Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM:

Fengjie Chen; Xiuming Jiang; Caifang Wu; He zhou; Bin Gao; Shasha Zhang

doi:10.1177/01445987221096750

Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM:

Chen, Fengjie; Jiang, Xiuming; Wu, Caifang; zhou, He; Gao, Bin; Zhang, Shasha 2022-05-16 00:00:00 Coalbed methane (CBM) resources are abundant in the Baoan blocks of the Xinjing coalﬁeld, China; their exploration and development remain in infancy. Suitable development blocks can be selected by evaluating their development potential; that of the No. 8 and 15 CBM systems in the Baoan blocks was evaluated using three quantitative evaluation methods via existing geo- logical data: multi-level fuzzy mathematics (MLFM), technique for order preference by similarity to ideal solution (TOPSIS), and principal component analysis (PCA). The results demonstrated that the CBM development potential in the central part of the Baoan blocks is low and unbecom- ing. The favorable areas of the No. 8 coal seam are distributed in the northeast and northwest, and that of No. 15 in the northwest and southwest Differences exist in quantitative selection; with limited data, the evaluation of the TOPSIS method is considerably different from actuality, and is unbecoming for evaluating the development potential. Keywords Coalbed methane, potential evaluation, multi-level fuzzy mathematics, TOPSIS, principal component analysis Introduction China is rich in coalbed methane (CBM) resources, and the commercial development of CBM is primarily concentrated in the eastern margin of the Qinshui and Ordos basins. Previous studies on the relevant theories of CBM development selection evaluation systems have been conducted. Department of Geological and Surveying Engineering, Shanxi Institute of Energy, Jinzhong, Shanxi, China Key Laboratory Coalbed Methane Resources & Reservoir formation, Ministry of Education, China University Mine & Technology, Xuzhou, Jiangsu, China Corresponding author: Xiuming Jiang, China University Mine & Technology, Key Laboratory Coalbed Methane Resources & Reservoir formation, Ministry of Education, Xuzhou 221008, Jiangsu, China. Email: cumt-jxm@cumt.edu.cn Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as speciﬁed on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 Energy Exploration & Exploitation 0(0) (Cai et al., 2014; Liu et al., 2022; Shen et al., 2016). Yangquan Coal mine, located in the east of Taiyuan city, is rich in CBM resources (Li et al., 2019b; Li et al., 2021a; Tao et al., 2022). Geological selection evaluation is a prerequisite for CBM development. Selecting only favorable blocks suitable for its development can be foundational (Bo et al., 2020; He et al., 2021; Okere et al., 2020). Many evaluation methods for CBM geological selection have been established, such as the search for high-permeability area, formation energy, fuzzy matrix, analytic hierarchy process, catas- trophe evaluation, grey clustering analysis, grey correlation, back-propagation neural network, and numerical simulation (Bo et al., 2020; Ghosh et al., 2016; Heo et al., 2010; Li et al., 2013; Lü et al., 2011). Based on the geological conditions of coal seams, these methods evaluate the advantages, disadvantages, and economy of CBM development from different aspects. However, CBM reser- voirs are complex geological systems, and the factors affecting their development often restrict one another. Therefore, a large number of mathematical methods have been introduced to comprehen- sively consider the interaction and intervention of various factors. Among them, multi-level fuzzy mathematics is a method combining hierarchical analysis and fuzzy mathematics, and the geo- logical index of mutual restriction is converted into a compatible geological index, eliminating the restriction between the inﬂuencing factors, which has a certain advantage for CBM selection evaluation. The technique for order preference by similarity to ideal solution (TOPSIS) method based on the coefﬁcient of variation is another multi-objective decision analysis method, conduct- ing a comprehensive evaluation based on the distance between the evaluation object and the most idealized decision target. TOPSIS has been widely used in evaluating oil and gas reservoir (Qu et al., 2021), resource and environment (Yan and Li, 2021), coal mining safety and other geological ﬁelds (Zhao et al., 2021a). However, there exists limited research on CBM selection using TOPSIS. In addition, factors of CBM development potential evaluation not only restrict and inﬂuence each other, but also possess repeated information among a large number of indicators, resulting in the complexity and uncertainty of CBM selection evaluation and decision-making system (Hower et al., 2021; Ye et al., 2022; Zhang et al., 2021a). Principal component analysis is a type of evalu- ation method that can deal with information jumbled in decision systems and evaluation systems. The main information in the data was extracted, and the target was evaluated by dimensionality reduction (Li et al., 2021b). Compared with single-factor analysis, these comprehensive evaluation methods have more advantages in CBM selection index analysis. Among them, fuzzy mathematics and PCA are the most widely used methods, in the evaluation of CBM development potential in many regions, including the southern Qinshui Basin in China (Zhang et al., 2016) and the Xishan coalﬁeld in Shanxi Province, China (Wang et al., 2018). The Teviot-Brook region of Australia (Yang et al., 2020a, 2020b). (Zhang et al., 2019) used PCA method to evaluate the development potential of multiple synclines in western Guizhou, China, and the results were consistent with the actual production capacity. Wang et al. (2022) used the PCA method to analyze the characteristics of CBM drainage and production and consid- ered that the gas content is the most important factor affecting CBM productivity. The TOPSIS method has also been widely used in geological resource-related research and favorable target opti- mization and has been successfully applied to coal resource safety and aquifer evaluation (Qiu et al., 2020; Yang et al., 2020b). However, previous studies have mainly focused on using a single method to evaluate CBM development potential, and few comparative studies exist on different methods in the same area. Therefore, this study considers the Baoan blocks of Xinjing Mine its research object. Based on ana- lyzing the geological elements in the research area, combined with the advantages and disadvan- tages of three evaluation index systems and methods, we conducted a comprehensive evaluation Chen et al. 3 of the development potential of the No. 8 and No. 15 coal seams, and determined the evaluation method most suitable for CBM development in the research area. Geological background Baoan blocks are located in the west of Yangquan mining of the Qinshui Basin, the west side of the Taihang Mountain north section, and the south foothill of the Liuzhao Mountain low and mid- mountain. The ﬁeld is 7.9 km long from east to west, 7.5 km wide from north to south, and covers an area of 64.747 7 km (Li et al., 2019a, 2019b; Zhao et al., 2021b) the surface topography in the mine ﬁeld is complex and ﬂuctuates substantially (Figure 1). Coal bearing strata The main coal-bearing strata in the study area are the Taiyuan Formation and the Shanxi Formation of the upper Carboniferous to lower Permian. Its average total thickness is 177.68 m, the average total thickness of the coal seam is 20.85 m, and the coal bearing coefﬁcient is 11.73% (Zhang et al., 2016). The Taiyuan Formation contains nine layers of coal, among which the No. 15 coal seam is stable and recoverable, the No. 8 and No. 9 coal seams are stable, and most of them are recoverable. The average total thickness of the coal seam is 15.57 m and the coal bearing coefﬁcient is 12.88%. The average total thickness of coal seam containing 6 layers in Shanxi Formation is 5.28 m, and the coal coefﬁcient is 9.30% (Figure 2)(Cao et al., 2021; Li et al., 2019a, 2019b). Tectonic Overall, the Qinshui Basin is an NNE syncline structure adjacent to the Taihang Mountain uplift in the east and the Luliang Mountain uplift in the west Its axis is approximately along the Yushe - Qinxian–Qinshui line. The overall structure was relatively simple, and fault development was Figure 1. Tectonic outline and location map of the study area. 4 Energy Exploration & Exploitation 0(0) Figure 2. Comprehensive histogram of strata in the study area. weak (Figure 1).The study area is located in the north of the Xining Coal Mine, which belongs to the Shenyang-Yangquan monocline zone at the northern end of the Qinshui Basin, Shanxi Province, and is a syncline formed by near-horizontal compression after the late Paleozoic coal- forming period in North China (Figure 1).(Hu et al., 2015; Wang et al., 2018). Selection evaluation index Evaluation index of reservoir conditions Coal thickness. The thickness of the coal seam is an important parameter in evaluating the CBM reservoir that affects the resource amount and abundance of its development. The larger the Chen et al. 5 thickness of the coal seam, the higher the resource amount there is (Liu et al., 2009). The results demonstrate a positive correlation between gas production and the thickness of the coal seam in the Qinshui Basin (Zhang et al., 2016). According to the borehole data of several coal ﬁelds in the research area, the thickness of the No. 8 coal seam is 0.4–3.95 m, with an average of 2.15 m. The distribution characteristics of the No. 8 coal seam on the plane is relatively complex. Multiple high-value coal thickness areas were developed in the study area. In general, the coal seam in the middle of the study area was thin and its surrounding areas were thick (Figure 3(a)). The thickness of the No. 15 coal seam was larger than that of No. 8. It is the main mining coal seam in the study area, with a thickness of 2.05–9.81 m and an average of 6.5 m. From the perspective of plane region, coal seam No. 15 is mainly developed in the northwest, and the overall trend depicts a gradually thinning from northwest to southeast (Figure 3(b)). Buried depth. The buried depth of the coal seam not only affects the reserves of CBM but is also an important evaluation index for developing CBM projects. The permeability of the coal seam decreases gradually, with increasing buried depth, which makes it difﬁcult to discharge and depres- surize CBM. However, with increasing burial depth, CBM sealing conditions improve and coal seam gas content and resource reserves increase (Li et al., 2015; Shen et al., 2016). Statistical data show that the buried depth of No. 8 in the research area is 431.94–744.75 m, with an average of 586.75 m. In terms of plane distribution, the buried depth of the No. 8 coal seam is the largest in the northeast and southwest and is greater than 700 m in some areas (Figure 4(a)). The buried depth of the No. 15 seam varies less than that of No.8, ranging from 523.6 m to 834.97 m, with an average of 674.86 m. The plane regional distribution trend of the buried depth was consistent with that of the No. 8 coal seam, with the largest buried depth in the northeast and southwest (Figure 4(b)). Gas content. Gas content is one of the most important factors in evaluating CBM selection, which directly affects the gas production of CBM (Lv et al., 2012; Tao et al., 2014). The results show that 3 3 the gas content of the No. 8 coal seam is 6.0–20.6 m /t with an average of 12.7 m /t; the gas content 3 3 of the No. 15 coal seam is 3.8–19.3 m /t with an average of 9.6 m /t, such that it is lower than that of No. 8. In terms of regional distribution, the gas content of the No. 8 coal seam is high in the Figure 3. Contour map of coal seam thickness in the study area (a: No.8, b: No.15). 