in ordovician strata of well lun dong-1in the Tarim Basin. Sci. China:Earth Sci. 56 (4), 618–627. doi:10.1007/s11430-012-4563-4

Wang, B. Q., and Al-Aasm, I. S. (2002). Karst-controlled diagenesis and reservoir development: example from the Ordovician main reservoir carbonate rocks on the eastern margin of the Ordos Basin, China. AAPG Bull. 86 (9), 1639–1658. doi:10.1306/61eedd28-173e-11d7-8645000102c1865d

Wang, C., Zhang, Q., and Xiang, W. (2017). Physical and numerical modeling of the stability of deep caverns in Tahe oil field in China. Energies 10 (6), 769. doi:10.3390/en10060769

Wang, Y., Gao, J. Y., and Yang, D. B.2023. Development conditions and geological models of large dendritic karst conduit in early Hercynian: a case study of B94 well block of Tahe Oilfield, Tarim Basin. Fault-Block Oil and Gas Field, 30(5): 758–769 .

Wei, D., Gao, Z. Q., Zhang, L. L., Fan, T., Wang, J., Zhang, C., et al. (2023). Application of blocky calcite vein LA-MC-ICP-MS U–Pb dating and geochemical analysis to the study of tectonic–fault–fluid evolutionary history of the Tabei Uplift, Tarim Basin. Sediment. Geol. 453, 106425. doi:10.1016/j.sedgeo.2023.106425

Winter, B. L., and Knauth, L. P. (1992). Stable isotope geochemistry of carbonate fracture fills in the Monterey Formation, California. J. Sediment. Petrology 62 (2), 208–219. doi:10.1306/d42678c5-2b26-11d7-8648000102c1865d

Wu, M., Zeng, C. B., and Huang, S. J. (2010). Fluid inclusion study of the Ordovician carbonate rock reservoir in the Tahe Oilfield. Geol. China 37 (5), 1360–1373.

Xia, R. Y., and Tang, J. S. (2004). Indication of mineral inclusion characteristics to palaeokarst actions. Acta Geosci. Sin. 25 (3), 373–377. doi:10.3321/j.issn:1006-3021.2004.03.017

Xia, R. Y., Tang, J. S., and Zou, S. Z. (2006). Features of the inclusion in paleo-karst fillings at the north edge of Tarim Basin. Carsologica Sin. 25 (3), 246–249. doi:10.3969/j.issn.1001-4810.2006.03.011

Xia, R. Y., Zou, S. Z., and Liang, B. (2011). Fracture-cavity system model and its genetic research in the ordivician carbonate, Tarim Basin. Beijing: Geological Publishing House.

Xu, J. H., Kang, Z. H., and Lan, Q. Q. (2020). Karst reservoir type, cave structure and genetic model of ordovician Tahe reservoirs:case study of fracture-cavity unit T615 in Tahe oilfield 7 block. Geoscience 3 (6), 1181–1192.

Xu, W., Cai, Z. X., and Jia, Z. Y. (2010). The study on ordovician carbonate reservoir karst cavern fillings characterization in Tahe oilfield. Geoscience 24 (2), 287–293. doi:10.3969/j.issn.1000-8527.2010.02.013

Xu, W., Cai, Z. X., and Lin, Z. M. (2012). Karst genesis classification of ordovician carbonate reservoir in Tahe oilfield, Tarim Basin. Mar. Orig. Pet. Geol. 17 (1), 66–72. doi:10.3969/j.issn.1672-9854.2012.01.010

Yu, H. F., Bai, Z. K., and Deng, L. P. (2011). Determination and geologic significance of Yingshan unconformity of lower Ordovician in Tazhong area, Tarim Basin (in Chinese with English abstract). Xinjiang Pet. Geol. 32, 231–234.

Yuan, D. X. (1993). China Karstology. Beijing: Geology Press, 4–8. (In Chinese).

Zhang, H. (2020). Formation mechanism of middle-lower Ordovician karst reservoirs in Tarim Basin and the comparison with similar reservoirs in Permian Basin. Wuhan: China University of Geosciences.

Zhang, H. T., Wang, J. Z., and Yang, M. (2020). Method research of quantitative evaluation on Ordovician carbonate reservoirs in western Tazhong area of Tarim Basin. Xinjiang Oil Gas 16 (1), 4–10. doi:10.3969/j.issn.1673-2677.2020.01.003

Zhang, H. T., Yang, M., and Wang, Y. (2021). Mechanical filling characteristics of Ordovician Karst paleo-subterranean rivers in Karst stage of Tahe oilfield. Petroleum Geol. Eng. 35 (6), 12–17. doi:10.3969/j.issn.1673-8217.2021.06.003

Zhang, J., Bao, D., and Yang, M. (2018). Analysis on fracture-cave structure characteristics and its controlling factor of paleo-subterranean rivers in the western Tahe Oilfield. Petroleum Geol. Recovery Effic. 25 (4), 33–39. doi:10.13673/j.cnki.cn37-1359/te.2018.04.006

Zhang, T., and Cai, X. Y. (2007). Caledonian paleo-karstification and its characteristics in Tahe area, Tarim Basin. Acta Geol. Sin. 81 (8), 1125–1134. doi:10.3321/j.issn:0001-5717.2007.08.012

Zhang, T., Yun, L., and Wu, X. W. (2005). The application of strontium isotopes in division of paleokarst stages in Tahe Oil Field. Petroleum Geol. Exp. 27 (3), 299–303. doi:10.3969/j.issn.1001-6112.2005.03.018

Zhang, W. B., Jin, Q., and Xu, S. Y. (2012). Filling characteristics of paleokarst cave in Ordovician outcrop in Tabei and its significance for oil and gas reservoir. Special Oil Gas Reservoirs 19 (3), 50–54. doi:10.3969/j.issn.1006-6535.2012.03.012

Zhang, X. L. (1985). Relationship between carbon and oxygen stable isotope in carbonate rocks and paleosalinity and paleotemperature of seawater. Acta Sedimentol. Sin. 3 (4), 17–30.

Zhang, Y., Peng, S. T., and Ji, S. Z. (2017). Filling degree prediction of ordovician paleocave and the application in yuqixi area of Tahe oilfield. Xinjiang Geol. 35 (1), 89–93. doi:10.3969/j.issn.1000-8845.2017.01.015

Zhang, Z. J., Lu, Y. P., and Ma, H. L. (2023). Fracture-cave system in collapsed underground paleo-river with subterranean flow in karst canyon area, Tahe Oilfield. Xinjiang Pet. Geol. 44 (1), 9–17. doi:10.7657/XJPG20230102

Zou, S. Z., Xia, R. Y., and Liu, L. (2016). Vertical zone characteristics and identification standard of Ordovician karst reservoirs in the Tahe Oilfield. Acta Geol. Sin. 90 (9), 2490–2501.

