煤体孔隙结构直接影响着煤体孔隙内部瓦斯渗流，探究煤体孔裂隙结构分布对瓦斯微细观渗流的影响规律，对于完善煤体瓦斯渗流理论十分重要。随着煤层瓦斯渗流问题研究的不断深入，仅通过宏观方法已难以完全揭示其内在的规律机理，而微观模拟方法局限于单个孔隙空间内的瓦斯分子间或与煤体间的相互作用，缺少局部与整体性之间的联系，因此，从微细观角度入手更容易发现和研究煤体孔隙结构对瓦斯渗流的影响规律。本文采用随机四参数生长法（QSGS）构建了随机分布的煤体微细观孔隙结构模型，研发了煤体孔裂隙图像识别分析软件，对煤体孔隙结构图形结构特征进行分析，通过格子玻尔兹曼方法（Lattice Boltzmann Method）对瓦斯微细观渗流过程进行数值模拟，讨论了煤体不同孔隙率、相同孔隙率的不同孔隙结构分布（核分布概率、孔隙倾角等）、压力等因素对瓦斯微细观渗流的影响规律，获得如下结论：

（1）对重构的煤体随机孔隙结构图像识别分析结果显示，随着孔隙率的增大，煤体孔隙数量、平均孔径、开放孔隙面积占比均会增加。孔隙率相同时，核分布概率P_c越小的煤体孔隙结构，孔隙数量越少，孔径尺寸越大，分形维数越小，甚至会出现“贯通型”孔隙。孔隙结构模型孔径分布呈现大孔数量较少，中孔数量占比较多的特征，中孔数量占比达75% ~ 90%。

（2）建立了REV尺度上的煤体瓦斯渗流LBM模型，研究煤体孔隙中瓦斯渗流局部特性。研究表明，孔隙率越大的煤体，孔隙内的瓦斯渗流速度越大，煤体渗透率越大，孔隙率大于0.18的孔隙结构渗透率增幅远大于孔隙率小于0.18的孔隙结构，高出10倍以上；相同压力条件下，孔隙率为0.32的孔隙结构的渗透率比孔隙率为0.12的孔隙结构的渗透率高2个数量级以上，其主要原因在于孔隙率越大的煤体，开放型孔隙占比相对更多，平均孔径相对更大，导致煤体连通性更好。

（3）对比相同孔隙率的不同孔隙分布结构中的瓦斯渗流规律，“贯通型”孔隙导致瓦斯渗流速度、渗透率均远大于没有贯通孔的孔隙结构，瓦斯平均渗流速度是没有贯通孔结构的3倍以上，渗透率是没有贯通孔结构的10倍以上。“贯通型”孔隙为煤体中瓦斯渗流提供优势渗流通道，造成煤体中瓦斯渗流速度分布不均匀，在连通性较好的孔隙区域瓦斯渗流速度较大，在封闭孔区域瓦斯渗流速度很小甚至停滞。在工程实际中应更关注“贯通型”孔隙分布特征对瓦斯渗流的影响。

（4）瓦斯渗流特性随孔隙倾角不同而不同，瓦斯平均渗流速度及渗透率均随着孔隙倾角的增大而减小，孔隙倾角为0°的孔隙结构中瓦斯平均渗流速度比倾角为45°和90°的孔隙结构高出3倍以上。因此，在实践中应该更关注与瓦斯压力梯度方向相同的孔裂隙分布对渗流的影响。瓦斯平均渗流速度随着压差的增加而增加，煤体孔隙率越大，压差对平均渗流速度的影响作用越大。

本文通过格子Boltzmann方法探究了煤体随机孔隙结构分布对瓦斯微细观渗流的影响规律，研究结果对瓦斯微细观渗流理论进行补充完善，有助于进一步认识煤层瓦斯渗流规律，为煤层瓦斯的高效开发提供理论基础。

