Study on initial fracture distribution and bearing damage evolution mechanism of disturbed coal samples in oil-rich coal deposits in western China

The simultaneous formation of stress transfer and structural optimization in deep underground engineering strongly influences the occurrence and severity of dynamic disasters, especially under complex stress–structure conditions. In western China, oil-rich coal is mainly distributed in complex geological structures, and the deformation, fracture, and failure of coal are essentially governed by fracture evolution under external loading. Therefore, the characteristics of the original fissure structure and the subsequent damage evolution process are critical for multi-source disaster prevention and control during oil-rich coal mining. In this study, the initiation, propagation, coalescence, and evolution of coal microfissures under multi-stage disturbances were investigated using CT scanning combined with mechanical testing. Differences in CT images among different regions were analyzed after initial oil and gas resource exploitation, and crack binary images were used to qualitatively describe fracture evolution. Meanwhile, coal CT number, crack number, trace width, gap length, and crack density were extracted to quantitatively characterize the spatio-temporal relationship between fracture structural features and multi-stage repeated disturbances, together with acoustic emission characteristics. The results provide a comprehensive description of fracture development and damage progression in disturbed coal samples, which is of great significance for understanding the fracture distribution and mechanical response mechanisms, thereby supporting safe and efficient coordinated mining of oil-rich coal in western China.

1 Introduction

The oil-rich coal in the west is mainly endowed in complex geological formations, the coal seams are usually thick and unevenly distributed, formed in ancient hot and humid swampy environments, rich in organic matter, with high oil and gas generating potentials, deeper deposits, and greater economic development value. Coal and oil resources co-storage phenomenon (Zheng et al., 2024) is common in Songliao, Junggar, Tarim, Qaidam and Ordos basins in China. Residual damage formed during long-term geological coupling, which leads to coal rock damage, overburden structural kinetic instability and homologous stress excitation, and breeds derivative kinetic hazards (roof hazards, gas hazards, and flooding, etc.) The disaster-causing mechanism is complicated (Ji et al., 2024; Zhang T. et al. 2024; Zhao et al., 2025), and there are obvious differences between coal mine kinetic hazards and conventional kinetic hazards. There are obvious differences between coal mine dynamic disasters, mainly manifested in the well field and mining two stress field coupling, coal rock has previously undergone temperature cycling and long-term hydrocarbon seepage in situ, coal seam mining stress field redistribution, seepage channel formation and other complex course, which has formed persistent residual fractures that continue to influence its bearing behavior under later disturbance in the mechanical properties of the coal rock and permeability, which will affect the safety of a variety of coal, oil and gas resources in the co-storage area of mineral resources. Efficient synergistic mining of multiple mineral resources in the coal, oil and gas resources co-storage area. In order to explore the internal structure of rock materials, many scholars at home and abroad have used a large number of cutting-edge technologies, including CT scanning imaging non-destructive detection technology (Wang et al., 2024; Zeng et al., 2025; Han et al., 2024), X-ray section scanning imaging, which can visualize the internal structure of coal rocks without damaging them, and provide reference for the study of the characteristics of the distribution of internal fractures and mineral impurities in rock materials (Lin et al., 2024; Xianglong et al., 2023).

To summarize: the fracture identification technology, quantitative characterization means such as dimensional computing and damage characteristics of coal rock, combined with stress-strain and acoustic emission data, have achieved fruitful results in studying the fracture evolution law and seepage damage characteristics of coal rock in the process of loading. However, the post-coupling degradation mechanism of such pre-damaged coal remains unclear, which restricts the research on the damage mechanism of coal rock in the coal, oil and gas resource co-storage area. Although CT-based fracture characterization has been widely reported, existing studies mainly focus on intact or mechanically pre-fractured coal. Little attention has been paid to coal bodies that have already experienced long-term oil–gas seepage disturbance in co-storage zones, where the inherited fissure network becomes the dominant control factor of subsequent mechanical instability during mining. Therefore, it is necessary to clarify the residual fissure structure and its evolution under loading, which is the technical gap addressed in this study. In this paper, the oil and gas seepage problem faced by coal mining in the coal oil and gas resource co-storage area is solved as the starting point, and the fissure distribution law of coal body damaged by oil and gas seepage is investigated in the Shuangma Coal Mine in Ningdong Mining Area, which is the typical engineering background of oil-rich coal in the west, and the results of the research are of great practical significance and popularization value for the safe and efficient exploitation of coal resources in the coal resource co-storage area and full utilization of the resources.

2 Engineering overview

2.1 Mine overview

Shuangma Coal Mine is located in Ningdong coal and oil resources common storage area (Figure 1), the mine design production capacity of 4.0Mt/a, the main coal seams can be extracted 8 seams, of which 4⁻¹ coal seam is the uppermost recoverable coal seam with an average thickness of 3.80 m, and 18⁻² coal seam is the lowermost recoverable coal seam with an average thickness of 2.82 m. The mine area is rich in petroleum and coal resources, the coal-bearing strata is mainly located in Jurassic Yan’an Formation, the recoverable coal seam is about 250 m away from the upper boundary of the main oil-bearing strata of the Yanchang Formation, the lower boundary is about 250 m away from the upper boundary of the main oil-bearing strata of the Yanchang Formation. The lower boundary is about 250 m away from the upper boundary of the main oil-bearing strata of Yan’an Formation.

Figure 1

Figure 1. The layout of coal seam and oil exploitation in Shuangma resource co-storage area.

Ningdong Shuangma Coal Mine is located in the Ordos Basin coal and oil resources co-storage area, the coal rock around the oil and gas wells of the first oil mining experience drilling engineering stress field redistribution, oil resources development injection and mining engineering cycle, the temperature field of the well field surrounding the rock between the injection wells and the production wells continues to change, the long term oil and gas seepage inside the bare eye wells and the poorly closed wells, the development of the coal resources leads to the change of the coal rock stress field and the seepage field, and the complex temperature, stress and seepage history with the increase of the depth of the coal bed mining the coal rock is located in the temperature field changes. As well as the complex temperature, stress, and seepage history of the coal rock with the increase of the coal seam mining depth, the temperature field of the coal rock is changed (Zhang et al., 2025).

