Cited by 124CO2 Sequestration and Geologic InteractionsReservoir Engineering and Simulation MethodsEnhanced Oil Recovery Techniques

Towards a predictor for CO2 plume migration using deep neural networks

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Wen, Gege, Tang, Meng, Benson, Sally M.·2021·International Journal of Greenhouse Gas Control

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Crossref · 10.1016/j.ijggc.2020.103223

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66 of 66 references verified0 citation errors · 0 not checkable

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  • Deep learning can help speed up simulation of carbon dioxide plume migration in sequestration reservoirs.

    “Deep learning can also help speed up simulation of carbon dioxide plume migration in sequestration reservoirs [836].”

    From Tackling climate change with machine learning (2023) · cites this paper as [836]

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Authors
Wen, G., Tang, M., & Benson, S. M.
Affiliations
Stanford University (US)
Corresponding author
Wen, Gege
Venue
International Journal of Greenhouse Gas Control
Publisher
Elsevier BV
Published
2021-02
Volume
105
Pages
103223
ISSN
1750-5836 (ISSN-L)
OpenAlex
W2981461641
Cited by
124
References
66
Citations by year
2026: 28 · 2025: 22 · 2024: 25 · 2023: 23 · 2022: 19 · 2021: 5 · 2020: 2
FWCI
4.40
Type
journal article
Language
en
Open access
Locations
  • International journal of greenhouse gas control (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › CO2 Sequestration and Geologic Interactions
  • Physical Sciences › Engineering › Ocean Engineering › Reservoir Engineering and Simulation Methods
  • Physical Sciences › Engineering › Ocean Engineering › Enhanced Oil Recovery Techniques
Keywords
CO2 plume migration, deep neural network, carbon capture and storage, heterogeneous reservoirs, permeability field, buoyancy-driven flow, transfer learning, extrapolation, injection wells, CO2 injection

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