Abstract
Due to the geometric heterogeneity of fault systems, quantitatively evaluating the controlling effect of complex fault networks on hydrocarbon accumulation remains challenging. Taking the Heidimiao oil layer as a case study, this work quantitatively characterizes fault-network complexity using topological parameters and systematically evaluates fault connectivity and development intensity. A topological differentiation framework is further established to describe spatial variations in fault architecture. Integrated with hydrocarbon migration data, the results indicate a close spatial correspondence between fault-network complexity and hydrocarbon distribution. Fault lines with intensive interconnections correspond to high-value topological zones. As fault-network complexity increases, the numbers of connected nodes (e.g., Y- and X-nodes) and connected branches (e.g., I–C and C–C branches) increase significantly, indicating enhanced geometric connectivity. The average connectivity of fault line (CL) is generally greater than 1.5, while the average connectivity of fault branch (CB) mostly ranges from 0.5 to 2.3 and is commonly above 0.5. Fault intensity (I) shows a close spatial association with oil distribution, with multiple high-value zones identified. Areas with fault intensity values ranging from 0.3 × 10−3 to 1.2 × 10−3 m-1 are widely distributed, indicating a relatively high degree of fault development. High-value topological zones, particularly within the range of 0.3 × 10−3 to 1.2 × 10−3 m-1, show a strong spatial coupling with hydrocarbon migration pathways. Compared with other fault-network domains, topologically complex domains provide a greater number of potential fluid-migration pathways and commonly act as preferential migration pathways for hydrocarbons. Combined with reservoir distribution characteristics, faults play a key role in promoting high-angle source–reservoir contact and facilitating sealing-controlled hydrocarbon accumulation, thereby creating favorable conditions for hydrocarbon enrichment.
1 Introduction
Fault belts represent one of the most important deformation styles in hydrocarbon-bearing basins, and their formation and evolution reflect the complexity and dynamic nature of geological structures. Under multi-stage tectonic stress regimes, faults of different orientations interact extensively, forming complex fault-network systems through processes such as fault branching and lateral linkage (Sun et al., 2000). The development of such fault networks not only controls basin-scale sedimentation, deformation, and hydrocarbon generation, but also provides critical pathways for hydrocarbon migration and accumulation via both vertical and lateral transport. At the same time, faults may also act as barriers to hydrocarbon migration (Jolley et al., 2007; Fossen and Rotevatn, 2016; Wang et al., 2023; Sun et al., 2022; Wu et al., 2022). Consequently, fault-network systems have long been a key focus of research in hydrocarbon-bearing basins worldwide.
Topology is an abstract approach to spatial analysis that ignores the geometric shape and volumetric properties of objects, simplifying them into discrete points with specific attributes. These points are connected through transmission relationships to form lines, which collectively constitute complex network structures. Such structures are particularly effective for addressing problems related to spatial connectivity and continuity (Jing and Stephansson 1997; Sanderson and Nixon, 2015). In recent years, numerous studies have proposed quantitative characterization methods based on topological parameters derived from the geometric morphology and spatial relationships of nodes and branches within fault systems (Wang et al., 2026; Cui et al., 2026; Liu et al., 2026). Through the identification and classification of node–branch systems, combined with quantitative analysis of key topological parameters and fault scale and density, these approaches can effectively describe the structural attributes of fracture networks. This enables quantitative evaluation of fault-network connectivity and fault-development intensity, as well as the overall complexity of fault zones (Wang et al., 2021; Ma et al., 2024; Richards et al., 2015). Although topological methods have been widely and successfully applied to characterize geometric connectivity in fault systems, relatively few studies have quantitatively integrated topological parameters with actual hydrocarbon and fluid distribution patterns.
To address these research gaps, this study takes the Aogula-Talaha Fault Zone as an example and applies topological theory combined with topological parameters to quantitatively characterize the topological features, planar connectivity, and structural complexity of the fault network in the study area. By integrating hydrocarbon and water distribution data, the study further investigates the controlling role of high-value topological zones in hydrocarbon migration and accumulation, thereby revealing the intrinsic relationship between fault-network topological parameters and hydrocarbon enrichment patterns. Accordingly, the fault-network evaluation framework based on topological analysis provides an effective means to characterize connectivity and offers a reliable theoretical basis and technical support for hydrocarbon exploration in fault-controlled reservoirs.