2.2 Characterization of hazards in coal, oil and gas resource co-storage areas

Oil mining in the 60s∼70s of the last century formed the oil and gas wells in the well field of Ningdong Shuangma Coal Mine, due to the number of oil wells in the mining area, the complexity of the well types, the long history, the serious lack of information, the oil and gas pressure in the wells (the measured pressure at the site is more than 15 MPa) and other characteristics, the coal mining process, by the impact of oil and gas wells, which can lead to the oil and gas leakage and explosions, the roof plate water protruding, H₂S gas poisoning and other major During the coal mining process, the influence of oil and gas wells can cause oil and gas leakage, explosion, water protrusion from roof slabs, H2S gas poisoning and other major disasters and accidents, which seriously affects the safety of coal mines in the coal, oil and gas resources storage area. There are more and more coal mines in the coal, oil and gas resource co-storage area, and the phenomenon of multiple resources constraining each other occurs frequently.

A total of 170 oil wells have been identified in Shuangma coal mine field, mainly concentrated in the northeast of Shuangma well field. According to the mining succession, 11 abandoned oil wells such as Ma Tan 31, Ma Tan 30, Ma Tan 29, etc., which affect the mining work in the recent mining range, are located in the working face of the south wing of the shaft in Ⅰ01 mining area.

3 Experimental programs

3.1 Sampling location

The coal samples used in this experiment were taken from the return roadway of I0104105 working face in 4-1 coal seam of Shuangma Coal Mine of Ningxia Coal Industry Co., Ltd. of National Energy Group, in the areas of 10m, 100m and 500 m away from the oil wells of MaDan30, and were numbered from near to far (Coal samples from co-storage area (CSCA)), and the specimens were in the order of CSCA-1, CSCA-2, CSCA-3, and were polished and treated by grinding stone machine for the test. CSCA-1, CSCA-2, CSCA-3, and the specimens were polished with a stone grinder to prepare for the test.

3.2 Integrated test platform

The test mainly consists of a CT scanning system, a mechanical loading system and an acoustic emission monitoring system (Figure 2). The CT scanning equipment is a UCT780 X-ray spiral CT machine of Shanghai Union Image Medical Technology Co. with a spatial resolution of 0.3 mm × 0.3 mm, a density-contrast resolution of 3HU, and a scanning layer thickness of 0.5 mm. The loading equipment is the RMT-150B rock mechanics test machine of the Key Laboratory of the Ministry of Education for Mining and Disaster Prevention of the Western Mining and Disaster Control (Xi’an, China) RMT-150B rock mechanics testing machine. Before loading, the specimen was loaded with PTFE sheets and silicone grease to ensure uniform stress distribution at the end of the specimen, and then loaded at a rate of 0.2 mm/min until the specimen was damaged. The acoustic emission monitoring system is a DS5-16B full information acoustic emission signal acquisition and analysis system with a 16-bit analog-to-digital converter, 10 MHz sampling rate, and six acoustic emission probes are arranged on three sides of the selected specimen. Between the sensor and the test piece is filled with the appropriate amount of coupling agent to ensure adequate contact, before the test, the test piece is first tapped with a signature pen to simulate the signal source, to ensure that the response of each probe channel is normal. According to the field environment test, the acoustic emission threshold is 38dB, the sampling frequency is 1MHz, the preamplifier gain is set to 40dB, and butter is applied between the probe and the coal sample to enhance the coupling effect. Before the test, knock the coal sample to simulate the signal source, observe the response of each probe channel, and ensure that each probe is normal before the test (Lai et al., 2025).

Figure 2

Figure 2. Experimental system of strength cracking characteristics of seepage damage coal.

4 Characterization of initial fracture geometry parameters of disturbed coal bodies in syngenetic mines

4.1 Qualitative characterization of the fracture structure of coal bodies

4.1.1 Precise identification of coal body fissure parameters

With the help of UCT780 X-ray spiral CT equipment of Union Image Medical Technology Co. Ltd, the CT slice thickness of coal samples was set at 2 mm, and a total of 50 CT slice images could be obtained for a single coal sample. In order to reflect the geometric parameters of the fissure of the whole coal sample, each coal sample selects 10 mm, 30 mm, 50 mm, 70 mm and 90 mm position slice images, establishes the coupling algorithm of fissure binarization processing and neighborhood interpolation, and writes the image binarization processing commands through the MATLAB software, the CT images were processed using a standard CT reconstruction workflow (denoising, enhancement and segmentation), and the specific software-level operations are omitted as they are not the focus of this study (Zhang L. et al., 2025).

Under the action of oil and gas seepage, there are significant differences in the fracture structure characteristics of coal bodies in different regions, which are mainly manifested in the trace lengths of primary and secondary fractures, the degree of openness, the depth of penetration, and the influence of mineral impurities on the fracture evolution paths, etc. The spatial position slices of the coal bodies in different regions and the dichotomized fracture characteristics are shown in Figure 3. In this paper, the term fissure is used as the unified expression for internal discontinuities in the coal body. The term micro-fissure refers to micro-scale initiation and early propagation, whereas fracture is reserved for macroscopic failure surfaces after instability. Earlier uses of “crack” are therefore conceptually included within the unified definition of fissure for terminological consistency.

Figure 3

Figure 3. Spatial position slice and binary fracture characteristics of coal in different regions. (a) CSCA-1 (10 m away from abandoned wells). (b) CSCA-2 (100 m away from abandoned wells). (c) CSCA-3 (500 m away from abandoned wells).

