Coal Geology & Exploration
Abstract
Objective Cleats in deep coal seams are characterized by strong heterogeneity, and their topological configurations, as well as their topological relationships with matrix blocks, remain poorly understood. On the other hand, the assumptions regarding the size and geometry of matrix blocks adopted in representative reservoir structure models and numerical simulations do not fully reflect actual reservoir conditions. Methods Deep coal cores collected from the northeastern Ordos Basin were investigated in this study. Using CT scanning and topological theory, a multi-dimensional topological parameter system was established to characterize cleats in deep coal seams. Furthermore, four topological configurations of cleats in deep coal reservoirs were identified, and corresponding discriminant models were developed. Results Cleats in the deep coal cores exhibited the synergistic development of I-, Y-, and X-type nodes, accounting for average proportions of 59.7%, 33.8%, and 6.5%, respectively, in the same sample. Despite representing the lowest proportion, X nodes played a key role in driving the evolution of cleats from isolated structures to complex, interconnected networks, thereby enhancing their global connectivity. Based on the clustering of multidimensional topological parameters and convex tessellation theory, four topological configurations of cleats in deep coal seams were identified, primarily controlled by the combined effects of cleat development and network connectivity. Among these, the dense reticulation type (Type I) accounted for 4.18%. This topological configuration was governed by Platonic attractors and exhibited extremely developed fracture networks, with the matrix partitioned into highly interconnected, tiny rectangular blocks. The complex branch type (Type Ⅱ) accounted for 23.34%. It was characterized by dense branches accompanied by a large number of ineffective blind ends, with matrix blocks transitioning toward irregular hexagons. The branch transition type (Type Ⅲ) accounted for 55.06%. This topological configuration exhibited fracture networks with a moderate scale but efficient connectivity and approached Voronoi attractors, with matrix blocks approximating regular hexagons. The sparse isolation type (Type Ⅳ) accounted for 17.42% and was characterized by a limited number of effectively connected cleats. Accordingly, the matrix remained largely intact, with matrix blocks represented by large circles. The geometrical quantitative characterization of the matrix in deep coal seams indicates that with an increase in the cleat development, the matrix blocks evolved from Type Ⅳ [Smed = 2320.09 mm2; median diffusion distance Lmed = 27.18 mm] toward types Ⅲ (Smed = 210.76 mm2; Lmed = 7.80‒9.01 mm) and Ⅱ (Smed = 16.87 mm2; Lmed = 2.21‒2.55 mm), ultimately broking into tiny rectangles (Type I; Smed = 4.75 mm2; Lmed = 1.09‒1.54 mm). This transition in topological configuration resulted in dramatic changes in gas desorption and diffusion radii of the matrix. Conclusions The results of this study refine the understanding of the topological configurations of cleats in deep coal reservoirs, along with their control over the geometric characteristics and gas diffusion scales of the matrix. The discriminant diagrams developed in this study provide a practical tool for coal reservoir classification and the selection of optimal intervals. Furthermore, the qualitified sizes and gas diffusion radii of matrix blocks provide a basis for numerical simulation and reservoir stimulation design.
Keywords
deep coal seam, matrix-cleat, topological structure, convex tessellation theory, gas diffusion, northeastern Ordos Basin
DOI
10.12363/issn.1001-1986.26.06.0388
Recommended Citation
SHEN Jian, LI Wei, TIAN Yongjing,
et al.
(2026)
"Spatial topological configurations of cleats and geometrical quantitative characterization of matrix blocks in deep coal seams,"
Coal Geology & Exploration: Vol. 54:
Iss.
8, Article 8.
DOI: 10.12363/issn.1001-1986.26.06.0388
Available at:
https://cge.researchcommons.org/journal/vol54/iss8/8
Reference
[1] 傅雪海,秦勇,韦重韬. 煤层气地质学[M]. 徐州:中国矿业大学出版社,2007.
[2] LAUBACH S E,MARRETT R A,OLSON J E,et al. Characteristics and origins of coal cleat:A review[J]. International Journal of Coal Geology,1998,35(1/2/3/4):175−207.
[3] 贾建称,张泓,贾茜,等. 煤储层割理系统研究:现状与展望[J]. 天然气地球科学,2015,26(9):1621−1628 JIA Jiancheng,ZHANG Hong,JIA Qian,et al. Status and prospect:Study on the cleat system in coal reservoir[J]. Natural Gas Geoscience,2015,26(9):1621−1628
[4] ABRAR S W,MENDHE V A,KAMBLE A D,et al. Cleat and fracture systems in coal with implications for strength,hydrofracking,and gas–water flow mechanisms:A critical review[J]. ACS Omega,2025,10(28):29909−29928.
