Physics-informed neural network-based petroleum reservoir simulation with sparse data using domain decomposition-Reference-Cited by-同舟云学术

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Physics-informed neural network-based petroleum reservoir simulation with sparse data using domain decomposition

Published:2023-12 Issue:6 Volume:20 Page:3450-3460
ISSN:1995-8226
Container-title:Petroleum Science
language:en
Short-container-title:Petroleum Science

Author:

Han Jiang-Xia^ORCID,Xue Liang^ORCID,Wei Yun-Sheng,Qi Ya-Dong,Wang Jun-Lei,Liu Yue-Tian,Zhang Yu-Qi

Funder

China National Petroleum Corporation

Natural Science Foundation of Beijing Municipality

Science Foundation of China University of Petroleum, Beijing

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Economic Geology,Geochemistry and Petrology,Geology,Geophysics,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference32 articles.

1. State-of-the-art in artificial neural network applications: a survey;Abiodun;Heliyon,2018

2. Simulating oil and water production in reservoirs with generative deep learning;Alakeely;SPE Reservoir Eval. Eng.,2022

3. Prediction of porous media fluid flow using physics informed neural networks;Almajid;J. Petrol. Sci. Eng.,2022

4. Mechanism of fluid displacement in sands;Buckley;Trans. AIME,1942

5. CNN-PFVS: integrating neural network and finite volume models to accelerate flow simulation on pore space images;Chung;Transp Porous Med.,2020

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