2 Geological background
The study area is located within the Dorbod Mongol Autonomous County and structurally lies in the western segment of the central depression of the northern Songliao Basin, spanning the Qijia–Gulong Sag and the Longhupao Terrace, two secondary structural belts (Figure 1a). The primary target for exploration is the Heidimiao reservoir. The Heidimiao reservoir is a lower-source, upper-reservoir hydrocarbon system. Source faults penetrate the Qingshankou and Nenjiang formations, providing abundant hydrocarbon supply. Locally developed mudstones in the Nenjiang Formation provide effective sealing conditions, while extensively developed delta-front sand bodies (channel sands, sand bars, and sheet sands) offer favorable reservoir space. Faults act both as hydrocarbon migration pathways and as barriers, creating excellent trapping conditions that have led to the formation of multiple hydrocarbon reservoir types.
FIGURE 1
The fault network in the study area belongs to the Aogula–Talaha Fault Zone (Figure 1b), which trends predominantly north–south. The northern segment of the fault zone is oriented nearly north–south, while the southern segment trends north-northeast, forming a relatively wide and gentle fault belt. Faults are classified into three categories based on the source rock intervals they penetrate: Type 1 faults cut through the third Member of the Nenjiang Formation down to the Qingshankou Formation, Type 2 faults penetrate down to the first Member of the Nenjiang Formation, and Type 3 faults are confined only within the third Member of the Nenjiang Formation (Yao et al., 2017). Multi-stage, multi-directional, and multi-level fault systems interact in complex ways, collectively controlling hydrocarbon generation, migration, accumulation, and entrapment. Exploration practices indicate that the Heidimiao reservoir has strong potential for reserve evaluation and enhancement, with hydrocarbons mainly distributed along fault strikes. The complexity of the fault network is closely correlated with the degree of hydrocarbon enrichment.Based on planar maps of the fault belts, geological data, and previous studies, this paper conducts an in-depth analysis of the complex fault network system of the Heidimiao reservoir. Through quantitative characterization of topological features, the complexity and connectivity of the fault system are assessed. Combined with hydrocarbon migration characteristics in the study area, this approach reveals the intrinsic relationship between fault network complexity and hydrocarbon migration patterns.
3 Methods
By constructing vector topological maps, structural elements are analyzed from two dimensions: fracture development characteristics and fault scale, allowing for the comparison of topological differences among different structural units. The research methodology encompasses core steps such as topological classification principles, node–branch characterization, and connectivity prediction (Figure 2), ultimately clarifying the topological characteristics and their geological significance in hydrocarbon exploration.
FIGURE 2
3.1 Topological structure method
In a two-dimensional fault network system, topological structure analysis characterizes the connectivity of the fault network by extracting key topological parameters such as nodes and branches, in combination with fault scale and density. Nodes refer to points where faults intersect, terminate, or are discontinuous, and can be classified into three types based on geometric configuration: I-nodes (isolated tips), Y-nodes (adjacent or oblique intersections), and X-nodes (cross-cutting fractures) (Duffy et al., 2017; Morley and Nixon, 2016). Similarly, branches can also be divided into three categories: isolated I-I branches, partially connected I-C branches, and fully connected C-C branches (Aydin, 2000; Wang et al., 2023; Sanderson and Nixon, 2018; Manzocchi, 2002) (Figure 3). These classifications provide a fundamental basis for quantitatively characterizing the topological structure of fault networks. From a topological perspective, this method systematically describes the structural attributes of fault networks and quantitatively evaluates their connectivity and development intensity, thereby providing a scientific basis for the detailed characterization of complex fault systems. By projecting the proportions of different types of fault nodes and branches onto ternary diagrams, the connectivity characteristics and topological patterns of fault networks can be intuitively revealed. In the node ternary diagram, a dominance of I-nodes indicates a relatively simple fault network structure, whereas higher proportions of Y-nodes and X-nodes reflect a more complex and strongly connected fault system. In the branch ternary diagram, a dominance of I-I branches with fewer I-C and C-C branches suggests a simple network with weak connectivity; conversely, an increasing proportion of I-C and C-C branches-particularly when C-C branches become dominant-indicates a highly complex network with significantly enhanced connectivity (Wang et al., 2025). Therefore, characterizing the topological structure of fault networks not only helps distinguish between simple extensional fault settings and complex extensional fault systems in hydrocarbon-bearing basins, but also provides important theoretical guidance for understanding the influence of fault network connectivity on hydrocarbon migration pathways.