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Dong, Liang, Zhang, Li, Wan, Nie, and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 12 July 2024
doi: 10.3389/feart.2024.1401624

Coupling noble gas and alkane gas isotopes to constrain normally pressured shale gas expulsion in SE Sichuan Basin, China

Jiaxin Li1,², Yunhai Wang², Dazhi Fang², Zhigang Wen¹ and Chenjun Wu¹*

¹Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, China

²Sinopec East China Oil and Gas Company, Nanjing, China

Edited by:
Wenguang Wang, Northeast Petroleum University, China

Reviewed by:
Mianmo Meng, China University of Geosciences Wuhan, China
Chao Li, Northwest Institute of Eco-Environment and Resources (CAS), China

* Correspondence: Chenjun Wu, chenjun.wu@yangtzeu.edu.cn

Received: 15 March 2024
Accepted: 10 June 2024
Published: 12 July 2024

Citation: Li J, Wang Y, Fang D, Wen Z and Wu C (2024) Coupling noble gas and alkane gas isotopes to constrain normally pressured shale gas expulsion in SE Sichuan Basin, China. Front. Earth Sci. 12:1401624. doi: 10.3389/feart.2024.1401624

The molecular and isotopic compositions of shale gases exhibit substantial differences under different storage conditions. Gas geochemistry is widely used when evaluating gas accumulation and expulsion in petroleum systems. Gas geochemical characteristics can provide important references for determining the enrichment mechanism of shale gas reservoirs and predicting shale gas production capacity in different regions. In tectonically stable regions with similar reservoir formation and evolution histories, shale gas reservoirs are expected to exhibit favorable storage conditions with only relatively small variations in gas geochemical characteristics. In tectonically active regions, shale gas preservation conditions are expected to be more variable. In this study, we systematically analyzed the stable isotope signatures (δ¹³C and δD) of alkane gases (CH₄, C₂H₆, and C₃H₈), along with noble gas compositions and isotopic signatures, of normally pressured Wufeng‒Longmaxi marine shale gas samples comprising a continuous pressure coefficient series from a structurally active region at the transition between an orogenic belt and the southeastern (SE) Sichuan Basin, China. The relationships between noble gas contents, isotopic signatures, and shale gas yields were evaluated, and a mechanism for normally pressured shale gas accumulation and expulsion was presented. The δ¹³C and δD data suggest that the normally pressured shale gas originated from late-mature thermogenic generation, equivalent to shale gas from other production areas in the inner Sichuan Basin. Gas dryness ratios [C₁/(C₂ + C₃)] exhibit negative relationships with δ¹³C₁ and δ¹³C₂. Normally pressured shale gas yields exhibit a negative correlation with δ¹³C and a positive correlation with [C₁/(C₂ + C₃)], suggesting differences in shale gas accumulation and expulsion across the studied region related to changes in the pressure coefficient. Noble gas isotope data suggest that the normally pressured Longmaxi shale gas received a substantial contribution of crust-derived He. Coupling noble gas and stable C/H isotope data reveals that the abundance of He and Ar, along with the δ¹³C signatures of alkane gases, is affected by the abundance of shale gas during the accumulation and expulsion process. The noble gas and stable isotope distribution trends presented herein can be used to evaluate Wufeng‒Longmaxi’s normally pressured shale gas accumulation and expulsion in complex structural areas of the southeastern Sichuan Basin. Better preservation conditions accompanying lower tectonic activity will normally result in higher shale gas production and a lower concentration of noble gases. The above findings show that gas geochemical characteristics could be used as effective evaluation indicators for determining shale gas accumulation mechanisms in tectonically active regions.

Keywords: normally pressured shale gas, noble gas, carbon isotope, accumulation, expulsion

1 INTRODUCTION

Gas geochemical characteristics are important tools for studying the formation mechanism of natural gas reservoirs. The fractionation of alkane gas carbon isotopes in natural gas, owing to mass transport, is widely used to identify genetic types of natural gas, as well as oil and gas source correlation and alteration processes (Schoell, 1988; Whiticar, 1996; Xia and Tang, 2012). Owing to chemical inertness, noble gases are not impacted by secondary chemical processes, no matter whether the chemical reactions are inorganic or organic (Ozima and Podosek, 2017; Byrne et al., 2018). Noble gas isotope data have been widely used as important gas tracers for a variety of subsurface fluid transport processes in petroleum systems (Zhou et al., 2012; Burnard et al., 2013; Wang et al., 2013; Byrne et al., 2017; Byrne et al., 2020; Zhang et al., 2019).

Shale gas is considered a notable discovery in terms of natural gas exploration in the Sichuan Basin, given its great resource potential (Dai et al., 2014; Zou et al., 2015; Dong et al., 2016). Multiple exploration breakthroughs have been achieved in the Sichuan Basin, with the discovery of several very large shale gas fields, including Fuling, Weiyuan, Weirong, and Zhaotong (Jiang et al., 2020; Ma et al., 2020; Qiu et al., 2020; Zhao et al., 2020; Zou et al., 2021). The aforementioned successful shale gas fields have all targeted deep and high/over-pressured shale layers at depths >2000 m and pressure coefficients >1.3. In contrast, orogenic processes have caused Wufeng‒Longmaxi shale to be markedly uplifted in Sichuan Basin–margin transitional regions. Hence, shale layers in the southeastern (SE) Sichuan Basin are now characterized by low burial depths (<2000 m) and low pressure coefficients (<1.3), leading to the formation of normally pressured shale gas.

Shale gas exploration in the United States began in normally pressured organic-rich shale layers (Guo et al., 2015; Hu et al., 2015). Before 2004, the main shale gas fields, such as the Marcellus shale gas field in the Appalachian Basin and Ohio shale gas field, were dominated by normally pressured shale gas, characterized by low-pressure or normal-pressure conditions (Lancaster et al., 1989; Curtis, 2002; Montgomery et al., 2006; Pollastro et al., 2007; Darabi et al., 2012; Meng et al., 2023). The typical characteristics of normally pressured shale gas in the United States are a continuous distribution of organic-rich shales, low thermal evolution, and a stable tectonic environment (Guo, 2016, Guo et al., 2020; Jiang et al., 2022). The thermal maturity of normally pressured shale gas in the United States is normally <1.5%, with an adsorbed shale gas content >50%. In contrast to low-maturity normally pressured shale gas in the United States, normally pressured shale gas exploration in China has achieved notable success in highly mature shales (Guo et al., 2020). Normally pressured organic-rich shale layers are widely distributed along the margins of the Sichuan Basin. Currently, the Nanchuan‒Wulong regions, located in the southeastern Sichuan Basin, are the only successfully developed normally pressured shale gas fields in this basin. Compared with high-/over-pressured shale gas reservoirs, normally pressured shale gas reservoirs have the characteristics of weak formation energy, complex ground stress fields, low single-well production, high drilling and production costs, a wide distribution area, a large total resource, and low resource abundance (Guo et al., 2020; He, 2021). Under existing technical and economic conditions, commercial development of normally pressured shale gas faces considerable challenges. Previous studies have revealed tectonic deformation, such as folding, to be the key factor in controlling the quality of Wufeng‒Longmaxi shale gas preservation conditions. More intense strain results in more free gas migrating to pressure-relief areas, such as eroded areas, permeable faults, and fracture zones. Meanwhile, owing to pressure reduction and desorption, the adsorbed gas will be converted into free gas and also dissipate, resulting in the further reduction of shale gas content or potentially no gas at all. Therefore, the intensity of the tectonic strain controls the quality of preservation conditions, affecting the gas content of shale and the proportion of free gas, and determines the enrichment of shale gas reservoirs (He et al., 2019; He, 2021).

Geological studies suggest that multi-stage tectonic movement has occurred in the southeastern Sichuan Basin, causing shale gas preservation conditions to be degraded (He et al., 2019). Gas geochemistry has been widely used to provide important information in the evaluation of shale gas type, origin, evolution, migration, and accumulation (Dai et al., 2014; Cao et al., 2018; Cao et al., 2020; Chen et al., 2020; Liu et al., 2021; Wang et al., 2022). Wang et al. (2022) studied the chemical compositions and isotopic signatures of alkane gases, He, and Ar associated with Lower Paleozoic shale gas in the southern Sichuan Basin. Cao et al. (2020) analyzed shale gas origin and evolution using the stable carbon isotope signature (δ¹³C) of alkane gases and the geochemistry of noble gases. Liu et al. (2021) used noble gas and C/H isotopic analyses to evaluate reservoir compartmentalization in the Wufeng‒Longmaxi organic-rich shale and determined the geologic causation mechanism (e.g., tectonic activity). Most of the aforementioned research has focused on well-developed high-/over-pressured shale reservoirs; however, the accumulation and expulsion mechanisms of normally pressured shale gas reservoirs have received much less attention.