From the spatial location slices and binarized fracture characteristics of coal bodies in different regions (see Figure 3), it can be seen that the number of fissures, degree of opening and penetration and fracture density show an increasing trend with the location of sampling close to the oil wells, which indicates that the damage of high-pressure oil and gas seepage to the surrounding coal bodies decreases with the increase of distance. The characteristics of the openness, penetration depth and mineral impurities of coal body fissures in different areas are as follows: the CSCA-1 coal sample at 10 m from the abandoned oil well has more fissures, higher fissure density, and the main fissures are developed, especially the longitudinal distribution of the main fissures in the middle has a large penetration depth, with an axial penetration depth of more than 50 mm, an openness of 4 mm, and a horizontal direction that runs through the surface of the coal body, and the coal body has fewer mineral impurities and simple characteristics; 100 m away from the abandoned oil well has a simple character; 100 m away from the abandoned oil well has a simple character; 100 m away from the abandoned oil well has a simple character. Characteristics are simple; 100 m away from the abandoned oil well, the number of cracks and crack density of CSCA-2 coal samples began to decrease, three kinds of mineral impurities appeared, including dark gray chondrites and dense coal body to form a 45° inclined direction of the interaction layer, the other in the slices show white spots for pyrite impurities, the main cracks in the coal body is only a single one, axial penetration depth of 30 mm, the degree of openness is less than 1 mm, the main independent cracks account for a large depth of penetration, axial penetration depth of more than 50 mm, the degree of openness is less than 1 mm, the main cracks are more than 1 mm. The independent cracks accounted for a larger proportion, mainly concentrated in the middle of the coal body; the CSCA-3 coal sample 500 m away from the abandoned oil well has a lower degree of fissure development, and there are more independent microfissures and pore structures.

4.1.2 Characterization of coal body fissure body data

The internal structure of the coal body is complex, and it is difficult to accurately describe the fracture structure by direct measurement. CT scanning, as a nondestructive detection technique (Xu, 2023; Yi, 2025; Chen, 2023; Zhang Q. et al., 2024; Li et al., 2024; Jianhang et al., 2023; Sun et al., 2023; Shan et al., 2023; Lu et al., 2023; Shan et al., 2021; Yin et al., 2013; Xie and Zhou, 1998) to characterize the fracture, mineral and pore penetration properties of the coal body, and the two characterization methods of CT counting and threshold segmentation are most widely used in the field of the identification of the fracture structure and the characterization of the evolution of the internal fracture structure of the rock material. The effect of CT scanning on the identification of the fine-scale cracks is obvious, but the effect of characterization of the microcrack is poor, resulting in the damage pattern of the rock material is not obvious. CT scanning is effective for the identification of microscopic cracks, but less effective for the characterization of microcracks, resulting in the damage pattern of rock materials is not obvious. The average CT number method refers to the linear attenuation coefficient of the material structure in CT scanning, which is closely related to the density of the measured material, and can determine the distribution characteristics of cracks and mineral impurities in rock materials according to the density change characteristics of the measured material, and the offset of the density of the measured material and the cracks will affect the test results of the average CT number. Based on this, this paper adopts CT scanning pictures to mark the coordinates of the position of the prime points of different media, and determines the CT value of each medium through the determination of the pixel points, determines the distribution characteristics of each medium, and combines with the average CT number to determine the density of the rock material, which can be used to characterize the anisotropy of the coal body through the density variability of the material at different locations.

The essence of CT scanning technology is the non-metallic materials on the X-ray absorption degree of variability, due to the different atomic structure of different media, the X-ray absorption ability of different strengths and weaknesses. Assuming that the initial energy of X-rays for I0, through the object under test after the energy projected onto the detector for I, due to the variability of the attenuation coefficients of the components of different media, resulting in the detector received energy is not the same, you can differentiate between the different media, the energy attenuation characteristics of the whole detection process follow Beer’s law, as shown in Equation 1:

$I=I_0{e}^{{\displaystyle \sum _{i=1}^n}{\mu }_i{x}_i}(1)$

Where: I₀-the initial energy of X-rays, V·ms;

I-the energy of X-rays after passing through the inspected material, V·ms;

μ_i-attenuation coefficient of the ith layer of the inspected material;

x_i-thickness of the ith layer of the inspected material, mm.

$\ln \left(\frac{I_0}{I}\right)={\displaystyle \sum _{i=1}^n}{\mu }_i{x}_i(2)$

As can be seen from Equation 2, the sum of the product of the attenuation coefficient of the rock material and the thickness of the material to be detected is equal to the logarithm of the ratio of the initial energy intensity of the X-rays to that of the received energy intensity. The attenuation coefficient can be used to reconstruct the scanned image during the CT scanning process.

Normally, the wavelength of the rays during CT scanning is constant, so the main factors affecting the attenuation coefficient of X-rays are Compton effect and photoelectric effect, from which it can be deduced that the relationship between the X-ray attenuation coefficient and the atomic structure as well as the density of the measured substance is given in Equation 3:

$\mu =\rho \left(a+b\frac{Z^{3.8}}{E^{3.2}}\right)(3)$

Where: μ-attenuation coefficient of X-rays through the measured substance, cm²/g;

Ρ-density of the measured substance, g/cm³;

a-Kline-Nishina coefficient; b = 9.8 × 10⁻²⁴;

Z-effective atomic number, ev/m²s;

E-X-ray energy, V·ms.

In the process of practical application, the variability of the attenuation coefficient of X-rays is small, so in order to make the attenuation coefficient more obvious, it is necessary to amplify it, so the attenuation coefficient of X-rays passing through the water as a benchmark, and the CT number (in HU) was introduced, and the expression of the relationship between the attenuation coefficient of X-rays passing through the detected substance and the CT number was defined as:

$CT=1000\times \frac{{\mu }_m-{\mu }_w}{{\mu }_w}(4)$

Where: μ_m-X-ray attenuation coefficient through the measured material;

μ_w-X-ray attenuation coefficient through the water.

