Simulating dynamics of pressure and fluid saturation at grid-scale by a deep learning-based surrogate reservoir modeling based on a fast-supply hybrid database and developing preliminary insights for future gas hydrate exploitations in China-Reference-Cited by-同舟云学术

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Simulating dynamics of pressure and fluid saturation at grid-scale by a deep learning-based surrogate reservoir modeling based on a fast-supply hybrid database and developing preliminary insights for future gas hydrate exploitations in China

Published:2023-03 Issue: Volume:222 Page:211415
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Saberali Behzad,Golsanami Naser^ORCID,Zhang Kai,Gong Bin,Ostadhassan Mehdi

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference53 articles.

1. Error amplification when updating deployed machine learning models;Adam,2022

2. A data-driven smart proxy model for a comprehensive reservoir simulation;Alenezi,2016

3. Developing a smart proxy for the SACROC water-flooding numerical reservoir simulation model;Alenezi,2017

4. Application of machine learning and artificial intelligence in proxy modeling for fluid flow in porous media;Amini;Fluid,2019

5. Comparison of strategies for multi-step-ahead prediction of time series using neural network;An,2015

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1. Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media;Geoenergy Science and Engineering;2024-08

2. Leveraging machine learning in porous media;Journal of Materials Chemistry A;2024

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