End-to-end neural network approach to 3D reservoir simulation and adaptation-Reference-Cited by-同舟云学术

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End-to-end neural network approach to 3D reservoir simulation and adaptation

Published:2022-01 Issue: Volume:208 Page:109332
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Illarionov E.^ORCID,Temirchev P.,Voloskov D.^ORCID,Kostoev R.,Simonov M.,Pissarenko D.,Orlov D.^ORCID,Koroteev D.^ORCID

Funder

Ministry of Education and Science of the Russian Federation

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference23 articles.

1. Robust locally-linear controllable embedding;Banijamali,2017

2. Computational Methods for Multiphase Flows in Porous Media;Chen,2006

3. Neural ordinary differential equations;Chen,2018

4. Gradient-based history matching with a global optimization method;Gómez;SPE J.,2001

5. Deep Learning;Goodfellow,2016

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2. Application of self-learning enhancing Huff and Puff gas injection optimization in shale reservoirs through sequence-based proxy reservoir simulation and reinforcement learning;Geoenergy Science and Engineering;2024-03

3. Construction of adaptive reduced-order reservoir models based on POD‑DEIM approach;Georesursy;2023-12-30

4. A deep learning method for multi-material diffusion problems based on physics-informed neural networks;Computer Methods in Applied Mechanics and Engineering;2023-12

5. Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II;Energies;2023-09-20

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