From Equation 4, it can be seen that when the examined substance is water, because the density of water is 1 × 10³ kg/m³, its attenuation coefficient μ_w = μ_m, that is, the CT number of water is 0. For air, its density is 0, the attenuation coefficient μ_m = 0, and at this time, the CT number of air is −1000HU, in other words, the CT number of examined substance is positively correlated with its density. The inhomogeneity of the examined material can be expressed on the CT image in detail and intuitively through the CT number, and the range of the CT number of the geotechnical medium is generally taken as −1024∼+3071. The CT values of the coal body, fissures and internal mineral impurities in Shuangma coal mine are shown in Figure 4.

Figure 4

Figure 4. Coal CT pixel position coordinates and CT value analysis.

Volume data (VD) records the values on each discrete grid point in a three-dimensional or multidimensional spatial range. Volume data consists of voxels, which are the smallest units that make up the volume data, and it can be understood as the points or a small area with arrangement and color in the three-dimensional space. The obtained images of CT slice scanning of coal body fissures can be obtained to extract the coordinates of fissure positions and CT values of different spatial layers, as shown in Figure 5.

Figure 5

Figure 5. CT value standard of coal medium and fracture CT characteristic value.

The CT value of the coal body refers to the unit of attenuation coefficient (HU) when X-ray transmits the coal body, assuming that the coal body consists of a large number of units, when the unit is small enough, the attenuation coefficient of X-ray transmitting through each unit is a constant, the average CT number of the slices of the coal body can be obtained through many times of the penetration test in different directions, the combination of all the slices is the average CT number of coal body, the average CT number of the slices of the coal body can reflect the coal body’s The average CT number of coal body slices can reflect the non-homogeneity of coal body, and through the difference of the average CT number of coal body, we can analyze the relationship between the damage characteristics of coal body and the location of oil wells in the oil and gas rich area, and further analyze the influence of oil and gas seepage on the damage characteristics of coal body (Zhang et al., 2026).

4.1.3 Analysis of coal body data from damaged coal bodies in symbiotic mining areas

The change amount of CT value of coal samples is compared to characterize the internal structural changes of coal body cracks and mineral impurities in the oil and gas rich area, so as to analyze the degree of damage and destruction of oil and gas seepage on the coal body. Therefore, when counting the average CT value of each section of coal samples, the section selection cannot be too sparse, nor need to be too dense, taking into account the distribution and size of the damaged cracks of coal samples, 50 sections of each coal sample are selected for counting the average CT value, and the average CT value of each section is obtained, and then the average value of the CT value of the 50 sections is summed and averaged, that is, the average CT value of the coal samples. Figure 6 shows the spatial stratigraphic analysis of CT eigenvalues of seepage damaged coal body.

Figure 6

Figure 6. CT characteristic value analysis of coal fracture in oil and gas enrichment area. (a) CSCA-1. (b) CSCA-2. (c) CSCA-3.

Figure 6 gives the CT value curves of typical layer CT slices and fissure characteristic pixel points of coal bodies in different regions of the oil and gas enrichment area, and the analysis results show that the CT value of coal bodies is closely related to their looseness and fissure development, with the approximate distribution range of 100∼800HU, the fissure development is higher, the CT value range of the condition of loose coal is 100∼500HU, the fissure development is lower The CT value range is 500∼800HU in the case of hard coal quality; the CT value range of rock medium is 1600∼2400HU; the CT value of air medium is −1000; the CT value of water is 0, and the CT value of slices is between −1000∼0HU, which indicates that the area is a distribution area of the fissures, and the size of the CT value can reflect the distribution characteristics of the fissure structure, and the size of the CT value can reflect the fissures, distribution characteristics of structures such as filler and mineral impurities, and also reflect the damage of oil and gas seepage on the coal body. The statistics of the penetration depth of air fissures in Figures 6a–c are as follows: the penetration depth of fissures in CSCA-1 pixel points are 56 mm, 42 mm and 26 mm; the penetration depth of fissures in CSCA-2 coal sample pixel points are 24 mm and 22 mm; and the penetration depth of fissures in CSCA-3 pixel points are 8 mm and 4 mm, respectively.

In summary, it can be seen that the X-ray attenuation degree of coal matrix, fissure rock media and mineral impurities within the coal body has obvious differences, the CT value of coal samples ranges from 100 to 800 HU, the CT value of rock media ranges from 1600 to 2400 HU, and the range of mineral impurities is greater than 2000 HU, the CT value of coal body can respond to the components and the distribution characteristics of each component, and can be used as a method to characterize the structure and evolution of the coal body. It can be used as a characterization method of coal body structure and evolution. The closer the coal samples are to the oil and gas wells, the higher the degree of fissure development, the larger the proportion of CT values between −1000 and 100 HU, and the more serious the damage to the coal body, indicating that the CT values can reflect the damage characteristics of the coal body and its own strength properties.

4.2 Quantitative characterization of the fracture structure of coal bodies

4.2.1 Fractal characterization of coal body fractures

Based on the fractal theory of rock materials proposed by Ren (2022), the number of lattices in the fracture region is counted by controlling the size of the square lattice, and the side lengths of the lattice are set as 0.2 mm, 0.4 mm, 0.8 mm, 12.8 mm, and 25.6 mm, and the average number of lattices of each side length in the fracture region is counted, and the fractal dimensions of fracture structure of oil and gas seepage coals are calculated by the fractal box dimensions method (Figure 7).

Figure 7

Figure 7. Coal fracture binarization processing and fractal calculation.

The linear fitting function for typical box dimension calculation of fracture distribution can be expressed as Equation 5:

$LogN\left(\delta \right)=-D\log \delta +C(5)$

Where: N(δ)-number of lattice distribution in the fracture region;

δ-side length of square lattice, mm;

C-constant;

D-the fractal dimension of coal sample cleavage.