In this study, normally pressured shale gas samples from various structural positions across the southeastern Sichuan Basin were collected, and their alkane gas (methane (CH₄), ethane (C₂H₆), and propane (C₃H₈)) stable isotope (δ¹³C and δD) and noble gas isotope signatures were analyzed. The main objectives of this study were to 1) reveal the coupling relationship between hydrocarbon gas isotope signatures and shale gas yield; 2) understand the origin of noble gas from normally pressured shale gas reservoirs; 3) determine the accumulation and expulsion mechanisms of normally pressured shale gas in the tectonically complex margin of the southeastern Sichuan Basin; and 4) provide insights into the enrichment mechanism of shale gas reservoirs and the prediction of shale gas production capacity in other structurally complex regions of the world.

2 GEOLOGICAL SETTING

The Sichuan Basin is located in southwest China, northwest of the Yangtze metaplatform; tectonically, it is an important gas-producing sedimentary basin in China. Wufeng‒Longmaxi organic-rich shales are widely distributed across the Sichuan Basin and surrounding regions and are key exploration and evaluation targets for shale gas reservoirs (Dai et al., 2014; Gao et al., 2014; Zhao et al., 2019). Our study area is located in the southeastern Sichuan Basin and comprises a basin-margin transition belt and an extra-basin complex fold belt (Figure 1).

FIGURE 1 | Location and degree of geologic deformation of the study area in the southeastern Sichuan Basin.

The basin-margin transition belt is located between the Sichuan Basin and a complex fold belt lying exterior to the basin; it shows a higher level of tectonic deformation compared with most parts of the Sichuan Basin, but deformation is weaker than that outside of the basin. The region has experienced multiple tectonic phases, including during the Caledonian, Hercynian, Indosinian, and Yanshan‒Himalayan periods, with the latter having the strongest impact. This has laid the foundation for the current structural pattern, dominated by northeast‒southwest-trending synclines and anticlines. From east to west, the basin-margin transition belt includes seven tectonic units: Shimen slope, Shiqiao fault depression, Pingqiao anticline, Yuanjiagou syncline, Dongsheng anticline, Shentongba syncline, and Yangchungou anticline. Wufeng‒Longmaxi organic-rich shales in this basin-margin transition belt are buried under high- to normal-pressure conditions, with pressure coefficients ranging between 1.10 and 1.35.

The extra-basin complex fold belt exhibits an increased degree of uplift-related erosion, with the Longmaxi Formation being almost completely denuded in anticlinal regions. Hence, Longmaxi organic-rich shales mainly occur in residual synclines, such as the Wulong, Sangzeping, and Daozhen synclines. The present burial depth of Longmaxi organic-rich shales in these residual synclines ranges from 1,000 to 3,500 m (He et al., 2019), where they are buried under typical normal-pressure conditions with pressure coefficients ranging from 0.95 to 1.10.

3 SAMPLING AND ANALYTICAL METHODOLOGY

3.1 Shale gas samples

Eighteen shale gas samples from the southeastern Sichuan Basin were collected using steel cylinder gas containers with a maximum gas pressure capacity of 15 MPa and a maximum volume of 1,000 mL. To avoid atmospheric contamination, the containers were flushed with wellhead gas at least 10 times before gas collection. The studied shale gas samples were collected from the sweet shale gas layer (Wufeng‒Longmaxi Formation) based on a continuous pressure coefficient series moving from the intra-basin and basin-margin transition belt toward the basin exterior; specifically, they were collected from Pingqiao (PQ), Dongsheng (DS), Jinfo (JF), Wulong (WL), and Pengshui (PS). The pressure coefficients for the Long-1 shale gas from these regions were 1.35 (PQ), 1.20–1.35 (DS), 1.12–1.20 (JF), 1.08 (WL), and 0.98 (PS), respectively.

3.2 Analytical methodology

The chemical compositions of non-hydrocarbon gases (CO₂, H₂, H₂S, N₂, and He) and hydrocarbon gases were examined. Non-hydrocarbon molecular compositions were analyzed using a gas composition mass spectrometer (MAT-271). Hydrocarbon gas compositions were analyzed using a gas chromatograph (GC, Agilent 6890N) equipped with an FID detector. To differentiate each of the individual hydrocarbon gas components (e.g., CH₄, C₂H₆, and C₃H₈), an aluminum oxide capillary column with a length of 50 m and a diameter of 0.53 mm was used. The oven temperature setting for GC analysis was set at 30°C for 10 min, followed by an increase to a target temperature of 180°C at a heating rate of 10°C/min, and then maintained at the target temperature for 20–30 min.

An Agilent 6890N GC equipped with an isotope ratio mass spectrometer (IRMS, Delta plus XP) was used to determine the δ¹³C signatures of individual hydrocarbon gas components. The operating temperature for δ¹³C analysis was set at 30°C for 3 min, followed by an increase to a target temperature of 250°C at a heating rate of 10°C/min, and then maintained at the target temperature for 50 min. To calculate the δ¹³C values of hydrocarbon gases, three pulses of standard pure CO₂ gas were injected into the GC‒IRMS instrument. The overall analytical precision was ±3‰.

A MAT-253 IRMS coupled with an HP6890 GC instrument was used to analyze shale gas H isotopes. CH₄ and C₂H₆ components were chromatographically distinguished using a fused silica capillary column (HP-PLOT Q, 30 m × 0.32 mm × 20 μm). The operating GC temperature for H isotope analysis was set at 40°C for 5 min, followed by an increase to 80°C at a heating rate of 5°C/min, a further increase to 140°C at a heating rate of 10°C/min, and a final increase to a target temperature of 260°C at a heating rate of 30°C/min (Xing et al., 2021). The pyrolysis-oven temperature was set at 1,450°C, and a standard H₂ sample was used as the reference gas. The accuracy of the H isotope analysis was approximately ±3‰. Hydrogen isotope data are reported in δ notation (δD, ‰) relative to Vienna Standard Mean Ocean Water (VSMOW = 0.0‰). An internal standard was measured to calibrate the δD data for the studied shale gas samples (Cao et al., 2020).

Abundances and isotopic compositions of noble gases (He, Ne, and Ar) were measured using a noble gas isotope mass spectrometer (Noblesse SFT). The shale gas sample container was first connected to the purification vacuum system. After the system reached vacuum, the gas sample was introduced into a gas pipette with a volume of 8.5 cm³. According to the pressure and temperature variations in the gas pipette before and after the gas sample introduction, the absolute amount of gas sample introduced was calculated. Then, 1.5 cm³ of the gas sample was further introduced to the online noble gas isotope mass spectrometer for noble gas purification. Details of the analytical procedure for noble gas abundance and isotope composition determination can be found in Cao et al. (2018).

4 RESULTS

4.1 Shale gas yields

Shale gas yields from the basin-margin transition belt and extra-basin complex fold belt vary markedly. Considering that drilling wells in different structural zones are currently in different stages of production, daily shale gas production values for the first 500 production days were compared herein (Table 1; Figure 2). Compared with the highly pressured Jiaoshiba shale gas production region (e.g., 20.3 × 10⁴ m³/day for well JY1), daily shale gas production values in the study area were much lower. Shale gas production in the PQ area was the highest, ranging from 4.19 × 10⁴ to 6.55 × 10⁴ m³/day (average: 5.55 × 10⁴ m³/day). The average daily shale gas production values for the DS and JF areas were 3.32 × 10⁴ and 3.40 × 10⁴ m³/day, respectively. Shale gas production in the extra-basin complex fold belt was much lower, with average values of 2.15 × 10⁴ m³/day for the WL area and 0.77 × 10⁴ m³/day for the PS area. In general, daily shale gas production exhibited a decreasing trend with a decreasing pressure coefficient across the study area.

TABLE 1 | Chemical and carbon isotope (δ¹³C) compositions of normally pressured shale gas samples in the southeastern Sichuan Basin.

FIGURE 2 | Variation in gas composition in normally pressured Longmaxi shale gas from the southeastern Sichuan Basin.

