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A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model:A case study from the Dongying Depression,Bohai Bay Basin,China

Gui-Ang Li Cheng-Yan Lin Chun-Mei Dong Li-Hua Ren Peng-Jie Ma Yu-Qi Wu Guo-Yin Zhang Xin-Yu Du Zi-Ru Zhao

石油科学（英文版）2026，Vol.23Issue(6)：3017-3036,20.

石油科学（英文版）2026，Vol.23Issue(6)：3017-3036,20.DOI:10.1016/j.petsci.2026.01.042

A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model:A case study from the Dongying Depression,Bohai Bay Basin,China

Gui-Ang Li ¹Cheng-Yan Lin ¹Chun-Mei Dong ¹Li-Hua Ren ¹Peng-Jie Ma ²Yu-Qi Wu ¹Guo-Yin Zhang ¹Xin-Yu Du ³Zi-Ru Zhao¹

作者信息

1. State Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China),Qingdao,266580,Shandong,China||School of Geosciences,China University of Petroleum(East China),Qingdao,266580,Shandong,China||Shandong Provincial Key Laboratory of Reservoir Geology,China University of Petroleum(East China),Qingdao,266580,Shandong,China
2. Sanya Offshore Oil & Gas Research Institute,Northeast Petroleum University,Sanya,572025,Hainan,China
3. Wuxi Research Institute of Petroleum Geology,Petroleum Exploration and Production Research Institute,SINOPEC,Wuxi,214126,Jiangsu,China
折叠

摘要

关键词

Lacustrine shale/Lithofacies prediction/Conditional diffusion probabilistic model/Data augmentation/Class imbalance

Key words

Lacustrine shale/Lithofacies prediction/Conditional diffusion probabilistic model/Data augmentation/Class imbalance

引用本文复制引用

Gui-Ang Li,Cheng-Yan Lin,Chun-Mei Dong,Li-Hua Ren,Peng-Jie Ma,Yu-Qi Wu,Guo-Yin Zhang,Xin-Yu Du,Zi-Ru Zhao..A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model:A case study from the Dongying Depression,Bohai Bay Basin,China[J].石油科学（英文版）,2026,23(6):3017-3036,20.

基金项目

This work is funded by the National Natural Science Foundation of China(Nos.42372156 （）

42202146 （）

42102153 （）

42472194),the Key R&D Program of Shandong Province,China(Grant No.2022CXPT048),and the Research Contract"Comprehensive Eval-uation and Prediction of Shale Oil Development Sweet Spots"(Contract No.30200018-19-ZC0613-0116).We acknowledge the Exploration and Development Research Institute of Shengli Oil-field Company,SINOPEC,for providing core samples,well-log data,and geological data for this study.Special thanks are extended to the editor and anonymous reviewers for their constructive com-ments and suggestions. （Grant No.2022CXPT048）

石油科学（英文版）

ISSN：1672-5107

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