Figure 8 and Table 1 demonstrate the fractal dimension characteristics of the fracture structure of coal samples at different locations, and the fracture structure has a significant effect on its fractal dimension value, and the fractal dimension of coal samples ranges from 1.68153 to 1.94138, which can be seen that the closer the location of coal samples is to the abandoned oil wells, the more the number of fissures is, and the bigger its fractal dimension is, which shows that the closer the abandoned oil wells are, the more the fissures of the coal body are developed, and the bigger the degree of damage is. Among them, the fractal dimension of 2.10 (e∼f) coal samples after 200 m from the abandoned oil and gas wells is discrete, which indicates that the oil and gas seepage from the abandoned oil wells has less damage to them.

Figure 8

Figure 8. Fractal dimension calculation of coal sample crack based on fractal dimension method. (a) CSCA-1 coal sample. (b) CSCA-2 coal sample. (c) CSCA-3 coal Sample.

Table 1

Table 1. Calculation of lattice distribution number N(δ) and fractal dimension D of different side lengths in fracture area of coal samples.

4.2.2 Three-dimensional volume reconstruction of coal body fissures

Three-dimensional reconstruction of oil and gas seepage damaged coal body can determine the spatial location of the cracks (Figure 9), and extract the statistical analysis of the data of different horizons, so as to reveal the relationship between the crack evolution characteristics of oil and gas damaged coal body and its relationship with the location of oil wells.

Figure 9

Figure 9. Three-dimensional fracture distribution characteristics of seepage damage coal. (a) CSCA-1. (b) CSCA-2. (c) CSCA-3.

4.2.3 Multi-scale characterization of fracture structure in coals

The previous section has reconstructed the coal body and the fracture structure at different locations from the oil wells in three dimensions, however, the three-dimensional spatial fracture structure characteristics can be quantitatively characterized by multiple parameters such as fracture dimensions, number, density multivolume and fractal dimension. From Figure 9, it can be seen that the internal fracture structure of the coal body is complex and varied, the single fracture is irregular geometry, and the overall fracture is interlocked, so in order to facilitate the statistics of the fracture often need to make some assumptions, and the specific fracture statistics are analyzed as follows:

4.2.3.1 Size and number of space fissures

In addition to macroscopic fissures, a large number of microscopic pore and fissure structures exist inside the coal body, resulting in a fissure structure that is difficult to quantitatively analyze, and the microscopic pore and fissure structures are usually equated to easily measurable shapes, and standard geometrical parameters are used to quantitatively analyze the fissure structure inside the coal body. In this paper, in order to simplify the fracture structure and realize simplified calculation, each independent pore-fracture structure is regarded as a sphere with its equivalent volume, and the equivalent diameter of the sp(c) CSCA-3 Fracture number and volume fractionhere can be obtained by the Equation 6, and the volume of the independent fracture is obtained by the product of each spatial pixel and the number of pixels. The formula for calculating the equivalent diameter of the fissure is:

$d_{ep}=\sqrt{6V_p/\pi 3}(6)$

Where: d_eq-equivalent diameter of the fissure;

V_p-volume of each individual fissure.

Figure 10 shows the statistical results of the number of fissures of different sizes and their volume fractions It can be seen that the number of fissures shows a decreasing trend with the increase of the equivalent diameter of the fissures, indicating that a large number of tiny fissures exist in the coal body. The volume fraction of the fissures as a whole shows an increasing and then decreasing trend; the fluctuation of the volume fraction is larger after the equivalent diameter is larger than about 2000 μm, indicating that the difference in the size of the fissures is the most significant in this scale range.

Figure 10

Figure 10. Number and volume fraction of cracks in different regions. (a) CSCA-1 Fracture number and volume fraction. (b) CSCA-2 Fracture number and volume fraction. (c) CSCA-3 Fracture number and volume fraction.

4.2.3.2 Density

Fracture density is an important parameter for its quantitative characterization, and the relative proportions of the number, area, and volume of fractures in a given range can be determined by the three metrics P_l, P_a, and P_V, as defined in Equations 7–9.

$P_{\mathrm{l}}=\frac{n_i}{V}(7)$

$P_a=\frac{A_i}{V}(8)$

$P_V=\frac{V_i}{V}(9)$

Where: P_l-linear density, mm^-3;

P_a- surface density, mm^-1;

P_V-body density;

n_i-number of fissures;

A_i-area of fissure, mm²;

V_i-volume of fissure, mm³;

V-total volume, mm³.

The calculated density statistics for each coal sample are shown in Table 2.

Table 2

Table 2. Coal sample fracture density statistics.

5 Evolution of mining damage in coal samples from coeval mines

In order to obtain more accurate mechanical parameters, the production of coal samples and the test process strictly comply with the relevant standards, the wave velocity of coal samples is detected by ultrasonic detector before the experiment, the coal samples with smaller wave velocity difference are selected, and the anisotropy of coal samples is controlled to ensure the reliability of the test. During the test process, the rock mechanics test and the use of instruments and equipment are strictly adhered to, and the study of the difference in mechanical response of coal samples with different levels of oil and gas seepage damage is carried out.

5.1 Statistical analysis of physical-mechanical parameters

In order to study the damage characteristics of coal bodies around abandoned oil wells by oil and gas seepage action, uniaxial compression tests were utilized to test the response of different oil and gas seepage damaged coal samples with respect to the basic mechanical parameters.

