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    A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media

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    Name:
    JFUE-D-21-06636_R1-Accepted.pdf
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    2.503Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Embargo End Date:
    2023-12-01
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    Type
    Article
    Authors
    Yan, Bicheng
    Harp, Dylan Robert
    Chen, Bailian
    Pawar, Rajesh
    KAUST Department
    Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
    Date
    2021-12
    Embargo End Date
    2023-12-01
    Permanent link to this record
    http://hdl.handle.net/10754/674979
    
    Metadata
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    Abstract
    Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological CO2 sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ∼ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.
    Citation
    Yan, B., Harp, D. R., Chen, B., & Pawar, R. (2021). A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media. Fuel, 122693. doi:10.1016/j.fuel.2021.122693
    Sponsors
    The authors acknowledge the financial support by US DOE's Fossil Energy Program Office through the project, Science-informed Machine Learning to Accelerate Real Time (SMART) Decisions in Subsurface Applications. Funding for SMART is managed by the National Energy Technology Laboratory (NETL). The authors also thank Dr. Seyyed A. Hosseini from University of Texas at Austin for providing the reservoir simulation data for CO geological sequestration, and thank Dr. Diana Bacon from Pacific Northwest National Laboratory for providing parsing tools to process simulation data.
    Publisher
    Elsevier BV
    Journal
    Fuel
    DOI
    10.1016/j.fuel.2021.122693
    arXiv
    2105.09467
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S001623612102559X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.fuel.2021.122693
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