FIGURE 3
3.2 Topological structure parameters
By analyzing the fault network structure in the study area, different types of nodes (NI, NX, NY) were obtained as fundamental parameters. This study primarily uses several parameters to characterize the complexity and connectivity of the fault network system: the number of connected nodes (NC) representing fault connectivity; the average connectivity of fault line (CL); the average connectivity of fault branch (CB); fault intensity (I) reflecting the degree of fault development.
3.2.1 Numbers of branches and nodes
Each I-node represents the endpoint of a single branch, whereas Y-nodes and X-nodes represent the endpoints of three and four branches, respectively. The Y-nodes and X-nodes are classified as connected nodes, playing a critical role in maintaining network connectivity. Since each branch has two endpoints, the number of branches (NB) can be calculated using Equation 1. The number of connected nodes (NC) is determined according to Equation 2. As I-nodes indicate isolated terminations, Y-nodes generally represent fault adjacency or oblique intersections, while X-nodes correspond to fault intersections. Because I-nodes and Y-nodes represent the endpoints of individual fault lines within a unit area, the number of fault lines (NL) can be calculated using Equation 3 (Sanderson and Nixon, 2015; Sanderson and Nixon, 2018).
In Equations 1–3, NB represents the number of branches within a unit area, NC denotes the number of connected nodes, and NL represents the number of fault lines. NI, NX, and NY indicate the numbers of I-nodes, X-nodes, and Y-nodes within the unit area, respectively.
3.2.2 Fault connectivity (definitions and calculations of CL, and CB)
The average connectivity of fault line (CL) is an important topological parameter that characterizes the degree of connectivity among faults within a unit area (Manzocchi, 2002). For an individual fault line, the number of connections with other fault lines depends on the quantities of X-nodes and Y-nodes. Since each X- or Y-node contributes two connected fault lines, the average connectivity of fault line (CL) can be calculated using Equation 4.
As indicated by Equation 4, networks dominated by I-nodes do not exhibit connectivity. The value of CL increases only with the increasing number of X-nodes and Y-nodes, and a higher CL value indicates a greater degree of interconnection among fault lines.
Among the three types of branch configurations, namely, isolated (I-I), partially connected (I-C), and fully connected (C-C) branches, the node type C corresponds to the connecting nodes formed by Y- or X-nodes (Sanderson and Nixon, 2015). Therefore, the average connectivity of fault branch (CB) can be determined based on the proportions of different node types. In Equation 5, NB represents the total number of branches within a unit area, and CB, as the average connectivity of fault branch, is a dimensionless parameter. The average connectivity of fault branch within a unit area (CB) can be used to characterize the overall connectivity of the fault system. Generally, a higher proportion of C-C branches indicates a better-connected fault network within the investigated area (Sanderson and Nixon, 2018).
3.2.3 Fault development degree (definitions and calculations of I)
The degree of fault development is represented by fault intensity (I), which is defined as the total length of fault lines within a unit grid area (A) (Rohrbaugh et al., 2002). Therefore, the fault intensity (I) can be calculated using Equation 6. Here, represents the total length of fault lines within the unit grid area (A).
