石油物探2026，Vol.65Issue(5)：835-843,9.DOI:10.12431/issn.1000-1441.2026.0453
微构造识别方法及其在煤层气储层中的应用
A microstructure identification method and its application in coalbed methane reservoirs
摘要
Abstract
Microstructures in coalbed methane(CBM)reservoirs are concealed and highly heterogeneous,which directly impacts horizontal well placement and productivity.To address the limitations of conventional methods in characterizing their small-scale,weak seismic responses,this study integrates artificial intelligence with multiple seismic attributes,including most positive curvature,maximum likelihood,and phase tuning,for microstructure identification.This method enhances the characterization of low-relief structures(relative relief<20 m,mostly<10 m)and low-grade faults(throw≤10 m,and even<5 m),both of which affect CBM well productivity.Application of the proposed method to CBM reservoirs of the Permian Shanxi Formation in the central-eastern Qinshui Basin identifies low-grade faults and low-relief structures with improved accuracy and high confidence,which significantly enhances engineering response capabilities.Comprehensive analysis suggests that well placement should be at least 500 m away from local negative microstructures and avoid densely populated net-shaped low-grade faults.This method provides technical support for the integrated seismic-geological-engineering development of CBM.关键词
微构造/多源地震属性/人工智能/煤层气/沁水盆地Key words
microstructure/multiple seismic attributes/artificial intelligence/coalbed methane(CBM)/Qinshui Basin分类
能源科技引用本文复制引用
王天云,叶金诚,郭旭升,申宝剑,侯艳,李陶,万小平,刘曾勤,刘禹含,赵石虎..微构造识别方法及其在煤层气储层中的应用[J].石油物探,2026,65(5):835-843,9.基金项目
国家油气重大专项(2025ZD1404204)和中国石化科技部项目(P26009_2)共同资助. This research is financially supported by the China National Oil and Gas Major Project(Grant No.2025ZD1404204)and the Project of the Ministry of Science and Technology of Sinopec(Grant No.P26009_2). （2025ZD1404204）
