地质科技通报2025，Vol.44Issue(2)：182-192,11.DOI:10.19509/j.cnki.dzkq.tb20230583
基于XGBoost算法的走滑断裂内部特征带的精细识别
Fine-grained identification of internal characteristic zones within strike-slip faults via the XGBoost algorithm
摘要
Abstract
Because of the strong heterogeneity of fault zones,diverse reservoir types,and complex fluid distributions,the logging responses between damage,fault breccias,and dissolution zones within strike-slip faults are complex and variable,making it difficult to identify the three characteristic zones effectively inside strike-slip faults using imaging and conventional logging data.[Objective]The extreme gradient boosting(XGBoost)algorithm is introduced to establish a model to improve the identification accuracy of the three characteristic zones within strike-slip faults.[Methods]The logging response characteristics of the three characteristic zones within strike-slip faults are analyzed,and the sensitive logging curves are selected to construct a feature vector space set based on the mean and variance.The XGBoost algorithm is applied to establish XGBoost regression prediction models for the dissolution,breccias,and damage zones of strike-slip faults.The key parameters of the XGBoost model are optimized through multiclass evaluation indicators to improve the identification accuracy of the characteristic zones within strike-slip faults.[Results]The constructed XGBoost model was used to identify the internal characteristic zones of strike-slip faults in the study area,with a total of 234 samples;208 samples were correctly identified,resulting in an identification accuracy of 88.89%.The prediction results reveal that,within the internal characteristic zones of strike-slip faults,the damage zone has the widest distribution,followed by the breccias zone,and the dissolution zone is the narrowest,which is consistent with the actual distribution of the internal characteristic zones of strike-slip faults.[Conclusion]The identification model of internal characteristic zones within strike-slip faults based on the XGBoost algorithm can be used to effectively identify the damage,breccias,and dissolution zones,thereby supporting more effective analysis of the distribution of small-scale dissolution cavities and fracture reservoir spaces inside strike-slip faults,and providing reference information for the accurate characterization of the internal structure of strike-slip faults.关键词
走滑断裂/XGBoost算法/碳酸盐岩/测井评价/特征带识别/四川盆地/高石梯-磨溪地区Key words
strike-slip fault/XGBoost algorithm/carbonate rock/logging evaluation/characteristic zone identification/Sichuan Basin/Gaoshiti-Moxi region引用本文复制引用
赵军,汪峻宇,赖强,文晓峰,邬光辉,焦世祥..基于XGBoost算法的走滑断裂内部特征带的精细识别[J].地质科技通报,2025,44(2):182-192,11.基金项目
中国石油-西南石油大学创新联合体项目(2020CX010204) （2020CX010204）