4 Results
4.1 Topological structure characteristics
This study focuses on the Heidimiao reservoir in the study area, aiming to characterize its fault-network topological structure and spatial distribution patterns. The analysis is primarily concerned with the planar distribution of the fault system within the Aogula-Talaha Fault Zone. By systematically extracting fault branches and node information, a detailed statistical analysis was conducted on different node types (I-nodes, Y-nodes, and X-nodes) and branches (I-I, I-C, and C-C). The results show that a total of 149 I-nodes, 33 Y-nodes, and 2 X-nodes were identified in the study area (Figure 4a), accounting for 81.0%, 17.9%, and 1.1%, respectively. Connected nodes account for 19.0% of the total population. Owing to the relatively dense development of small-scale faults, I-nodes dominate the system, whereas X-nodes and Y-nodes are relatively limited. In terms of branches, 47 I-I branches, 55 I-C branches, and 25 C-C branches were identified (Figure 4b), accounting for 37.0%, 43.3%, and 19.8%, respectively. Connected branches (I-C and C-C types) account for 63.0% of the total, indicating a relatively high degree of network connectivity. However, when evaluating connectivity, relying solely on node-type proportions has inherent limitations. To ensure a more comprehensive and robust assessment, this study adopts a multi-dimensional framework integrating both node and branch characteristics. This approach more effectively captures the overall connectivity of the fault network and provides a more reliable basis for quantitative characterization of the fault system in the study area.
FIGURE 4
Based on node-branch analysis of the fault network in the study area, a 2 km × 2 km grid was constructed according to the spatial extent of the region. Within each grid cell, a series of topological parameters were calculated, including the number of connected nodes (NC), the average connectivity of fault line (CL), the average connectivity of fault branch (CB), and fault intensity (I). The spatial distributions of these parameters are presented in Figure 5.
FIGURE 5
The number of connected nodes (NC) is defined as the total number of Y-nodes and X-nodes, and its magnitude directly reflects the connectivity degree of the fault network. Increasing NC values indicate enhanced fault density and connectivity per unit area, thereby improving the overall connectivity of the fault network. High NC values are primarily concentrated in the northwestern part of the study area, generally exceeding 1, with most values ranging from 2 to 4, whereas NC is zero across much of the remaining area. The average connectivity of fault line (CL), a widely used connectivity metric, shows an increasing trend with the abundance of X-nodes and Y-nodes. This pattern intuitively reflects the progressive strengthening of fault-network connectivity. High CL values are distributed in eight distinct zones and are mainly concentrated along the main fault belt. Most CL values range from 1 to 4.5 and are generally greater than 1.5. The average connectivity of fault branch (CB) is a dimensionless parameter, used to evaluate the quality of fault-network connectivity across different regions. Eight high-value CB zones are identified, with peak values also concentrated along the main fault belt. Most CB values range from 0.5 to 2.3 and are generally above 0.5. A higher proportion of C-C branches within the fault core typically indicates stronger connectivity in these areas. In terms of fault intensity (I), multiple high-value zones are observed, with values widely distributed between 0.3 × 10−3 and 1.2 × 10−3 m-1.
To further characterize the spatial variability in fault-network topology, the Aogula–Talaha Fault Zone was subdivided into four fault-network domains (Figure 6). These domains differ markedly in fault development, node composition, and branch composition, thereby allowing direct comparison. In the node ternary diagram (Figure 7a), data for the overall fault zone plot along the I-Y edge, reflecting the scarcity of X-nodes and a position relatively distant from the I-node end member. In the branch ternary diagram (Figure 7b), I-I branches account for the smallest proportion, whereas I-C and C-C branches predominate. The data plot toward the I-C end member and exhibit relatively high CB values. Collectively, these characteristics indicate that the fault network has a relatively complex topology and a high degree of geometric connectivity. Fault-network domains 1, 2, and 3 contain relatively high proportions of Y-nodes and I-C and C-C branches, with CL values ranging from 1.00 to 1.23. Their data points plot close to the I-C end member in the branch ternary diagram, and their CB values range from 1.10 to 1.20. These relatively high topological parameter values indicate well-connected fault networks. In contrast, domain four is dominated by isolated I-nodes and I-I branches, with a CL value of 0.40 and a CB value of 0.62. Its topological parameter values are comparatively low, indicating relatively poor fault connectivity. The differences among the four fault-network domains suggest that increasing topological complexity is generally associated with enhanced geometric connectivity and, where favorable hydraulic conditions are present, greater potential for fault-assisted hydrocarbon migration. This relationship represents a consistent spatial association within the study area, rather than evidence that topological complexity alone determines hydrocarbon migration efficiency.
