IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v251y2024ics0951832024004642.html
   My bibliography  Save this article

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Author

Listed:
  • Jiang, Zhongyi
  • Zhu, Min
  • Lu, Lu

Abstract

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (< 15%) and GPU memory (< 35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Moreover, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

Suggested Citation

  • Jiang, Zhongyi & Zhu, Min & Lu, Lu, 2024. "Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004642
    DOI: 10.1016/j.ress.2024.110392
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024004642
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110392?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kröker, Ilja & Oladyshkin, Sergey, 2022. "Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Kandel, Rajesh & Baroud, Hiba, 2024. "A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Das, Sourav & Tesfamariam, Solomon, 2024. "Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Wu, Hao & Xu, Yanwen & Liu, Zheng & Li, Yumeng & Wang, Pingfeng, 2023. "Adaptive machine learning with physics-based simulations for mean time to failure prediction of engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    6. Alireza Yazdani & Lu Lu & Maziar Raissi & George Em Karniadakis, 2020. "Systems biology informed deep learning for inferring parameters and hidden dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-19, November.
    7. Song, Chaolin & Xiao, Rucheng & Zhang, Chi & Zhao, Xinwei & Sun, Bo, 2024. "Simulation-free reliability analysis with importance sampling-based adaptive training physics-informed neural networks: Method and application to chloride penetration," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    8. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    9. Rehme, Michael F. & Franzelin, Fabian & Pflüger, Dirk, 2021. "B-splines on sparse grids for surrogates in uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    10. Benjamin Fan & Edward Qiao & Anran Jiao & Zhouzhou Gu & Wenhao Li & Lu Lu, 2023. "Deep Learning for Solving and Estimating Dynamic Macro-Finance Models," Papers 2305.09783, arXiv.org.
    11. Shujie Ma & Peter X.-K. Song, 2015. "Varying Index Coefficient Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 341-356, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fan, Hanwen & Jia, Haiying & He, Xuzhuo & Lyu, Jing, 2024. "Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    2. Kröker, Ilja & Oladyshkin, Sergey, 2022. "Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Kong, Dewei & Lin, Zelong & Li, Wei & He, Wei, 2024. "Development of an improved Bayesian network method for maritime accident safety assessment based on multiscale scenario analysis theory," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    4. Guan, Xuefei, 2024. "Sparse moment quadrature for uncertainty modeling and quantification," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2022. "Estimation of varying coefficient models with measurement error," Journal of Econometrics, Elsevier, vol. 230(2), pages 388-415.
    7. Jing Lv & Chaohui Guo, 2019. "Quantile estimations via modified Cholesky decomposition for longitudinal single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1163-1199, October.
    8. Li, Huanhuan & Çelik, Cihad & Bashir, Musa & Zou, Lu & Yang, Zaili, 2024. "Incorporation of a global perspective into data-driven analysis of maritime collision accident risk," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    9. Guo, Zehua & Dailey, Ryan & Feng, Tangtao & Zhou, Yukun & Sun, Zhongning & Corradini, Michael L & Wang, Jun, 2021. "Uncertainty analysis of ATF Cr-coated-Zircaloy on BWR in-vessel accident progression during a station blackout," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    10. Chen, Yu & Yu, Hui & Liu, Chengjie & Xie, Jin & Han, Jun & Dai, Houde, 2024. "Synergistic fusion of physical modeling and data-driven approaches for parameter inference to enzymatic biodiesel production system," Applied Energy, Elsevier, vol. 373(C).
    11. Bai, Guo-Peng & Er, Guo-Kang & Iu, Vai Pan, 2024. "A novel stochastic approach to investigate the probabilistic characteristics of the ship roll system with sinusoidal restoring force," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    12. Wang, Yangpeng & Li, Shuxiang & Lee, Kangkuen & Tam, Hwayaw & Qu, Yuanju & Huang, Jingyin & Chu, Xianghua, 2023. "Accident risk tensor-specific covariant model for railway accident risk assessment and prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    13. Liu, Hefei & Song, Xinyuan & Zhang, Baoxue, 2022. "Varying-coefficient hidden Markov models with zero-effect regions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    14. Hongyu An & Boping Tian, 2024. "Varying Index Coefficient Model for Tail Index Regression," Mathematics, MDPI, vol. 12(13), pages 1-35, June.
    15. Breunig, Christoph, 2021. "Varying random coefficient models," Journal of Econometrics, Elsevier, vol. 221(2), pages 381-408.
    16. Zhou, Fei & Ren, Jie & Ma, Shuangge & Wu, Cen, 2023. "The Bayesian regularized quantile varying coefficient model," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    17. Grabill, Nicholas & Wang, Stephanie & Olayinka, Hammed A. & De Alwis, Tharindu P. & Khalil, Yehia F. & Zou, Jian, 2024. "AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    18. Zhu, Hanbing & Zhang, Yuanyuan & Li, Yehua & Lian, Heng, 2023. "Semiparametric function-on-function quantile regression model with dynamic single-index interactions," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    19. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    20. Guan, Yu & Li, Wei & Kozak, Drazan & Zhao, Junfeng, 2024. "Response and reliability analysis of a nonlinear VEH systems with FOPID controller by improved stochastic averaging method and LBFNN algorithm," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.