From the statistical results of the basic mechanical parameters of coal samples in Table 3, it can be seen that under uniaxial compression, the average peak strengths of coal samples in the positions of 10m, 100m and 500 m from the oil well were 6.64MPa, 12.05 MPa and 15.97MPa, respectively, and taking the CSCA-3 coal samples which were 500 m away from the oil well as a reference, the peak strengths of coal samples in the positions of 10m and 100 m from the oil well were reduced by 58% and 24.55%, respectively. Decreased by 58.42% and 24.55%. For the coal samples at 10m, 100 m, and 500 m from the oil well, the average strains corresponding to the peaks were 0.0113, 0.0134, and 0.0156, respectively, which were reduced by 27.56% and 14.10%, respectively, with respect to the CSCA-3 coal sample at 500 m from the oil well. The elastic modulus was 1.072 GPa, 1.224 Pa and 1.280 GMPa for the coal samples at 10m, 100m and 500 m positions from the abandoned oil wells, respectively, and the elastic modulus was reduced by 16.25% and 4.38% with respect to the CSCA-3 coal samples 500 m away from the oil wells, respectively, for the positions 10m and 100 m away from the oil wells.

Table 3

Table 3. Statistical table of mechanical parameters of coal sample foundation with seepage damage.

Obviously, for the bearing coal samples, the oil and gas seepage effect significantly reduced their mechanical properties, and the peak strength and its corresponding strain and elastic modulus showed obvious positive correlation, indicating that the oil and gas seepage damage is very sensitive to the mechanical response of the coal body in the enriched area, which is mainly reflected in the data as the increment of the mechanical parameters.

5.2 Acoustic emission characteristics of mining damage coal samples and strain stage evolution law

5.2.1 Characteristics of acoustic emission evolution of damaged coal body

The generation of AE events in the process of coal damage can reflect the spatial evolution of its internal microcracks (Xi et al., 2011). Figures 11–13 show the spatial distribution characteristics of the loaded AE events in the coal samples damaged by oil and gas seepage at the locations of 10m, 100m, and 500 m from the wells, the color of the spheres is the time dimension (i.e., the number of AE events in different phases), and the sphere size is the energy dimension (i.e., the energy value of the AE events), and the Table 4 shows the AE event number, ratio and energy characteristics of the three groups of coal samples in different loading phases. Table 4 shows the number, proportion and energy characteristics of AE events in different loading stages for the group of coal samples. The staged CT scans were performed at characteristic stress levels corresponding to the key points of the stress–strain curve (compaction, elastic deformation, pre-peak accumulation and post-peak instability), so that each scan reflects a mechanically meaningful stage of internal fracture evolution. The CT and AE tests were carried out simultaneously, where AE continuously recorded the dynamic crack nucleation and propagation process, and CT was conducted at the key stress stages to provide structural verification of the internal fractures corresponding to the AE activity peaks (Xu et al., 2025).

Figure 11

Figure 11. Crack evolution characteristics of CSCA-1-2 coal sample in loading stage (10 m away from abandoned oil well). (a) Densification stage. (b) Elasticity stage. (c) Plasticity stage.

Figure 12

Figure 12. Crack evolution characteristics of CSCA-3-3 coal sample in loading stage (100 m away from abandoned oil well). (a) Densification stage. (b) Elasticity stage. (c) Plasticity stage.

Figure 13

Figure 13. Crack evolution characteristics of CSCA-5-3 coal sample in loading stage (200 m from abandoned oil well). (a) Densification stage. (b) Elasticity stage. (c) Plasticity stage.

Table 4

Table 4. Number of acoustic emission events, energy and proportion of each stage in loading stage of coal body with different oil and gas seepage damage.

As can be seen from Figures 11–13, the number of acoustic emissions from coal samples with oil and gas damage at the positions of 10∼500 m from the oil wells are 674, 977 and 1281, respectively, which can be seen that the number of acoustic emissions from coal samples increases as the coal body is far away from the abandoned oil wells. In different loading stages, the ratio of the number of events shows different patterns of change as the coal body is far away from the abandoned oil wells: in the initial compression stage, the ratio of the number of events decreases from 24.18% to 12.38% and 3.52%; in the elastic stage, the ratio of the number of events does not have any obvious pattern; in the plastic damage stage, the ratio of the number of events increases from 42.43% to 54.96% and 64.32%; in the post-peak stage, the corresponding ratio increases from 3.42% to 3.42%; in the post-peak stage, the corresponding ratio increases from 3.42% to 3.43%. Ratio increased from 3.42% to 5.43% and then decreased to 3.59%. As the coal body is close to the abandoned oil wells, the strength of the coal samples decreases, and the ratio of the number of acoustic emission events in the initial compaction and elasticity stages is larger, but the energy is smaller, which indicates that the oil and gas seepage damages the coal body with a high number of pores and cavities, and the compaction has more fissures. The proportion of the number of events in the plastic damage stage fluctuates, mainly because the internal cracks in the coal body have not yet penetrated and the expansion is unstable, showing strong anisotropy leading to the coal body.

5.2.2 Characteristics of strain stage evolution of damaged coal body

Combined with the selection rules of feature points, the stage share of the feature point area was counted separately to analyze the stage share of each stage of the bearing coal samples under the action of oil and gas seepage. As shown in Table 5.

Table 5

Table 5. Statistics of the proportion of bearing coal samples under the action of oil and gas seepage.

As shown in the stress-strain curve graphs and stage characteristic histograms of the coal body in Figure 14, the proportion of each stage of the coal samples under different oil and gas seepage damage conditions changes greatly with the change of the sampling location, and the comparison found that in the compaction stage, the stress corresponding to the end point of the compaction continues to increase with the increase of the sampling location from the location of the oil wells, and as the coal body is far away from the abandoned oil wells, the percentage of the compaction stage in the stress curve continues to decrease. After the relative distance between the sampling location and the oil well exceeds 100 m, the difference in the proportion of the compacting stage gradually decreases, indicating that the relative location of the coal body and the abandoned oil well affects the compacting stage of the coal body, and also affects the stress value of the compacting stage of the coal body.

Figure 14

Figure 14. Proportion of characteristic strength stage of typical bearing coal samples. (a) CSCA-1-2. (b) CSCA-3-3. (c) CSCA-5-3.