6 Energy Exploration & Exploitation 0(0) Figure 4. Contour map of buried depth of coal seam in the study area (a: No.8, b: No.15). Figure 5. Contour map of coal seam gas content in the study area (a: No.8, b: No.15). northeast, high in the south, and low in the southwest and northwest, and the gas content of local and regional coal seams is characterized by aggregation. Combined with the tectonic development in the study area, the regional distribution of gas content is consistent with the tectonic track, and the complex structural development is the main reason for this difference. The regional distribution characteristics of the gas content in the No. 15 coal seam is different from that in No. 8. The gas content in the No.15 coal seam was higher in the south than in the middle (Figure 5(b)). Permeability. The permeability of the coal reservoir is the main factor restricting CBM production. The higher the permeability, the more easily methane could be desorbed and transported. Therefore, the main goal in the early potential evaluation process of CBM development, is to ﬁnd a “high permeability area” as the dominant blocks of CBM development (Fu et al., 2016; Liu et al., 2020). Due to the lack of exploration in the research area, the permeability data of the No. 8 coal seam are missing. This study mainly analyzed the No.15 coal seam, for which −2 −2 the permeability was 0.996–1.08 × 10 mD, and the average was 1.02 × 10 mD. In terms of Chen et al. 7 regional distribution, the hypertonic areas were mainly distributed in the west and southwest of the study area (Figures 6 and 7). Resource abundance. Resource abundance is one of the main economic indicators of CBM devel- opment. The higher the resource abundance, the greater the economic potential of CBM develop- ment (Liu et al., 2015; Zhao, 2021; Zhou et al., 2021). The borehole data in an study area show that 8 3 2 the resource abundance of the No. 8 coal seam is 0.42–0.92 × 10 m /km , with an average of 0.60 8 3 2 8 3 2 8 3 × 10 m /km ; and that of No. 15 is 0.58–1.53 × 10 m /km , with an average of 0.96 × 10 m /km. The resource abundance of the No. 15 coal seam was evidently better than that of No. 8. Combined with the plane distribution of resource abundance, we can see that the resource abundance of the No. 8 coal seam is lower in the middle and largest in the northeast The No. 15 coal seam shows similar distribution characteristics, and the resource abundance in the middle region is low. In con- trast to No. 8, the resource abundance of the No. 15 coal seam in the southwest was the highest Evaluating the index of conditions of preservation Tectonic coal distribution in the study area. A large amount of measured data and research results have revealed that the coal structure type has an important effect on permeability and CBM production. Figure 6. Contour map of permeability of No. 15 coal seam in the study area. 8 Energy Exploration & Exploitation 0(0) Figure 7. Contour map of CBM resource abundance in the study area (a: No.8, b: No.15). According to the degree of damage, the coal structure can be divided into four types: primary struc- ture coal, fragmentary coal, fragmentary coal and mylonite coal. Fragmentary and mylonite coal are also known as tectonic coal, which is not conducive to CBM development owing to its structure, disordered fracture direction, poor connectivity, and relatively low permeability (Wu et al., 2021; Zhang et al., 2021a, 2021b). Based on the analysis of well drilling results and logging data, we can see that both primary and structural coal developed in the Baoan blocks. The average tectonic coal thickness of the No. 8 coal seam was 0.59 m, accounting for 34%, and the average tectonic coal thickness of the No. 15 coal seam was 1.29 m, accounting for 20%. In terms of regional distribu- tion, the tectonic coal of the No. 8 coal seam was relatively developed along the southeast direction near small faults. The tectonic coal of the No. 15 coal seam mainly developed along the NNE dir- ection of fault DF , which is subjected to weak tectonic stress. Most faults only developed in the coal seam, and the coal structure did not suffer severe damage (Cao et al., 2021; Zhang et al., 2021b, 2021c) (Figure 8). Roof and ﬂoor. The lithology of the coal roof and ﬂoor affects the preservation of CBM and is a key factor affecting gas saturation. Because of their low permeability, mudstones are ideal roofs and ﬂoors for CBM sealing. According to the drilling data, the roof and ﬂoor of the No. 8 coal seam are mainly mudstone; the roof is 1.66–9.67 m with an average of 5.48 m. The thickness of the bottom plate was 0.3–9.35 m, with an average of 4.07 m. According to the contour map of the ratio of mudstone thickness to thickness of the roof of the No. 8 coal seam, we can see that the roof mudstone of No. 8 is thick in the southeast and northwest and thin in the middle (Figure 9(a)). The No. 15 coal seam roof is mainly limestone, 0.35–7.44 m thick, averaging 1.9 m. The bottom mudstone is 0.55–16.6 m thick with an average of 6.56 m. The thickness of the bottom plate was greater than that of the top plate. Combined with the contour map of the mud- stone thickness ratio of the roof of the No.15 coal seam, we can see that the roof thickness of the No. 15 coal seam gradually thins from NE to SW (Figure 9(b)) (Ni et al., 2020; Tao et al., 2012). Fault fractal dimension. Fault development is an important condition for CBM storage. Faults not only affect the preservation and migration of CBM but also need to consider faults and other struc- tures during fracturing and exploitation (Wu et al., 2021). According to the structural outline map of Chen et al. 9 Figure 8. Tectonic coal thickness contour map (a: No.8, b: No.15). Figure 9. Contour map of mudstone thickness ratio of coal seam roof in the study area (a: No.8, b: No.15). the research area, the faults in the east of the No. 8 coal seam are more developed than those in the west, and there are 22 faults in total, including 11 normal and 10 reverse faults. The dip angle of the faults was large, ranging from 31.5° to 70°, with an average of 55° (Figure 10(a)). Small faults are more developed in No. 15, with 44 faults in the study area, mainly normal faults, with dip angles ranging from 38° to 75° and an average of 56° (Figure 10(b)). To quantify the degree of fault development in the study area, the fault fractal dimension was used to evaluate the degree of structural failure of the coal seams. We can see from the fractal dimension contour map of the fault of the No.8 coal seam that the study area is moderately complex. Among them, due to a large number of faults, the northeast region is locally developed as a complex structural region, whereas the southwest region is the simplest structural region (Figure 10(a)). The northeastern area of the No. 15 coal seam is of medium structural complexity, whereas the structural development trend of the southwestern area is consistent with that of No. 8, which is a simple area. The structural complexity of the No.15 coal seam responded adequately to the fault trace of the fault in the Baoan blocks; the simple structural area is distributed between two 10 Energy Exploration & Exploitation 0(0) Figure 10. Fault fractal dimension contour map of the study area (a: No.8, b: No.15). Figure 11. Contour map of structural curvature in the study area (a: No.8, b: No.15). large fault traces (Figure 10(b)). The Figures show that the fractal dimension of the fault in the study area is consistent with the degree of development of the fault (Figure 10). Tectonic curvature. The structure of the study area is a synanticline with wide and slow undulations. The NNE-NE strike of the fold, such as the S syncline, is consistent with the strike of the fault. The NE parts of the S syncline are characterized by multiple secondary small folds, showing alternate anticline development. The No. 15 coal seam is simpler than the No. 8 coal seam, and the secondary small syncline of the No. 15 coal is less than that of No.8. (Li et al., 2013; Li et al., 2015; Zhang et al., 2016) (Figure 11). Tectonic curvature is the result of the tectonic stress ﬁeld. The curvature reﬂects the relative development degree of tensile fractures in curved strata, and a high curvature is located in areas with strong folding. Herein, the maximum curvature of the study area was calculated using the contour line of the coal ﬂoor to characterize the deformation degree of the fold. (Banerjee et al., 2021; Qi et al., 2022; Sun et al., 2021). Figure 11 shows that there is a good coincidence and Chen et al. 11 relationship between fold development and tectonic trace in the study area, and the maximum curvature can be used to characterize the degree of fold development. Potential evaluation and favorable area prediction of CBM After years of development, CBM selection methods and indicators have reached a consensus. The important indicators are gradually becoming clearer, such as resource conditions, reservoir condi- tions, preservation conditions, and so on. The selection method developed gradually from the initial qualitative evaluation to quantitative evaluation and then to qualitative and quantitative comprehen- sive considerations (Bo et al., 2020; Li et al., 2019a, 2019b; Wang et al., 2018). Herein, we selected three methods, namely multi-level fuzzy mathematics, the TOPSIS method based on the coefﬁcient of variation, and principal component analysis, to evaluate the CBM development potential in Baoan blocks. Multi-level fuzzy mathematics The analytic hierarchy process is a decision-making method of system theory, which is decomposed into several levels, making index evaluation complex at different decision-making levels. Furthermore, considering the problem of insufﬁcient data, the theory of analytic hierarchy process and fuzzy mathematics is introduced, and a multi-level fuzzy mathematics method is devel- oped as an evaluation method for CBM constituency. The basic idea of this method is to decompose the problem to be solved into several components, group these components according to the dom- inant relationship, and establish a hierarchical structural model. According to a certain scale, a pair- wise comparison of the importance of each element at the same level with respect to the previous level is made, and a pairwise comparison judgment matrix is constructed, by which the relative weight of the compared element to its criterion is calculated (Heo et al., 2010; Wang et al., 2018). First, the decision objective was stratiﬁed and a discriminant matrix was established. Based on previous research methods, coal seam thickness, buried depth, gas content, permeability, and resource abundance were selected as the reservoir evaluation conditions of CBM. The structural coal content, roof mudstone thickness ratio, fault fractal dimension value, and structural curvature value were selected as the key factors of CBM preservation conditions, and a three-level evaluation model was established (Wang et al., 2018). Second, the importance of each parameter was compared at the same level. We used the 1–9 scale method to rank the importance of the evaluation indicators and MATLAB to calculate the maximum feature root and feature vector of the judgment matrix, and the weight coefﬁcients of the indicators at each level were obtained (Table 1). To ensure the accuracy and reliability of the calculation results, it was necessary to conduct a random consistency test on the calculation results. The following formula was used to test the results. λ − n max C.I. = (1) n − 1 C.I. C.R. = (2) R.I. where n is the order of the matrix, λ is the characteristic root, and R.I. is the random consistency max index. 12 Energy Exploration & Exploitation 0(0) Table 1. Importance coefﬁcients of evaluation indices. No.8 W λ C.R. No.15 W λ C.R. B max B max A∼BA B B -- - W 2.00 0.00 A∼BAB B -- -- W 2.00 0.00 1 2 B 1 2 B B 1 4 - - - 0.8 B 1 4 -- -- 0.8 1 1 B 0.25 0.1 - - - 0.2 B 0.25 0.1 - - - - 0.2 2 2 B ∼C B C C C C C W 5.21 0.05 B ∼C B C C C C C C W 6.25 0.04 1 1 1 11 12 13 14 15 B 1 1 1 11 12 13 14 15 16 B C 1 0.33 0.33 4 0.5 0.13 C 1 0.33 0.33 4 0.5 0.25 0.09 11 11 C 3 1 1 5 3 0.34 C 3 1 1 5 3 0.5 0.22 12 12 C 3 1 1 4 3 0.33 C 3 1 1 4 3 0.5 0.21 13 13 C 0.25 0.2 0.25 1 0.5 0.06 C 0.25 0.2 0.25 1 0.5 0.2 0.04 14 14 C 2 0.33 0.33 2 1 0.14 C 2 0.33 0.33 2 1 0.33 0.1 15 15 --- - - - - - - C 4 2 2 5 1 3 0.33 B ∼C B C C C -- W 3.02 0.02 B ∼C B C C C -- - W 3.05 0.05 2 2 2 21 22 23 B 2 2 2 21 22 23 B C 1 0.25 0.33 - - 0.12 C 1 0.33 0.33 - - - 0.14 21 21 C 4 1 2 - - 0.56 C 3 1 2 - - - 0.53 22 22 C 3 0.5 1 - - 0.32 C 3 0.5 1 - - - 0.33 23 23 Note: In the table, W is the eigenvector, λ is the maximum eigenroot, and C.R. is the random consistency ratio. B max Chen et al. 13 Table 2. Weight table of evaluation indices and evaluation parameters. No. 8 No. 15 The evaluation indices weight Evaluation parameters weight The evaluation indices weight Evaluation parameters weight Reservoir conditionsB 0.8 Coal seam thicknessC 0.10 Reservoir conditionsB 0.8 Coal seam thicknessC 0.07 1 11 1 11 gas contentC 0.27 gas contentC 0.18 12 12 Resource abundanceC 0.26 Resource abundanceC 0.17 13 13 Tectonic coalC 0.05 Tectonic coalC 0.04 14 14 Buried depthC 0.11 Buried depthC 0.08 15 15 - - Permeability C - 0.27 Preservation 0.2 Ratio of roof mudstone 0.02 Preservation 0.2 Ratio of roof mudstone 0.03 conditionsB thicknessC conditionsB thicknessC 2 21 2 21 Fault fractal dimension valueC 0.11 Fault fractal dimension valueC 0.11 22 22 Structural curvature valueC 0.06 Structural curvature valueC 0.07 23 23 14 Energy Exploration & Exploitation 0(0) A consistency test was performed on each discriminant matrix, and was continuously iterated adjusting the matrix parameters until the C.R was < 10%. Through the fuzzy evaluation model constructed above, combined with the weight of each evaluation index in Table 2, multi-level fuzzy comprehensive evaluation results of the study area can be obtained, and the advantage map of CBM development potential evaluation of Baoan blocks. As shown in Figure 12, the favorable areas of the No. 8 coal seam were mainly distributed in the northeast of the study area, where the potential for CBM development was low in the middle. The evaluation results of the No. 15 coal seam were