Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning-Reference-Cited by-同舟云学术

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Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning

Published:2024-05 Issue: Volume:361 Page:122914
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Mao Shaowen^ORCID,Chen Bailian,Malki Mohamed^ORCID,Chen Fangxuan^ORCID,Morales Misael^ORCID,Ma Zhiwei,Mehana Mohamed^ORCID

Funder

Los Alamos National Laboratory

Publisher

Elsevier BV

Reference70 articles.

1. European commission;Energy 2020,2010

2. Agreement, P;United nations,2015

3. Monthly energy review;US Energy Information Administration (EIA),2022

4. Net generation by energy source: Total (all sectors);US Energy Information Administration (EIA),2022

5. Renewable energy market update-outlook for 2023 and 2024;US Energy Information Administration (EIA),2023

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2. Underground hydrogen storage in depleted gas reservoirs with hydraulic fractures: Numerical modeling and simulation;Journal of Energy Storage;2024-09

3. A critical review of underground hydrogen storage: From fundamentals to applications, unveiling future frontiers in energy storage;International Journal of Hydrogen Energy;2024-08

4. An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks;Geoenergy Science and Engineering;2024-08

5. Strategies for hydrogen storage in a depleted sandstone reservoir from the San Joaquin Basin, California (USA) based on high-fidelity numerical simulations;Journal of Energy Storage;2024-07

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