The pore structure of coal directly affect the gas seepage behavior inside the pores. It was very important to explore the influence of pore and fissure structure distribution on gas mesoscopic seepage theory in coal, so the microscopic seepage characteristics of gas need further study. With the deepening of research on coal seam gas seepage, it was difficult to fully reveal its inherent law mechanism only by macroscopic method, while the microscopic simulation method was limited to the interaction between gas molecules or coal in a single pore space, and lacks the connection between local and whole. Therefore, it was easier to find and study the influence of microscopic pore structure of coal on gas seepage from the mesoscopic perspective. In this paper, the Quartet Structure Generation Set (QSGS) method was used to restructure random pore structure of coal, and the image recognition and analysis software on pore structure of coal was developed to analyze the characteristics of coal pore structure. On this basis, the lattice Boltzmann numerical method was used to simulate gas seepage, and the influence of different porosity, different pore distribution with the same porosity and pressure on gas seepage was discussed. The main conclusions are as follows:

(1)The image recognition analysis results of the reconstructed random pore structure of coal showed that, with the increase of porosity, the number of pores, the average pore size and the proportion of open pores will increase. Under the same porosity, the less the number of pores, the larger the pore size, and the smaller the fractal dimension with the smaller the probability of core distribution (P_c), and the "through" pores would appear. The pore size distribution showed that the number of macropores was less, and the number of mesopores accounted for more, the number of mesopores accounted for 75% ~ 90%.

(2)The LBM seepage model at REV scale was established to explore the local characteristics of gas seepage in coal micro pores. The results showed that the larger the porosity was, the greater the gas seepage velocity and permeability were. When the porosity is greater than 0.18, the increase in permeability of pore structure was much larger than that of pore structure below 0.18, reaching more than ten-fold. Under the same pressure condition, the permeability of pore structure with porosity of 0.32 was as high as two orders of magnitude higher than that of pore structure with porosity of 0.12. The main reason was that the proportion of open pores was relatively more and the average pore size was relatively larger, which leads to better connectivity of coal.

(3)By comparing the gas seepage laws in different structures with the same porosity, it could be found that gas seepage velocity and permeability were much higher in the pore structure with through-hole than those without through-hole. The average seepage velocity of gas was more than three-fold that of other structures, and the permeability was more than ten-fold that of other structures. The through-hole provided advantageous seepage channels for gas seepage in coal, leading to uneven distribution of gas seepage velocity in coal pore structure. Gas seepage velocity was larger in the well connected pore area, and very small or even stagnant in the closed pore area. In engineering practice, more attention should be paid to influence of this kind of pore distribution characteristics on gas seepage.

(4)The gas seepage characteristic was vary with different pore inclination angles. The average gas seepage velocity and permeability decreased with the increase of pore inclination angle. The average gas seepage velocity in the pore structure with 0° pore angle was more than three times higher than that in the pore structure with 45° and 90° pore angles. Therefore, in practice, more attention should be paid to the influence of pore-fracture distribution in the same direction as gas seepage on seepage. The average seepage velocity of gas increased with the increase of pressure difference. The greater the porosity of coal was, the greater the influence of pressure difference on the average seepage velocity was.

In this paper, the influence of random pore structure of coal on gas microscopic seepage was explored by lattice Boltzmann method. The research results supplemented and improved the microscopic seepage of gas, which was helpful to further understand the law of gas seepage and provided theoretical basis for the efficient exploitation of gas in coal seam.

[1] 孙旭东, 张蕾欣, 张博. 碳中和背景下我国煤炭行业的发展与转型研究[J]. 中国矿业, 2021, 30(02):1-6.

[2] 桑树勋, 王冉, 周效志, 等. 论煤地质学与碳中和[J]. 煤田地质与勘探, 2021, 49(01):1-11.

[3] 邹才能, 杨智, 黄士鹏, 等. 煤系天然气的资源类型、形成分布与发展前景[J]. 石油勘探与开发, 2019, 46(03):21-30.

