A Caltech Library Service

U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow

Wen, Gege and Li, Zongyi and Azizzadenesheli, Kamyar and Anandkumar, Anima and Benson, Sally M. (2021) U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow. . (Unpublished)

[img] PDF - Submitted Version
Creative Commons Attribution Non-commercial No Derivatives.


Use this Persistent URL to link to this item:


Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical simulation data can provide a faster alternative to traditional simulators. Here we present U-FNO, a novel neural network architecture for solving multiphase flow problems with superior accuracy, speed, and data efficiency. U-FNO is designed based on the newly proposed Fourier neural operator (FNO), which has shown excellent performance in single-phase flows. We extend the FNO-based architecture to a highly complex CO₂-water multiphase problem with wide ranges of permeability and porosity heterogeneity, anisotropy, reservoir conditions, injection configurations, flow rates, and multiphase flow properties. The U-FNO architecture is more accurate in gas saturation and pressure buildup predictions than the original FNO and a state-of-the-art convolutional neural network (CNN) benchmark. Meanwhile, it has superior data utilization efficiency, requiring only a third of the training data to achieve the equivalent accuracy as CNN. U-FNO provides superior performance in highly heterogeneous geological formations and critically important applications such as gas saturation and pressure buildup "fronts" determination. The trained model can serve as a general-purpose alternative to routine numerical simulations of 2D-radial CO₂ injection problems with significant speed-ups than traditional simulators.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper Itempython code and dataset used
https://ccsnet.aiRelated ItemWeb application - trained U-FNO models
Wen, Gege0000-0003-1668-3777
Li, Zongyi0000-0003-2081-9665
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) G. Wen and S. M. Benson gratefully acknowledges the supported by ExxonMobil through the Strategic Energy Alliance at Stanford University and the Stanford Center for Carbon Storage. Z. Li gratefully acknowledges the financial support from the Kortschak Scholars Program. A. Anandkumar is supported in part by Bren endowed chair, LwLL grants, Beyond Limits, Raytheon, Microsoft, Google, Adobe faculty fellowships, and DE Logi grant. The authors would like to acknowledge the reviewers and editors for the constructive comments. Code and data availability. The python code for U-FNO model architecture and the data set used in training is available at Web application hosts the trained U-FNO models to provide real time predictions.
Funding AgencyGrant Number
Stanford UniversityUNSPECIFIED
Kortschak Scholars ProgramUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Learning with Less Labels (LwLL)UNSPECIFIED
Raytheon CompanyUNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
Google Faculty Research AwardUNSPECIFIED
Caltech De Logi FundUNSPECIFIED
Subject Keywords:Multiphase flow, Fourier neural operator, Convolutional neural network, Carbon capture and storage, Deep learning
Record Number:CaltechAUTHORS:20220714-224722475
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115614
Deposited By: George Porter
Deposited On:15 Jul 2022 22:56
Last Modified:15 Jul 2022 22:56

Repository Staff Only: item control page