Towards a predictor for CO2 plume migration using deep neural networks
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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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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- 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
- green
- arxiv.org
- 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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