In the elastic deformation stage, as the sampling location is far away from the abandoned wells, the elastic strain is increasing in the stress-strain curve, but the proportion of the elastic deformation stage in the whole stress-strain process is decreasing, and the value of the characteristic point stress corresponding to the elastic deformation is increasing, and in the elastic stage, the proportion of the elastic stage in the different areas of oil and gas seepage damage conditions is almost a straight line; in the yield stage, as the sampling location is far away from the abandoned wells, the yield stage is more obvious, and the proportion of the yield stage in the whole compression process is increasing. In the yielding stage, as the sampling location is far away from the abandoned oil wells, the yielding stage is more obvious, the characteristic point stress value of the yielding stage is increasing, the proportion of the yielding stage in the whole compression process is increasing, and the proportion of the elastic deformation stage is about the same as that of the yielding stage, which indicates that the brittleness of the coal body will be altered and the load-bearing capacity of the coal body will be increased as the sampling location is far away from the abandoned oil wells.

6 Statistical analysis of geometric features of fracture structures

6.1 Relationship between fissure structure and fractal dimension

The volume fraction and fractal dimension of the fissures in the three-dimensional space of the coal samples in the experiment are calculated separately, and the final results are shown in Figure 15. Comparing the volume fraction and fractal dimension of the coal fracture in the figure, it can be found that the volume fraction of the fracture increases, and the fractal dimension also increases, and the overall trend of the volume fraction and fractal dimension is consistent, which indicates that the fractal dimension can reflect the change of the target volume to a certain extent.

Figure 15

Figure 15. Coal sample fracture density and fractal dimension.

In order to analyze the effect of each density of the fissure on its fractal dimension, the relationship between the P_l line density, P_a surface density and P_v bulk density of the coal sample fissure and the fractal dimension was statistically calculated, as shown in Figure 15 the fractal dimension of the coal sample fissure increases with the increase of the density of the fissure, which indicates that the damage characteristics of the coal sample are closely related to its spatial location and the time of the seepage, i.e., the closer the sampling location is to the wells, the higher the density of the fissure is, and the higher the fractal dimension is. That is, the closer the sampling location is to the well, the higher the fractal density, the higher the fractal dimension, and the greater the damage degree.

6.2 Statistical analysis of fissure structure

Since the shape of the cleft is irregular and it is very difficult to inscribe its geometrical features, this paper introduces the Feret diameter in the calculation of the length and width of the cleft. The Feret straight warp is not a diameter in the practical sense but a set of diameters determined by a set of two parallel tangent distances tangent to the particle.

The Feret diameter is derived from the distance between two tangents to the profile of a particle in a well-defined direction. In a simple expression, it corresponds to the measurement of a vernier caliper (principle of the vernier caliper). In general, for irregular shapes Ferret diameters are available as the distance between two parallel tangents at an arbitrary angle. In practice, the minimum and maximum Feret diameters Feret_min and Feret_max are used, as well as the average Feret diameter. The minimum Feret diameter is often used as the equivalent diameter for sieve analysis. In this paper, the minimum Feret diameter is used as the width of the fissure and the maximum Feret diameter as the length of the fissure when measuring irregular fissures. It is shown that fissures in 3D network simulations of rock fissures are usually associated with probability distributions such as uniform, normal, lognormal, and negative exponential (or exponential) distributions (Xingping et al., 2020). The simplest one is the uniform distribution, whose probability density function is given in Equation 10:

$f\left(x\right)=\left\{\begin{array}{l}\frac{1}{b-a}\left(a\le x\le b\right)\\ 0\end{array}\right.(10)$

The probability density function of the normal distribution is given in Equation 11:

$f\left(x\right)=\frac{1}{{\sigma }_x\sqrt{2\pi }}{e}^{-\frac{1}{2}{\left(\frac{x-{\mu }_x}{{\sigma }_x}\right)}^2}\left(-\infty <x<\infty \right)(11)$

Where: μ_x-Expectation of normal distribution;

σ_x-variance of normal distribution.

The probability density function of the negative exponential distribution can be expressed as Equation 12:

$f\left(x\right)=\lambda e^{-\lambda x}(12)$

Where: λ-the inverse of the expected value of the random variable E(x), $\lambda =1/E\left(x\right)$.

The obtained data on the fracture structure of the coal body were statistically analyzed, and the obtained parameters were plotted as distribution histograms, as shown in Figure 16, and the length and width of the fracture conformed to the negative exponential distribution.

Figure 16

Figure 16. The distribution of fracture structure. (a) CSCA-1. (b) CSCA-2. (c) CSCA-3.

The analysis of the CT scan results of the coal body shows that as the sampling location is close to the oil well, the extension length, openness, penetration depth of the main fissure of the coal sample is larger, the number of fissures is larger, the fractal dimension is larger, and the degree of damage of high-pressure hydrocarbon seepage to the coal sample is larger, and the ability of storing hydrocarbons is larger.

7 Discussion

7.1 Analysis of the deterioration causes of oil and gas seepage on the physical and mechanical properties of coal

In the early stage of the plastic stage, the coal sample CSCA-1 near the oil well has two longitudinal main cracks in the middle of the coal body and penetrates under the shear action. At this time, the compressive stress and energy of the coal sample are still in the accumulation state. Although the cracks penetrate, the overall structure is not completely destroyed. Subsequently, the edge of the coal sample was subjected to tensile failure, but the crack did not penetrate the entire coal sample, and the stress and energy continued to accumulate. The final fracture of the coal sample was induced by the shear stress in the middle. The combined action of tensile and shear failure led to the rapid release of the stress accumulated by the overall disintegration of the coal sample. The CSCA-2 coal sample at 100 m from the abandoned oil well is in the middle area where the mineral impurities are densely distributed. Due to the large difference in the strength of the two media, the tip damage is formed near the mineral impurities in the rapid fracture stage, and the cracks continue to evolve to both sides. Finally, at the peak stress, the crack penetrates the entire coal sample and the coal body is destroyed. The CSCA-3 coal sample at a distance of 500 m from the abandoned oil well is subjected to tensile failure at the edge of the coal body at the initial stage of loading failure. With the increase of loading strength, the fracture deformation expands and penetrates to form a fracture network. Finally, the fracture is induced by the shear failure in the middle, resulting in the stress release accumulated by the disintegration of the coal body under the combined action of tensile and shear failure. The failure path of the coal sample is similar to the conjugate failure, and the residual strength of the coal body is higher after the yield strength (Dai et al., 2025).