FIGURE 6
FIGURE 7
The above analysis indicates that fault-network connectivity progressively decreases from north to south. The proportions of connected nodes (Y- and X-nodes) and connected branches (I-C and C-C branches) gradually decrease, whereas the proportions of I-nodes and isolated I-I branches increase. Zones of complex faulting are mainly concentrated along the master fault and correspond to areas with high topological parameter values. These zones are characterized by intense fault development and well-developed splay, abutting, and linkage relationships. Under tectonic stress, the master fault interacts and links with secondary faults, forming a complex and highly connected fault network. In contrast, areas farther from the master fault exhibit a lower degree of fault development, with less-developed fault linkage and splay structures. These areas are dominated by isolated I-nodes and I-I branches and are therefore characterized by simpler fault-network topologies and relatively poor connectivity (Ma et al., 2024).
4.2 Relationship between structural complexity and hydrocarbon migration
The regional tectonic evolution, fault-network topology, and spatial distribution of hydrocarbons provide an integrated basis for interpreting the principal controls on hydrocarbon accumulation in the Heidimiao reservoir. Because the Heidimiao reservoir is vertically separated from the source rocks in Member 1 of the Qingshankou Formation by a considerable distance, vertically connected faults linking the source rocks to the reservoir constitute a critical prerequisite for hydrocarbon accumulation (Dai et al., 2022; Li et al., 2023; Huang and Guo, 2014; Wang et al., 2016; Yao et al., 2017). Within the Aogula-Talaha Fault Zone, faults formed at the end of Mingshui Formation deposition, together with faults initiated at the end of Member 1 of the Qingshankou Formation and reactivated at the end of Yaojia Formation deposition, effectively connected the source rocks with the Heidimiao reservoir (Yao et al., 2017). During periods of fault activity, hydrocarbons migrated vertically from Member 1 of the Qingshankou Formation into the Heidimiao reservoir along the fault system. In the absence of effective fault conduits, the considerable source-to-reservoir separation and the intervening mudstone units would have impeded vertical hydrocarbon migration. Late-stage structural inversion and fault reactivation promoted linkage among the master faults, secondary faults, and newly formed faults, producing a topologically complex and well-connected fault network characterized by high topological parameter values. This interconnected network provided potential pathways for upward hydrocarbon migration into the Heidimiao reservoir. In areas where reservoir sand bodies are well developed, the faults intersect the sand bodies at high angles. After facilitating vertical migration, these faults may also act as lateral barriers where favorable sandstone-mudstone juxtaposition and fault-zone sealing occur, thereby restricting further updip migration and promoting lateral accumulation within adjacent sandstone bodies. This conduit–barrier interaction facilitated the formation of fault–lithologic reservoirs in the study area (Figure 8).
FIGURE 8
The master fault of the Aogula-Talaha Fault Zone is located within a first-order regional structural transition zone between the western slope uplift and the Qijia-Gulong Sag. The master fault and its adjacent secondary faults are well developed. Connected Y- and X-nodes and fully connected C-C branches are concentrated within this zone, resulting in a topologically complex fault network that provides multiple potential pathways for hydrocarbon migration. Areas with relatively abundant hydrocarbon accumulation in the Heidimiao reservoir are preferentially distributed along the fault zone and are mainly located within areas characterized by high topological parameter values (Figure 9a), indicating a close spatial correspondence between fault-network topology and hydrocarbon distribution. Frequency analysis of fault intensity (I) within oil-bearing grid cells of the Heidimiao reservoir (Figure 9b) shows that 84.38% of the oil-bearing grid cells have fault-intensity values ranging from 0.3 × 10−3[m]−1-1.2 × 10−3[m]−1. Here, 0.3 × 10−3 m-1 is not a statistically determined threshold, but represents the lower boundary of the dominant oil-bearing interval identified from the frequency distribution in Figure 9b. The largest proportion, accounting for 37.5%, occurs within the range of 0.3 × 10−3[m]−1-0.6 × 10−3[m]−1. These results suggest a close spatial association between hydrocarbon enrichment and the degree of fault development in the study area.
