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Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Author

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  • Anna Samnioti

    (School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Vassilis Gaganis

    (School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece
    Institute of Geoenergy, Foundation for Research and Technology-Hellas, 73100 Chania, Greece)

Abstract

In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6079-:d:1221008
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    References listed on IDEAS

    as
    1. Vassilis Gaganis & Dirar Homouz & Maher Maalouf & Naji Khoury & Kyriaki Polychronopoulou, 2019. "An Efficient Method to Predict Compressibility Factor of Natural Gas Streams," Energies, MDPI, vol. 12(13), pages 1-20, July.
    2. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    3. 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.
    4. Peyman Bahrami & Farzan Sahari Moghaddam & Lesley A. James, 2022. "A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering," Energies, MDPI, vol. 15(14), pages 1-32, July.
    5. Anna Samnioti & Vassiliki Anastasiadou & Vassilis Gaganis, 2022. "Application of Machine Learning to Accelerate Gas Condensate Reservoir Simulation," Clean Technol., MDPI, vol. 4(1), pages 1-21, March.
    6. Anna Samnioti & Eirini Maria Kanakaki & Evangelia Koffa & Irene Dimitrellou & Christos Tomos & Paschalia Kiomourtzi & Vassilis Gaganis & Sofia Stamataki, 2023. "Wellbore and Reservoir Thermodynamic Appraisal in Acid Gas Injection for EOR Operations," Energies, MDPI, vol. 16(5), pages 1-26, March.
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    Cited by:

    1. Panagiotis Papanikolaou & Eirini Maria Kanakaki & Stefanos Lempesis & Vassilis Gaganis, 2024. "Mass Balance-Based Quality Control of PVT Results of Reservoir Oil DL Studies," Energies, MDPI, vol. 17(13), pages 1-29, July.
    2. Eirini Maria Kanakaki & Anna Samnioti & Evangelia Koffa & Irene Dimitrellou & Ivan Obetzanov & Yannis Tsiantis & Paschalia Kiomourtzi & Vassilis Gaganis & Sofia Stamataki, 2023. "Prospects of an Acid Gas Re-Injection Process into a Mature Reservoir," Energies, MDPI, vol. 16(24), pages 1-27, December.

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