considerably different from those of the No. 8 coal seam, and the area with the largest CBM development potential was located southeast of the study area. Similar to the evaluation results of the No. 8 coal seam, the development potential in the middle of the study area was the lowest TOPSIS based on variation coefﬁcient method Technique for order preference by similarity to an ideal solution (TOPSIS), also known as an ideal solution, is a common and effective method in multi-objective decision analysis. TOPSIS sorts the evaluation objects according to their proximity to the most ideal goal, calculates the distance between the evaluation objects and the optimal and worst schemes, and evaluates the relative advantages and disadvantages on this basis (Sun and Li, 2021; Yang et al., 2020a, 2020b). Compared with the analytic hierarchy process, the TOPSIS method does not require the selection of the membership function and index weight; therefore, there is no subjective inﬂuence of the membership function and index weight. Herein, we chose the variation coefﬁcient method to deter- mine the weights of the evaluation indices, which is based on the general law of data samples, using the arithmetic mean and variance to determine the relative weight of each index. It can better reﬂect the relative weights of every index (Qiu et al., 2020; Sivaraja et al., 2020). First, the original data were positively and normalized. For some indicators, the larger the data value, the greater the advantages of the evaluation object. However, for others, the opposite was true. Therefore, the more advantageous the evaluation object, the more appropriate the data needed to be forward-processed to increase the index. Equation 3 was used to forward the extremely small data, and to eliminate the impact of data dimension and the data needed to be standardized. Figure 12. Distribution map of CBM potential evaluation by multi-level fuzzy mathematics (a: No.8, b: No.15). Chen et al. 15 Equation 4 was used to standardize the data: x = x − x (3) i max i t = (4) i=1 i After processing the data, a standardized decision matrix T comprising n blocks and m evaluation indices was constructed. ⎡ ⎤ t .. . t 11 1n ⎣ ⎦ T = .. . . .. .. . (5) t .. . t m1 nm The weight of each evaluation index was calculated using the coefﬁcient of variation method. 1 n t = t (6) j ij i=1 1 n 2 2 S = (t − t ) (7) ij j i=1 n − 1 Assuming v =|t | / S , then, normalized v was the weight of each indicator, i i i i ω = (8) According to the above method, the weights of the No. 8 and No. 15 coal seams in the study area were determined, as shown in Table 3. + − The matrix of positive ideal solution Z and negative ideal solution Z were established: T =(max t , max t , ... , max t ) (9) i1 i2 im T = (min t , min t , .. . , min t ) (10) i1 i2 im The Euclidean distance between each target value and the ideal value is calculated, and the distance D from the evaluated blocks d to the positive ideal solution is: i i D = (t − t ) (11) ij i j j=1 The distance from alternative d to the negative ideal solution, D –i is: i i - 2 D = (t − t ) (12) ij i j j=1 The comprehensive evaluation index of each program to be evaluated is calculated, and Equation 13 is used to calculate the proximity between each evaluation object and the optimal program f . f (13) i= − + D +D i i The larger the f value, the farther scheme d was from the negative ideal solution and the closer it i i was to the positive ideal solution, and block d had more advantages. Based on the above calculation process, the potential evaluation advantages of CBM in the study area can be obtained, and 16 Energy Exploration & Exploitation 0(0) Table 3. Weight of each selected indices of No. 8 and No. 15 coal seam in the study area. Coal Buried gas Resource structure roof mudstone fault fractal structural Indices thickness depth content abundance coal thickness ratio permeability dimension curvature No.8 0.06 0.05 0.09 0.11 0.21 0.03 - 0.22 0.23 No.15 0.07 0.05 0.09 0.12 0.15 0.07 0.02 0.26 0.17 Chen et al. 17 Figure 13. Distribution map of CBM potential based on TOPSIS coefﬁcient of variation method (a: No.8, b: No.15). Figure 13 shows the potential evaluation advantages of CBM development in the Baoan blocks. We can see from Figure 13 that the dominant areas of coal seam development in the No. 8 and No. 15 coal seams were mainly distributed in the middle of the research area, which was considerably dif- ferent from the evaluation results of the multilevel fuzzy mathematics method. Principal component analysis Principal component analysis is a method of dimension reduction. The dimensionality reduction of a set of variables with a large correlation and mutual inﬂuence was transformed into a new set of vari- ables with a weak correlation. During data processing, the total variance of the variable remained unchanged, and the variance of the new variable was controlled to ensure the new variable contained as much original information as possible. The greater the variance of the new variable, the less the original information is lost. The new variable with the greatest variance was deﬁned as the ﬁrst and second principal components (Hower et al., 2021; Ye et al., 2022). Due to the large difference in the order of magnitude of each indicator, data should be standar- dized ﬁrst in data processing, and the original data should be converted into dimensionless data to make it more adequately comparable. A standardized decision data matrix, Z, was constructed. Equation 4 was used for data standardization, and Equations 14 and 15 were used to calculate the correlation coefﬁcient matrix, R. R = (r ) (14) k n×m 1 n r = Z Z (k = 1, 2, .. . , m) (15) k ij ik i=1 n − 1 The eigenvalues and variances of the decision matrix were calculated (Table 4), and the principal components with eigenvalues greater than one were retained in order of magnitude. The principal component scores of CBM potential evaluation in the study area were calculated by combining the principal component score coefﬁcients (Table 5). Using principal component scores, the characteristic roots of the principal components were taken as the weights of each principal component to conduct a comprehensive evaluation of CBM development potential in the study area, as shown in Figure 14. 18 Energy Exploration & Exploitation 0(0) Table 4. Characteristic values and variance of evaluation indices. No.8 No.15 Components The eigenvalue Percentage of variance% The cumulative % Component The eigenvalue Percentage of variance% The cumulative % 1 2.37 29.66 29.66 1 2.64 29.36 29.36 2 1.75 21.87 51.53 2 1.64 18.24 47.60 3 1.19 14.91 66.44 3 1.31 14.57 62.17 4 0.97 12.13 78.56 4 1.10 12.20 74.37 5 0.72 9.01 87.57 5 0.70 7.75 82.12 6 0.67 8.42 95.99 6 0.61 6.73 88.85 7 0.32 3.97 99.96 7 0.59 6.59 95.44 8 0.00 0.04 100.00 8 0.40 4.47 99.92 - - - - 9 0.01 0.08 100.00 Chen et al. 19 Table 5. Principal component score coefﬁcient table No.8 No.15 Indices Components 1 Components 2 Components 3 Components 1 Components 2 Components 3 Components 4 Resource abundance 0.52 0.40 −0.12 0.59 0.07 −0.07 0.07 Tectonic coal 0.46 −0.36 −0.07 −0.41 0.30 −0.14 −0.12 Coal thickness 0.46 −0.14 0.28 0.39 −0.13 −0.43 0.31 Ratio of roof mudstone thickness 0.31 0.09 0.35 0.03 0.11 0.55 0.54 gas content 0.29 0.57 −0.33 0.52 0.19 0.19 −0.16 Fault fractal dimension −0.25 0.49 −0.08 −0.10 −0.45 −0.37 0.43 Buried depth 0.23 −0.29 −0.58 −0.18 0.35 0.02 0.61 Tectonic curvature 0.12 0.15 0.57 0.03 −0.38 0.55 0.01 permeability - - - 0.11 0.61 −0.11 0.04 20 Energy Exploration & Exploitation 0(0) Figure 14. Distribution of evaluation of CBM development potential by principal component analysis (a: No.8, b: No.15). As seen in Figure 14, the favorable areas for CBM development in the Baoan blocks were mainly concentrated in the surrounding areas of the study area, and the favorable areas for CBM development in the No. 8 and No. 15 coal seams showed similar distribution ranges. The difference was that the favorable areas for CBM development in the No. 8 coal seam were mainly distributed in the northeast and northwest of the study area. Combined with the above research results, we found that the favorable area of CBM development was consistent with the gas content distribution range, and the main controlling factor of CBM development was gas content. The most favorable area for CBM development in the No. 15 coal seam is distributed in the southwest, followed by the northwest According to the contour map above, permeability was the main controlling factor for the CBM development potential in the No. 15 coal seam. Comparison of selection methods According to the above research results, the multilevel fuzzy mathematics method, TOPSIS, and principal component analysis method differed signiﬁcantly in the evaluation results of CBM devel- opment potential in Baoan blocks, especially the evaluation results of TOPSIS. The results of the principal component analysis and fuzzy mathematics method are relatively similar, and it is believed that the development potential of CBM around the study area is the greatest However, the evaluation results of TOPSIS indicate that the central part of the study area is the most favorable area for CBM development. Combined with previous research results (Hu et al., 2015), the evalu- ation results of multilevel fuzzy mathematics and principal component analysis were better, which accords with the actual development situation of the research area. The evaluation results of the three evaluation methods adopted in this study are considerably different, especially those based on TOPSIS. The three methods were based on the geological char- acteristics of the study area. However, TOPSIS is more dependent on the overall law and the char- acteristics of the data samples. The more numerous the data, the more evident the law, and the more consistent the real situation. Qiu et al. (2020) predicted the groundwater in the Xinwen Coalﬁeld based on over 90 data points. Li et al. (2020) evaluated the development potential of shale gas blocks in China based on 55 sary indicators using the TOPSIS. These studies used a large number of data samples to reﬂect the real situation, such that results were more reasonable and Chen et al. 21 in line with the actual development situation. In addition, compared with the coefﬁcient of variation method adopted in this study to determine the weight of indicators, other studies chose to use grey relative analysis (GRA) or the entropy weight method to determine the weight of indicators. Unnecessary information can be removed during data preprocessing (Qiu et al., 2020; Sun and Li, 2021). Multilevel fuzzy mathematics and principal component analysis have been widely applied in the Qinshui Basin and Ordos Basin of China (Wang et al., 2018; Zhang et al., 2016). Compared with TOPSIS, these two methods are more suitable for the evaluation of favorable CBM development areas under complex geological conditions. In addition, when using fuzzy mathematics to evaluate the potential of CBM development, the importance of each index at the same level is subjective, but the evaluation results are basically con- sistent with the evaluation results of principal component analysis, indicating that the multilevel fuzzy mathematics method is within an acceptable range, with the importance of each index set and the error of the result of subjective factors of this method being reasonable. In the evaluation and analysis of the CBM potential in the study area, due to the different devel- opment levels of different coal seams in the study area, the permeability index of the CBM potential evaluation of the No. 8 coal seam was lower than that of No. 15. Comprehensive analysis of the above evaluation results shows that the No. 8 coal seam principal component analysis and multi- level fuzzy favorable area evaluation results are consistent with the No. 15 coal seam evaluation results, indicating that the loss of some parameters has little effect on the overall development potential evaluation results. However, more detailed data are required to select suitable locations for further CBM development. Conclusions In this study, the development potential of CBM in Baoan blocks was evaluated using multilevel fuzzy mathematics, TOPSIS based on coefﬁcient of variation, and principal component analysis. The favorable area of CBM development in Baoan blocks was predicted by comprehensive analysis of the three evaluation results. The conclusions of this study are as follows: 1. The coal of No. 8 is relatively thin, with an average thickness of 2.15 m, and its development is unstable and the average burial depth was 586.75 m. The coal of No.15 was relatively thick, with an average thickness of 6.5 m. The burial depth was 674.86 m. The roof and ﬂoor of the No. 8 coal are mainly mudstone with a thickness of 0.3–9.6 m, and the sealing effect is adequate. 