中国地质2026，Vol.53Issue(2)：466-475,10.DOI:10.12029/gc20250718003
基于Kmeans-LightGBM算法的页岩含气量预测及其在筇竹寺组的应用
Prediction of gas content in shale based on the Kmeans-LightGBM algorithm and its application in Qiongzhusi Formation
摘要
Abstract
[Objective]Gas content is a crucial parameter for evaluating shale gas resources potential.Accurate prediction of gas content can guide shale gas exploration deployment.[Methods]This study takes the shale of the Qiongzhusi Formation in the Sichuan Basin as an example.Based on measured gas content data and logging data of the shale,a model that combines the Kmeans clustering algorithm and the Light Gradient Boosting Machine(LightGBM)algorithm for predicting gas content in shale reservoirs is proposed,and its prediction results are compared with those of Extreme Gradient Boosting(XGBoost)and LightGBM algorithms.[Results]The error rate of the XGBoost algorithm's predictions is 9.76％,with a root mean square error of 0.734 and a coefficient of determination of 0.8714.The prediction results of the LightGBM algorithm show an error rate of 9.48％,a root mean square error of 0.6478,and a coefficient of determination of 0.9427.The prediction results of the Kmeans-LightGBM algorithm show an error rate of 7.96％,a root mean square error of 0.5805,and a coefficient of determination of 0.96.[Conclusions]The LightGBM prediction model enhanced by Kmeans clustering features can effectively improve the prediction accuracy of gas content in deep shale reservoirs.Based on the Kmeans-LightGBM algorithm prediction,the gas content of the Qiongzhusi shale ranges from 0.21 m3/t to 13.27 m3/t in a great difference,with the high gas content in the Qiong 2 member in the vertical direction.关键词
页岩气/筇竹寺组/Kmeans-LightGBM算法/含气性预测/油气勘查工程/四川盆地Key words
shale gas/Qiongzhusi Formation/Kmeans-LightGBM algorithm/prediction of gas content/oil and gas exploration engineering/Sichuan Basin分类
天文与地球科学引用本文复制引用
刘鑫,缪欢,田鹤,何亮,黎丁源,杨雨然,伍秋姿,姜振学,吴建晨,史德民..基于Kmeans-LightGBM算法的页岩含气量预测及其在筇竹寺组的应用[J].中国地质,2026,53(2):466-475,10.基金项目
中国石油天然气股份有限公司科技项目(2023ZZ21YJ04)与中国石油西南油气田分公司科技项目(2025D00401)联合资助. Supported by China National Petroleum Corporation's Science and Technology Project(No.2023ZZ21YJ04)and China Petroleum Southwest Oil and Gas Field Company's Science and Technology Project(No.2025D00401). （2023ZZ21YJ04）
