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Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure

Author

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  • Tao Zhang

    (Computational Transport Phenomena Laboratory (CTPL), Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia)

  • Shuyu Sun

    (Computational Transport Phenomena Laboratory (CTPL), Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia)

Abstract

The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.

Suggested Citation

  • Tao Zhang & Shuyu Sun, 2021. "Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure," Energies, MDPI, vol. 14(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7724-:d:681820
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    References listed on IDEAS

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    1. Wang, Hui & Chen, Li & Qu, Zhiguo & Yin, Ying & Kang, Qinjun & Yu, Bo & Tao, Wen-Quan, 2020. "Modeling of multi-scale transport phenomena in shale gas production — A critical review," Applied Energy, Elsevier, vol. 262(C).
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    2. Hariharan Ramachandran & Andreia Plaza-Faverola & Hugh Daigle, 2022. "Impact of Gas Saturation and Gas Column Height at the Base of the Gas Hydrate Stability Zone on Fracturing and Seepage at Vestnesa Ridge, West-Svalbard Margin," Energies, MDPI, vol. 15(9), pages 1-25, April.
    3. Song, Haoran & Zhong, Zheng & Lin, Baiquan, 2023. "Mechanical degradation model of porous coal with water intrusion," Energy, Elsevier, vol. 278(C).
    4. Piotr Pałka & Robert Olszewski & Agnieszka Wendland, 2022. "Using Spatial Data Science in Energy-Related Modeling of Terraforming the Martian Atmosphere," Energies, MDPI, vol. 15(14), pages 1-24, July.

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