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Learning multi-phase flow and transport in fractured porous media with auto-regressive and recurrent graph neural networks

Mohammed Al Kobaisi Wenjuan Zhang Waleed Diab Hadi Hajibeygi

Earth Energy Science2026，Vol.2Issue(1)：P.58-73,16.

Earth Energy Science2026，Vol.2Issue(1)：P.58-73,16.DOI:10.1016/j.ees.2025.11.002

Learning multi-phase flow and transport in fractured porous media with auto-regressive and recurrent graph neural networks

Mohammed Al Kobaisi ¹Wenjuan Zhang ²Waleed Diab ³Hadi Hajibeygi⁴

作者信息

1. Department of Chemical and Petroleum Engineering,Khalifa University of Science and Technology,Abu Dhabi,UAE Delft Institute of Applied Mathematics,Delft University of Technology,Delft,the Netherlands
2. School of Civil Engineering,Qingdao University of Technology,Qingdao 266520,China
3. Department of Chemical and Petroleum Engineering,Khalifa University of Science and Technology,Abu Dhabi,UAE
4. Department of Geoscience and Engineering,Delft University of Technology,Delft,the Netherlands
折叠

摘要

关键词

Graph neural networks/Deep learning/SciML/EDFM/Fractured reservoirs

分类

信息技术与安全科学

引用本文复制引用

Mohammed Al Kobaisi,Wenjuan Zhang,Waleed Diab,Hadi Hajibeygi..Learning multi-phase flow and transport in fractured porous media with auto-regressive and recurrent graph neural networks[J].Earth Energy Science,2026,2(1):P.58-73,16.

基金项目

supported by ADNOC(Grant No.8434000476) （Grant No.8434000476）

the National Natural Science Foundation of China(Grant No.52304030) （Grant No.52304030）

the Natural Science Foundation of Shandong Province,China(Grant No.ZR2023QA034). （Grant No.ZR2023QA034）

Earth Energy Science

ISSN：2950-1547

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