A probabilistic lithofacies classification method for deep coal measures

Authors

• XU Aiguo, School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China; Huaibei Mining (Group) Co., Ltd., Huaibei 235000, ChinaFollow
• ZHANG Pingsong, School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China
• WU Haibo, School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, ChinaFollow
• LI Shenglin, State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science & Technology, Huainan 232001, China

Abstract

Background Deep coal measures contain multiple unconventional reservoir types, including coal, shale, and tight sandstone. Accurate lithofacies classification helps identify the resource and engineering targets of unconventional gas within these deep sequences, thus guiding category-based extraction and efficient development. However, conventional lithofacies classification methods largely rely on elastic parameters derived based on the isotropic-medium assumption or linear amplitude variation with offset (AVO) inversion while neglecting the pronounced vertical transverse isotropy (VTI) anisotropy of deep coal measures, severely limiting the lithofacies classification accuracy. Methods This study develops a probabilistic lithofacies classification method for deep coal measures. First, high-accuracy elastic parameters, including P- and S-wave velocities and density, are determined through anisotropic AVO inversion, and the joint probability distributions of the three parameters are acquired based on log data. Then, taking the inverted elastic parameters as input, the posterior probabilities of coal, shale, and carbonate are calculated using Bayesian classification, with the lithofacies corresponding to the maximum probability designated as the final lithofacies category. Results and Conclusions The proposed method was verified using measured seismic data along a cross-well section. The test and analysis results indicate that anisotropic AVO inversion outperformed conventional isotropic inversion in the accuracy of P- and S-wave velocities and density, and the results of the former can be directly used for the probabilistic lithofacies classification of deep coal measures. The lithologies at the well location in the seismic section agreed well with the lithofacies classification results. Therefore, the proposed method enables the effective lithofacies classification of deep coal measures and can also be expanded to lithofacies or fluid classification for other reservoir types, thereby facilitating the efficient exploration and development of resources such as unconventional gas.

Keywords

coal measure, lithofacies, Bayesian classification, anisotropy

DOI

10.12363/issn.1001-1986.26.06.0394

Recommended Citation

XU Aiguo, ZHANG Pingsong, WU Haibo, et al. (2026) "A probabilistic lithofacies classification method for deep coal measures," Coal Geology & Exploration: Vol. 54: Iss. 9, Article 16.
DOI: 10.12363/issn.1001-1986.26.06.0394
Available at: https://cge.researchcommons.org/journal/vol54/iss9/16

Reference

[1] ZOEPPRITZ K. Erdbebenwellen VIII B. Uber Reflexion and Durchgang seismischer wellen durch Unstetigkeitsflachen[J]. Gottinger Nachr,1919,1919:66−84.

[2] AKI K,RICHARDS P G. Quantitative seismology (Second Edition)[M]. San Francisco: W. H. Freeman, 1980.

[3] SHUEY R T. A simplification of the Zoeppritz equations[J]. Geophysics,1985,50(4):609−614.

[4] SMITH G C,GIDLOW P M. Weighted stacking for rock property estimation and detection of gas[J]. Geophysical Prospecting,1987,35(9):993−1014.

[5] FATTI J L,SMITH G C,VAIL P J,et al. Detection of gas in sandstone reservoirs using AVO analysis; a 3–D seismic case history using the Geostack technique[J]. Geophysics,1994,59(9):1362−1376.

[6] GOODWAY B,CHEN Taiwen,DOWNTON J. Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters; "λρ","μρ",& "λ/μ fluid stack",from P and S inversions[C]//SEG Technical Program Expanded Abstracts 1997. Dallas: Society of Exploration Geophysicists,1997: 183−186.

[7] GRAY D,GOODWAY B,CHEN Taiwen. Bridging the gap: Using AVO to detect changes in fundamental elastic constants[C]//SEG Technical Program Expanded Abstracts 1999. Houston: Society of Exploration Geophysicists,1999: 852−855.

[8] OSTRANDER W J. Plane–wave reflection coefficients for gas sands at nonnormal angles of incidence[J]. Geophysics,1984,49(10):1637−1648.

[9] RUTHERFORD S R,WILLIAMS R H. Amplitude–versus–offset variations in gas sands[J]. Geophysics,1989,54(6):680−688.

[10] CASTAGNA J P,SWAN H W. Principles of AVO crossplotting[J]. The Leading Edge,1997,16(4):337−344.

[11] BULAND A,OMRE H. Bayesian linearized AVO inversion[J]. Geophysics,2003,68(1):185−198.

[12] BULAND A,KOLBJØRNSEN O,HAUGE R,et al. Bayesian lithology and fluid prediction from seismic prestack data[J]. Geophysics,2008,73(3):C13−C21.