[5] 赵海燕,宫伟力. 基于图像分割的煤岩割理CT图像各向异性特征[J]. 煤田地质与勘探,2009,37(6):14−18 ZHAO Haiyan,GONG Weili. Characterization on anisotropic fractures of coal and rocks by computed X–ray tomography based on image segmentation[J]. Coal Geology & Exploration,2009,37(6):14−18
[6] 屈晶,申建,韩磊,等. 基于CT图像的高阶煤不同宏观煤岩组分裂隙差异发育规律[J]. 天然气工业,2022,42(6):76−86 QU Jing,SHEN Jian,HAN Lei,et al. Characteristics of fractures in different macro–coal components in high–rank coal based on CT images[J]. Natural Gas Industry,2022,42(6):76−86
[7] JING Yu,ARMSTRONG R T,RAMANDI H L,et al. Topological characterization of fractured coal[J]. Journal of Geophysical Research:Solid Earth,2017,122(12):9849−9861.
[8] 石钰,马玉华,李树刚,等. 基于CT扫描技术的煤样三维重构及气体表观渗透率研究[J]. 矿业安全与环保,2024,51(1):27−35 SHI Yu,MA Yuhua,LI Shugang,et al. Three–dimensional reconstruction of coal based on CT scan technology and the study on gas apparent permeability[J]. Mining Safety & Environmental Protection,2024,51(1):27−35
[9] BIAN Liheng,HE Yanxiang,SHI Rui,et al. Fracture system characteristics and their control on permeability anisotropy in bright and dull coal[J]. Processes,2025,13(5):1509.
[10] 王鹏翔,张洲,余婉莹,等. 深/浅部煤储层孔裂隙结构及三维空间分布差异特征:以准噶尔盆地为例[J]. 油气藏评价与开发,2025,15(2):227−236 WANG Pengxiang,ZHANG Zhou,YU Wanying,et al. Characteristics of pore–fracture structure and three–dimensional spatial distribution differences in deep and shallow coal reservoirs:A case study of Junggar Basin[J]. Petroleum Reservoir Evaluation and Development,2025,15(2):227−236
[11] ZHENG Qiangqiang,LI Pingfeng,XU Ying,et al. Crack propagation and CT imaging of internal cracks in rocks damaged by pre–compression under explosive loading[J]. Ain Shams Engineering Journal,2025,16(3):103302.
[12] 印兴耀,李坤,宗兆云,等. 五维地震数据解释与储层预测进展[J]. 中国科学:地球科学,2026,56(2):411−431 YIN Xingyao,LI Kun,ZONG Zhaoyun,et al. Advances in five–dimensional seismic data interpretation and reservoir prediction[J]. Science China:Earth Sciences,2026,56(2):411−431
[13] 张和伟,申建,李可心,等. 烟煤中CH4–H2O动态产出的温压控制机制[J]. 煤田地质与勘探,2026,54(3):69−80 ZHANG Hewei,SHEN Jian,LI Kexin,et al. Mechanisms behind the controlling effects of temperature–pressure conditions on the dynamic CH4–H2O production from bituminous coal[J]. Coal Geology & Exploration,2026,54(3):69−80
[14] SANDERSON D J,NIXON C W. The use of topology in fracture network characterization[J]. Journal of Structural Geology,2015,72:55−66.
[15] SANDERSON D J,NIXON C W. Topology,connectivity and percolation in fracture networks[J]. Journal of Structural Geology,2018,115:167−177.
[16] LAHIRI S. Estimating effective permeability using connectivity and branch length distribution of fracture network[J]. Journal of Structural Geology,2021,146:104314.
[17] NIXON C W,NÆRLAND K,ROTEVATN A,et al. Connectivity and network development of carbonate–hosted fault damage zones from western Malta[J]. Journal of Structural Geology,2020,141:104212.
[18] WANG Mingyang,WANG Congcong,WANG Enzhi,et al. Graph theoretical analysis of limestone fracture network damage patterns based on uniaxial compression test[J]. Journal of Rock Mechanics and Geotechnical Engineering,2024,16(9):3485−3510.