FIGURE 9
5 Discussion
5.1 Advantages of topological analysis
Conventional fault evaluation commonly relies on geometric and statistical parameters, including fault strike, dip, length, linkage pattern, and lithologic juxtaposition across faults. Fault geometries interpreted from structural maps and seismic profiles have been widely used to characterize the degree of fault development qualitatively or semi-quantitatively (Feng et al., 2014; Huang and Guo, 2014; Wang et al., 2016; Yao et al., 2017). However, geometric characterization primarily describes the external configuration of a fault network and may not adequately quantify the spatial intersections and connectivity relationships among individual fault lines. In particular, conventional geometric parameters alone cannot readily distinguish differences in fault-network connectivity, even though connectivity is an important control on hydrocarbon migration and accumulation. For example, two fault networks with identical fault densities may exhibit markedly different hydrocarbon-conveyance capacities. A network composed predominantly of isolated and unlinked fault lines is unlikely to form continuous migration pathways. In contrast, a network containing numerous intersecting and linked faults may contain multiple fault-interaction zones. Local stress perturbations within these interaction zones may promote the development of secondary fractures, enhance formation permeability, and consequently create preferential pathways for hydrocarbon migration and accumulation (Ma et al., 2024; Wang et al., 2021). Evaluation based solely on conventional fault parameters may therefore overlook these fundamental differences, potentially resulting in incomplete assessments of fault transmissibility and hydrocarbon accumulation potential.
Topological analysis provides a complementary quantitative approach by classifying node types and characterizing the connectivity of fault branches. The study area contains densely distributed and extensively linked faults with complex geometric patterns. Conventional parameters such as fault strike, length, and density alone are therefore insufficient to characterize the internal connectivity of this complex fault system. Topological parameters can quantify the degree of linkage among fault lines and the continuity of potential migration pathways, thereby providing a geometric basis for identifying possible preferential hydrocarbon migration zones. Nevertheless, topological analysis cannot replace conventional evaluations of fault displacement, timing of fault activity, stress conditions, sandstone–mudstone juxtaposition, or fault sealing. Topological parameters should therefore be integrated with tectonic evolution, fault-seal analysis, and petroleum-system elements to provide a more comprehensive assessment of fault-controlled hydrocarbon migration and accumulation.
5.2 Controls of fault-network topology on hydrocarbon migration and accumulation
Fault-network topology primarily characterizes the geometric connectivity among faults and can therefore provide a geometric basis for identifying potential hydrocarbon migration pathways and favorable accumulation zones. High proportions of isolated I-nodes and I-I branches generally indicate poor connectivity and a limited capacity to form continuous, long-distance migration networks. Y-nodes commonly occur where a fault splays, abuts, or terminates against another fault, whereas X-nodes represent intersections between different faults. An increase in the proportions of I-C and C-C branches indicates that one or both ends of a branch are connected to other faults. Such an increase provides a greater number of alternative migration pathways and enhances the overall connectivity potential of the fault network (Sanderson and Nixon, 2015; Wang et al., 2021; Ma et al., 2024; Sanderson and Nixon, 2018).
Fault-linkage patterns can further influence fluid-migration efficiency. Overlap zones or relay zones may develop between initially unconnected fault segments and may act as local bottlenecks to fluid flow. Under continued tectonic deformation, secondary faults and associated fractures may develop within these zones, allowing an initially soft-linked fault system to evolve into a hard-linked system and thereby establishing continuous hydrocarbon migration pathways. Consequently, areas containing abundant Y- and X-nodes and C-C branches not only exhibit complex fault geometries but may also correspond to zones of localized strain concentration, enhanced secondary fracturing, and increased potential for fluid transmission (Aydin, 2000; Fossen and Rotevatn, 2016).
