Investigating the mechanisms controlling gas flow in coal reservoirs based on multi-scale digital rocks

Authors

• HAN Lei, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China; Science and Technology R & D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, Jiaozuo 454003, ChinaFollow
• HUANG Dongxiang, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China; Science and Technology R & D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, Jiaozuo 454003, China
• ZHANG Xiaodong, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China; Science and Technology R & D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, Jiaozuo 454003, China
• SHEN Jian, Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, China University of Mining and Technology, Xuzhou 221008, China; School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221008, China
• ZHANG Shuo, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China; Science and Technology R & D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, Jiaozuo 454003, China
• NI Xiaoming, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China; Science and Technology R & D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, Jiaozuo 454003, China
• LIU Xiao, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China; Science and Technology R & D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, Jiaozuo 454003, China

Abstract

Background Deep coal reservoirs exhibit highly heterogeneous pore-fracture structures. However, limited by a single imaging resolution, conventional digital rock techniques struggle to characterize complex pore-fracture networks across multiple scales. This poses significant scientific challenges for the fine-scale characterization of deep coal reservoirs and the investigation of their seepage mechanisms. Methods This study investigated deep coalbed methane (CBM) reservoirs in the north-central Qinshui Basin. The pore-fracture structures of coal samples were characterized by integrating X-ray computed tomography (CT), mercury injection capillary pressure (MICP), and scanning electron microscopy (SEM). Digital rock models incorporating multi-scale pores and fractures were then constructed by integrating physical experiments with numerical reconstruction. Using the lattice Boltzmann method (LBM), single-phase seepage in deep coal reservoirs was simulated. Based on the simulation results, key factors influencing the seepage capacity of the reservoirs were analyzed. Results The method combining physical experiments with numerical reconstruction constrains fractures using CT, corrects pores through MICP experiments, and characterizes structural anisotropy with SEM. Using this method, digital rock models incorporating multi-scale pores and fractures were constructed, enabling the integrated characterization of fractures and multi-scale pores. Compared to experimentally derived reference porosities, the reconstructed models showed total porosity errors ranging from 0% to 0.08%. Furthermore, the volumetric proportions of pores at varying scales and the trends in pore size distributions in the reconstructed models were generally consistent with the MICP experiment results. These findings demonstrate a high degree of consistency between the models and experimental results in terms of pore volume and pore scale distribution. The seepage simulation results reveal pronounced anisotropy in gas flow in deep coal reservoirs. Preferentially interconnected channels were more likely to develop parallel to bedding planes, with permeability ranging from 0.28×10^–3 μm² to 2.33×10^–3 μm². In contrast, lower permeability was observed perpendicular to bedding planes, ranging from 0.29×10^–3 μm² to 0.82×10^–3 μm². Across all the samples, interconnected porosity was positively correlated with permeability, with a correlation coefficient (r) of 0.192. In contrast, pore-fracture tortuosity and channeling index exhibited relatively strong negative correlations with permeability, with r values of −0.742 and −0.745, respectively. These results reveal the mechanisms underlying the coupled control of multi-scale pore-fracture structures on permeability. Rather than being solely determined by the number of pores, the differences in permeability are governed by a two-stage controlling mechanism. Specifically, pore interconnectivity determines the occurrence of effective flow, whereas the channel architecture governs the flow efficiency. In other words, interconnected porosity governs the formation of effective flow pathways; after interconnection is established, tortuosity and the degree of channeling further regulate the fluid transport efficiency. Conclusion The developed digital rock models incorporating multi-scale pores and fractures preserve actual fracture frameworks using CT, constrain multi-scale pore distributions through MICP, and characterize structural anisotropy with SEM. These models enable the integrated characterization of actual fracture structures and trans-scale pores, thereby overcoming the limitation associated with a single imaging resolution and addressing the insufficient fidelity of numerical reconstruction alone. Furthermore, the developed models provide an approach to revealing the mechanisms controlling gas flow in deep coal reservoirs.