2. The fault dip angle in the study area is relatively large, with an average dip angle of 55°. There are few faults in No. 8 and a large number of small faults in No. 15. The tectonic development in the eastern part of the study area is complex, whereas that in the western part is relatively simple. The average thickness of No. 8 is 0.59 m, and the average thickness of No. 15 is 1.29 m. The gas content of the No. 8 coal seam is high in the northeast and south and low in the northwest and southwest The gas content of sample No. 15 was high in the south and low in the middle. The distribution of gas content in the study area was mainly controlled by the structure. 3. The northwestern and northeastern parts of the No. 8 coal seam are suitable for CBM develop- ment. The northwest and southwest parts of the No. 15 coal seam are favorable blocks for CBM. Considering the evaluation results comprehensively, we determined that the amount of data in the study area affects the evaluation results of the favorable area, especially the TOPSIS, which is not suitable for evaluating the CBM of the Xinjing Baoan blocks. In the evaluation process of CBM favorable area selection, the impact of missing geological evaluation indicators on the results can be ignored. 22 Energy Exploration & Exploitation 0(0) Acknowledgements This work was supported by the National Natural Science Foundation of China (41872170), the National Major Science and Technology Projects of China (2016ZX05044)，The Fundamental Research Funds for the Central Universities (2020CXNL11), The Priority Academic Program Development of Jiangsu Higher Education Institutions; Coal seam anti-reﬂection and efﬁcient gas extraction technology in structural coal area（20191102002). Declaration of Conﬂicting Interests The author(s) declared no potential conﬂicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following ﬁnancial support for the research, authorship, and/or publica- tion of this article: This work was supported by THE Fundamental Research Funds for the Central Universities, The Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China, (grant number 2020CXNL11, 41872170, 4213080); Coal seam anti-reﬂection and efﬁcient gas extraction technology in structural coal area(20191102002). ORCID iDs Xiuming Jiang https://orcid.org/0000-0003-1006-5070 He zhou https://orcid.org/0000-0002-1001-8912 References Banerjee A, Sequeira N and Bhattacharya A (2021) Tectonics of the greater India proterozoic fold belt, with emphasis on the nature of curvature of the belt in west-central India. Earth-Science Reviews 221: 103758. Bo H, Yong Q, Zhang W, et al. (2020) Prediction of high-quality coalbed methane reservoirs based on the fuzzy gray model: an investigation into coal seam No. 8 in Gujiao, Xishan, North China. Energy Exploration & Exploitation 38(4): 1054–1081. Cai Y, Liu D, Zhang K, et al. (2014) Preliminary evaluation of gas content of the No. 2 coal seam in the Yanchuannan area, southeast Ordos basin, China. Journal of Petroleum Science & Engineering 122: 675–689. Cao T, Yang Z, Qin Y, et al. (2021) Characteristics of modern geostress and removability of No. 15 coal res- ervoir, Yangquan mining area, China. Natural Resources Research 30(3): 2211–2223. Fu H, Tang D, Xu H, et al. (2016) Geological characteristics and CBM exploration potential evaluation: A case study in the middle of the southern Junggar Basin, NW China. Journal of Natural Gas Science and Engineering 30: 557–570. Ghosh S, Chatterjee R and Shanker P (2016) Estimation of ash, moisture content and detection of coal litho- facies from well logs using regression and artiﬁcial neural network modelling. Fuel 177: 279–287. He J, Okere CJ, Su G, et al. (2021) Formation damage mitigation mechanism for coalbed methane wells via refracturing with fuzzy-ball ﬂuid as temporary blocking agents. Journal of Natural Gas Science and Engineering 90: 103956. Heo E, Kim J and Boo K (2010) Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews 14(8): 2214–2220. Hower JC, Eble CF, Wang N, et al. (2021) Distribution of rare earth elements and other critical elements in beneﬁciated pennsylvania anthracites. Fuel 304: 121400. Hu G, Xu J, Zhang F, et al. (2015) Coal and coalbed methane co-extraction technology based on the ground movement in the Yangquan coalﬁeld. China. Energies 8(7): 6881–6897. Chen et al. 23 Li F, Jiang B, Cheng G, et al. (2019a) Multifractal analysis and evolution rules of micro-fractures in brittle tectonically deformed coals of Yangquan mining area. Arabian Journal of Geosciences 12(19). Li F, Jiang B, Cheng G, et al. (2019b) Structural and evolutionary characteristics of pores-microfractures and their inﬂuence on coalbed methane exploitation in high-rank brittle tectonically deformed coals of the Yangquan mining area, northeastern Qinshui basin, China. Journal of Petroleum Science And Engineering 174: 1290–1302. Li F, Jiang B, Wei C, et al. (2021a) Simulation of coalbed methane generation, dissipation, and preservation and analysis of the geological inﬂuencing factors: A case study of the Xinjing coal mine, northeastern Qinshui basin, China. Arabian Journal of Geosciences 14(5). Li S, Tang D, Pan Z, et al. (2015) Evaluation of coalbed methane potential of different reservoirs in western Guizhou and eastern Yunnan, China. Fuel 139: 257–267. Li T, Zhang F and Meng L (2021b) Occurrence, distribution, and prediction of iron and manganese in ground- water of opencast mines: An example from Inner Mongolia, China. Environmental Monitoring and Assessment 193(8). Li Y, Liu DM, Yao YB, et al. (2013) Evaluation of favorable area in the weibei coalbed methane ﬁeld: A study by using grey correlation analysis. Applied Mechanics and Materials 295–298: 3342–3345. Li Z, Zhang J, Tang X, et al. (2020) Approaches for the evaluation of favorable shale gas areas and applications: implications for China’s exploration strategy. Energy Science & Engineering 8(2): 270– Liu D, Yao Y, Tang D, et al. (2009) Coal reservoir characteristics and coalbed methane resource assessment in Huainan and Huaibei coalﬁelds, Southern North China. International Journal of Coal Geology 79(3): 97–112. Liu H, Su G, Okere CJ, et al. (2022) Working ﬂuid-induced formation damage evaluation for commingled pro- duction of multi-layer natural gas reservoirs with ﬂow rate method. Energy 239: 122107. Liu J, Jiang B, Li M, et al. (2015) Structural control on pore-fracture characteristics of coals from Xinjing coal mine, northeastern Qinshui basin, China. Arabian Journal of Geosciences 8(7): 4421–4431. Liu X, Wu C, Wei G, et al. (2020) Adsorption deformation characteristics of coal and coupling with perme- ability during gas injection. Journal of Petroleum Science and Engineering 195: 107875. Lü Y, Tang D, Xu H, et al. (2011) Productivity matching and quantitative prediction of coalbed methane wells based on BP neural network. Science China Technological Sciences 54(5): 1281–1286. Lv Y, Tang D, Xu H, et al. (2012) Production characteristics and the key factors in high-rank coalbed methane ﬁelds: A case study on the Fanzhuang Block, Southern Qinshui Basin, China. International Journal of Coal Geology 96–97: 93–108. Ni X, Zhao Z, Wang Y, et al. (2020) Optimisation and application of well types for ground development of coalbed methane from no. 3 coal seam in shizhuang south block in Qinshui basin, Shanxi province, China. Journal of Petroleum Science and Engineering 193: 107453. Okere CJ, Su G, Zheng L, et al. (2020) Experimental, algorithmic, and theoretical analyses for selecting an optimal laboratory method to evaluate working ﬂuid damage in coal bed methane reservoirs. Fuel 282: Qi Y, Ju Y, Yu K, et al. (2022) The effect of grain size, porosity and mineralogy on the compressive strength of tight sandstones: A case study from the eastern Ordos basin, China. Journal of Petroleum Science and Engineering 208: 109461. Qiu M, Huang F, Wang Y, et al. (2020) Prediction model of water yield property based on GRA, FAHP and TOPSIS methods for ordovician top aquifer in the Xinwen coalﬁeld of China. Environmental Earth Sciences 79(10). Qu X, Shi L, Qu X, et al. (2021) Multi-model fusion for assessing risk of inrush of limestone karst water through the mine ﬂoor. Energy Reports 7: 1473–1487. Shen Y, Qin Y, Guo Y, et al. (2016) Characteristics and sedimentary control of a coalbed methane-bearing system in lopingian (late permian) coal-bearing strata of western Guizhou province. Journal of Natural Gas Science and Engineering 33: 8–17. 24 Energy Exploration & Exploitation 0(0) Sivaraja CM, Sakthivel G and Jegadeeshwaran R (2020) Selection of optimum bio-diesel fuel blend using fuzzy TOPSIS and fuzzy VIKOR approaches. International Journal of Oil Gas and Coal Technology 23(2): 261–291. Sun D, Chen Y, Li X, et al. (2021) Control and application of terrain curvature on dynamic disasters: A case study of coal and gas outbursts in the Pingdingshan mining area. China. Advances in Civil Engineering 2021: 1–6. Sun Z and Li J (2021) Study on open-pit mine slope stability based on improved TOPSIS method. IOP con- ference series. Earth and Environmental Science 781(2): 22045. Tao S, Tang D, Xu H, et al. (2014) Factors controlling high-yield coalbed methane vertical wells in the Fanzhuang block, Southern Qinshui basin. International Journal of Coal Geology 134–135: 38–45. Tao S, Wang Y, Tang D, et al. (2012) Dynamic variation effects of coal permeability during the coalbed methane development process in the Qinshui basin, China. International Journal of Coal Geology 93: 16–22. Tao X, Okere CJ, Su G, et al. (2022) Experimental and theoretical evaluation of interlayer interference in multi- layer commingled gas production of tight gas reservoirs. Journal of Petroleum Science and Engineering 208: 109731. Wang G, Qin Y, Xie Y, et al. (2018) Coalbed methane system potential evaluation and favourable area pre- diction of Gujiao blocks, Xishan coalﬁeld, based on multi-level fuzzy mathematical analysis. Journal of Petroleum Science and Engineering 160: 136–151. Wang H, Zhang X, Wu C, et al. (2022) Drainage type classiﬁcation and key controlling factors of product- ivity for CBM wells in the Zheng Zhuang area, southern Qinshui basin, north China. ACS Omega 7(2): 1883–1892. Wu Y, Tao S, Tian W, et al. (2021) Advantageous seepage channel in coal seam and its effects on the distri- bution of high-yield areas in the fanzhuang CBM block, southern Qinshui basin, China. Natural Resources Research 30(3): 2361–2376. Yan S and Li L (2021) An empirical analysis of China’s energy security based on TOPSIS model. IOP con- ference series. Earth and Environmental Science 826(1). Yang Y, Bie A, Bie H, et al. (2020a) Geological Inﬂuential Factors on the SIS Well Productivity in Teviot-Brook Block, Moranbah Gas Field. Singapore: Springer Singapore, pp.794–808. Yang Y, Zheng X and Sun Z (2020b) Coal resource security assessment in China: A study using entropy-weight-based TOPSIS and BP neural network. Sustainability 12(6): 2294. Ye Y, Tang S, Xi Z, et al. (2022) A new method to predict brittleness index for shale gas reservoirs: insights from well logging data. Journal of Petroleum Science and Engineering 208: 109431. Zhang Y, He Z, Tian H, et al. (2021a) Hydrochemistry appraisal, quality assessment and health risk evaluation of shallow groundwater in the Mianyang area of Sichuan basin, southwestern China. Environmental Earth Sciences 80(17). Zhang K, Meng Z and Li G (2021b) Analysis of logging identiﬁcation and methane diffusion properties of coals with various deformation structures. Energy & Fuels 35(15): 12091–12103. Zhang Z, Qin Y, Bai J, et al. (2016) Evaluation of favorable regions for multi-seam coalbed methane joint exploitation based on a fuzzy model: A case study in southern Qinshui basin, China. Energy Exploration & Exploitation 34(3): 400–417. Zhang Z, Qin Y, Wang G, et al. (2021c) Evaluation of coal body structures and their distributions by geophys- ical logging methods: case study in the Laochang block, eastern Yunnan, China. Natural Resources Research 30(3): 2225–2239. Zhang Z, Qin Y, Yang Z, et al. (2019) Fluid energy characteristics and development potential of coalbed methane reservoirs with different synclines in Guizhou, China. Journal of Natural Gas Science and Engineering 71: 102981. Zhao B, Zhao Y and Wang J (2021a) New stability forecasting model for goaf slope based on the AHP– TOPSIS theory. Arabian Journal of Geosciences 14(1). Zhao L, Cao Y, Shi X, et al. (2021b) A ﬁeld application of methane drainage by surface vertical well fracturing in complicated geologic structure area: a case study. Energy sources. Part A, Recovery, utilization, and environmental effects, 1–18. Chen et al. 25 Zhao Z (2021) Evaluation of coalbed methane resources in the Huainan-Huaibei coalﬁeld of Anhui province, China, using an analytic hierarchy process and grey cluster method. Arabian Journal of Geosciences 14(17). Zhou S, Tang J and Cai Y (2021) Reservoir characterization of the upper cretaceous shale for the Changling Sag, southern Songliao basin, NE China. Arabian Journal of Geosciences 14(8). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Energy Exploration & Exploitation SAGE http://www.deepdyve.com/lp/sage/evaluation-of-coalbed-methane-resources-in-xinjing-baoan-block-based-1ympgBPavf