It should be emphasized that geometric fault connectivity does not necessarily imply hydraulic connectivity. Fault transmissibility is also controlled by the intensity and timing of fault activity, the stress state, and the internal architecture of the fault zone. During extensional faulting, a reduction in effective normal stress may promote the opening of faults and associated fractures, increasing along-fault permeability and facilitating vertical hydrocarbon migration. Following the cessation or weakening of fault activity, compaction, fault-rock development, clay smearing, and diagenetic cementation may reduce across-fault permeability, causing a fault to evolve from a migration conduit into a lateral sealing boundary. During structural inversion, moderate compression may promote fault closure and hydrocarbon preservation, whereas intense fault reactivation may reopen previously sealed faults and lead to hydrocarbon redistribution or leakage (Huang et al., 2026). The role of faults in hydrocarbon accumulation is therefore both stage-dependent and direction-dependent. During hydrocarbon charging, faults may primarily act as vertical conduits, whereas after accumulation they may restrict across-fault fluid flow and provide lateral sealing (Huang and Guo, 2014; Yao et al., 2017). In the study area, the approximately north–south-striking faults intersect the reservoir sand bodies at high angles. Where fault displacement produces sandstone–mudstone juxtaposition and the fault rock has sufficient sealing capacity, the faults may restrict further updip and lateral migration, promoting hydrocarbon accumulation in sandstone bodies on one side of the fault and facilitating the formation of fault–lithologic reservoirs. Conversely, where laterally extensive sand bodies have high reservoir connectivity and multiphase fault activity has not produced effective sealing, faults may serve as leakage pathways, thereby limiting the scale of hydrocarbon accumulation. Overall, hydrocarbon enrichment in the Heidimiao reservoir is jointly controlled by the degree of fault development, favorable fault–sand-body configuration, reservoir quality, and effective preservation conditions.
5.3 Coupling between topological parameters and petroleum-system elements
Topological parameters characterize only the geometric connectivity of a fault network. Their significance for hydrocarbon accumulation must therefore be evaluated within a petroleum-system framework that integrates source, migration pathways, reservoirs, traps, and preservation conditions. In the study area, the Heidimiao reservoir is separated vertically from the source rocks in Member 1 of the Qingshankou Formation by a considerable distance, with several intervening mudstone units. A fault can function as an effective source-to-reservoir carrier only if it connects the source rocks with the Heidimiao reservoir and its period of activity coincides with the timing of hydrocarbon generation and charging (Feng et al., 2014; Huang and Guo, 2014; Yao et al., 2017). Accordingly, high topological parameter values, such as high CL and CB, indicate complex geometric linkage and a greater abundance of potential migration pathways, but do not necessarily demonstrate effective hydrocarbon transmission. If a fault does not penetrate an effective source-rock interval, or if its activity does not coincide with the principal hydrocarbon-charging period, it is unlikely to form an effective migration conduit. After hydrocarbons migrate along faults into the Heidimiao reservoir, their subsequent migration and accumulation are controlled by reservoir porosity, permeability, and sandstone-body distribution. Laterally continuous sandstone bodies with favorable porosity and permeability facilitate hydrocarbon migration and redistribution, whereas poorly connected sandstone bodies with low reservoir quality are less favorable for large-scale hydrocarbon accumulation. Consequently, topological connectivity should be evaluated together with reservoir quality, sandstone-body architecture, sandstone–mudstone juxtaposition, clay smearing, trap configuration, and regional preservation conditions when assessing the effectiveness of fault-controlled hydrocarbon accumulation (Qin et al., 2026).
