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Parallel Automatic History Matching Algorithm Using Reinforcement Learning

Author

Listed:
  • Omar S. Alolayan

    (Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

  • Abdullah O. Alomar

    (Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

  • John R. Williams

    (Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

Abstract

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such a formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.

Suggested Citation

  • Omar S. Alolayan & Abdullah O. Alomar & John R. Williams, 2023. "Parallel Automatic History Matching Algorithm Using Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:860-:d:1032906
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    References listed on IDEAS

    as
    1. Li, Hao & Misra, Siddharth, 2021. "Reinforcement learning based automated history matching for improved hydrocarbon production forecast," Applied Energy, Elsevier, vol. 284(C).
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    Cited by:

    1. Anna Samnioti & Vassilis Gaganis, 2023. "Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I," Energies, MDPI, vol. 16(16), pages 1-43, August.

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