中国地质调查2026，Vol.13Issue(2)：1-12,12.DOI:10.19388/j.zgdzdc.2026.173
基于机器学习测井反演的煤系岩性组合识别与评价
Identification and evaluation of lithological assemblages in the coal measure based on machine learning log inversion:A case study of the northern Mizhi area of Ordos Basin
摘要
Abstract
The gas sealing behavior of coal reservoirs is affected by lithological assemblages in coal measures,and the accurate identification and prediction of such coal assemblages is critical for evaluating source reservoir cap configurations and predicting gas bearing potential.Traditional lithological assemblages rely on labor interpreta-tion,with disadvantages of high cost,low inefficiency and strong subjectivity.Three machine learning algorithms were compared in this research,including light gradient boosting machine(LightGBM),random forest(RF)and extreme gradient boosting(XGBoost),and the roof/floor strata of No.8 coal seam of Benxi Formation in the northern Mizhi area of Ordos Basin was chosen as the study area.And the favorable lithological assemblages in the coal measure were assessed using logging parameters inversion by optimal algorithm in conjunction with coalbed methane content,based on well logging and core observation data.Results indicated that LightGBM model yielded the highest prediction accuracy at 0.93,compared with 0.92 for Random Forest and 0.91 for XGBoost.Regional inversion by LightGBM showed that mudstone-coal-mudstone assemblage was the most widely distributed,fol-lowed by sandstone-coal-mudstone assemblage and the mudstone-coal-sandstone assemblage.Integrated a-nalysis of coal thickness and measured gas content revealed that the limestone-coal-mudstone assemblage ex-hibited the highest gas abundance,with an average of 168.6 m3/m2.And it was the most favorable lithological assemblage in the study area under present samples and gas bearing content indices,and it was predominantly dis-tributed in the southeastern part of the study area.This study effectively overcomes the limitations of traditional li-thological assemblage identification,providing new insights and technical references for the comprehensive explo-ration and development of coal-measure unconventional natural gas.关键词
煤系/岩性组合/机器学习/LightGBM/随机森林/XGBoostKey words
Coal measure/lithological assemblage/machine learning/LightGBM/Random Forest/XGBoost分类
天文与地球科学引用本文复制引用
谷美珊,张云骥,李安,王子豪,蔡益栋..基于机器学习测井反演的煤系岩性组合识别与评价[J].中国地质调查,2026,13(2):1-12,12.基金项目
国家重点研发计划"煤系流-固复合矿藏协同勘查理论与技术(编号:2024YFC2909400)"、国家自然科学基金"煤系地层流体压力系统及煤系气叠置成藏机理(编号:42130806)""深部煤储层固-液-气多级联动的冷冲击响应机理研究(编号:42372195)"、中央高校基本科研业务费深时数字地球前沿科学中心"深时数字地球"中央高校科技领军人才团队项目(编号:2652023001)及中央高校青年教师科研创新能力支持项目"非常规油气高精度勘探开发的理论与技术(编号:ZYGXQNJSKY-CXNLZCXM-E14)"联合资助. （编号:2024YFC2909400）