[4] 孙德强, 高文凯, 郑军卫, 等. 制约中国煤层气发展瓶颈问题及政策建议[J]. 中国能源, 2021, 43(01):33-38.

[5] 姜杉钰, 王峰. 中国煤系天然气共探合采的战略选择与发展对策[J]. 天然气工业, 2020, 40(01):152-159.

[6] 李松, 汤达祯, 许浩, 等. 深部煤层气储层地质研究进展[J]. 地学前缘, 2016, 23(03):10-16.

[7] 孙钦平, 赵群, 姜馨淳, 等. 新形势下中国煤层气勘探开发前景与对策思考[J]. 煤炭学报, 2021, 46(01):65-76.

[8] 孙德强, 习成威, 郑军卫, 等. 第四次工业革命对我国能源的发展影响和启示[J]. 中国能源, 2019, 41(11):26-29+41.

[9] 袁亮. 我国深部煤与瓦斯共采战略思考[J]. 煤炭学报, 2016, 41(1):1-6.

[10] 金毅, 宋慧波, 潘结南, 等. 煤微观结构三维表征及其孔-渗时空演化模式数值分析[J]. 岩石力学与工程学报, 2013, 32(S1):2632-2641.

[11] 刘世奇, 王鹤, 王冉, 等. 煤层孔隙与裂隙特征研究进展[J]. 沉积学报, 2021, 39(01):212-230.

[12] 降文萍, 张群, 姜在炳, 等. 构造煤孔隙结构对煤层气产气特征的影响[J]. 天然气地球科学, 2016, 27(01):173-179.

[13] 潘结南, 张召召, 李猛, 等. 煤的多尺度孔隙结构特征及其对渗透率的影响[J]. 天然气工业, 2019, 39(01):64-73.

[14] 刘大锰, 李振涛, 蔡益栋. 煤储层孔-裂隙非均质性及其地质影响因素研究进展[J].煤炭科学技术, 2015, 43(02):10-15.

[15] 刘永茜, 侯金玲, 张浪, 等. 孔隙结构控制下的煤体渗透实验研究[J]. 煤炭学报, 2016, 41(S2):434-440.

[16] 李振涛. 煤储层孔裂隙演化及对煤层气微观流动的影响[D]. 中国地质大学(北京), 2018.

[17] 杨甫, 贺丹, 马东民, 等. 低阶煤储层微观孔隙结构多尺度联合表征[J]. 岩性油气藏, 2020, 32(03):14-23.

[18] 程庆迎, 黄炳香, 李增华. 煤岩体孔隙裂隙实验方法研究进展[J]. 中国矿业, 2012, 21(01):115-118.

[19] 蔺亚兵, 贾雪梅, 马东民. 基于液氮吸附法对煤的孔隙特征研究与应用[J]. 煤炭科学技术, 2016, 44(003):135-140.

[20] Du Z, Zhang X, Huang Q , et al. Investigation of coal pore and fracture distributions and their contributions to coal reservoir permeability in the Changzhi block, middle-southern Qinshui Basin, North China[J]. Arabian Journal of Geosciences, 2019, 12(16):505.

[21] 李祥春, 李忠备, 张良, 等. 不同煤阶煤样孔隙结构表征及其对瓦斯解吸扩散的影响[J]. 煤炭学报, 2019, 44(S1):142-156.

[22] 林海飞, 卜婧婷, 严敏, 等. 中低阶煤孔隙结构特征的氮吸附法和压汞法联合分析[J]. 西安科技大学学报, 2019, 39(01):1-8.

[23] 戚灵灵, 王兆丰, 杨宏民, 等. 基于低温氮吸附法和压汞法的煤样孔隙研究[J]. 煤炭科学技术, 2012, 40(08):36-39+87.