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References (72)

X. Tao, Chinedu Okere, Guan-qiao Su, Lihui Zheng (2022)
Experimental and theoretical evaluation of interlayer interference in multi-layer commingled gas production of tight gas reservoirs
Journal of Petroleum Science and Engineering
(2021)
2021a) Simulation of coalbed methane generation, dissipation, and preservation
J. Hower, C. Eble, Na Wang, S. Dai (2021)
Distribution of rare earth elements and other critical elements in beneficiated Pennsylvania anthracites
Fuel, 304
Jiayuan He, Chinedu Okere, Guandong Su, Pengjie Hu, Longsheng Zhang, W. Xiong, Zhonghui Li (2021)
Formation damage mitigation mechanism for coalbed methane wells via refracturing with fuzzy-ball fluid as temporary blocking agents
Journal of Natural Gas Science and Engineering
(2021)
Occurrence, distribution, and prediction of iron and manganese in groundwater of opencast mines: An example from Inner Mongolia
(2016)
Geological characteristics and CBM exploration potential evaluation: A case study in the middle of the southern Junggar Basin
Yunhui Zhang, Zhihao He, Haohao Tian, Xun Huang, Zhixiong Zhang, Yang Liu, Yong Xiao, Rui Li (2021)
Hydrochemistry appraisal, quality assessment and health risk evaluation of shallow groundwater in the Mianyang area of Sichuan Basin, southwestern China
Environmental Earth Sciences, 80
(2022)
Drainage type classi fi cation and key controlling factors of productivity for CBM wells in the Zheng Zhuang area , southern Qinshui basin , north China
Gang Wang, Yong Qin, Yiwei Xie, Jian Shen, Long Zhao, Bo Huang, Wenqiang Zhao (2018)
Coalbed methane system potential evaluation and favourable area prediction of Gujiao blocks, Xishan coalfield, based on multi-level fuzzy mathematical analysis
Journal of Petroleum Science and Engineering, 160
(2022)
Working fl uid - induced formation damage evaluation for commingled production of multi - layer natural gas reservoirs with fl ow rate method
Hao Liu, Guandong Su, Chinedu Okere, Guozhang Li, Xiangchun Wang, Yuzhe Cai, Tong Wu, Lihui Zheng (2022)
Working fluid-induced formation damage evaluation for commingled production of multi-layer natural gas reservoirs with flow rate method
Energy
Yong Yang, A. Bie, Han-yu Bie, Ming Zhang, Ze-hong Cui, Xiao-yan Liu (2019)
Geological Influential Factors on the SIS Well Productivity in Teviot-Brook Block, Moranbah Gas Field
Jiegang Liu, B. Jiang, Ming Li, Zhenghui Qu, Linlin Wang, Lei Li (2015)
Structural control on pore-fracture characteristics of coals from Xinjing coal mine, northeastern Qinshui basin, China
Arabian Journal of Geosciences, 8
(2021)
Distribution of rare earth elements and other critical elements
Eunnyeong Heo, Jinsoo Kim, K. Boo (2010)
Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP
Renewable & Sustainable Energy Reviews, 14
Xingyue Qu, Longqing Shi, Xingwei Qu, Ahmer Bilal, Mei Qiu, Weifu Gao (2021)
Multi-model fusion for assessing risk of inrush of limestone karst water through the mine floor
Energy Reports, 7
(2021)
2021a) New stability forecasting model for goaf slope based on the AHP
Zhong-qiang Sun, Jiangteng Li (2021)
Study on Open-pit Mine Slope Stability Based on Improved TOPSIS Method
IOP Conference Series: Earth and Environmental Science, 781
S. Tao, D. Tang, Hao Xu, Lijun Gao, Yuan Fang (2014)
Factors controlling high-yield coalbed methane vertical wells in the Fanzhuang Block, Southern Qinshui Basin
International Journal of Coal Geology, 134
Yidong Cai, Dameng Liu, Keming Zhang, D. Elsworth, Yanbin Yao, D. Tang (2014)
Preliminary evaluation of gas content of the No. 2 coal seam in the Yanchuannan area, southeast Ordos basin, China
Journal of Petroleum Science and Engineering, 122
Yapei Ye, Shuheng Tang, Z. Xi, Dexin Jiang, Yang Duan (2022)
A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data
Journal of Petroleum Science and Engineering, 208
(2021)
2021b) A field application of methane drainage by surface vertical well fracturing
Yulin Shen, Yulin Shen, Yong Qin, Yong Qin, Yinghai Guo, Yinghai Guo, Tongsheng Yi, Xuexu Yuan, Yubao Shao (2016)
Characteristics and sedimentary control of a coalbed methane-bearing system in lopingian (late permian) coal-bearing strata of western Guizhou Province
Journal of Natural Gas Science and Engineering, 33
Fengli Li, B. Jiang, Chong-tao Wei, Guoxi Cheng, Yu Song (2021)
Simulation of coalbed methane generation, dissipation, and preservation and analysis of the geological influencing factors: a case study of the Xinjing coal mine, northeastern Qinshui basin, China
Arabian Journal of Geosciences, 14
Bo Zhao, Yuqiong Zhao, Jiamin Wang (2021)
New stability forecasting model for goaf slope based on the AHP–TOPSIS theory
Arabian Journal of Geosciences, 14
(2022)
The effect of grain size, porosity and mineralogy on the compressive strength of tight sandstones: A case study from the eastern Ordos basin
X. Ni, Zheng Zhao, Yanbin Wang, Li Wang (2020)
Optimisation and application of well types for ground development of coalbed methane from no. 3 coal seam in shizhuang south block in Qinshui basin, Shanxi province, China
Journal of Petroleum Science and Engineering, 193
S. Tao, Yanbin Wang, D. Tang, Hao Xu, Yumin Lv, Wei He, Yong Li (2012)
Dynamic variation effects of coal permeability during the coalbed methane development process in the Qinshui Basin, China
International Journal of Coal Geology, 93
(2022)
The effect of grain size, porosity and mineralogy on the compressive strength
Structural and evolutionary characteristics of pores-microfractures and their influence on coalbed methane exploitation in high-rank brittle tectonically deformed coals of the Yangquan mining area, northeastern Qinshui basin, China
(2020)
2020b) Coal resource security assessment in China: A study
Mei Qiu, Fengjun Huang, Ying Wang, Tao Guan, Longqing Shi, Jin Han (2020)
Prediction model of water yield property based on GRA, FAHP and TOPSIS methods for Ordovician top aquifer in the Xinwen coalfield of China
Environmental Earth Sciences, 79
Tengfei Cao, Zhaobiao Yang, Yong Qin, Zhiming Yan, Zhaoying Chen, Chao Li (2021)
Characteristics of Modern Geostress and Removability of No. 15 Coal Reservoir, Yangquan Mining Area, China
Natural Resources Research, 30
Zhigen Zhao (2021)
Evaluation of coalbed methane resources in the Huainan-Huaibei coalfield of Anhui Province, China, using an analytic hierarchy process and grey cluster method
Arabian Journal of Geosciences, 14
(2019)
Structural and evolutionary characteristics of pores-microfractures
A. Banerjee, N. Sequeira, A. Bhattacharya (2021)
Tectonics of the Greater India Proterozoic Fold Belt, with emphasis on the nature of curvature of the belt in west-central India
Earth-Science Reviews, 221
Chinedu Okere, Guandong Su, Lihui Zheng, Yuzhe Cai, Zhaochuan Li, Hao Liu (2020)
Experimental, algorithmic, and theoretical analyses for selecting an optimal laboratory method to evaluate working fluid damage in coal bed methane reservoirs
Fuel, 282
(2016)
Geological characteristics and CBM exploration potential evaluation: A case
(2020)
Experimental, algorithmic, and theoretical analyses
Fengli Li, B. Jiang, Guoxi Cheng, Yu Song (2019)
Multifractal analysis and evolution rules of micro-fractures in brittle tectonically deformed coals of Yangquan mining area
Arabian Journal of Geosciences, 12
Yuexiang Yang, Xiaoyu Zheng, Zhen Sun (2020)
Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network
Sustainability
Yumin Lv, D. Tang, Hao Xu, Haohan Luo (2012)
Production characteristics and the key factors in high-rank coalbed methane fields: A case study on the Fanzhuang Block, Southern Qinshui Basin, China
International Journal of Coal Geology, 96
Liu Xiaolei, Caifang Wu, Wei Guoying, Xiaodong Zhang, Jia Tianrang, Huaizhen Li, Mingyang Du (2020)
Adsorption deformation characteristics of coal and coupling with permeability during gas injection
Journal of Petroleum Science and Engineering, 195
(2021)
An empirical analysis of China ’ s energy security based on TOPSIS model . IOP conference series
(2021)
Distribution of rare earth elements and other critical elements in bene fi ciated pennsylvania anthracites
Dequan Sun, Ying Chen, Xiaoyan Li, Chun-Hsiang Huang, Huairui Su, Rujiao Chen (2021)
Control and Application of Terrain Curvature on Dynamic Disasters: A Case Study of Coal and Gas Outbursts in the Pingdingshan Mining Area, China
Advances in Civil Engineering
Huang Bo, Qin Yong, Wanhong Zhang, Qiming Zheng, Songlin Shi, Gang Wang (2020)
Prediction of high-quality coalbed methane reservoirs based on the fuzzy gray model: An investigation into coal seam No. 8 in Gujiao, Xishan, North China
Energy Exploration & Exploitation, 38
H. Wang, Xiaoyang Zhang, Caifang Wu, G. Cheng, Binbin Hu, Licui Fu, Meiru Zhang (2022)
Drainage Type Classification and Key Controlling Factors of Productivity for CBM Wells in the Zheng Zhuang Area, Southern Qinshui Basin, North China
ACS Omega, 7
Yu Qi, Y. Ju, Kun Yu, S. Meng, Peng Qiao (2022)
The effect of grain size, porosity and mineralogy on the compressive strength of tight sandstones: A case study from the eastern Ordos Basin, China
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(2022)
Experimental and theoretical evaluation of interlayer interference
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Advantageous Seepage Channel in Coal Seam and its Effects on the Distribution of High-yield Areas in the Fanzhuang CBM Block, Southern Qinshui Basin, China
Natural Resources Research, 30
Yumin Lü, D. Tang, Hao Xu, S. Tao (2011)
Productivity matching and quantitative prediction of coalbed methane wells based on BP neural network
Science China Technological Sciences, 54
Zhengguang Zhang, Yong Qin, Zhaobiao Yang, Jun Jin, Congcong Wu (2019)
Fluid energy characteristics and development potential of coalbed methane reservoirs with different synclines in Guizhou, China
Journal of Natural Gas Science and Engineering
Kun Zhang, Z. Meng, Guofu Li (2021)
Analysis of Logging Identification and Methane Diffusion Properties of Coals with Various Deformation Structures
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Sandong Zhou, Jiguang Tang, Yidong Cai (2021)
Reservoir characterization of the Upper Cretaceous shale for the Changling Sag, Southern Songliao Basin, NE China
Arabian Journal of Geosciences, 14
Song Li, D. Tang, Z. Pan, Hao Xu, Lele Guo (2015)
Evaluation of coalbed methane potential of different reservoirs in western Guizhou and eastern Yunnan, China
Fuel, 139
(2021)
Occurrence, distribution, and prediction of iron and manganese
C. Sivaraja, G. Sakthivel, R. Jegadeeshwaran (2020)
Selection of optimum bio-diesel fuel blend using fuzzy TOPSIS and fuzzy VIKOR approaches
International Journal of Oil, Gas and Coal Technology
Zhuguo Li, Jinchuan Zhang, Xuan Tang, Yu Gao, Z. Huo, Pei Li, Junlan Liu, D. Gong (2019)
Approaches for the evaluation of favorable shale gas areas and applications: Implications for China's exploration strategy
Energy Science & Engineering, 8
Sayan Ghosh, R. Chatterjee, P. Shanker (2016)
Estimation of ash, moisture content and detection of coal lithofacies from well logs using regression and artificial neural network modelling
Fuel, 177
Haijiao Fu, D. Tang, Hao Xu, Ting Xu, Baoli Chen, Peng Hu, Zhenyong Yin, Peng Wu, Guang He (2016)
Geological characteristics and CBM exploration potential evaluation: A case study in the middle of the southern Junggar Basin, NW China
Journal of Natural Gas Science and Engineering, 30
Lipeng Zhao, Yunxing Cao, Xiaohong Shi, Zuxun Zhang, Baoan Xian, Gaofeng Liu, Qizhi Wang, Q. Niu (2021)
A field application of methane drainage by surface vertical well fracturing in complicated geologic structure area: a case study
Energy Sources Part A-recovery Utilization and Environmental Effects
Dameng Liu, Yanbin Yao, D. Tang, Shuheng Tang, Yao Che, Wenhui Huang (2009)
Coal reservoir characteristics and coalbed methane resource assessment in Huainan and Huaibei coalfields, Southern North China
International Journal of Coal Geology, 79
(2022)
A new method to predict brittleness index for shale gas reservoirs: insights
Zheng Zhang, Yong Qin, Jianping Bai, Xuehai Fu, Dong-lie Liu (2016)
Evaluation of favorable regions for multi-seam coalbed methane joint exploitation based on a fuzzy model: A case study in southern Qinshui Basin, China
Energy Exploration & Exploitation, 34
Li Tianxin, Z. Fang, Meng Linglong (2021)
Occurrence, distribution, and prediction of iron and manganese in groundwater of opencast mines: an example from Inner Mongolia, China
Environmental Monitoring and Assessment, 193
(2021)
2021a) Hydrochemistry appraisal, quality assessment and health risk evaluation
G. Hu, Jialin Xu, Fuxi Zhang, Changchun Zhao, W. Qin, Z. Yiran (2015)
Coal and Coalbed Methane Co-Extraction Technology Based on the Ground Movement in the Yangquan Coalfield, China
Energies, 8
Ying Li, D. Liu, Yanbin Yao, Xiao Wang, Zhaoyin Yin (2013)
Evaluation of Favorable Area in the Weibei Coalbed Methane Field: a Study by Using Grey Correlation Analysis
Applied Mechanics and Materials, 295-298
Zhengguang Zhang, Yong Qin, Geoff Wang, Hansen Sun, Z. You, Jun Jin, Zhaobiao Yang (2021)
Evaluation of Coal Body Structures and Their Distributions by Geophysical Logging Methods: Case Study in the Laochang Block, Eastern Yunnan, China
Natural Resources Research, 30
(2021)
A (2021) Tectonics of the greater India proterozoic fold belt
Study on open-pit mine slope stability based on improved TOPSIS method. IOP conference series