4.2 Major gas compositions

Shale gas compositions from 18 wells within the Wufeng‒Longmaxi Formation in the southeastern Sichuan Basin are shown in Table 1. All samples were dominated by CH₄, with minor amounts of C₂H₆ and non-hydrocarbon gases (e.g., N₂, CO₂, and He). The CH₄ content ranged from 95.99% to 98.8%, with an average of 97.2%; the C₂H₆ content ranged from 0.28% to 0.99%, with an average of 0.48%; and the C₃H₈ content in all shale gas samples was very low, with an average of 0.01%. Non-hydrocarbon gases in the studied shale gas samples mainly consisted of N₂ (0.76%–3.11%, average: 1.39%) and CO₂ (0.01%–0.321%, average: 0.16%). The non-hydrocarbon gases He and Ar occurred in trace amounts in the shale gas samples and were detected using more precise noble gas mass spectrometry (the results are presented in Section 4.4).

4.3 Carbon and hydrogen isotope signatures of alkane gases

The δ¹³C signatures of alkane gases varied among shale gas samples collected from different areas. The δ¹³C₁ values ranged from −36.5‰ to −29.6‰, with average values of −34.3‰, −33.8‰, −33.1‰, −30.7‰, and −30.8‰ for the PQ, DS, JF, WL, and PS areas, respectively. The δ¹³C₂ values ranged from −38.2‰ to −33.4‰, with average values of −37.1‰, −37.4‰, −36.2‰, −30.7‰, and −33.4‰ for the PQ, DS, JF, WL, and PS areas, respectively. The δ¹³C signatures of alkane gases exhibited an increasing trend with a decreasing pressure coefficient. In contrast to the δ¹³C data, the δD signatures of alkane gases showed no evident pattern of change with a decreasing pressure coefficient, with δDC₁ ranging from −162.2‰ to −165.4‰ and δDC₂ ranging from −166.1‰ to −135.1‰.

4.4 Noble gas compositions and isotopes

He, Ne, and Ar concentrations and isotopic data from the 18 studied Longmaxi shale gas samples are presented in Table 2. The He concentration ranged from 383.6 to 976.9 ppm, with an average of 550.3 ppm. The He concentration varied among shale gas samples collected from different areas, showing an increasing trend with a decreasing pressure coefficient moving from the intra-basin toward the basin exterior. The average He contents of shale gas from the PQ, DS, JF, WL, and PS areas were 551.0, 446.2, 444.8, 530.5, and 954.3 ppm, respectively. Ne and Ar concentrations had ranges of 0.01–0.278 and 26–96 ppm, respectively.

TABLE 2 | Noble gas composition and isotopic data from normally pressured shale gas samples in the southeastern Sichuan Basin.

³He/⁴He ratios ranged from 0.0045 to 0.013 Ra, where Ra is the atmospheric ratio of (1.384 ± 0.013) × 10–6 (Ozima and Podosek, 2017; Mishima et al., 2018). ²⁰Ne/²²Ne ratios (9.3–11.7, average: 10.6) were higher than the atmospheric value (9.80; Sarda et al., 1988) and lower than the mantle value (12.2; Ballentine et al., 2005). ⁴He/²⁰Ne ratios ranged from 2,425.8 to 75,716.2, much higher than the atmospheric value of 0.288 (Kipfer et al., 2002). Most ²¹Ne/²²Ne ratios (0.028–0.039, average: 0.033) were higher than the atmospheric value (0.02959; Gyore et al., 2019), indicating a mixed source of two end-members: crustal (0.03–0.70; Ozima and Podosek, 2017) and atmospheric Ne. The ⁴⁰Ar/³⁶Ar ratios of the studied shale gas samples varied from 730.1 to 1,648.2, with an average of 1,286.7; these ratios are much higher than those of air, indicating no apparent noble gas contamination from the atmosphere during shale gas sampling and analysis.

5 DISCUSSION

5.1 Coupling relationship between alkane gas isotopes and shale gas yield

The origin of Longmaxi shale gas has been widely discussed in previous studies (Hao et al., 2013; Dai et al., 2014; Wu et al., 2017; Cao et al., 2018; Liu et al., 2021). Methane is the dominant component in the high-/over-pressured Longmaxi shale gas produced (Dai et al., 2014; Liu et al., 2021). Normally pressured shale gas samples in the studied southeastern margin of the Sichuan Basin show similar characteristics, with high CH₄ abundance and high gas dryness ratios [C₁/(C₂ + C₃)] (94.3–339.7, avg. 216.8). The δDC₁ values of normally pressured shale gas have a similar distribution range to those of Wufeng‒Longmaxi shale gas from Weiyuan and Changning, as reported by Dai et al. (2014). Plots of δ¹³C₁ versus [C₁/(C₂ + C₃)] (Figure 3A) and δ¹³C₁ versus δDC₁ (Figure 3B) both suggest that the Longmaxi shale gas is of late-mature thermogenic origin. A kinetic isotope effect resulting from differences in the energy required to cleave ¹²C\¹²C and ¹²C\¹³C bonds in shale gas precursors has been reported (Tang et al., 2000). Such a kinetic isotope effect results in a more positive δ¹³C composition in hydrocarbons at higher maturity (Xia et al., 2013).

FIGURE 3 | Gas molecular and stable isotope composition plots of normally pressured Longmaxi shale gas from the southeastern Sichuan Basin, along with other shales. (A) [C₁/(C₂ + C₃)] vs δ¹³C₁. (B) δ¹³C₁ vs δDC₁. (C) Isotopic composition of C₂H₆ exhibiting a reversal trend with increasing maturity. Fayetteville and Barnett shale gas data and genetic diagrams are adapted from Milkov et al. (2020), while previously studied shale gas data are adapted from Dai et al. (2014). Abbreviations: SM, secondary microbial; EMT, early-mature thermogenic gas; OA, oil-associated (mid-mature) thermogenic gas; LMT, late-mature thermogenic gas.

The δ¹³C₁ and δ¹³C₂ values of our normally pressured shale gas samples are plotted in Figure 3C, together with those of Barnett (Milkov et al., 2020), Fayetteville (Milkov et al., 2020), and Wufeng‒Longmaxi (Dai et al., 2014) shale gas samples studied previously, in which the dashed line represents the relationship δ¹³C₁ = δ¹³C₂. Above the dashed line, a normal δ¹³C trend exists, characterized by δ¹³C₁ < δ¹³C₂. The turning point in this normal trend area has been suggested to represent the start of secondary hydrocarbon cracking and mixing of gas (primary and secondary cracking), which would likely cause a δ¹³C reversal among alkane gases (Tilley and Muehlenbachs, 2013; Xia et al., 2013). Shale gas with a normal δ¹³C trend is mostly related to the low‒middle stage of maturity. As maturity increases, a reversal in the trend (δ¹³C₁ > δ¹³C₂) can be observed. A further turning point in this reversed trend area has been suggested to represent a post-reversal stage at an extremely high or over-mature stage of evolution (Dai et al., 2014). This was confirmed by Xia et al. (2013) when applying a δ¹³C correlated with the maturity model and by Wu et al. (2017) when studying even higher-maturity Lower Cambrian Niutitang shale gas.

For our normally pressured shale gas, δ¹³C and δD data suggest that the shale gas originated from late-mature thermogenic generation, equivalent to shale gas in other production areas of the inner Sichuan Basin. The [C₁/(C₂ + C₃)] data exhibit a negative relationship with δ¹³C₁ and δ¹³C₂ (Figure 4). Shale gas from the basin-margin transition belt (areas PQ, DS, and JF) is characterized by higher [C₁/(C₂ + C₃)] and lighter δ¹³C compared with shale gas samples from the extra-basin complex fold belt (areas WL and PS). The slope trend of decreasing [C₁/(C₂ + C₃)] with heavier δ¹³C matches the decreasing trend of the pressure coefficient for the selected shale gas samples. Meanwhile, daily shale gas yields for the selected production wells exhibit a negative correlation with δ¹³C and a positive correlation with [C₁/(C₂ + C₃)] (Figure 5), indicating that shale gas production wells in the basin-margin transition belt and extra-basin complex fold belt have completely different shale gas yields and gas geochemistry characteristics. The PS, DS, and JF areas have better gas production than WL and PS. The reason for this phenomenon may be that shale gas expulsion efficiency and gas source supply are different in different structural belts.