[24] Li X, Kang Y, Haghighi M. Investigation of pore size distributions of coals with different structures by nuclear magnetic resonance (NMR) and mercury intrusion porosimetry (MIP)[J]. Measurement, 2017, 116:122-128.

[25] 王金波. 岩石孔隙结构三维重构及微细观渗流的数值模拟研究[D]. 中国矿业大学（北京）, 2014.

[26] 张平, 王登科, 于充, 等. 基于工业CT扫描的数字煤心构建过程及裂缝形态表征[J]. 河南理工大学学报(自然科学版), 2019, 38(6):10-16.

[27] 聂百胜, 杨龙龙, 李默庚, 等. 煤体微观孔隙结构与受载细观变形特征规律研究[J]. 煤炭科学技术, 2016, 44(6):123-126.

[28] Gang W, Qin X, Shen J, 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.

[29] Yang T, Xu T, Liu H, et al. Stress-damage-flow coupling model and its application to pressure relief coal bed methane in deep coal seam[J]. International Journal of Coal Geology, 2011, 86(4):357-366.

[30] Cai Y, Liu D, Mathews J P, et al. Permeability evolution in fractured coal-Combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques[J]. International Journal of Coal Geology, 2014, 122:91-104.

[31] Soheil, Saraji, Mohammad, et al. The representative sample size in shale oil rocks and nano-scale characterization of transport properties[J]. International Journal of Coal Geology, 2015, 146:42-54.

[32] Espinoza D, Shovkun I, Makni O, et al. Natural and induced fractures in coal cores imaged through X-ray computed microtomography-Impact on desorption time[J]. International Journal of Coal Geology, 2016, 154-155:165-175 .

[33] Tahmasebi P, Javadpour F, Sahimi M. Stochastic Shale Permeability Matching: Three-Dimensional Characterization and Modeling[J]. International Journal of Coal Geology, 2016:231-242.

[34] 李伟, 要惠芳, 刘鸿福, 等. 基于显微CT的不同煤体结构煤三维孔隙精细表征[J]. 煤炭学报, 2014, 39(06):1127-1132.

[35] 王登科, 张平, 魏建平, 等. CT可视化的受载煤体三维裂隙结构动态演化试验研究[J]. 煤炭学报, 2019, 44( S2):574-584.

[36] 秦跃平, 刘鹏, 郝永江. 煤体双重孔隙瓦斯双渗流模型及无因次解算[J]. 岩石力学与工程学报, 2017, 36(01):43-52.

[37] 张春会, 于永江, 岳宏亮, 等. 随机分布裂隙煤岩体模型及其应用[J]. 岩土力学, 2010, 31(01):265-270.

[38] Raeini A Q, Blunt M J, Bijeljic B. Direct simulations of two-phase flow on micro-CT images of porous media and upscaling of pore-scale forces[J]. Advances in Water Resources, 2014, 74(dec.):116-126.

[39] A M J B, A B B, B H D, et al. Pore-scale imaging and modelling[J]. Advances in Water Resources, 2013, 51:197-216.

[40] Blunt M J, Jackson M D, Piri M, et al. Detailed physics, predictive capabilities and macroscopic consequences for pore-network models of multiphase flow[J]. Advances in Water Resources, 2002, 25(8/12):1069-1089.

[41] Mehmani A, Prodanovic M, Javadpour F. Multiscale, multiphysics network modeling of shale matrix gas flows[J]. Transport in Porous Media, 2013, 99(2):377-390.

[42] Zhou X P, Xiao N. 3D numerical reconstruction of porous sandstone using improved simulated annealing algorithms[J]. Rock Mechanics & Rock Engineering, 2018, 51:2135-2151.

[43] 张烈辉, 贾鸣, 张芮菡, 等. 裂缝性油藏离散裂缝网络模型与数值模拟[J]. 西南石油大学学报(自然科学版), 2017, 39(03):121-127.