[13] HAMMER H,KOLBJØRNSEN O,TJELMELAND H,et al. Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior[J]. Geophysical Prospecting,2012,60(3):500−515.

[14] ZHAO Luanxiao,GENG Jianhua,CHENG Jiubing,et al. Probabilistic lithofacies prediction from prestack seismic data in a heterogeneous carbonate reservoir[J]. Geophysics,2014,79(5):M25−M34.

[15] ZHOU Lin,LI Jingye,LIAO Jianping,et al. Nonlinear prestack inversion method for the effective pore–fluid bulk modulus based on the Zoeppritz equations[J]. Journal of Applied Geophysics,2026,252:106390.

[16] LIAO Jianping,WEN Ping,XU Yungui,et al. An integrated study on fracture prediction using 3D P–wave field seismic data[J]. Geophysics,2022,87(6):B313−B328.

[17] 张青,廖建平,刘和秀,等. 基于黏弹性Chapman–Kelvin模型的裂缝储层频变AVAZ反演研究[J]. 地球物理学报,2024,67(2):711−737. ZHANG Qing,LIAO Jianping,LIU Hexiu,et al. Inversion study of frequency–dependent AVAZ fractured reservoirs based on the viscoelastic Chapman–Kelvin model[J]. Chinese Journal of Geophysics,2024,67(2):711−737.

[18] WANG Lin,ZHANG Feng,LI Xiangyang,et al. Quantitative seismic interpretation of rock brittleness based on statistical rock physics[J]. Geophysics,2019,84(4):IM63−IM75.

[19] GRANA D,BRONSTON M. Probabilistic formulation of AVO modeling and AVO–attribute–based facies classification using well logs[J]. Geophysics,2015,80(4):D343−D354.

[20] 刘兴业,余沛林,贺珩峻. 地震相智能识别方法研究进展[J]. 煤田地质与勘探,2026,54(6):272−299. LIU Xingye,YU Peilin,HE Hengjun. Advances in research on methods for intelligent identification of seismic facies[J]. Coal Geology & Exploration,2026,54(6):272−299.

[21] 王小琼,葛洪魁,王文文,等. 致密储层岩石应力各向异性与材料各向异性的实验研究[J]. 地球物理学报,2021,64(12):4239−4251. WANG Xiaoqiong,GE Hongkui,WANG Wenwen,et al. Experimental study on stress–related and matrix–related anisotropy in tight reservoirs[J]. Chinese Journal of Geophysics,2021,64(12):4239−4251.

[22] WU Haibo,GUO Jinran,JI Guangzhong,et al. Estimating the anisotropy of the vertical transverse isotropy coal seam by rock physics model–based inversion[J]. Geophysical Prospecting,2024,72(5):2064−2075.

[23] 龚飞,彭苏萍,程绩伟,等. 深部煤层气储层各向异性岩石物理建模及应用[J]. 煤炭学报,2025,50(增刊1):476−491. GONG Fei,PENG Suping,CHENG Jiwei,et al. Anisotropic rock physics modeling and its application for deep coalbed methane reservoirs[J]. Journal of China Coal Society,2025,50(Sup.1):476−491.

[24] RÜGER A. P–wave reflection coefficients for transversely isotropic models with vertical and horizontal axis of symmetry[J]. Geophysics,1997,62(3):713−722.

[25] WU Haibo,HUANG Yaping,HUANG Yanhui,et al. Joint Lamé constants and P–wave anisotropic parameter estimation of vertical transverse isotropy coal–measure gas reservoir[J]. Journal of Applied Geophysics,2025,233:105638.

[26] WU Haibo,GAO Congshun,XIANG Wang,et al. Joint fluid factor and brittleness index inversion of a coal–measure gas reservoir with vertical transverse isotropy[J]. Lithosphere,2025,2025(3):lithosphere_2024_254.

[27] WU Fan,LI Jingye,GENG Weiheng,et al. A VTI anisotropic media inversion method based on the exact reflection coefficient equation[J]. Frontiers in Physics,2022,10:926636.

[28] WU Haibo,WU Rongxin,ZHANG Pingsong,et al. Combined fluid factor and brittleness index inversion for coal–measure gas reservoirs[J]. Geophysical Prospecting,2022,70(4):751−764.

[29] THOMSEN L. Weak elastic anisotropy[J]. Geophysics,1986,51(10):1954−1966.

[30] LI Y E,CHENG A C H,YOU Nan. Shale anisotropy estimation from logs in vertical wells[J]. Journal of Geophysical Research: Solid Earth,2019,124(7):6602−6611.

[31] MAVKO G,MUKERJI T,DVORKIN J. The rock physics handbook: Tools for seismic analysis of porous media (Second Edition)[M]. Cambridge: Cambridge University Press, 2009.