7.2 Analysis of fracture evolution characteristics of mining coal with seepage damage

In the early stage of loading failure of coal samples, tensile failure leads to crack expansion, forming an independent crack structure, and the crack density increases, resulting in the accumulation of high-pressure oil and gas into the coal body. After loading to the yield strength, the shear failure gradually increases, and the extension of the initial crack and the germination of the new crack lead to the rapid cross expansion of the crack to form a network structure. The crack structure is in good conduction state within the region and poor conduction between regions. At this time, the oil and gas concentration in the coal body increases rapidly. When loaded to the ultimate strength, the fracture develops into the whole coal sample, and the generation of oil and gas macroscopic channels leads to the diffusion of a large amount of oil and oil layer gas to the mining face, forming oil and gas disasters. At the same time, the stress and energy accumulated by the final overall disintegration of the coal body are rapidly released, which can easily induce the dynamic disaster of coal mine oil and gas emission and even ejection in the overlapping area of coal and oil resources.

Therefore, the fracture evolution and failure mode of coal in the co-storage area of coal and oil resources are the key to the research on the prevention technology of oil and gas disasters in such coal mines. The damage degree of high-pressure oil and gas diffusion to coal body increases with the sampling position close to the oil well, which significantly reduces the compressive strength, cumulative energy and number of fracture events of coal body. The deterioration of mechanical properties of coal body is conducive to the formation of oil and gas storage and migration channels.

8 Conclusion

In this paper, the CT scanning equipment was used to test the CT scanning of coal samples from oil and gas rich areas, and on this basis, the three-dimensional reconstruction and fracture extraction of coal body fractures were carried out by using MATLAB software, and the distribution characteristics of the fractures within the coal body were analyzed qualitatively, and the fracture edge detection was carried out by CT tomography images, and the correlation of the fracture traces, numbers, fracture densities, and fractal dimensions in seepage-damaged coal body were quantitatively analyzed, and the relationship between the location of coal body relative to abandoned oil wells and the damage characteristics of the coal body was investigated. The relationship between the position of the coal body relative to the abandoned oil wells and the damage characteristics of the coal body was investigated. The main conclusions are as follows:

1. The correlation between the number of fractures, density, volume fraction and fractal dimension of the coal body was analyzed, and it was found that the number of fractures, density and volume fraction were positively correlated with the fractal dimension, and the fractal dimension of the detected coal body ranged from 1.68153 to 1.94138.

2. The average CT number of the coal samples is relatively small when the degree of fracture development is high, and the trace length and width of the fracture structure conform to a negative exponential distribution. The fracture equivalent diameter volume fraction and fractal dimension decrease with the sampling location far away from the oil wells, indicating that the fracture structure characteristics are closely related to the spatial location.

Through the coal body strain softening and mechanical parameter evolution law, pick up the acoustic emission signal released during the test, analyze the relationship between the damage pattern of coal samples with different damage degree and the original fissure, and obtain the evolution law and characteristics of coal body physical properties. The main conclusions are as follows:

1. With the sampling location far away from the oil well, the maximum increase of compressive strength and cumulative energy of coal samples were 61.06% and 112.38%, respectively, and the maximum increase of rupture number at key rupture location was 81.93%, which was mainly due to the fact that oil and gas seepage action degraded the mechanical properties of the coal samples and lowered the energy released during the damage of coal samples.

2. The degradation of coal body physical properties by oil and gas seepage is conducive to the formation of oil and gas disaster channels, and at the same time, the stress-sparing effect of oil and gas diffusion on the damage of the coal body reduces all kinds of dynamic disasters produced by coal body due to mining.

This study reveals the evolution characteristics of the residual fissure network in oil-rich coal under mechanical loading, and clarifies the structural damage mechanism induced by long-term seepage disturbance. Although the tests were conducted at the laboratory scale, the observed multiscale fissure hierarchy and the AE-based evolution characteristics correspond to the same precursory patterns that control large-scale instability in engineering applications. Therefore, the findings of this study can be directly transferred to field-scale practice by mapping the AE-derived precursors onto microseismic/AE monitoring indicators used in actual coal–oil–gas co-storage mining.

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

YS: Funding acquisition, Project administration, Writing – original draft, Writing – review and editing. LZ: Software, Supervision, Writing – original draft, Writing – review and editing. CX: Conceptualization, Investigation, Writing – original draft, Writing – review and editing. GH: Data curation, Project administration, Writing – original draft, Writing – review and editing. HaL: Formal Analysis, Methodology, Writing – original draft, Writing – review and editing. HuL: Validation, Visualization, Writing – original draft, Writing – review and editing. JX: Funding acquisition, Resources, Writing – original draft, Writing – review and editing. MS: Data curation, Resources, Writing – original draft, Writing – review and editing. JW: Methodology, Validation, Writing – original draft, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was funded by the National Natural Science Foundation of China (Grant No. 52274138) and the Shaanxi Province postdoctoral research project (Grant No. 2023BSHEDZZ313). Their support is gratefully acknowledged.

Conflict of interest

Authors YS, LZ, GH, HuL, JX, MS, and JW were employed by China Coal Energy Research Institute Co., Ltd. Authors YS, LZ, GH, HuL, JX, MS, and JW were employed by China Coal (Ordos) Energy Technology Co., Ltd. Authors YS, LZ, GH, HuL, JX, MS, and JW were employed by Engineering Research Center for Rock Burst Prevention of China Coal Energy Company Limited.

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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