[44] 张丽, 孙建孟, 孙志强, 等. 多点地质统计学在三维岩心孔隙分布建模中的应用[J]. 中国石油大学学报(自然科学版), 2012, 36(02):105-109.

[45] 刘学锋, 孙建孟, 王海涛, 等. 顺序指示模拟重建三维数字岩心的准确性评价[J]. 石油学报, 2009, 30(03):391-395.

[46] Chen L, Zhang L, Kang Q, et al. Nanoscale simulation of shale transport properties using the lattice Boltzmann method: permeability and diffusivity[J]. Scientific Report, 2015, 5:8089.

[47] 李滔, 李骞, 胡勇, 等. 不规则微裂缝网络定量表征及其对多孔介质渗流能力的影响[J]. 石油勘探与开发, 2021, 48(02):368-378.

[48] Wang Z, Jin X, Wang X, et al. Pore-scale geometry effects on gas permeability in shale:[J]. Journal of Natural Gas Science and Engineering, 2016, 34:948-957.

[49] Chen L, Kang Q, Dai Z, et al. Permeability prediction of shale matrix reconstructed using the elementary building block model[J]. Fuel, 2015, 160(15):346-356.

[50] 金毅, 宋慧波, 胡斌, 等. 煤储层分形孔隙结构中流体运移格子Boltzmann模拟[J]. 中国科学:地球科学, 2013, 43(12):1984-1995.

[51] 郭平, 曹树刚, 张遵国, 等. 含瓦斯煤体固气耦合数学模型及数值模拟[J]. 煤炭学报, 2012, 37(S2):330-335.

[52] 李宝林, 魏国营. 不同温度煤裂隙流动优势性的热流固耦合数值模拟[J]. 煤炭科学技术, 2020, 48(11):141-146.

[53] 尹光志, 王登科, 张东明, 等. 含瓦斯煤岩固气耦合动态模型与数值模拟研究[J].岩土工程学报, 2008(10):1430-1436.

[54] 程国强, 赵芳, 尚永会, 等. 非均质煤层瓦斯渗流的SPH方法模拟研究[J]. 煤炭学报, 2016, 41(05):1152-1157.

[55] Xia T, Gao F, Kang J, et al. A fully coupling coal-gas model associated with inertia and slip effects for CBM migration[J]. Environmental Earth Sciences, 2016, 75(7):1-10.

[56] Li S Z, Jiang C B, Yao J W, et al. Solid-Gas Coupling Model and Numerical Simulation of Coal Containing Gas Based on Comsol Multiphysic[J]. Advanced Materials Research, 2013, 616-618:515-520.

[57] Xu C, Qin L, Wang K, et al. Gas seepage laws based on dual porosity and dual permeability: Numerical simulation and coalbed methane extraction practice[J]. Energy Science & Engineering, 2021,9(4):509-519.

[58] Wang J G, Kabir A, Liu J, et al. Effects of non-Darcy flow on the performance of coal seam gas wells[J]. International Journal of Coal Geology, 2012, 93:62-74.

[59] 秦玉金, 罗海珠, 姜文忠, 等. 非等温吸附变形条件下瓦斯运移多场耦合模型研究[J]. 煤炭学报, 2011, 36(03):412-416.

[60] 林柏泉, 刘厅, 杨威. 基于动态扩散的煤层多场耦合模型建立及应用[J]. 中国矿业大学学报, 2018, 47(01):32-39+112.

[61] Fan N, J Wang, Deng C, et al. Quantitative characterization of coal microstructure and visualization seepage of macropores using CT-based 3D reconstruction[J]. Journal of Natural Gas Science and Engineering, 2020, 81:103384.

[62] Ni X, Miao J, Lv R, et al. Quantitative 3D spatial characterization and flow simulation of coal macropores based on μCT technology[J]. Fuel, 2017, 200(JUL.15):199-207.

