A novel interpretable machine learning framework for predicting gas-bearing properties of tight sandstone reservoirsOA
A novel interpretable machine learning framework for predicting gas-bearing properties of tight sandstone reservoirs
Liu Cao;Ben-Jie-Ming Liu;Yong Ma;Xiao-Juan Wang;Zhi-Min Jin;Ao-Bo Zhang;Fu-Jie Jiang;Zhang-Xing Chen;Li-Na Huo;Run-Hai Feng;Di Chen;Meng-Yang Wang;Jian Li;Yang Gao
College of Artificial Intelligence,China University of Petroleum(Beijing),Beijing,102249,China||State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,ChinaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,China||College of Safety and Ocean Engineering,China University of Petroleum(Beijing),Beijing,102249,ChinaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaResearch Institute of Exploration and Development,PetroChina Southwest Oil & Gas Field Company,Chengdu,610041,Sichuan,ChinaResearch Institute of Exploration and Development,PetroChina Southwest Oil & Gas Field Company,Chengdu,610041,Sichuan,ChinaResearch Institute of Exploration and Development,PetroChina Southwest Oil & Gas Field Company,Chengdu,610041,Sichuan,ChinaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||Ningbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,China||Chemical and Petroleum Engineering,Schulich School of Engineering,University of Calgary,Calgary,T2N 1N4,CanadaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaAramco Research Center-Beijing,Aramco Asia,Beijing,100020,ChinaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaState Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNingbo Key Laboratory of Low-Carbon Hydrogen Energy,Eastern Institute of Technology,Ningbo,315200,Zhejiang,ChinaKey Laboratory of Orogenic Belts and Crustal Evolution,Ministry of Education,School of Earth and Space Sciences,Peking University,Beijing,100871,China
Tight sandstoneGas-bearing property predictionInterpretable machine learning frameworkSemi-quantitative prediction
Tight sandstoneGas-bearing property predictionInterpretable machine learning frameworkSemi-quantitative prediction
《石油科学（英文版）》 2026 (7)
3805-3833,29
This work was supported by the National Science and Tech-nology Major Project of China(No.2025ZD1400400),National Natural Science Foundation of China(NSFC)(Nos.42302142 and 42372147)and Collaborative Project between the PetroChina Southwest Oil & Gas Field Company and China University of Pe-troleum(Beijing)(HX20231362).We appreciate the Research Institute of Exploration and Development,PetroChina Southwest Oil & Gas Field Company for providing the data.We are also grateful to the reviewers for the helpful comments to improve our paper.
评论