FIGURE 4 | Correlations between the dryness coefficient (C₁/C2‒3) and δ¹³C. (A) C₁/C2‒3 vs δ¹³C₁. (B) C₁/C2‒3 vs δ¹³C₂.

FIGURE 5 | Correlations between daily shale gas production and hydrocarbon gas geochemistry characteristics. Daily shale gas production vs δ¹³C₁ (A), δ¹³C₂ (B), and dryness coefficient (C₁/C2‒3) (C).

Meng et al. (2022) studied the CH₄ and C₂H₆ δ¹³C changes in the Triassic Yanchang Formation from the Ordos Basin using pressure coring to avoid the problem of lost gas. During shale gas desorption, both δ¹³C₁ and δ¹³C₂ values became heavier, and gas dryness coefficients (C₁/C1‒5) decreased with increasing desorption rates. Smaller molecules (e.g., CH₄) desorb more rapidly than longer-chain hydrocarbon gases. For normally pressured shale gas, variations in gas geochemistry characteristics provide evidence for the gas accumulation and expulsion processes during geological time. The WL and PS areas within the extra-basin complex fold belt experienced earlier uplift compared with the PQ, DS, and JF areas within the basin-margin transition belt (Figure 6, modified after Teng et al., 2020). Although shale layers in the WL and PS areas have similar top and bottom cap-rock conditions to those in the inner basin area (e.g., Fulin shale gas field), their relatively poor self-sealing capacity and low displacement pressure mean that an episodic hydrocarbon expulsion process is more likely to occur. More oil and gas escape from these shale layers during the peak hydrocarbon generation period, reducing the potential source supply for later shale gas generation during higher maturity stages. During expulsion, hydrocarbons with a lighter δ¹³C escape first, leaving the residual hydrocarbons richer in ¹³C. As the pressure coefficient decreases, the hydrocarbon expulsion efficiency increases and residual gas abundance is reduced. Therefore, the PS shale gas samples are characterized by the heaviest δ¹³C values, lowest gas dryness coefficients, and lowest daily gas yields among all studied shale gas samples.

FIGURE 6 | Burial history, thermal history, and paleo-pressure of the Wufeng‒Longmaxi Formation in the (A) basin-margin transition belt and (B) extra-basin complex fold belt.

The H atom is much smaller than the C atom; hence, H isotope fractionation occurs more readily during shale gas expulsion or desorption. Notably, unlike δ¹³C, the δD values of CH₄ and C₂H₆ show little variation with changes in pressure coefficient across all studied shale gas samples. A previous study suggested that δD values in alkane gases desorbed from pressure coring remained stable with increasing gas desorption rates (Meng et al., 2022).

5.2 Origin of noble gases in a normally pressured shale gas system

In subsurface fluid systems, noble gases can have atmospheric, mantle, or radiogenic origins, with distinct isotopic signatures associated with each component (Ballentine et al., 2005). According to its origin, there are three types of He: atmospheric, mantle-derived, and crust-derived. Helium has two isotopes: primordial ³He and ⁴He sourced from the radioactive decay of ²³⁸U, ²³⁵U, and ²³²Th over geological time (Graham, 2002). The origin of He is usually determined using the ratio of the isotopic abundances of ³He and ⁴He, i.e., R = ³He/⁴He. Atmospheric He is ubiquitous in the air, showing no industrial significance, owing to its low concentration. The R value for atmospheric helium is 1.4 × 10⁶, normally referred to as Ra. Mantle-derived He refers to He of deep magmatic origin, including the original ³He captured in the early stage of the formation of the Earth, and ⁴He produced by radioactive decay, with a high R ratio of approximately 8 Ra. Crust-derived He is mainly sourced from α radioactive decay of U and Th in ancient sedimentary rocks, metamorphic rocks, migmatites, and granites. This crust-derived He, with a low R ratio of approximately 0.02 Ra, is an important source of He input in natural gas reservoirs (Oxburgh et al., 1986; Ballentine and Lollar, 2002; Kennedy and van Soest, 2005; Wang et al., 2020). As shown in a plot of R/Ra vs ⁴He/²⁰Ne (Figure 7A), the ³He/⁴He ratios (R/Ra) of our normally pressured shale gas samples range from 0.0045 to 0.013 Ra, all plotted within the crust end-member region. A plot of CH₄/³He vs R/Ra (Figure 7B) also indicates that the studied shale gas samples lie in the crustal field, suggesting that the Longmaxi shale gas received a substantial contribution of crust-derived He; this is consistent with the Weiyuan and Changning shale gas samples studied by Cao et al. (2020).

FIGURE 7 | (A) Plot of ³He/⁴He (R/Ra) vs ⁴He/²⁰Ne showing mixing lines between the atmosphere and upper mantle and between the atmosphere and crust. Ratios of the atmosphere, continental crust, and upper mantle end-members are obtained from Ozima and Podosek (2017), Ballentine and Burnard (2002), and Graham (2002). (B) Plot of CH₄/³He vs ³He/⁴He (R/Ra) demonstrating a crustal origin for noble gases.

Argon has three isotopes, namely, ⁴⁰Ar, ³⁸Ar, and ³⁶Ar, with ⁴⁰Ar mainly sourced from the radioactive decay of K, and ³⁸Ar and ³⁶Ar of primordial origin (Ballentine and Burnard, 2002). The ⁴⁰Ar/³⁶Ar ratios of our shale gas samples are relatively high (Figure 8; ranging from 1,194.3 to 4,604.5), indicating a radiogenic origin consistent with that suggested by the He isotope data. The ⁴⁰Ar/³⁶Ar ratios of our shale gas samples exhibit similar values to those of Jiaoshiba shale gas but are higher than those of Changning shale gas samples from the southern Sichuan Basin (Wang et al., 2022).

FIGURE 8 | Plots of (A) ³He/⁴He (R/Ra) vs ⁴⁰Ar/³⁶Ar and (B) ⁴⁰Ar/³⁶Ar vs 1/³⁶Ar.

5.3 Shale gas accumulation and expulsion reflected in noble gas isotope signatures

Compared with conventional natural gas reservoirs, the enrichment and preservation of noble gas and the controlling factors of the formation of He-rich natural gas reservoirs show some similarities and also differences. Regions with high hydrocarbon generation intensity that are conducive to the formation of large oil and gas reservoirs may not be conducive to the formation of He-rich natural gas reservoirs. However, tectonic uplift and denudation areas with relatively low hydrocarbon generation intensity may be favorable for forming He-rich natural gas. The same applies to shale gas reservoirs, in which self-generation and self-storage occur. Excessive hydrocarbon generation intensity greatly dilutes the concentration of He. Therefore, for self-generated and self-stored He-rich shale gas reservoirs, despite the high content of U and Th in shale, it is difficult to achieve commercial development of He, owing to the dilution effect of hydrocarbon gases (Wang et al., 2020). The He concentration of Wufeng‒Longmaxi shale gas is relatively low in the high-yield Jiaoshiba and Changning areas, with average concentrations of 205 and 376 ppm, respectively. However, in extra-basin areas that are structurally complex, the He concentration can reach 941 ppm (Wang et al., 2022).

The spatial distributions of He and Ar concentrations have been suggested to be closely related to the accumulation and dissipation of shale gas (Wang et al., 2022). Owing to mixing and dilution, the concentration of He decreases with an increase in the shale gas content. Compared with shale gas development areas in a structurally stable basin, such as Changning and Jiaoshiba, the preservation conditions of shale gas in structurally complex areas outside the basin, such as Pengshui in southeastern Chongqing, are relatively poor. At the over-mature evolution stage, owing to the continuous decay of U and Th releasing ⁴He, higher He contents are found in structurally complex areas outside the basin compared with inner basin areas with high shale gas production.