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Abstract

Coalbed methane (CBM) resources are abundant in the Baoan blocks of the Xinjing coalﬁeld, China; their exploration and development remain in infancy. Suitable development blocks can be selected by evaluating their development potential; that of the No. 8 and 15 CBM systems in the Baoan blocks was evaluated using three quantitative evaluation methods via existing geo- logical data: multi-level fuzzy mathematics (MLFM), technique for order preference by similarity to ideal solution (TOPSIS), and principal component analysis (PCA). The results demonstrated that the CBM development potential in the central part of the Baoan blocks is low and unbecom- ing. The favorable areas of the No. 8 coal seam are distributed in the northeast and northwest, and that of No. 15 in the northwest and southwest Differences exist in quantitative selection; with limited data, the evaluation of the TOPSIS method is considerably different from actuality, and is unbecoming for evaluating the development potential. Keywords Coalbed methane, potential evaluation, multi-level fuzzy mathematics, TOPSIS, principal component analysis Introduction China is rich in coalbed methane (CBM) resources, and the commercial development of CBM is primarily concentrated in the eastern margin of the Qinshui and Ordos basins. Previous studies on the relevant theories of CBM development selection evaluation systems have been conducted. Department of Geological and Surveying Engineering, Shanxi Institute of Energy, Jinzhong, Shanxi, China Key Laboratory Coalbed Methane Resources & Reservoir formation, Ministry of Education, China University Mine & Technology, Xuzhou, Jiangsu, China Corresponding author: Xiuming Jiang, China University Mine & Technology, Key Laboratory Coalbed Methane Resources & Reservoir formation, Ministry of Education, Xuzhou 221008, Jiangsu, China. Email: cumt-jxm@cumt.edu.cn Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as speciﬁed on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 Energy Exploration & Exploitation 0(0) (Cai et al., 2014; Liu et al., 2022; Shen et al., 2016). Yangquan Coal mine, located in the east of Taiyuan city, is rich in CBM resources (Li et al., 2019b; Li et al., 2021a; Tao et al., 2022). Geological selection evaluation is a prerequisite for CBM development. Selecting only favorable blocks suitable for its development can be foundational (Bo et al., 2020; He et al., 2021; Okere et al., 2020). Many evaluation methods for CBM geological selection have been established, such as the search for high-permeability area, formation energy, fuzzy matrix, analytic hierarchy process, catas- trophe evaluation, grey clustering analysis, grey correlation, back-propagation neural network, and numerical simulation (Bo et al., 2020; Ghosh et al., 2016; Heo et al., 2010; Li et al., 2013; Lü et al., 2011). Based on the geological conditions of coal seams, these methods evaluate the advantages, disadvantages, and economy of CBM development from different aspects. However, CBM reser- voirs are complex geological systems, and the factors affecting their development often restrict one another. Therefore, a large number of mathematical methods have been introduced to comprehen- sively consider the interaction and intervention of various factors. Among them, multi-level fuzzy mathematics is a method combining hierarchical analysis and fuzzy mathematics, and the geo- logical index of mutual restriction is converted into a compatible geological index, eliminating the restriction between the inﬂuencing factors, which has a certain advantage for CBM selection evaluation. The technique for order preference by similarity to ideal solution (TOPSIS) method based on the coefﬁcient of variation is another multi-objective decision analysis method, conduct- ing a comprehensive evaluation based on the distance between the evaluation object and the most idealized decision target. TOPSIS has been widely used in evaluating oil and gas reservoir (Qu et al., 2021), resource and environment (Yan and Li, 2021), coal mining safety and other geological ﬁelds (Zhao et al., 2021a). However, there exists limited research on CBM selection using TOPSIS. In addition, factors of CBM development potential evaluation not only restrict and inﬂuence each other, but also possess repeated information among a large number of indicators, resulting in the complexity and uncertainty of CBM selection evaluation and decision-making system (Hower et al., 2021; Ye et al., 2022; Zhang et al., 2021a). Principal component analysis is a type of evalu- ation method that can deal with information jumbled in decision systems and evaluation systems. The main information in the data was extracted, and the target was evaluated by dimensionality reduction (Li et al., 2021b). Compared with single-factor analysis, these comprehensive evaluation methods have more advantages in CBM selection index analysis. Among them, fuzzy mathematics and PCA are the most widely used methods, in the evaluation of CBM development potential in many regions, including the southern Qinshui Basin in China (Zhang et al., 2016) and the Xishan coalﬁeld in Shanxi Province, China (Wang et al., 2018). The Teviot-Brook region of Australia (Yang et al., 2020a, 2020b). (Zhang et al., 2019) used PCA method to evaluate the development potential of multiple synclines in western Guizhou, China, and the results were consistent with the actual production capacity. Wang et al. (2022) used the PCA method to analyze the characteristics of CBM drainage and production and consid- ered that the gas content is the most important factor affecting CBM productivity. The TOPSIS method has also been widely used in geological resource-related research and favorable target opti- mization and has been successfully applied to coal resource safety and aquifer evaluation (Qiu et al., 2020; Yang et al., 2020b). However, previous studies have mainly focused on using a single method to evaluate CBM development potential, and few comparative studies exist on different methods in the same area. Therefore, this study considers the Baoan blocks of Xinjing Mine its research object. Based on ana- lyzing the geological elements in the research area, combined with the advantages and disadvan- tages of three evaluation index systems and methods, we conducted a comprehensive evaluation Chen et al. 3 of the development potential of the No. 8 and No. 15 coal seams, and determined the evaluation method most suitable for CBM development in the research area. Geological background Baoan blocks are located in the west of Yangquan mining of the Qinshui Basin, the west side of the Taihang Mountain north section, and the south foothill of the Liuzhao Mountain low and mid- mountain. The ﬁeld is 7.9 km long from east to west, 7.5 km wide from north to south, and covers an area of 64.747 7 km (Li et al., 2019a, 2019b; Zhao et al., 2021b) the surface topography in the mine ﬁeld is complex and ﬂuctuates substantially (Figure 1). Coal bearing strata The main coal-bearing strata in the study area are the Taiyuan Formation and the Shanxi Formation of the upper Carboniferous to lower Permian. Its average total thickness is 177.68 m, the average total thickness of the coal seam is 20.85 m, and the coal bearing coefﬁcient is 11.73% (Zhang et al., 2016). The Taiyuan Formation contains nine layers of coal, among which the No. 15 coal seam is stable and recoverable, the No. 8 and No. 9 coal seams are stable, and most of them are recoverable. The average total thickness of the coal seam is 15.57 m and the coal bearing coefﬁcient is 12.88%. The average total thickness of coal seam containing 6 layers in Shanxi Formation is 5.28 m, and the coal coefﬁcient is 9.30% (Figure 2)(Cao et al., 2021; Li et al., 2019a, 2019b). Tectonic Overall, the Qinshui Basin is an NNE syncline structure adjacent to the Taihang Mountain uplift in the east and the Luliang Mountain uplift in the west Its axis is approximately along the Yushe - Qinxian–Qinshui line. The overall structure was relatively simple, and fault development was Figure 1. Tectonic outline and location map of the study area. 4 Energy Exploration & Exploitation 0(0) Figure 2. Comprehensive histogram of strata in the study area. weak (Figure 1).The study area is located in the north of the Xining Coal Mine, which belongs to the Shenyang-Yangquan monocline zone at the northern end of the Qinshui Basin, Shanxi Province, and is a syncline formed by near-horizontal compression after the late Paleozoic coal- forming period in North China (Figure 1).(Hu et al., 2015; Wang et al., 2018). Selection evaluation index Evaluation index of reservoir conditions Coal thickness. The thickness of the coal seam is an important parameter in evaluating the CBM reservoir that affects the resource amount and abundance of its development. The larger the Chen et al. 5 thickness of the coal seam, the higher the resource amount there is (Liu et al., 2009). The results demonstrate a positive correlation between gas production and the thickness of the coal seam in the Qinshui Basin (Zhang et al., 2016). According to the borehole data of several coal ﬁelds in the research area, the thickness of the No. 8 coal seam is 0.4–3.95 m, with an average of 2.15 m. The distribution characteristics of the No. 8 coal seam on the plane is relatively complex. Multiple high-value coal thickness areas were developed in the study area. In general, the coal seam in the middle of the study area was thin and its surrounding areas were thick (Figure 3(a)). The thickness of the No. 15 coal seam was larger than that of No. 8. It is the main mining coal seam in the study area, with a thickness of 2.05–9.81 m and an average of 6.5 m. From the perspective of plane region, coal seam No. 15 is mainly developed in the northwest, and the overall trend depicts a gradually thinning from northwest to southeast (Figure 3(b)). Buried depth. The buried depth of the coal seam not only affects the reserves of CBM but is also an important evaluation index for developing CBM projects. The permeability of the coal seam decreases gradually, with increasing buried depth, which makes it difﬁcult to discharge and depres- surize CBM. However, with increasing burial depth, CBM sealing conditions improve and coal seam gas content and resource reserves increase (Li et al., 2015; Shen et al., 2016). Statistical data show that the buried depth of No. 8 in the research area is 431.94–744.75 m, with an average of 586.75 m. In terms of plane distribution, the buried depth of the No. 8 coal seam is the largest in the northeast and southwest and is greater than 700 m in some areas (Figure 4(a)). The buried depth of the No. 15 seam varies less than that of No.8, ranging from 523.6 m to 834.97 m, with an average of 674.86 m. The plane regional distribution trend of the buried depth was consistent with that of the No. 8 coal seam, with the largest buried depth in the northeast and southwest (Figure 4(b)). Gas content. Gas content is one of the most important factors in evaluating CBM selection, which directly affects the gas production of CBM (Lv et al., 2012; Tao et al., 2014). The results show that 3 3 the gas content of the No. 8 coal seam is 6.0–20.6 m /t with an average of 12.7 m /t; the gas content 3 3 of the No. 15 coal seam is 3.8–19.3 m /t with an average of 9.6 m /t, such that it is lower than that of No. 8. In terms of regional distribution, the gas content of the No. 8 coal seam is high in the Figure 3. Contour map of coal seam thickness in the study area (a: No.8, b: No.15). 6 Energy Exploration & Exploitation 0(0) Figure 4. Contour map of buried depth of coal seam in the study area (a: No.8, b: No.15). Figure 5. Contour map of coal seam gas content in the study area (a: No.8, b: No.15). northeast, high in the south, and low in the southwest and northwest, and the gas content of local and regional coal seams is characterized by aggregation. Combined with the tectonic development in the study area, the regional distribution of gas content is consistent with the tectonic track, and the complex structural development is the main reason for this difference. The regional distribution characteristics of the gas content in the No. 15 coal seam is different from that in No. 8. The gas content in the No.15 coal seam was higher in the south than in the middle (Figure 5(b)). Permeability. The permeability of the coal reservoir is the main factor restricting CBM production. The higher the permeability, the more easily methane could be desorbed and transported. Therefore, the main goal in the early potential evaluation process of CBM development, is to ﬁnd a “high permeability area” as the dominant blocks of CBM development (Fu et al., 2016; Liu et al., 2020). Due to the lack of exploration in the research area, the permeability data of the No. 8 coal seam are missing. This study mainly analyzed the No.15 coal seam, for which −2 −2 the permeability was 0.996–1.08 × 10 mD, and the average was 1.02 × 10 mD. In terms of Chen et al. 7 regional distribution, the hypertonic areas were mainly distributed in the west and southwest of the study area (Figures 6 and 7). Resource abundance. Resource abundance is one of the main economic indicators of CBM devel- opment. The higher the resource abundance, the greater the economic potential of CBM develop- ment (Liu et al., 2015; Zhao, 2021; Zhou et al., 2021). The borehole data in an study area show that 8 3 2 the resource abundance of the No. 8 coal seam is 0.42–0.92 × 10 m /km , with an average of 0.60 8 3 2 8 3 2 8 3 × 10 m /km ; and that of No. 15 is 0.58–1.53 × 10 m /km , with an average of 0.96 × 10 m /km. The resource abundance of the No. 15 coal seam was evidently better than that of No. 8. Combined with the plane distribution of resource abundance, we can see that the resource abundance of the No. 8 coal seam is lower in the middle and largest in the northeast The No. 15 coal seam shows similar distribution characteristics, and the resource abundance in the middle region is low. In con- trast to No. 8, the resource abundance of the No. 15 coal seam in the southwest was the highest Evaluating the index of conditions of preservation Tectonic coal distribution in the study area. A large amount of measured data and research results have revealed that the coal structure type has an important effect on permeability and CBM production. Figure 6. Contour map of permeability of No. 15 coal seam in the study area. 8 Energy Exploration & Exploitation 0(0) Figure 7. Contour map of CBM resource abundance in the study area (a: No.8, b: No.15). According to the degree of damage, the coal structure can be divided into four types: primary struc- ture coal, fragmentary coal, fragmentary coal and mylonite coal. Fragmentary and mylonite coal are also known as tectonic coal, which is not conducive to CBM development owing to its structure, disordered fracture direction, poor connectivity, and relatively low permeability (Wu et al., 2021; Zhang et al., 2021a, 2021b). Based on the analysis of well drilling results and logging data, we can see that both primary and structural coal developed in the Baoan blocks. The average tectonic coal thickness of the No. 8 coal seam was 0.59 m, accounting for 34%, and the average tectonic coal thickness of the No. 15 coal seam was 1.29 m, accounting for 20%. In terms of regional distribu- tion, the tectonic coal of the No. 8 coal seam was relatively developed along the southeast direction near small faults. The tectonic coal of the No. 15 coal seam mainly developed along the NNE dir- ection of fault DF , which is subjected to weak tectonic stress. Most faults only developed in the coal seam, and the coal structure did not suffer severe damage (Cao et al., 2021; Zhang et al., 2021b, 2021c) (Figure 8). Roof and ﬂoor. The lithology of the coal roof and ﬂoor affects the preservation of CBM and is a key factor affecting gas saturation. Because of their low permeability, mudstones are ideal roofs and ﬂoors for CBM sealing. According to the drilling data, the roof and ﬂoor of the No. 8 coal seam are mainly mudstone; the roof is 1.66–9.67 m with an average of 5.48 m. The thickness of the bottom plate was 0.3–9.35 m, with an average of 4.07 m. According to the contour map of the ratio of mudstone thickness to thickness of the roof of the No. 8 coal seam, we can see that the roof mudstone of No. 8 is thick in the southeast and northwest and thin in the middle (Figure 9(a)). The No. 15 coal seam roof is mainly limestone, 0.35–7.44 m thick, averaging 1.9 m. The bottom mudstone is 0.55–16.6 m thick with an average of 6.56 m. The thickness of the bottom plate was greater than that of the top plate. Combined with the contour map of the mud- stone thickness ratio of the roof of the No.15 coal seam, we can see that the roof thickness of the No. 15 coal seam gradually thins from NE to SW (Figure 9(b)) (Ni et al., 2020; Tao et al., 2012). Fault fractal dimension. Fault development is an important condition for CBM storage. Faults not only affect the preservation and migration of CBM but also need to consider faults and other struc- tures during fracturing and exploitation (Wu et al., 2021). According to the structural outline map of Chen et al. 9 Figure 8. Tectonic coal thickness contour map (a: No.8, b: No.15). Figure 9. Contour map of mudstone thickness ratio of coal seam roof in the study area (a: No.8, b: No.15). the research area, the faults in the east of the No. 8 coal seam are more developed