Keywords

multi-scale digital rock, physical experiment–numerical reconstruction method, gas flow, deep coal reservoir, Qinshui Basin

DOI

10.12363/issn.1001-1986.26.04.0246

Recommended Citation

HAN Lei, HUANG Dongxiang, ZHANG Xiaodong, et al. (2026) "Investigating the mechanisms controlling gas flow in coal reservoirs based on multi-scale digital rocks," Coal Geology & Exploration: Vol. 54: Iss. 9, Article 9.
DOI: 10.12363/issn.1001-1986.26.04.0246
Available at: https://cge.researchcommons.org/journal/vol54/iss9/9

Reference

[1] 秦勇. 中国深部煤层气地质研究进展[J]. 石油学报,2023,44(11):1791−1811. QIN Yong. Progress on geological research of deep coalbed methane in China[J]. Acta Petrolei Sinica,2023,44(11):1791−1811.

[2] 鞠玮,肖宇航,田永净,等. 深部煤层气储层地质力学研究与进展[J]. 地球科学,2026,51(7):2528−2554. JU Wei,XIAO Yuhang,TIAN Yongjing,et al. Study and progress of reservoir geomechanics within deep coalbed methane[J]. Earth Science,2026,51(7):2528−2554.

[3] 曹代勇,郭爱军,陈利敏,等. 煤田构造演化新解:从成煤盆地到赋煤构造单元[J]. 煤田地质与勘探,2016,44(1):1−8. CAO Daiyong,GUO Aijun,CHEN Limin,et al. New interpretation of coalfield tectonic evolution: From coal–forming basins to coal–bearing tectonic units[J]. Coal Geology & Exploration,2016,44(1):1−8.

[4] 韩磊,申建,张小东,等. 构造复杂区深部煤储层可压性评价方法及应用:以沁水盆地H区块为例[J/OL]. 煤炭科学技术,2025:1−19 (2026-06-23) [2025-12-01]. https://link.cnki.net/urlid/11.2402.TD.20251201.1459.002. HAN Lei,SHEN Jian,ZHANG Xiaodong,et al. Method and application of fracability of deep coal reservoirs in complex structural areas of Hengling Block,Qinshui Basin[J/OL]. Coal Science and Technology,2025:1−19 (2026-06-23) [2025-12-01]. https://link.cnki.net/urlid/11.2402.TD.20251201.1459.002.

[5] 吴翔,肖占山,张永浩,等. 多尺度数字岩石建模进展与展望[J]. 吉林大学学报(地球科学版),2024,54(5):1736−1751. WU Xiang,XIAO Zhanshan,ZHANG Yonghao,et al. Progress and prospect of multiscale digital rock modeling[J]. Journal of Jilin University (Earth Science Edition),2024,54(5):1736−1751.

[6] LIU Shiqi,SANG Shuxun,WANG G,et al. FIB–SEM and X–ray CT characterization of interconnected pores in high–rank coal formed from regional metamorphism[J]. Journal of Petroleum Science and Engineering,2017,148:21−31.

[7] HAN Lei,SHEN Jian,QU Jing,et al. Characteristics of a multi–scale fracture network and its contributions to flow properties in anthracite[J]. Energy & Fuels,2021,35(14):11319−11332.

[8] HAN Lei,SHEN Jian,WANG Jinyue,et al. Characteristics of pore evolution and its maceral contributions in the Huolinhe lignite during coal pyrolysis[J]. Natural Resources Research,2021,30(3):2195−2210.

[9] JING Yu,RABBANI A,ARMSTRONG R T,et al. A hybrid fracture–micropore network model for multiphysics gas flow in coal[J]. Fuel,2020,281:118687.

[10] WANG Moran,PAN Ning. Numerical analyses of effective dielectric constant of multiphase microporous media[J]. Journal of Applied Physics,2007,101(11):114102.

[11] 吴玉其,林承焰,任丽华,等. 基于多点地质统计学的数字岩心建模[J]. 中国石油大学学报(自然科学版),2018,42(3):12−21. WU Yuqi,LIN Chengyan,REN Lihua,et al. Digital core modeling based on multiple–point statistics[J]. Journal of China University of Petroleum (Edition of Natural Science),2018,42(3):12−21.

[12] 王晨晨,姚军,杨永飞,等. 基于CT扫描法构建数字岩心的分辨率选取研究[J]. 科学技术与工程,2013,13(4):1049−1052. WANG Chenchen,YAO Jun,YANG Yongfei,et al. Study on resolution selection for digital rock construction with CT scanning method[J]. Science Technology and Engineering,2013,13(4):1049−1052.

[13] 孙朗秋,闫博鸿,马英珑,等. 机器学习方法在数字岩心研究中的应用进展[J]. 石油物探,2026,65(2):308−322. SUN Langqiu,YAN Bohong,MA Yinglong,et al. Application progress of machine learning to digital core research[J]. Geophysical Prospecting for Petroleum,2026,65(2):308−322.

