Application of physics-informed neural networks for self-similar and transient solutions of spontaneous imbibition-Reference-Cited by-同舟云学术

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Application of physics-informed neural networks for self-similar and transient solutions of spontaneous imbibition

Published:2021-08 Issue: Volume:203 Page:108644
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Deng Lichi,Pan Yuewei

Funder

Texas A and M University

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference45 articles.

1. Improving the simulation of waterflood performance with the use of neural networks;Aminian,2000

2. Early and late time analytical solutions for Co-current spontaneous imbibition and generalized scaling;Anderson,2020

3. Imaging spontaneous imbibition in full Darcy-scale samples at pore scale resolution by fast X-ray tomography;Bartels;Water Resour. Res.,2019

4. Automatic differentiation in machine learning: a survey;Baydin;J. Mach. Learn. Res.,2017

5. Geological carbon storage;Bickle;Nat. Geosci.,2009

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1. A novel governing equation for shale gas production prediction via physics-informed neural networks;Expert Systems with Applications;2024-08

2. A critical review of physics-informed machine learning applications in subsurface energy systems;Geoenergy Science and Engineering;2024-08

3. Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions;Advances in Water Resources;2024-03

4. Advancing Relative Permeability and Capillary Pressure Estimation in Porous Media through Physics-Informed Machine Learning and Reinforcement Learning Techniques;Day 3 Wed, February 14, 2024;2024-02-12

5. Application of Physics-Informed Neural Networks for Estimation of Saturation Functions from Countercurrent Spontaneous Imbibition Tests;SPE Journal;2024-01-10

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