As shown in Figure 9, the He and Ar concentrations of shale gas samples from the basin-margin transition belt (PQ, DS, and JF areas) are lower than those of the extra-basin complex fold belt (WL and PS areas). The He and Ar concentrations of PS shale gas samples in this study are consistent with previous data (He: 716–941 ppm, Ar: 42–56 ppm; Wang et al., 2022). The contents of ⁴He and ⁴⁰Ar show a weak positive correlation (Figure 10A), indicating a radiogenic contribution for both noble gases.

FIGURE 9 | Noble gas abundances and gas yield distribution for the normally pressured shale gas studied. (A) He abundance. (B) Ar abundance.

FIGURE 10 | Plots of (A) ⁴He vs ⁴⁰Ar content and (B) ⁴He vs gas yield.

The He content exhibits a negative correlation with daily shale gas yield for the studied shale gas wells (Figure 10B). High-gas yield wells located in structural regions with higher in situ stress and higher pressure coefficients have low concentrations of He and Ar. As an unconventional gas, shale gas has the characteristics of self-generation and self-storage. A large amount of gas can be generated during the hydrocarbon gas generation stage. However, the abundance of noble gas (He and Ar) source elements, such as U, Th, and K, is very low in geological bodies, with an extremely long half-life and a very low rate of He or Ar generation (1 m³ km−3 year−¹). Furthermore, there is no peak of concentrated noble gas generation during the generation and storage processes. After the termination of the hydrocarbon generation process, the concentrations of He and Ar will inevitably be diluted with increasing CH₄ concentrations (Li et al., 2022).

The correlations between He and δ¹³C are shown in Figure 11. The ⁴He content exhibits positive correlations with both δ¹³C₁ and δ¹³C₂, with WL and PS shale gas samples having the highest ⁴He contents and heaviest δ¹³C signatures. The PQ and DS shale gas samples have the lowest ⁴He contents and the lightest δ¹³C signatures, with JF shale gas samples having intermediate values.

FIGURE 11 | Correlations between ⁴He abundance and δ¹³C. (A) ⁴He vs δ¹³C₁. (B) ⁴He vs δ¹³C₂.

The above coupled noble gas and stable isotope analyses reveal that the concentrations of He and Ar and δ¹³C signatures of alkane gases are closely related to the accumulation and expulsion of shale gas. Generally, with an increasing pressure coefficient, shale gas preservation conditions become better, and high shale gas yields are attained, which will dilute the concentration of noble gases. Furthermore, better preservation conditions equate to less shale gas escaping during the gas generation period, causing δ¹³C values to remain closer to their initial values. In contrast, shale gas preserved under poor conditions loses lighter hydrocarbons and isotopes, causing decreasing pressure coefficients and residual gases rich in heavy components. For shale gas reservoirs characterized by a wide range of pressure coefficients, such as the normally pressured Marcellus (pressure coefficient between 0.9 and 1.4; Chalmers et al., 2012) and highly pressured Haynesville (pressure coefficient between 1.61 and 2.07; Hammes et al., 2011) shale gas fields in North America, shale gas production exhibits a similar positive correlation with an increasing pressure coefficient to that found herein. Therefore, gas geochemical data may also provide effective indicators for shale gas production evaluation in these regions. Compared with tectonically stable regions, which exhibit fewer differences in gas geochemical characteristics, the noble gas and stable isotope distribution trends presented herein for tectonically active regions will be more effective in evaluating shale gas accumulation and expulsion in shale gas reservoirs worldwide.

6 CONCLUSION

On the basis of our systematic study of the noble gas and stable isotope characteristics of normally pressured shale gas samples, which are part of a continuous pressure coefficient series from the southeastern Sichuan Basin, our preliminary conclusions are as follows:

(1) Stable δ¹³C and δD data revealed that the shale gas originated from late-mature thermogenic generation, similar to shale gas from other production areas within the inner Sichuan Basin. As the pressure coefficient decreased, the hydrocarbon expulsion efficiency increased and residual gas abundance decreased. Shale gas from the basin-margin transition belt (PQ, DS, and JF areas) was characterized by a higher dryness ratio and a lighter δ¹³C signature than shale gas from the extra-basin complex fold belt (WL and PS areas).

(2) Noble gas isotopes were used to reveal the origin of noble gas in the normally pressured shale gas system studied. The ³He/⁴He ratios (R/Ra) ranged from 0.0045 to 0.013 Ra, and the ⁴⁰Ar/³⁶Ar ratios were relatively high, suggesting that the shale gas was sourced from a typically radiogenic origin.

(3) High-gas-yield wells located in structural areas with higher in situ stress and higher pressure coefficients had low concentrations of He and Ar. The He content showed positive correlations with δ¹³C₁ and δ¹³C₂ values. Shale gas samples from the WL and PS areas were characterized by the highest He contents and heaviest δ¹³C signatures. Our coupled noble gas and δ¹³C and δD data revealed that the concentrations of He and Ar and δ¹³C signatures of alkane gases are closely related to the accumulation and expulsion of shale gas.

(4) In tectonically stable regions, shale gas reservoirs normally exhibit favorable conditions with only relatively small differences in gas geochemistry. In tectonically active regions, shale gas geochemical characteristics exhibit substantial differences related to changes in structure. Better preservation conditions accompanied by lower-intensity tectonic movement normally result in higher shale gas production and a lower concentration of noble gases. The above results indicate that gas geochemical characteristics can be used as effective evaluation indicators for determining shale gas accumulation mechanisms in structurally active regions.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

AUTHOR CONTRIBUTIONS

JL: writing–original draft and writing–review and editing. YW: writing–original draft and writing–review and editing. DF: project administration and writing–review and editing. ZW: writing–review and editing. CW: writing–original draft.

FUNDING

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was conducted as part of a project funded by Sinopec East China Oil and Gas Company with grant no. 34600355-23-FW2099-0051.

ACKNOWLEDGMENTS

The authors are grateful to Sinopec East China Oil and Gas Company for access to samples and associated data. They are sincerely grateful to the Chief Editor, Valerio Acocella, and the reviewers who provided valuable comments.

PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

REFERENCES

Ballentine, C. J., and Burnard, P. G. (2002). Production, release and transport of noble gases in the continental crust. Rev. Mineral. Geochem. 47, 481–538. doi:10.2138/rmg.2002.47.12

Ballentine, C. J., and Lollar, B. S. (2002). Regional groundwater focusing of nitrogen and noble gases into the Hugoton-Panhandle giant gas field, USA. Geochem. Cosmochim. Acta 66, 2483–2497. doi:10.1016/S0016-7037(02)00850-5

Ballentine, C. J., Marty, B., and Sherwood, L. B. (2005). Neon isotopes constrain convection and volatile origin in the Earth's mantle. Nature 433, 33–38. doi:10.1038/nature03182

Burnard, P., Zimmermann, L., and Sano, Y. (2013). “The noble gases as geochemical tracers: history and background,” in The noble gases as geochemical tracers. Advances in isotope geochemistry ed . Editor P. Burnard (Berlin, Heidelberg: Springer), 1–15. doi:10.1007/978-3-642-28836-4_1

Byrne, D. J., Barry, P. H., Lawson, M., and Ballentine, C. (2018). Determining gas expulsion vs retention during hydrocarbon generation in the Eagle Ford Shale using noble gases. Geochim. Cosmochim. Acta 241, 240–254. doi:10.1016/j.gca.2018.08.042

Byrne, D. J., Barry, P. H., Lawson, M., and Ballentine, C. (2020). The use of noble gas isotopes to constrain subsurface fluid flow and hydrocarbon migration in the East Texas Basin. Geochim. Cosmochim. Acta 268, 186–208. doi:10.1016/j.gca.2019.10.001