5.4 Cross-area comparison of fault-network topology
Hydrocarbon migration is a complex process controlled by fault-linkage patterns, fault orientation, activity history, and other geological factors. Topological analysis enhances the understanding of fault-controlled hydrocarbon migration by providing a complementary quantitative framework for evaluating the relationship between fault-network complexity and potential migration pathways. Previous studies in the Linnan Sag and the Wenchang A Sag have shown that areas characterized by abundant connected Y- and X-nodes and I-C and C-C branches generally exhibit relatively high fault-network connectivity. Areas with high topological parameter values also show close spatial correspondence with preferential hydrocarbon migration pathways and reservoir distribution (Ma et al., 2024; Wang et al., 2021; Wang et al., 2025). The Aogula–Talaha Fault Zone is similarly characterized by a complex and well-connected fault-network topology. Within the hydrocarbon-enriched area, 37.5% of the oil-bearing grid cells have fault-intensity (I) values ranging from 0.3 × 10−3[m]−1-0.6 × 10−3[m]−1, representing the largest proportion among the defined fault-intensity intervals. This distribution indicates a close spatial association between hydrocarbon enrichment and the degree of fault development. These observations are broadly consistent with results reported from the Linnan Sag and the Wenchang A Sag, suggesting that fault-network topology can provide a useful quantitative basis for identifying potential preferential hydrocarbon migration pathways in structurally complex fault zones.
6 Conclusion
This study comprehensively applies topological parameters, including the number of connected nodes (NC), the average connectivity of fault line (CL), the average connectivity of fault branch (CB), and fault intensity (I), to characterize the connectivity of the fault network system. The results indicate that fault networks in different regions exhibit distinct topological architectures, accompanied by corresponding variations in connectivity. High-value topological zones are dominated by large-scale faults that, under tectonic stress, interconnect with secondary fault systems to form complex fault networks with relatively strong connectivity. In contrast, low-value topological zones are mainly composed of small-scale faults with fewer connected nodes, resulting in simpler topological configurations and weaker connectivity.
This study establishes a coupled relationship between fault-network topological characteristics and hydrocarbon migration, and demonstrates a strong spatial correspondence among structurally complex fault zones, high-value topological parameter zones, and hydrocarbon migration pathways. This approach complements conventional assessments based on fault strike, length, and density by explicitly quantifying fault-network connectivity. By incorporating topological parameters such as nodes, branches, and fault intensity, the method enables the identification of highly connected fault zones and potential preferential hydrocarbon migration pathways within complex fault networks. Overall, this methodology provides a practical quantitative framework and a useful reference for investigating hydrocarbon accumulation in faulted rift basins with similar geological settings.
Statements
Data availability statement
The datasets presented in this study are not publicly available due to institutional confidentiality restrictions. Requests to access these datasets should be directed to Bingxi Li, libx@sina.com.
Author contributions
SC: Writing – original draft, Writing – review and editing. FX: Writing – review and editing. RZ: Writing – review and editing. BL: Writing – review and editing. XL: Writing – original draft.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the National Natural Science Foundation of China (Project Number: 42172162) and the Hebei Provincial Natural Science Foundation for Young Scientists (Grant No. D2024107006).
Acknowledgments
We gratefully acknowledge the support provided by the National Natural Science Foundation of China (Grant No. 42172162), as well as the contributions from Northeast Petroleum University. We also extend our sincere thanks to the numerous investigators who generously allowed us to use their data.
Conflict of interest
Author SC was employed by Beijing Tianyuan Yunkai Technology Co., Ltd. FX was employed by Exploration Department of PetroChina Daqing Oilfield Co., Ltd. RZ was employed by No. 9 Oil Production Plant of Daqing Oilfield Co., Ltd. BL was employed by Beijing Xunhe Technology Co., Ltd.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
complex fault segments, fault intensity, fault network system, hydrocarbon migration, topological structure
Citation
Chen S, Xu F, Zhang R, Li B and Li X (2026) Topological structure of fault network system and control factors of reservoirs. Front. Earth Sci. 14:1919615. doi: 10.3389/feart.2026.1919615
Received
25 June 2026
Revised
28 August 2026
Accepted
31 August 2026
Published
28 September 2026
Volume
14 - 2026
Edited by
Hu Li, Sichuan University of Science and Engineering, China
Reviewed by
Ahmed E. Radwan, Wrocław University of Science and Technology, Poland
Rong Chu, Northeast Petroleum University, China
Updates
Copyright
© 2026 Chen, Xu, Zhang, Li and Li.
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*Correspondence: Bingxi Li, libx@sina.com
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