than those in the west, and there are 22 faults in total, including 11 normal and 10 reverse faults. The dip angle of the faults was large, ranging from 31.5° to 70°, with an average of 55° (Figure 10(a)). Small faults are more developed in No. 15, with 44 faults in the study area, mainly normal faults, with dip angles ranging from 38° to 75° and an average of 56° (Figure 10(b)). To quantify the degree of fault development in the study area, the fault fractal dimension was used to evaluate the degree of structural failure of the coal seams. We can see from the fractal dimension contour map of the fault of the No.8 coal seam that the study area is moderately complex. Among them, due to a large number of faults, the northeast region is locally developed as a complex structural region, whereas the southwest region is the simplest structural region (Figure 10(a)). The northeastern area of the No. 15 coal seam is of medium structural complexity, whereas the structural development trend of the southwestern area is consistent with that of No. 8, which is a simple area. The structural complexity of the No.15 coal seam responded adequately to the fault trace of the fault in the Baoan blocks; the simple structural area is distributed between two 10 Energy Exploration & Exploitation 0(0) Figure 10. Fault fractal dimension contour map of the study area (a: No.8, b: No.15). Figure 11. Contour map of structural curvature in the study area (a: No.8, b: No.15). large fault traces (Figure 10(b)). The Figures show that the fractal dimension of the fault in the study area is consistent with the degree of development of the fault (Figure 10). Tectonic curvature. The structure of the study area is a synanticline with wide and slow undulations. The NNE-NE strike of the fold, such as the S syncline, is consistent with the strike of the fault. The NE parts of the S syncline are characterized by multiple secondary small folds, showing alternate anticline development. The No. 15 coal seam is simpler than the No. 8 coal seam, and the secondary small syncline of the No. 15 coal is less than that of No.8. (Li et al., 2013; Li et al., 2015; Zhang et al., 2016) (Figure 11). Tectonic curvature is the result of the tectonic stress ﬁeld. The curvature reﬂects the relative development degree of tensile fractures in curved strata, and a high curvature is located in areas with strong folding. Herein, the maximum curvature of the study area was calculated using the contour line of the coal ﬂoor to characterize the deformation degree of the fold. (Banerjee et al., 2021; Qi et al., 2022; Sun et al., 2021). Figure 11 shows that there is a good coincidence and Chen et al. 11 relationship between fold development and tectonic trace in the study area, and the maximum curvature can be used to characterize the degree of fold development. Potential evaluation and favorable area prediction of CBM After years of development, CBM selection methods and indicators have reached a consensus. The important indicators are gradually becoming clearer, such as resource conditions, reservoir condi- tions, preservation conditions, and so on. The selection method developed gradually from the initial qualitative evaluation to quantitative evaluation and then to qualitative and quantitative comprehen- sive considerations (Bo et al., 2020; Li et al., 2019a, 2019b; Wang et al., 2018). Herein, we selected three methods, namely multi-level fuzzy mathematics, the TOPSIS method based on the coefﬁcient of variation, and principal component analysis, to evaluate the CBM development potential in Baoan blocks. Multi-level fuzzy mathematics The analytic hierarchy process is a decision-making method of system theory, which is decomposed into several levels, making index evaluation complex at different decision-making levels. Furthermore, considering the problem of insufﬁcient data, the theory of analytic hierarchy process and fuzzy mathematics is introduced, and a multi-level fuzzy mathematics method is devel- oped as an evaluation method for CBM constituency. The basic idea of this method is to decompose the problem to be solved into several components, group these components according to the dom- inant relationship, and establish a hierarchical structural model. According to a certain scale, a pair- wise comparison of the importance of each element at the same level with respect to the previous level is made, and a pairwise comparison judgment matrix is constructed, by which the relative weight of the compared element to its criterion is calculated (Heo et al., 2010; Wang et al., 2018). First, the decision objective was stratiﬁed and a discriminant matrix was established. Based on previous research methods, coal seam thickness, buried depth, gas content, permeability, and resource abundance were selected as the reservoir evaluation conditions of CBM. The structural coal content, roof mudstone thickness ratio, fault fractal dimension value, and structural curvature value were selected as the key factors of CBM preservation conditions, and a three-level evaluation model was established (Wang et al., 2018). Second, the importance of each parameter was compared at the same level. We used the 1–9 scale method to rank the importance of the evaluation indicators and MATLAB to calculate the maximum feature root and feature vector of the judgment matrix, and the weight coefﬁcients of the indicators at each level were obtained (Table 1). To ensure the accuracy and reliability of the calculation results, it was necessary to conduct a random consistency test on the calculation results. The following formula was used to test the results. λ − n max C.I. = (1) n − 1 C.I. C.R. = (2) R.I. where n is the order of the matrix, λ is the characteristic root, and R.I. is the random consistency max index. 12 Energy Exploration & Exploitation 0(0) Table 1. Importance coefﬁcients of evaluation indices. No.8 W λ C.R. No.15 W λ C.R. B max B max A∼BA B B -- - W 2.00 0.00 A∼BAB B -- -- W 2.00 0.00 1 2 B 1 2 B B 1 4 - - - 0.8 B 1 4 -- -- 0.8 1 1 B 0.25 0.1 - - - 0.2 B 0.25 0.1 - - - - 0.2 2 2 B ∼C B C C C C C W 5.21 0.05 B ∼C B C C C C C C W 6.25 0.04 1 1 1 11 12 13 14 15 B 1 1 1 11 12 13 14 15 16 B C 1 0.33 0.33 4 0.5 0.13 C 1 0.33 0.33 4 0.5 0.25 0.09 11 11 C 3 1 1 5 3 0.34 C 3 1 1 5 3 0.5 0.22 12 12 C 3 1 1 4 3 0.33 C 3 1 1 4 3 0.5 0.21 13 13 C 0.25 0.2 0.25 1 0.5 0.06 C 0.25 0.2 0.25 1 0.5 0.2 0.04 14 14 C 2 0.33 0.33 2 1 0.14 C 2 0.33 0.33 2 1 0.33 0.1 15 15 --- - - - - - - C 4 2 2 5 1 3 0.33 B ∼C B C C C -- W 3.02 0.02 B ∼C B C C C -- - W 3.05 0.05 2 2 2 21 22 23 B 2 2 2 21 22 23 B C 1 0.25 0.33 - - 0.12 C 1 0.33 0.33 - - - 0.14 21 21 C 4 1 2 - - 0.56 C 3 1 2 - - - 0.53 22 22 C 3 0.5 1 - - 0.32 C 3 0.5 1 - - - 0.33 23 23 Note: In the table, W is the eigenvector, λ is the maximum eigenroot, and C.R. is the random consistency ratio. B max Chen et al. 13 Table 2. Weight table of evaluation indices and evaluation parameters. No. 8 No. 15 The evaluation indices weight Evaluation parameters weight The evaluation indices weight Evaluation parameters weight Reservoir conditionsB 0.8 Coal seam thicknessC 0.10 Reservoir conditionsB 0.8 Coal seam thicknessC 0.07 1 11 1 11 gas contentC 0.27 gas contentC 0.18 12 12 Resource abundanceC 0.26 Resource abundanceC 0.17 13 13 Tectonic coalC 0.05 Tectonic coalC 0.04 14 14 Buried depthC 0.11 Buried depthC 0.08 15 15 - - Permeability C - 0.27 Preservation 0.2 Ratio of roof mudstone 0.02 Preservation 0.2 Ratio of roof mudstone 0.03 conditionsB thicknessC conditionsB thicknessC 2 21 2 21 Fault fractal dimension valueC 0.11 Fault fractal dimension valueC 0.11 22 22 Structural curvature valueC 0.06 Structural curvature valueC 0.07 23 23 14 Energy Exploration & Exploitation 0(0) A consistency test was performed on each discriminant matrix, and was continuously iterated adjusting the matrix parameters until the C.R was < 10%. Through the fuzzy evaluation model constructed above, combined with the weight of each evaluation index in Table 2, multi-level fuzzy comprehensive evaluation results of the study area can be obtained, and the advantage map of CBM development potential evaluation of Baoan blocks. As shown in Figure 12, the favorable areas of the No. 8 coal seam were mainly distributed in the northeast of the study area, where the potential for CBM development was low in the middle. The evaluation results of the No. 15 coal seam were considerably different from those of the No. 8 coal seam, and the area with the largest CBM development potential was located southeast of the study area. Similar to the evaluation results of the No. 8 coal seam, the development potential in the middle of the study area was the lowest TOPSIS based on variation coefﬁcient method Technique for order preference by similarity to an ideal solution (TOPSIS), also known as an ideal solution, is a common and effective method in multi-objective decision analysis. TOPSIS sorts the evaluation objects according to their proximity to the most ideal goal, calculates the distance between the evaluation objects and the optimal and worst schemes, and evaluates the relative advantages and disadvantages on this basis (Sun and Li, 2021; Yang et al., 2020a, 2020b). Compared with the analytic hierarchy process, the TOPSIS method does not require the selection of the membership function and index weight; therefore, there is no subjective inﬂuence of the membership function and index weight. Herein, we chose the variation coefﬁcient method to deter- mine the weights of the evaluation indices, which is based on the general law of data samples, using the arithmetic mean and variance to determine the relative weight of each index. It can better reﬂect the relative weights of every index (Qiu et al., 2020; Sivaraja et al., 2020). First, the original data were positively and normalized. For some indicators, the larger the data value, the greater the advantages of the evaluation object. However, for others, the opposite was true. Therefore, the more advantageous the evaluation object, the more appropriate the data needed to be forward-processed to increase the index. Equation 3 was used to forward the extremely small data, and to eliminate the impact of data dimension and the data needed to be standardized. Figure 12. Distribution map of CBM potential evaluation by multi-level fuzzy mathematics (a: No.8, b: No.15). Chen et al. 15 Equation 4 was used to standardize the data: x = x − x (3) i max i t = (4) i=1 i After processing the data, a standardized decision matrix T comprising n blocks and m evaluation indices was constructed. ⎡ ⎤ t .. . t 11 1n ⎣ ⎦ T = .. . . .. .. . (5) t .. . t m1 nm The weight of each evaluation index was calculated using the coefﬁcient of variation method. 1 n t = t (6) j ij i=1 1 n 2 2 S = (t − t ) (7) ij j i=1 n − 1 Assuming v =|t | / S , then, normalized v was the weight of each indicator, i i i i ω = (8) According to the above method, the weights of the No. 8 and No. 15 coal seams in the study area were determined, as shown in Table 3. + − The matrix of positive ideal solution Z and negative ideal solution Z were established: T =(max t , max t , ... , max t ) (9) i1 i2 im T = (min t , min t , .. . , min t ) (10) i1 i2 im The Euclidean distance between each target value and the ideal value is calculated, and the distance D from the evaluated blocks d to the positive ideal solution is: i i D = (t − t ) (11) ij i j j=1 The distance from alternative d to the negative ideal solution, D –i is: i i - 2 D = (t − t ) (12) ij i j j=1 The comprehensive evaluation index of each program to be evaluated is calculated, and Equation 13 is used to calculate the proximity between each evaluation object and the optimal program f . f (13) i= − + D +D i i The larger the f value, the farther scheme d was from the negative ideal solution and the closer it i i was to the positive ideal solution, and block d had more advantages. Based on the above calculation process, the potential evaluation advantages of CBM in the study area can be obtained, and 16 Energy Exploration & Exploitation 0(0) Table 3. Weight of each selected indices of No. 8 and No. 15 coal seam in the study area. Coal Buried gas Resource structure roof mudstone fault fractal structural Indices thickness depth content abundance coal thickness ratio permeability dimension curvature No.8 0.06 0.05 0.09 0.11 0.21 0.03 - 0.22 0.23 No.15 0.07 0.05 0.09 0.12 0.15 0.07 0.02 0.26 0.17 Chen et al. 17 Figure 13. Distribution map of CBM potential based on TOPSIS coefﬁcient of variation method (a: No.8, b: No.15). Figure 13 shows the potential evaluation advantages of CBM development in the Baoan blocks. We can see from Figure 13 that the dominant areas of coal seam development in the No. 8 and No. 15 coal seams were mainly distributed in the middle of the research area, which was considerably dif- ferent from the evaluation results of the multilevel fuzzy mathematics method. Principal component analysis Principal component analysis is a method of dimension reduction. The dimensionality reduction of a set of variables with a large correlation and mutual inﬂuence was transformed into a new set of vari- ables with a weak correlation. During data processing, the total variance of the variable remained unchanged, and the variance of the new variable was controlled to ensure the new variable contained as much original information as possible. The greater the variance of the new variable, the less the original information is lost. The new variable with the greatest variance was deﬁned as the ﬁrst and second principal components (Hower et al., 2021; Ye et al., 2022). Due to the large difference in the order of magnitude of each indicator, data should be standar- dized ﬁrst in data processing, and the original data should be converted into dimensionless data to make it more adequately comparable. A standardized decision data matrix, Z, was constructed. Equation 4 was used for data standardization, and Equations 14 and 15 were used to calculate the correlation coefﬁcient matrix, R. R = (r ) (14) k n×m 1 n r = Z Z (k = 1, 2, .. . , m) (15) k ij ik i=1 n − 1 The eigenvalues and variances of the decision matrix were calculated (Table 4), and the principal components with eigenvalues greater than one were retained in order of magnitude. The principal component scores of CBM potential evaluation in the study area were calculated by combining the principal component score coefﬁcients (Table 5). Using principal component scores, the characteristic roots of the principal components were taken as the weights of each principal component to conduct a comprehensive evaluation of CBM development potential in the study area, as shown in Figure 14. 18 Energy Exploration & Exploitation 0(0) Table 4. Characteristic values and variance of evaluation indices. No.8 No.15 Components The eigenvalue Percentage of variance% The cumulative % Component The eigenvalue Percentage of variance% The cumulative % 1 2.37 29.66 29.66 1 2.64 29.36 29.36 2 1.75 21.87 51.53 2 1.64 18.24 47.60 3 1.19 14.91 66.44 3 1.31 14.57 62.17 4 0.97 12.13 78.56 4 1.10 12.20 74.37 5 0.72 9.01 87.57 5 0.70 7.75 82.12 6 0.67 8.42 95.99 6 0.61 6.73 88.85 7 0.32 3.97 99.96 7 0.59 6.59 95.44 8 0.00 0.04 100.00 8 0.40 4.47 99.92 - - - - 9 0.01 0.08 100.00 Chen et al. 19 Table 5. Principal component score coefﬁcient table No.8 No.15 Indices Components 1 Components 2 Components 3 Components 1 Components 2 Components 3 Components 4 Resource abundance 0.52 0.40 −0.12 0.59 0.07 −0.07 0.07 Tectonic coal 0.46 −0.36 −0.07 −0.41 0.30 −0.14 −0.12 Coal thickness 0.46 −0.14 0.28 0.39 −0.13 −0.43 0.31 Ratio of roof mudstone thickness 0.31 0.09 0.35 0.03 0.11 0.55 0.54 gas content 0.29 0.57 −0.33 0.52 0.19 0.19 −0.16 Fault fractal dimension −0.25 0.49 −0.08 −0.10 −0.45 −0.37 0.43 Buried depth 0.23 −0.29 −0.58 −0.18 0.35 0.02 0.61 Tectonic curvature 0.12 0.15 0.57 0.03 −0.38 0.55 0.01 permeability - - - 0.11 0.61 −0.11 0.04 20 Energy Exploration & Exploitation 0(0) Figure 14. Distribution of evaluation of CBM development potential by principal component analysis (a: No.8, b: No.15). As seen in Figure 14, the favorable areas for CBM development in the Baoan blocks were mainly concentrated in the surrounding areas of the study area, and the favorable areas for CBM development in the No. 8 and No. 15 coal seams showed similar distribution ranges. The difference was that the favorable areas for CBM development in the No. 8 coal seam were mainly distributed in the northeast and northwest of the study area. Combined with the above research results, we found that the favorable area of CBM development was consistent with the gas content distribution range, and the main controlling factor of CBM development was gas content. The most favorable area for CBM development in the No. 15 coal seam is distributed in the southwest, followed by the northwest According to the contour map above, permeability was the main controlling factor for the CBM development potential in the No. 15 coal seam. Comparison of selection methods According to the above research results, the multilevel fuzzy mathematics method, TOPSIS, and principal component analysis method differed signiﬁcantly in the evaluation results of CBM devel- opment potential in Baoan blocks, especially the evaluation results of TOPSIS. The results of the principal component analysis and fuzzy mathematics method are relatively similar, and it is believed that the development potential of CBM around the study area is the greatest However, the evaluation results of TOPSIS indicate that the central