[14] 李忠新,曹小朋,吕琦,等. 基于深度学习的纳米级页岩三维数字岩心重构及导电机理研究[J]. 油气地质与采收率,2026,33(2):36−47. LI Zhongxin,CAO Xiaopeng,LYU Qi,et al. Research on 3D digital rock reconstruction and conductive mechanism of nanoscale shale based on deep learning[J]. Petroleum Geology and Recovery Efficiency,2026,33(2):36−47.

[15] LI Qian,LIU Dameng,CAI Yidong,et al. Scale–span pore structure heterogeneity of high volatile bituminous coal and anthracite by FIB–SEM and X–ray μ–CT[J]. Journal of Natural Gas Science and Engineering,2020,81:103443.

[16] TAHMASEBI P,SAHIMI M. Reconstruction of three–dimensional porous media using a single thin section[J]. Physical Review E,2012,85(6):066709.

[17] TINET A J,CORLAY Q,COLLON P,et al. Comparison of various 3D pore space reconstruction methods and implications on transport properties of nanoporous rocks[J]. Advances in Water Resources,2020,141:103615.

[18] MATHEWS J P,CAMPBELL Q P,XU Hao,et al. A review of the application of X–ray computed tomography to the study of coal[J]. Fuel,2017,209:10−24.

[19] LI Zhentao,LIU Dameng,CAI Yidong,et al. Multi–scale quantitative characterization of 3–D pore–fracture networks in bituminous and anthracite coals using FIB–SEM tomography and X–ray μ–CT[J]. Fuel,2017,209:43−53.

[20] WANG Gang,QIN Xiangjie,SHEN Junnan,et al. Quantitative analysis of microscopic structure and gas seepage characteristics of low–rank coal based on CT three–dimensional reconstruction of CT images and fractal theory[J]. Fuel,2019,256:115900.

[21] WANG Kai,GUO Lin,XU Chao,et al. Multiscale characteristics of pore–fracture structures in coal reservoirs and their influence on coalbed methane (CBM) transport: A review[J]. Geoenergy Science and Engineering,2024,242:213181.

[22] LI Yanghui,WANG Yunhui,DING Jiping,et al. Pore–scale permeability characteristics of deep coalbed methane reservoirs[J]. Energy & Fuels,2024,38(17):16149−16158.

[23] 张宏图,韦佳琪,王登科,等. 基于改进QSGS法的瓦斯多机制统一传输模型与流动特性研究[J]. 中国矿业大学学报,2025,54(3):640−652. ZHANG Hongtu,WEI Jiaqi,WANG Dengke,et al. Research on the unified multi–mechanism gas transport model and flow characteristics based on improved QSGS method[J]. Journal of China University of Mining & Technology,2025,54(3):640−652.

[24] LIU Peng,NIE Baisheng,ZHAO Zhengduo,et al. Permeability of micro–scale structure in coal: Insights from μ–CT image and pore network modelling[J]. Gas Science and Engineering,2023,111:204931.

[25] SHEN Jian,QIN Yong,GEOFF W,et al. Relative permeabilities of gas and water for different rank coals[J]. International Journal of Coal Geology,2011,86:266−275.

[26] 阙云,邱婷,蔡沛辰,等. 基于QSGS法3D重构土体渗流场的LBM数值模拟[J]. 湖南大学学报(自然科学版),2023,50(9):119−130. QUE Yun,QIU Ting,CAI Peichen,et al. LBM numerical simulation of seepage field of 3D reconstructed soil based on QSGS method[J]. Journal of Hunan University (Natural Sciences),2023,50(9):119−130.

[27] DE ROSIS A,COREIXAS C. Multiphysics flow simulations using D3Q19 lattice Boltzmann methods based on central moments[J]. Physics of Fluids,2020,32(11):117101.

[28] 赵华伟,宁正福,段太忠,等. 基于微米CT扫描成像实验及格子Boltzmann模拟方法的致密砂岩孔隙结构表征[J]. 东北石油大学学报,2019,43(5):1−10. ZHAO Huawei,NING Zhengfu,DUAN Taizhong,et al. Pore structure characterization of tight sandstones by X–ray computed tomography experiment combined with lattice Boltzmann method[J]. Journal of Northeast Petroleum University,2019,43(5):1−10.

