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Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators

Wen, Gege and Li, Zongyi and Long, Qirui and Azizzadenesheli, Kamyar and Anandkumar, Anima and Benson, Sally M. (2022) Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators. . (Unpublished)

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Carbon capture and storage (CCS) is an important strategy for reducing carbon dioxide emissions and mitigating climate change. We consider the storage aspect of CCS, which involves injecting carbon dioxide into underground reservoirs. This requires accurate and high-resolution predictions of carbon dioxide plume migration and reservoir pressure buildup. However, such modeling is challenging at scale due to the high computational costs of existing numerical methods. We introduce a novel machine learning approach for four-dimensional spatial-temporal modeling, which speeds up predictions nearly 700,000 times compared to existing methods. It provides highly accurate predictions under diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework, Nested Fourier Neural Operator (FNO), learns the solution operator for the family of partial differential equations governing the carbon dioxide-water multiphase flow. It uses a hierarchy of FNO models to produce outputs at different refinement levels. Thus, our approach enables unprecedented real-time high-resolution modeling for carbon dioxide storage.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Wen, Gege0000-0003-1668-3777
Li, Zongyi0000-0003-2081-9665
Long, Qirui0000-0002-6572-4021
Azizzadenesheli, Kamyar0000-0001-8507-1868
Anandkumar, Anima0000-0002-6974-6797
Benson, Sally M.0000-0002-3733-4296
Additional Information:Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). The authors gratefully acknowledge Yanhua Yuan from ExxonMobil for many helpful conversations and suggestions. G.W. and S.B. gratefully acknowledge the support by ExxonMobil through the Strategic Energy Alliance at Stanford University and the Stanford Center for Carbon Storage. Z.L. gratefully acknowledges the financial support from the Kortschak Scholars, PIMCO Fellows, and Amazon AI4Science Fellows programs. A.A. is supported in part by Bren endowed chair. Author contributions statement. G.W. Conceptualization, Methodology, Software, Data acquisition, Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing. Z.L. Methodology, Investigation, Validation, Writing – original draft, Writing – review & editing. Q.L. Data acquisition. K.A. Methodology, Software, Investigation, Validation, Writing – review & editing. A.A. Funding acquisition, Supervision, Writing – review & editing. S.B. Conceptualization, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Writing – review & editing. The authors declare no competing interests.
Funding AgencyGrant Number
Stanford UniversityUNSPECIFIED
Kortschak Scholars ProgramUNSPECIFIED
Amazon AI4Science FellowshipUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Record Number:CaltechAUTHORS:20221221-004723629
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118558
Deposited By: George Porter
Deposited On:22 Dec 2022 18:34
Last Modified:02 Jun 2023 01:29

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