part of the study area is the most favorable area for CBM development. Combined with previous research results (Hu et al., 2015), the evalu- ation results of multilevel fuzzy mathematics and principal component analysis were better, which accords with the actual development situation of the research area. The evaluation results of the three evaluation methods adopted in this study are considerably different, especially those based on TOPSIS. The three methods were based on the geological char- acteristics of the study area. However, TOPSIS is more dependent on the overall law and the char- acteristics of the data samples. The more numerous the data, the more evident the law, and the more consistent the real situation. Qiu et al. (2020) predicted the groundwater in the Xinwen Coalﬁeld based on over 90 data points. Li et al. (2020) evaluated the development potential of shale gas blocks in China based on 55 sary indicators using the TOPSIS. These studies used a large number of data samples to reﬂect the real situation, such that results were more reasonable and Chen et al. 21 in line with the actual development situation. In addition, compared with the coefﬁcient of variation method adopted in this study to determine the weight of indicators, other studies chose to use grey relative analysis (GRA) or the entropy weight method to determine the weight of indicators. Unnecessary information can be removed during data preprocessing (Qiu et al., 2020; Sun and Li, 2021). Multilevel fuzzy mathematics and principal component analysis have been widely applied in the Qinshui Basin and Ordos Basin of China (Wang et al., 2018; Zhang et al., 2016). Compared with TOPSIS, these two methods are more suitable for the evaluation of favorable CBM development areas under complex geological conditions. In addition, when using fuzzy mathematics to evaluate the potential of CBM development, the importance of each index at the same level is subjective, but the evaluation results are basically con- sistent with the evaluation results of principal component analysis, indicating that the multilevel fuzzy mathematics method is within an acceptable range, with the importance of each index set and the error of the result of subjective factors of this method being reasonable. In the evaluation and analysis of the CBM potential in the study area, due to the different devel- opment levels of different coal seams in the study area, the permeability index of the CBM potential evaluation of the No. 8 coal seam was lower than that of No. 15. Comprehensive analysis of the above evaluation results shows that the No. 8 coal seam principal component analysis and multi- level fuzzy favorable area evaluation results are consistent with the No. 15 coal seam evaluation results, indicating that the loss of some parameters has little effect on the overall development potential evaluation results. However, more detailed data are required to select suitable locations for further CBM development. Conclusions In this study, the development potential of CBM in Baoan blocks was evaluated using multilevel fuzzy mathematics, TOPSIS based on coefﬁcient of variation, and principal component analysis. The favorable area of CBM development in Baoan blocks was predicted by comprehensive analysis of the three evaluation results. The conclusions of this study are as follows: 1. The coal of No. 8 is relatively thin, with an average thickness of 2.15 m, and its development is unstable and the average burial depth was 586.75 m. The coal of No.15 was relatively thick, with an average thickness of 6.5 m. The burial depth was 674.86 m. The roof and ﬂoor of the No. 8 coal are mainly mudstone with a thickness of 0.3–9.6 m, and the sealing effect is adequate. 2. The fault dip angle in the study area is relatively large, with an average dip angle of 55°. There are few faults in No. 8 and a large number of small faults in No. 15. The tectonic development in the eastern part of the study area is complex, whereas that in the western part is relatively simple. The average thickness of No. 8 is 0.59 m, and the average thickness of No. 15 is 1.29 m. The gas content of the No. 8 coal seam is high in the northeast and south and low in the northwest and southwest The gas content of sample No. 15 was high in the south and low in the middle. The distribution of gas content in the study area was mainly controlled by the structure. 3. The northwestern and northeastern parts of the No. 8 coal seam are suitable for CBM develop- ment. The northwest and southwest parts of the No. 15 coal seam are favorable blocks for CBM. Considering the evaluation results comprehensively, we determined that the amount of data in the study area affects the evaluation results of the favorable area, especially the TOPSIS, which is not suitable for evaluating the CBM of the Xinjing Baoan blocks. In the evaluation process of CBM favorable area selection, the impact of missing geological evaluation indicators on the results can be ignored. 22 Energy Exploration & Exploitation 0(0) Acknowledgements This work was supported by the National Natural Science Foundation of China (41872170), the National Major Science and Technology Projects of China (2016ZX05044)，The Fundamental Research Funds for the Central Universities (2020CXNL11), The Priority Academic Program Development of Jiangsu Higher Education Institutions; Coal seam anti-reﬂection and efﬁcient gas extraction technology in structural coal area（20191102002). Declaration of Conﬂicting Interests The author(s) declared no potential conﬂicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following ﬁnancial support for the research, authorship, and/or publica- tion of this article: This work was supported by THE Fundamental Research Funds for the Central Universities, The Priority Academic Program Development of Jiangsu Higher Education Institutions, National Natural Science Foundation of China, (grant number 2020CXNL11, 41872170, 4213080); Coal seam anti-reﬂection and efﬁcient gas extraction technology in structural coal area(20191102002). ORCID iDs Xiuming Jiang https://orcid.org/0000-0003-1006-5070 He zhou https://orcid.org/0000-0002-1001-8912 References Banerjee A, Sequeira N and Bhattacharya A (2021) Tectonics of the greater India proterozoic fold belt, with emphasis on the nature of curvature of the belt in west-central India. Earth-Science Reviews 221: 103758. Bo H, Yong Q, Zhang W, et al. (2020) Prediction of high-quality coalbed methane reservoirs based on the fuzzy gray model: an investigation into coal seam No. 8 in Gujiao, Xishan, North China. Energy Exploration & Exploitation 38(4): 1054–1081. Cai Y, Liu D, Zhang K, et al. (2014) Preliminary evaluation of gas content of the No. 2 coal seam in the Yanchuannan area, southeast Ordos basin, China. Journal of Petroleum Science & Engineering 122: 675–689. Cao T, Yang Z, Qin Y, et al. (2021) Characteristics of modern geostress and removability of No. 15 coal res- ervoir, Yangquan mining area, China. Natural Resources Research 30(3): 2211–2223. Fu H, Tang D, Xu H, et al. (2016) Geological characteristics and CBM exploration potential evaluation: A case study in the middle of the southern Junggar Basin, NW China. Journal of Natural Gas Science and Engineering 30: 557–570. Ghosh S, Chatterjee R and Shanker P (2016) Estimation of ash, moisture content and detection of coal litho- facies from well logs using regression and artiﬁcial neural network modelling. Fuel 177: 279–287. He J, Okere CJ, Su G, et al. (2021) Formation damage mitigation mechanism for coalbed methane wells via refracturing with fuzzy-ball ﬂuid as temporary blocking agents. Journal of Natural Gas Science and Engineering 90: 103956. Heo E, Kim J and Boo K (2010) Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews 14(8): 2214–2220. Hower JC, Eble CF, Wang N, et al. (2021) Distribution of rare earth elements and other critical elements in beneﬁciated pennsylvania anthracites. Fuel 304: 121400. Hu G, Xu J, Zhang F, et al. (2015) Coal and coalbed methane co-extraction technology based on the ground movement in the Yangquan coalﬁeld. China. Energies 8(7): 6881–6897. Chen et al. 23 Li F, Jiang B, Cheng G, et al. (2019a) Multifractal analysis and evolution rules of micro-fractures in brittle tectonically deformed coals of Yangquan mining area. Arabian Journal of Geosciences 12(19). Li F, Jiang B, Cheng G, et al. (2019b) Structural and evolutionary characteristics of pores-microfractures and their inﬂuence on coalbed methane exploitation in high-rank brittle tectonically deformed coals of the Yangquan mining area, northeastern Qinshui basin, China. Journal of Petroleum Science And Engineering 174: 1290–1302. Li F, Jiang B, Wei C, et al. (2021a) Simulation of coalbed methane generation, dissipation, and preservation and analysis of the geological inﬂuencing factors: A case study of the Xinjing coal mine, northeastern Qinshui basin, China. Arabian Journal of Geosciences 14(5). Li S, Tang D, Pan Z, et al. (2015) Evaluation of coalbed methane potential of different reservoirs in western Guizhou and eastern Yunnan, China. Fuel 139: 257–267. Li T, Zhang F and Meng L (2021b) Occurrence, distribution, and prediction of iron and manganese in ground- water of opencast mines: An example from Inner Mongolia, China. Environmental Monitoring and Assessment 193(8). Li Y, Liu DM, Yao YB, et al. (2013) Evaluation of favorable area in the weibei coalbed methane ﬁeld: A study by using grey correlation analysis. Applied Mechanics and Materials 295–298: 3342–3345. Li Z, Zhang J, Tang X, et al. (2020) Approaches for the evaluation of favorable shale gas areas and applications: implications for China’s exploration strategy. Energy Science & Engineering 8(2): 270– Liu D, Yao Y, Tang D, et al. (2009) Coal reservoir characteristics and coalbed methane resource assessment in Huainan and Huaibei coalﬁelds, Southern North China. International Journal of Coal Geology 79(3): 97–112. Liu H, Su G, Okere CJ, et al. (2022) Working ﬂuid-induced formation damage evaluation for commingled pro- duction of multi-layer natural gas reservoirs with ﬂow rate method. Energy 239: 122107. Liu J, Jiang B, Li M, et al. (2015) Structural control on pore-fracture characteristics of coals from Xinjing coal mine, northeastern Qinshui basin, China. Arabian Journal of Geosciences 8(7): 4421–4431. Liu X, Wu C, Wei G, et al. (2020) Adsorption deformation characteristics of coal and coupling with perme- ability during gas injection. Journal of Petroleum Science and Engineering 195: 107875. Lü Y, Tang D, Xu H, et al. (2011) Productivity matching and quantitative prediction of coalbed methane wells based on BP neural network. Science China Technological Sciences 54(5): 1281–1286. Lv Y, Tang D, Xu H, et al. (2012) Production characteristics and the key factors in high-rank coalbed methane ﬁelds: A case study on the Fanzhuang Block, Southern Qinshui Basin, China. International Journal of Coal Geology 96–97: 93–108. Ni X, Zhao Z, Wang Y, et al. (2020) Optimisation and application of well types for ground development of coalbed methane from no. 3 coal seam in shizhuang south block in Qinshui basin, Shanxi province, China. Journal of Petroleum Science and Engineering 193: 107453. Okere CJ, Su G, Zheng L, et al. (2020) Experimental, algorithmic, and theoretical analyses for selecting an optimal laboratory method to evaluate working ﬂuid damage in coal bed methane reservoirs. Fuel 282: Qi Y, Ju Y, Yu K, et al. (2022) The effect of grain size, porosity and mineralogy on the compressive strength of tight sandstones: A case study from the eastern Ordos basin, China. Journal of Petroleum Science and Engineering 208: 109461. Qiu M, Huang F, Wang Y, et al. (2020) Prediction model of water yield property based on GRA, FAHP and TOPSIS methods for ordovician top aquifer in the Xinwen coalﬁeld of China. Environmental Earth Sciences 79(10). Qu X, Shi L, Qu X, et al. (2021) Multi-model fusion for assessing risk of inrush of limestone karst water through the mine ﬂoor. Energy Reports 7: 1473–1487. Shen Y, Qin Y, Guo Y, et al. (2016) Characteristics and sedimentary control of a coalbed methane-bearing system in lopingian (late permian) coal-bearing strata of western Guizhou province. Journal of Natural Gas Science and Engineering 33: 8–17. 24 Energy Exploration & Exploitation 0(0) Sivaraja CM, Sakthivel G and Jegadeeshwaran R (2020) Selection of optimum bio-diesel fuel blend using fuzzy TOPSIS and fuzzy VIKOR approaches. International Journal of Oil Gas and Coal Technology 23(2): 261–291. Sun D, Chen Y, Li X, et al. (2021) Control and application of terrain curvature on dynamic disasters: A case study of coal and gas outbursts in the Pingdingshan mining area. China. Advances in Civil Engineering 2021: 1–6. Sun Z and Li J (2021) Study on open-pit mine slope stability based on improved TOPSIS method. IOP con- ference series. Earth and Environmental Science 781(2): 22045. Tao S, Tang D, Xu H, et al. (2014) Factors controlling high-yield coalbed methane vertical wells in the Fanzhuang block, Southern Qinshui basin. International Journal of Coal Geology 134–135: 38–45. Tao S, Wang Y, Tang D, et al. (2012) Dynamic variation effects of coal permeability during the coalbed methane development process in the Qinshui basin, China. International Journal of Coal Geology 93: 16–22. Tao X, Okere CJ, Su G, et al. (2022) Experimental and theoretical evaluation of interlayer interference in multi- layer commingled gas production of tight gas reservoirs. Journal of Petroleum Science and Engineering 208: 109731. Wang G, Qin Y, Xie Y, et al. (2018) Coalbed methane system potential evaluation and favourable area pre- diction of Gujiao blocks, Xishan coalﬁeld, based on multi-level fuzzy mathematical analysis. Journal of Petroleum Science and Engineering 160: 136–151. Wang H, Zhang X, Wu C, et al. (2022) Drainage type classiﬁcation and key controlling factors of product- ivity for CBM wells in the Zheng Zhuang area, southern Qinshui basin, north China. ACS Omega 7(2): 1883–1892. Wu Y, Tao S, Tian W, et al. (2021) Advantageous seepage channel in coal seam and its effects on the distri- bution of high-yield areas in the fanzhuang CBM block, southern Qinshui basin, China. Natural Resources Research 30(3): 2361–2376. Yan S and Li L (2021) An empirical analysis of China’s energy security based on TOPSIS model. IOP con- ference series. Earth and Environmental Science 826(1). Yang Y, Bie A, Bie H, et al. (2020a) Geological Inﬂuential Factors on the SIS Well Productivity in Teviot-Brook Block, Moranbah Gas Field. Singapore: Springer Singapore, pp.794–808. Yang Y, Zheng X and Sun Z (2020b) Coal resource security assessment in China: A study using entropy-weight-based TOPSIS and BP neural network. Sustainability 12(6): 2294. Ye Y, Tang S, Xi Z, et al. (2022) A new method to predict brittleness index for shale gas reservoirs: insights from well logging data. Journal of Petroleum Science and Engineering 208: 109431. Zhang Y, He Z, Tian H, et al. (2021a) Hydrochemistry appraisal, quality assessment and health risk evaluation of shallow groundwater in the Mianyang area of Sichuan basin, southwestern China. Environmental Earth Sciences 80(17). Zhang K, Meng Z and Li G (2021b) Analysis of logging identiﬁcation and methane diffusion properties of coals with various deformation structures. Energy & Fuels 35(15): 12091–12103. Zhang Z, Qin Y, Bai J, et al. (2016) Evaluation of favorable regions for multi-seam coalbed methane joint exploitation based on a fuzzy model: A case study in southern Qinshui basin, China. Energy Exploration & Exploitation 34(3): 400–417. Zhang Z, Qin Y, Wang G, et al. (2021c) Evaluation of coal body structures and their distributions by geophys- ical logging methods: case study in the Laochang block, eastern Yunnan, China. Natural Resources Research 30(3): 2225–2239. Zhang Z, Qin Y, Yang Z, et al. (2019) Fluid energy characteristics and development potential of coalbed methane reservoirs with different synclines in Guizhou, China. Journal of Natural Gas Science and Engineering 71: 102981. Zhao B, Zhao Y and Wang J (2021a) New stability forecasting model for goaf slope based on the AHP– TOPSIS theory. Arabian Journal of Geosciences 14(1). Zhao L, Cao Y, Shi X, et al. (2021b) A ﬁeld application of methane drainage by surface vertical well fracturing in complicated geologic structure area: a case study. Energy sources. Part A, Recovery, utilization, and environmental effects, 1–18. Chen et al. 25 Zhao Z (2021) Evaluation of coalbed methane resources in the Huainan-Huaibei coalﬁeld of Anhui province, China, using an analytic hierarchy process and grey cluster method. Arabian Journal of Geosciences 14(17). Zhou S, Tang J and Cai Y (2021) Reservoir characterization of the upper cretaceous shale for the Changling Sag, southern Songliao basin, NE China. Arabian Journal of Geosciences 14(8).

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Energy Exploration & Exploitation – SAGE

Published: May 16, 2022

Keywords: Coalbed methane; potential evaluation; multi-level fuzzy mathematics; TOPSIS; principal component analysis

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Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM:

Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM:

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Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM:

Evaluation of coalbed methane resources in Xinjing Baoan block based on PCA, TOPSIS, & MLFM:

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