[29] 王振伟,马克,田洪圆,等. 基于RFPA2D–Flow软件对裂隙岩体渗透特性表征单元体的研究[J]. 煤炭学报,2019,44(10):3012−3021. WANG Zhenwei,MA Ke,TIAN Hongyuan,et al. Study on the element volume of permeability characteristics in fractured rock mass based on RFPA2D–Flow[J]. Journal of China Coal Society,2019,44(10):3012−3021.

[30] 石学文,王青,张景缘,等. 川南长宁区块龙潭组煤系储层孔裂隙结构、连通性特征及其地质意义[J]. 地质学报,2025,99(9):3133−3146. SHI Xuewen,WANG Qing,ZHANG Jingyuan,et al. Pore fracture structure,connectivity characteristics and its geological significance of Longtan Formation coal measures reservoir in Changning Block,southern Sichuan[J]. Acta Geologica Sinica,2025,99(9):3133−3146.

[31] HAN Lei,MISCH D,SHEN Jian,et al. Influence of viscous and capillary forces on residual water in anthracite fracture networks[J]. Natural Resources Research,2023,32(2):603−617.

[32] 周立宏,李曙光,王渊,等. 深层煤岩气效益开发关键技术与实践:以鄂尔多斯盆地大宁–吉县区块为例[J]. 石油学报,2025,46(8):1536−1549. ZHOU Lihong,LI Shuguang,WANG Yuan,et al. Key technologies and practices for cost-effective development of deep coal-rock gas: a case study of the Daning-Jixian block in Ordos Basin[J]. Acta Petrolei Sinica,2025,46(8):1536−1549.

[33] 车禹恒. 基于X-ray μCT扫描的煤孔隙瓦斯微观渗流各向异性特征研究[J]. 矿业安全与环保,2021,48(4):12−17. CHE Yuheng. Research on the anisotropic characteristics of gas microscopic seepage in coal pores based on X-ray μCT scanning[J]. Mining Safety & Environmental Protection,2021,48(4):12−17.

[34] 王小良. 深部复杂应力路径下煤力学行为各向异性研究[D]. 北京: 中国矿业大学(北京), 2023. WANG Xiaoliang. Anisotropic study of coal mechanical behavior under deep complex stress paths[D]. Beijing: China University of Mining & Technology (Beijing), 2023.

[35] 王登科,吕瑞环,彭明,等. 含瓦斯煤渗透率各向异性研究[J]. 煤炭学报,2018,43(4):1008−1015. WANG Dengke,LYU Ruihuan,PENG Ming,et al. Experimental study on anisotropic permeability rule of coal bearing methane[J]. Journal of China Coal Society,2018,43(4):1008−1015.

[36] 李雄炎,秦瑞宝,曹景记,等. 复杂储层连通孔隙度评价与渗透率定量计算方法[J]. 石油地球物理勘探,2022,57(2):377−385. LI Xiongyan,QIN Ruibao,CAO Jingji,et al. Method of connected porosity evaluation and quantitative permeability calculation for complex reservoirs[J]. Oil Geophysical Prospecting,2022,57(2):377−385.

[37] 朱瑜明,岳文正,张星,等. 多孔介质孔隙结构迂曲度计算方法研究进展[J]. 地球物理学进展,2023,38(3):1293−1304. ZHU Yuming,YUE Wenzheng,ZHANG Xing,et al. Advances on the calculation methods of tortuosity in porous media[J]. Progress in Geophysics,2023,38(3):1293−1304.

[38] 祝博轩,刘乐乐,刘涛. 基于格子玻尔兹曼方法的含水合物多孔介质水力迂曲度与绝对渗透率演变机理研究[J]. 力学学报,2026,58(7):2028−2042. ZHU Boxuan,LIU Lele,LIU Tao. Numerical study on hydraulic tortuosity and absolute permeability of hydrate–bearing porous media based on lattice Boltzmann method[J]. Chinese Journal of Theoretical and Applied Mechanics,2026,58(7):2028−2042.

[39] SIENA M,ILIEV O,PRILL T,et al. Identification of channeling in pore–scale flows[J]. Geophysical Research Letters,2019,46(6):3270−3278.

[40] TANG Yanbing,LI Min,LIU Xiangjun,et al. Pore–scale heterogeneity,flow channeling and permeability: Network simulation and comparison to experimental data[J]. Physica A: Statistical Mechanics and its Applications,2019,535:122533.