[63] Zhao T, Zhao H, Ning Z, et al. Permeability prediction of numerical reconstructed multiscale tight porous media using the representative elementary volume scale lattice Boltzmann method[J]. International Journal of Heat and Mass Transfer, 2018, 118:368-377.

[64] Zhao T, Zhao H, Li X, et al. Pore scale characteristics of gas flow in shale matrix determined by the regularized lattice Boltzmann method[J]. Chemical Engineering Science, 2018, 187:245-255.

[65] Zhou L, Qu Z G, Chen L, et al. Lattice Boltzmann simulation of gas-solid adsorption processes at pore scale level[J]. Journal of Computational Physics, 2015, 300:800-813.

[66] Yu H, Jie C, Zhu Y B, et al. Multiscale transport mechanism of shale gas in micro/nano-pores[J]. International Journal of Heat and Mass Transfer, 2017, 111(aug.):1172-1180.

[67] Liu L, Yao J, Zhang L, et al. REV-scale simulation of micro-fractured unconventional gas reservoir[J]. Journal of Natural Gas Science and Engineering, 2017, 48(9):100-110.

[68] 滕桂荣, 谭云亮, 高明, 等. 基于LBM方法的裂隙煤体内瓦斯抽放的模拟分析[J].煤炭学报, 2008(08):914-919.

[69] 滕桂荣, 谭云亮, 高明. 基于Lattice Boltzmann方法对裂隙煤体中瓦斯运移规律的模拟研究[J]. 岩石力学与工程学报, 2007(S1):3503-3508.

[70] 谭云亮, 尹延春, 滕桂荣, 等. 基于Lattice Boltzmann方法的瓦斯渗流模拟研究[J]. 煤炭学报, 2014(8):1446-1454.

[71] 王登科, 于充, 魏建平, 等. 基于LBM方法的裂隙煤岩应力-应变过程中渗流特性研究[J]. 岩石力学与工程学报, 2020, 39(04):695-704.

[72] 田智威, 谭云亮, 刘兆霞. 含裂隙煤体瓦斯渗流规律的LBM数值模拟[J]. 煤炭学报, 2013, 38(08):1376-1380.

[73] Gao J, Xing H, Turner L, et al. Pore-Scale Numerical Investigation on Chemical Stimulation in Coal and Permeability Enhancement for Coal Seam Gas Production[J]. Transport in Porous Media, 2017, 116(1):335-351.

[74] Wang Q, Li C, Zhao Y, et al. Study of gas emission law at the heading face in a coal-mine tunnel based on the Lattice Boltzmann method[J]. Energy Science and Engineering, 2020, 8(5):1705-1715.

[75] Jin Y, Song H, Hu B, et al. Lattice Boltzmann simulation of fluid flow through coal reservoir fractal pore structure[J]. Science China Earth Sciences, 2013, 56(9):1519-1530.

[76] Jin Y, Zheng J, Liu X, et al. Control mechanisms of self-affine, rough cleat networks on flow dynamics in coal reservoir[J]. Energy, 2019, 189(PT.1):116146.

[77] Peng Z G, Liu S, Tang S, et al. Multicomponent Lattice Boltzmann Simulations of Gas Transport in a Coal Reservoir with Dynamic Adsorption[J]. Geofluids, 2018, 2018:1-13.

[78] Li C, Fang Y, Ju Y. Three-Dimensional Reconstruction of Coalʼs Microstructure Using Randomly Packing-Sphere and Pore-Growing and Lattice Boltzmann Method[J]. Journal of Nanoscience and Nanotechnology, 2017, 17(9):6867-6872.

[79] Zhao Y L, Wang Z M, Yang H, et al. Multi-scale analysis on coal permeability using the lattice Boltzmann method[J]. Journal of Petroleum Science and Engineering, 2018, 174:1269-1278.

[80] Zhao Y L, Wang Z M, Qin X, et al. Stress-dependent permeability of coal fracture networks: A numerical study with Lattice Boltzmann method[J]. Journal of Petroleum Science & Engineering, 2019, 173:1053-1064.