[19] 罗亚飞,朱永建,黄飞,等. 基于拓扑图论的煤岩裂隙网络分形渗透率模型[J]. 煤炭学报,2024,49(8):3561−3570 LUO Yafei,ZHU Yongjian,HUANG Fei,et al. Fractal permeability model of fracture network based on topological graph theory[J]. Journal of China Coal Society,2024,49(8):3561−3570
[20] LI Wei,SHEN Jian,MA Zhuang,et al. The migration and deposition patterns of coal fines and their initiation mechanisms during the gas–water two–phase flow stage in coalbed methane wells[J]. International Journal of Coal Science & Technology,2025,12:83.
[21] LIM K T,AZIZ K. Matrix–fracture transfer shape factors for dual–porosity simulators[J]. Journal of Petroleum Science and Engineering,1995,13(3/4):169−178.
[22] WARREN J E,ROOT P J. The behavior of naturally fractured reservoirs[J]. Society of Petroleum Engineers Journal,1963,3(3):245−255.
[23] 王刚,肖智勇,王长盛,等. 基于非平衡状态的煤层中气体运移规律研究[J]. 岩土工程学报,2022,44(8):1512−1520 WANG Gang,XIAO Zhiyong,WANG Changsheng,et al. Gas transport in coal seams based on non–equilibrium state[J]. Chinese Journal of Geotechnical Engineering,2022,44(8):1512−1520
[24] ROSTAMI P,SHARIFI M,DEJAM M. Shape factor for regular and irregular matrix blocks in fractured porous media[J]. Petroleum Science,2020,17(1):136−152.
[25] ZHANG Kaizhong,WANG Xiaoke,WANG Liang,et al. Coal microstructure connectivity and pore–scale flow simulation integrated with reconstruction and topologization:Implications on control mechanism of cross–scale mass transfer[J]. Gas Science and Engineering,2026,149:205882.
[26] JING Yu,ARMSTRONG R T,RAMANDI H L,et al. Coal cleat reconstruction using micro–computed tomography imaging[J]. Fuel,2016,181:286−299.
[27] BUSSE J,DE DREUZY J R,GALINDO TORRES S,et al. Image processing based characterisation of coal cleat networks[J]. International Journal of Coal Geology,2017,169:1−21.
[28] BALBERG I,ANDERSON C H,ALEXANDER S,et al. Excluded volume and its relation to the onset of percolation[J]. Physical Review B,1984,30(7):3933−3943.
[29] SÆVIK P N,NIXON C W. Inclusion of topological measurements into analytic estimates of effective permeability in fractured media[J]. Water Resources Research,2017,53(11):9424−9443.
[30] DERSHOWITZ W S,EINSTEIN H H. Characterizing rock joint geometry with joint system models[J]. Rock Mechanics and Rock Engineering,1988,21(1):21−51.
[31] TOKHMECHI B,MEMARIAN H,MOSHIRI B,et al. Investigating the validity of conventional joint set clustering methods[J]. Engineering Geology,2011,118(3/4):75−81.
[32] IKOTUN A M,EZUGWU A E,ABUALIGAH L,et al. K–means clustering algorithms:A comprehensive review,variants analysis,and advances in the era of big data[J]. Information Sciences,2023,622:178−210.
[33] 邹冠贵,任珂,吉寅,等. 基于主成分分析和最近邻算法的断层识别研究[J]. 煤田地质与勘探,2021,49(4):15−23 ZOU Guangui,REN Ke,JI Yin,et al. Fault recognition based on principal component analysis and k–nearest neighbor algorithm[J]. Coal Geology & Exploration,2021,49(4):15−23
[34] DOMOKOS G,JEROLMACK D J,KUN F,et al. Plato’s cube and the natural geometry of fragmentation[J]. Proceedings of the National Academy of Sciences of the United States of America,2020,117(31):18178−18185.
[35] DOMOKOS G,LÁNGI Z. On some average properties of convex mosaics[J]. Experimental Mathematics,2022,31(3):783−793.
[36] SILVER S,REGŐS K,JEROLMACK D J,et al. Decoding planetary surfaces by counting cracks[J]. Proceedings of the National Academy of Sciences of the United States of America,2025,122(10):e2411738122.
[37] YANG Xin,GUO Tiankui,YU Mengqi,et al. Optimization of engineering parameters of deflagration fracturing in shale reservoirs based on hybrid proxy model[J]. Geoenergy Science and Engineering,2023,231:212318.
Included in
Earth Sciences Commons, Mining Engineering Commons, Oil, Gas, and Energy Commons, Sustainability Commons