Byrne, D. J., Barry, P. H., Lawson, M., and Ballentine, C. J. (2017). Noble gases in conventional and unconventional petroleum systems. Geol. Soc. Lond., Spec. Publ. 468, 127–149. doi:10.1144/sp468.5

Cao, C., Zhang, M., Li, L., Wang, Y., Li, Z., Du, L., et al. (2020). Tracing the sources and evolution processes of shale gas by coupling stable (C, H) and noble gas isotopic compositions: cases from Weiyuan and Changning in Sichuan Basin, China. J. Nat. Gas. Sci. Eng. 78, 103304. doi:10.1016/j.jngse.2020.103304

Cao, C., Zhang, M., Tang, Q., Yang, Y., Lv, Z., Zhang, T., et al. (2018). Noble gas isotopic variations and geological implication of Longmaxi shale gas in Sichuan Basin, China. China. Mar. Petrol. Geol. 89, 38–46. doi:10.1016/j.marpetgeo.2017.01.022

Chalmers, G., Bustin, R., and Power, I. (2012). Characterization of gas shale pore systems by porosimetry, pycnometry, surface area, and field emission scanning electron microscopy/transmission electron microscopy image analyses: examples from the Barnett, Woodford, Haynesville, Marcellus, and Doig units. Aapg Bull. 96, 1099–1119. doi:10.1306/10171111052

Chen, Z., Chen, L., Wang, G., Zou, C., Jiang, S., Si, Z., et al. (2020). Applying isotopic geochemical proxy for gas content prediction of Longmaxi shale in the Sichuan Basin, China. China. Mar. Petrol. Geol. 116, 104329. doi:10.1016/j.marpetgeo.2020.104329

Curtis, J. B. (2002). Fractured shale-gas systems. AAPG Bull. 86, 1921–1938. doi:10.1306/61EEDDBE-173E-11D7-8645000102C1865D

Dai, J., Zou, C., Liao, S., Dong, D., Ni, Y., and Huang, J. (2014). Geochemistry of the extremely high thermal maturity Longmaxi shale gas, southern Sichuan Basin. Org. Geochem. 74, 3–12. doi:10.1016/j.orggeochem.2014.01.018

Darabi, H., Ettehad, A., Javadpour, F., and Sepehrnoori, K. (2012). Gas flow in ultra-tight shale strata. J. Fluid. Mech. 710, 641–658. doi:10.1017/jfm.2012.424

Dong, D., Zou, C., Dai, J. X., Huang, S., Zheng, J., Gong, J., et al. (2016). Suggestions on the development strategy of shale gas in China. J. Nat. Gas. Geosci. 1, 413–423. doi:10.1016/j.jnggs.2016.11.011

Gao, L., Schimmelmann, A., Tang, Y., and Mastalerz, M. (2014). Isotope rollover in shale gas observed in laboratory pyrolysis experiments: insight to the role of water in thermogenesis of mature gas. Org. Geochem. 68, 95–106. doi:10.1016/j.orggeochem.2014.01.010

Graham, D. W. (2002). Noble gas isotope geochemistry of mid-ocean ridge and ocean island basalts: characterization of mantle source reservoirs. Rev. Mineral. Geochem. 47, 247–317. doi:10.2138/rmg.2002.47.8

Guo, C., Xu, J., Wu, K., Wei, M., and Liu, S. (2015). Study on gas flow through nano pores of shale gas reservoirs. Fuel 143, 107–117. doi:10.1016/j.fuel.2014.11.032

Guo, T. (2016). Key geological issues and main controls on accumulation and enrichment of Chinese shale gas. Petrol. explor. Dev. 43, 349–359. doi:10.1016/S1876-3804(16)30042-8

Guo, T., Jiang, S., and Zhang, P. (2020). Progress and direction of exploration and development of normally-pressured shale gas from the periphery of Sichuan Basin. J. Petroleum Geol. Exp. 42, 837–845. doi:10.11781/sysydz202005837

Györe, D., Tait, A., Hamilton, D., and Stuart, F. M. (2019). The formation of NeH⁺ in static vacuum mass spectrometers and re-determination of ²¹Ne/²⁰Ne of air. Geochim. Cosmochim. Acta 263, 1–12. doi:10.1016/j.gca.2019.07.059

Hammes, U., Hamlin, H., and Ewing, T. (2011). Geologic analysis of the upper jurassic Haynesville shale in east Texas and west Louisiana. J. Biol. Chem. 95, 1643–1666. doi:10.1306/02141110128

Hao, F., Zou, H., and Lu, Y. (2013). Mechanisms of shale gas storage: implications for shale gas exploration in China. AAPG Bull. 97, 1325–1346. doi:10.1306/02141312091

He, X. (2021). Sweet spot evaluation system and enrichment and high yield influential factors of shale gas in Nanchuan area of eastern Sichuan Basin. Nat. Gas. Ind. 41, 59–71. doi:10.3787/j.issn.1000-0976.2021.01.005

He, X., Qi, Y., and He, G. (2019). Further understanding of main controlling factors of normal pressure shale gas enrichment and high yield in the area with complex structure of the southeast area of Chongqing. Reserv. Eval. Dev. 5, 32–39. doi:10.13809/j.cnki.cn32-1825/te.2019.05.004

Hu, Q., Liu, X., Gao, Z., Liu, S. G., Zhou, W., and Hu, W. X. (2015). Pore structure and tracer migration behavior of typical American and Chinese shales. Petrol. Sci. 12, 651–663. doi:10.1007/s12182-015-0051-8

Jiang, S., Li, C., and Chen, G. (2022). Occurrence of normally-pressured shale gas in China and the United States and their effects on mobility and production: a case study of southeast Sichuan Basin and Appalachia Basin. Petroleum Reserv. Eval. Dev. 12, 399–406. doi:10.13809/j.cnki.cn32-1825/te.2022.03.001

Jiang, Z., Song, Y., Tang, X., Li, Z., Wang, X., Wang, G., et al. (2020). Controlling factors of marine shale gas differential enrichment in southern China. Petrol. explor. Dev. 47, 661–673. doi:10.1016/S1876-3804(20)60083-0

Kennedy, B. M., and van Soest, M. C. (2005). A helium isotope perspective on the Dixie Valley, Nevada, hydrothermal system. Geothermics 35, 26–43. doi:10.1016/j.geothermics.2005.09.004

Kipfer, R., Aeschbach-Gertig, W., Peeteres, F., and Stute, M. (2002). Noble gases in lakes and ground waters. Rev. Mineral. Geochem. 47, 615–700. doi:10.2138/rmg.2002.47.14

Lancaster, D. E., Guldry, F. K., Graham, R. L., Curtis, J., Shaw, J., and Blake, T. (1989). A case study of the evaluations completion and testing of a Devonian shale gas well. J. Petrol. Technol. 41, 509–518. doi:10.2118/17037-PA

Li, Y., Li, J., and Zhou, J. (2022). Research progress and new views of evaluation of helium resources. J. Sci. Environ. 44, 363–373. doi:10.19814/j.jese.2021.11008

Liu, R., Wen, T., Julien, A., Zheng, J., Hao, F., and Jiang, D. (2021). The dichotomy in noble gas signatures linked to tectonic deformation in Wufeng-Longmaxi Shale, Sichuan Basin. Chem. Geol. 581, 120412. doi:10.1016/j.chemgeo.2021.120412

Ma, X., Li, X., Liang, F., Wan, Y., Shi, Q., Wang, Y., et al. (2020). Dominating factors on well productivity and development strategies optimization in Weiyuan shale gas play, Sichuan Basin, SW China. Petrol. explor. Dev. 47, 594–602. doi:10.1016/S1876-3804(20)60076-3