[81] Wang M, Wang J, Pan N, et al. Mesoscopic predictions of the effective thermal conductivity for microscale random porous media[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2007, 75(3):036702.

[82] Wang M, Pan N. Elastic property of multiphase composites with random microstructures[J]. Journal of Computational Physics, 2009, 228(16):5978-5988.

[83] Wang M, Pan N, Wang J, et al. Mesoscopic simulations of phase distribution effects on the effective thermal conductivity of microgranular porous media[J]. Journal of Colloid & Interface Science, 2007, 311(2):562-570.

[84] 徐加祥, 杨立峰, 丁云宏, 等. 基于四参数随机生长模型的页岩储层应力敏感分析[J]. 天然气地球科学, 2019, 30(09):1341-1348.

[85] Li C, Fang Y, Ju Y, et al. Three-dimensional reconstruction of coal's microstructure using randomly packing-sphere and pore-growing and lattice boltzmann method[J]. Journal of Nanoscience and Nanotechnology, 2017, 17(9):6867-6872.

[86] 李滔, 李闽, 荆雪琪, 等. 孔隙尺度各向异性与孔隙分布非均质性对多孔介质渗透率的影响机理[J]. 石油勘探与开发, 2019, 46(03):569-579.

[87] 申林方, 王志良, 李邵军. 基于土体细观结构重构技术的渗流场数值模拟[J]. 岩土力学, 2015, 36(11):3307-3314.

[88] 吴子森, 董平川, 袁忠超, 等. 多孔介质流动及传热的格子Boltzmann方法研究[J]. 石油科学通报, 2017, 2(01):76-85.

[89] 王刚, 杨鑫祥, 张孝强, 等. 基于CT三维重建的煤层气非达西渗流数值模拟[J].煤炭学报, 2016, 041(004):931-940.

[90] Liu C, Tang C S, Shi B, et al. Automatic quantification of crack patterns by image processing[J]. Computers & Geosciences, 2013, 57:77-80.

[91] Grove C, Jerram D A. An ImageJ macro to quantify total optical porosity from blue-stained thin sections[J]. Computers & Geosciences, 2011, 37(11):1850-1859.

[92] 李海涛, 邓少贵, 牛云峰, 等. 低孔渗砂岩孔隙结构分类方法研究[J]. CT理论与应用研究, 2018, 27(05):551-560.

[93] B.B.霍多特, 宋士钊, 王佑安. 煤与瓦斯突出[M]. 北京: 中国工业出版社, 1966.

[94] 李子文, 林柏泉, 郝志勇. 煤体多孔介质孔隙度的分形特征研究[J]. 采矿与安全工程学报, 2013, 30(3):437-442.

[95] 杨宇, 孙晗森, 彭小东, 等. 煤层气储层孔隙结构分形特征定量研究[J]. 特种油气藏, 2013, 20(01):31-33+88+152.

[96] 王小垚, 曾联波, 周三栋, 等. 低阶煤储层微观孔隙结构的分形模型评价[J]. 天然气地球科学, 2018, 29(002):277-288.

[97] 姚铭檑, 邵龙义, 侯海海, 等. 两淮煤田煤储层吸附孔孔隙结构及分形特征[J]. 中国煤炭地质, 2018, 30(01):30-36+47.

[98] 傅雪海, 秦勇, 薛秀谦. 分形理论在煤储层物性研究中的应用[J]. 煤, 2000(04):1-3.

[99] 叶桢妮, 侯恩科, 段中会, 等. 不同煤体结构煤的孔隙-裂隙分形特征及其对渗透性的影响[J]. 煤田地质与勘探, 2019, 47(05):70-78.

[100]彭瑞东, 杨彦从, 鞠杨, 等. 基于灰度CT图像的岩石孔隙分形维数计算[J]. 科学通报, 2011, 56(26):2256-2266.

