Graph network surrogate model for subsurface flow optimization-Reference-Cited by-同舟云学术

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Graph network surrogate model for subsurface flow optimization

Published:2024-09 Issue: Volume:512 Page:113132
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Tang Haoyu^ORCID,Durlofsky Louis J.

Publisher

Elsevier BV

Reference33 articles.

1. An integrated closed-loop solution to assisted history matching and field optimization with machine learning techniques;Chai;J. Pet. Sci. Eng.,2021

2. Convolutional–recurrent neural network proxy for robust optimization and closed-loop reservoir management;Kim;Comput. Geosci.,2023

3. Deep reinforcement learning for optimizing well settings in subsurface systems with uncertain geology;Nasir;J. Comput. Phys.,2023

4. Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network;Xu;J. Hydrol.,2022

5. Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network;Wang;J. Comput. Phys.,2022

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