An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling
Lei Liu Jia-Gen Hou Wei Li Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong
石油科学(英文版)2026,Vol.23Issue(4):1754-1772,19.
石油科学(英文版)2026,Vol.23Issue(4):1754-1772,19.DOI:10.1016/j.petsci.2026.02.018
An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling
An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling
摘要
关键词
Sedimentary facies models/Attention-guided generative adversarial network/Interpretable framework/Sedimentary patterns/Multi-condition modelingKey words
Sedimentary facies models/Attention-guided generative adversarial network/Interpretable framework/Sedimentary patterns/Multi-condition modeling引用本文复制引用
Lei Liu,Jia-Gen Hou,Wei Li,Jian Gao,Da-Li Yue,De-Gang Wu,Wu-Rong Wang,Jin Lin,Zhi-Bo Li,Qian Zhong..An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling[J].石油科学(英文版),2026,23(4):1754-1772,19.基金项目
This work was supported by National Science and Technology Major Project"CO2 Flooding for Significantly Enhancing Recovery Rate and Long-Term Sequestration Technology"(No.2024ZD1406601),National Natural Science Foundation of China(Nos.42272186,42472179,42302128,42202109),Frontier Interdis-ciplinary Exploration Research Program of China University of Petroleum,Beijing(No.2462024XKQY003),and Science Foundation of China University of Petroleum(Beijing)(Nos.2462023BJRC024,and 2462023YJRC039). (No.2024ZD1406601)