古地理学报2025，Vol.27Issue(3)：763-776,14.DOI:10.7605/gdlxb.2025.00.026
基于XGBoost算法的页岩岩相测井预测方法
Shale lithofacies prediction method with well-logging data based on XGBoost algorithm
摘要
Abstract
The identification and prediction of shale lithofacies are crucial for identifying favorable intervals("sweet spots")in shale oil and gas reservoirs.In the absence of core data,logging data plays a key role in lithofacies analysis at the single-well level.By applying the XGBoost algorithm,useful litho-facies information can be extracted from multidimensional logging data,enabling effective prediction of shale lithofacies in individual wells.In this study,the XGBoost machine learning method,a supervised learning algorithm,is used to build a predictive model based on conventional logging datasets.First,a lithofacies classification scheme tailored to the specific study area is established,which captures the varia-bility in shale lithofacies identification.The boundaries of mineral compositions and TOC content for dif-ferent lithofacies types are then determined using statistical proportion analysis.During model construction,care must be taken to eliminate redundant variables,as highly correlated features may provide overlapping information and cause overfitting.XGBoost's grid search approach allows comprehensive parameter tuning.Multiple rounds of optimization should be conducted,with the search range gradually narrowed to deter-mine the optimal parameter set.Using the Zanzijing block in the Songnan area as a case study,five major shale lithofacies types are defined based on mineral composition,sedimentary structures,and TOC con-tent.During variable selection,for instance,only one of the highly correlated LLD and LLS logs is re-tained,which results in a model accuracy improvement of approximately 4％.After feature selection and parameter tuning,the final model achieves a lithofacies prediction accuracy of up to 90.03％.关键词
页岩岩相预测/XGBoost算法/变量选择/参数调优/测井信息/青山口组/松辽盆地Key words
shale lithofacies prediction/XGboost algorithm/variable selection/parameter tuning/well-logging data/Qingshankou Formation/Songliao Basin分类
天文与地球科学引用本文复制引用
闫佳飞,李胜利,魏泽德,吴忠宝,陈建阳..基于XGBoost算法的页岩岩相测井预测方法[J].古地理学报,2025,27(3):763-776,14.基金项目
国家自然科学基金项目(编号:42172112)资助.Financially supported by the National Natural Science Foundation of China(No.42172112) （编号:42172112）