Meng, M., Zhang, Y., Yuan, B., and Li, Z. (2023). Imbibition behavior of oil-saturated rock: implications for enhanced oil recovery in unconventional reservoirs. Energy and Fuels 37 (18), 13759–13768. doi:10.1021/acs.energyfuels.3c02501

Meng, Q., Wang, X., Shi, B., Lei, Y., Zhao, H., Zhao, Q., et al. (2022). The ¹³C-depleted methane in terrigenous shale gas: a case study in the Triassic Yanchang Formation, Ordos Basin. Mar. Petrol. Geol. 141, 105688. doi:10.1016/j.marpetgeo.2022.105688

Milkov, A. V., Faiz, M., and Etiope, G. (2020). Geochemistry of shale gases from around the world: composition, origins, isotope reversals and rollovers, and implications for the exploration of shale plays. Org. Geochem. 143, 103997. doi:10.1016/j.orggeochem.2020.103997

Mishima, K., Sumino, H., Yamada, T., Ieki, S., Nagakura, N., Otono, H., et al. (2018). Accurate determination of the absolute ³He/⁴He ratio of a synthesized helium standard gas (helium standard of Japan, HESJ): toward revision of the atmospheric ³He/⁴He ratio. Geochem. Geophys. Geosyst. 19, 3995–4005. doi:10.1029/2018GC007554

Montgomery, S. L., Jarvie, D. M., Bowker, K. A., and Pollastro, R. M. (2006). Mississippian Barnett Shale, Fort Worth basin, north-central Texas: gas-shale play with multi–trillion cubic foot potential. AAPG Bull. 89, 155–175. doi:10.1306/09170404042

Oxburgh, E. R., O’Nions, R. K., and Hill, R. I. (1986). Helium isotopes in sedimentary basins. Nature 324, 632–635. doi:10.1038/324632a0

Ozima, M., and Podosek, F. A. (2017). Noble gas geochemistry. 2nd edition. New York: Cambridge University Press. doi:10.1017/cbo9780511545986

Pollastro, R. M., Jarvie, D. M., Hill, R. J., and Adams, C. W. (2007). Geologic framework of the mississippian Barnett shale, barnett-paleozoic total petroleum system, bend arch–fort worth basin, Texas. AAPG Bull. 91, 405–436. doi:10.1306/10300606008

Qiu, Z., Zou, C., and Wang, H. (2020). Discussion on the characteristics and controlling factors of differential enrichment of Wufeng-Longmaxi formations shale gas in south China. Nat. Gas. Geosci. 31, 163–175. doi:10.1016/j.jnggs.2020.05.004

Sarda, P., Staudacher, T., and Allegre, C. J. (1988). Neon isotopes in submarine basalts. Earth Planet. Sci. Lett. 91, 73–88. doi:10.1016/0012-821X(88)90152-5

Schoell, M. (1988). Multiple origins of methane in the Earth. Chem. Geol. 71 (1), 1–10. doi:10.1016/0009-2541(88)90101-5

Tang, Y., Perry, J. K., Jenden, P. D., and Schoell, M. (2000). Mathematical modeling of stable carbon isotope ratios in natural gases. Geochimica Cosmochimica Acta 64, 2673–2687. doi:10.1016/s0016-7037(00)00377-x

Teng, G., Tao, C., and Hu, G. (2020). Effect of hydrocarbon expulsion efficiency on shale gas formation and enrichment. Pet. Geol. Exp. 42, 325–334. doi:10.11781/sysydz202003325

Tilley, B., and Muehlenbachs, K. (2013). Isotope reversals and universal stages and trends of gas maturation in sealed, self-contained petroleum systems. Chem. Geol. 339, 194–204. doi:10.1016/j.chemgeo.2012.08.002

Wang, P., Shen, Z., Liu, S., Lv, Z., Zhu, T., and Gong, Y. (2013). Geochemical characteristics of noble gases in natural gas and their application in tracing natural gas migration in the middle part of the western Sichuan Depression, China. Petrol. Sci. 10, 327–335. doi:10.1007/s12182-013-0281-6

Wang, X., Liu, W., Li, X., Liu, Q., Tao, C., and Xu, Y. (2020). Radiogenic helium concentration and isotope variations in crustal gas pools from Sichuan Basin, China. Appl. Geochem. 117, 104586. doi:10.1016/j.apgeochem.2020.104586

Wang, X., Liu, W., Li, X., Tao, C., Borjigin, T., Liu, P., et al. (2022). Application of noble gas geochemistry to the quantitative study of the accumulation and expulsion of lower Paleozoic shale gas in southern China. Appl. Geochem. 146, 105446. doi:10.1016/j.apgeochem.2022.105446

Whiticar, M. J. (1996). Stable isotope geochemistry of coals, humic kerogens and related natural gases. Int. J. Coal Geol. 32, 191–215. doi:10.1016/S0166-5162(96)00042-0

Wu, C. J., Tuo, J. C., Zhang, L. F., Zhang, M., Li, J., Liu, Y., et al. (2017). Pore characteristics differences between clay-rich and clay-poor shales of the Lower Cambrian Niutitang Formation in the Northern Guizhou area, and insights into shale gas storage mechanisms. Int. J. Coal Geol. 178, 13–25. doi:10.1016/j.coal.2017.04.009

Xia, X., Chen, J., Braun, R., and Tang, Y. (2013). Isotopic reversals with respect to maturity trends due to mixing of primary and secondary products in source rocks. Chem. Geol. 339, 205–212. doi:10.1016/j.chemgeo.2012.07.025

Xia, X. Y., and Tang, Y. C. (2012). Isotope fractionation of methane during natural gas flow with coupled diffusion and adsorption/desorption. Geochem. Cosmochim. Acta 77, 489–503. doi:10.1016/j.gca.2011.10.014

Xing, L., Li, Z., Li, L., and Liu, Y. (2021). Application of chromium catalysis technology to compound-specific hydrogen isotope analysis of natural gas samples. Talanta 239, 123133. doi:10.1016/j.talanta.2021.123133

Zhang, W., Li, Y., Zhao, F., Han, W., Zhou, J., Holland, G., et al. (2019). Quantifying the helium and hydrocarbon accumulation processes using noble gases in the North Qaidam Basin, China. Chem. Geol. 523, 368–379. doi:10.1016/j.chemgeo.2019.07.020

Zhao, W., Jia, A., and Wei, Y. (2020). Progress in shale gas exploration in China and prospects for future development. China Pet. Explor. 25, 31–44. doi:10.3969/j.issn.1672-7703.2020.01.004

Zhao, W., Zhang, S., He, K., Zeng, H., Hu, G., Zhang, B., et al. (2019). Origin of conventional and shale gas in Sinian–lower Paleozoic strata in the Sichuan Basin: relayed gas generation from liquid hydrocarbon cracking. AAPG Bull. 103, 1265–1296. doi:10.1306/11151817334

Zhou, Z., Ballentine, C. J., Schoell, M., and Stevens, S. H. (2012). Identifying and quantifying natural CO₂ sequestration processes over geological timescales: the Jackson Dome CO₂ Deposit, USA. Geochim. Cosmochim. Acta 86, 257–275. doi:10.1016/j.gca.2012.02.028

Zou, C., Yang, Z., Dai, J., Dong, D., Zhang, B., Wang, Y., et al. (2015). The characteristics and significance of conventional and unconventional Sinian–Silurian gas systems in the Sichuan Basin, central China. Mar. Petrol. Geol. 64, 386–402. doi:10.1016/j.marpetgeo.2015.03.005

Zou, C., Zhao, Q., and Wang, H. (2021). Theory and technology of unconventional oil and gas exploration and development helps China increase oil and gas reserves and production. Petroleum Sci. Technol. Forum. 40, 72–79. doi:10.3969/j.issn.1002-302x.2021.03.007

Conflict of interest: Authors JL, YW, and DF were employed by Sinopec East China Oil and Gas Company.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2024 Li, Wang, Fang, Wen and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.