[101]王登科, 曾凡超, 王建国, 等. 显微工业CT的受载煤样裂隙动态演化特征与分形规律研究[J]. 岩石力学与工程学报, 2020, 39(06):1165-1174.

[102]郭照立, 郑楚光. 格子Boltzmann方法的原理及应用[M]. 北京：科学出版社, 2009.

[103]Guo Z, Zhao T S. Lattice Boltzmann model for incompressible flows through porous media[J]. Physical Review E, 2002, 66(3 Pt 2B):036304.

[104]李江涛, 汪志明, 魏建光, 等. 基于数字岩心和LBM的页岩气流固耦合数值模拟[J]. 中国科学:技术科学, 2018, 48(05):499-509.

[105]Yu X, Xu L, Regenauer-Lieb K, et al. Modeling the effects of gas slippage, cleat network topology and scale dependence of gas transport in coal seam gas reservoirs[J]. Fuel, 2020, 264:116715.

[106]Yang X, Zhou W, Liu X, et al. A multiscale approach for simulation of shale gas transport in organic nanopores[J]. Energy, 2020:118547.

[107]Zhang T, Sun S. A coupled Lattice Boltzmann approach to simulate gas flow and transport in shale reservoirs with dynamic sorption[J]. Fuel, 2019, 246:196-203.

[108]金毅, 祝一搏, 吴影, 等. 煤储层粗糙割理中煤层气运移机理数值分析[J]. 煤炭学报, 2014, 39(09):1826-1834.

[109]Nithiarasu P, Seetharamu K N, Sundararajan T. Natural convective heat transfer in a fluid saturated variable porosity medium[J]. International journal of heat and mass transfer, 1997, 40(16):3955-3967.

[110]Ergun S. Fluid flow through packed columns[J]. Journal of Materials Science and Chemical Engineering, 1952, 48(2):89-94.

[111]Wang J, Kang Q, Wang Y, et al. Simulation of gas flow in micro-porous media with the regularized lattice Boltzmann method[J]. Fuel, 2017, 205(1):232-246.

[112]周刚, 邱磊, 王家远. 基于显微CT技术的低压瓦斯渗流特性模拟分析[J]. 煤炭科学技术, 2018, 046(001):120-126.

[113]方辉煌, 桑树勋, 刘世奇, 等. 基于微米焦点CT技术的煤岩数字岩石物理分析方法—以沁水盆地伯方3号煤为例[J]. 煤田地质与勘探, 2018, 046(005):167-174+181.

[114]苏政睿, 韦善阳. 基于X形真实裂隙通道的煤层瓦斯渗流模拟[J]. 煤田地质与勘探, 2020, 48(06):109-115.

[115]王刚, 江成浩, 刘世民, 等. 基于CT三维重建煤骨架结构模型的渗流过程动态模拟研究[J]. 煤炭学报, 2018, 43(005):1390-1399.

[116]马斌, 史琳, 黄超, 等. 孔隙分布不均匀性对渗透率影响的MRT-LBM法研究[J]. 中国科学院大学学报, 2018, 35(02):193-199.

[117]赵宇, 张玉贵, 岳高伟, 等. 煤层渗透性各向异性规律的实验研究[J]. 中国煤层气, 2017, 014(001):32-35.

[118]田坤云, 齐雷. 应力加卸载下层理裂隙煤体瓦斯渗流试验研究[J]. 中国安全科学学报, 2017, 27(01):93-97.

[119]孙国文, 罗甲渊, 罗斌玉. 煤层层理对瓦斯渗流影响理论分析与实验研究[J]. 湖南科技大学学报(自然科学版), 2017, 32(01):12-17.

[120]刘佳佳, 杨明, 张俊伟, 等. 受载含层理煤体裂隙演化与渗流特性模拟研究[J]. 中国安全科学学报, 2017, 27(10):103-108.