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Optimal Well Control Based on Auto-Adaptive Decision Tree—Maximizing Energy Efficiency in High-Nitrogen Underground Gas Storage

Author

Listed:
  • Edyta Kuk

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
    Edyta Kuk is currently working with Hitachi Energy, Pawia 7, 31-154 Krakow, Poland.)

  • Jerzy Stopa

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

  • Michał Kuk

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

  • Damian Janiga

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

  • Paweł Wojnarowski

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

To move the world toward a more sustainable energy future, it is crucial to use the limited hydrocarbon geological resources efficiently and to develop technologies that facilitate this. More rational management of petroleum reservoirs and underground gas storage can be obtained by optimizing well control. This paper presents a novel approach to optimal well control based on the combination of optimal control theory, innovative artificial intelligence methods, and numerical reservoir simulations. In the developed algorithm, well control is based on an auto-adaptive parameterized decision tree. Its parameters are optimized by state-of-the-art machine learning, which uses previous results to determine favorable parameters. During optimization, a numerical reservoir simulator is applied to compute the objective function. The developed solution enables full automation of the wells for optimal control. An exemplary application of the developed solution to optimize underground storage of gas with high nitrogen content confirmed its effectiveness. The total nitrogen content in the gas decreased by 2.4%, increasing energy efficiency without increasing expense, as only well control was modified.

Suggested Citation

  • Edyta Kuk & Jerzy Stopa & Michał Kuk & Damian Janiga & Paweł Wojnarowski, 2022. "Optimal Well Control Based on Auto-Adaptive Decision Tree—Maximizing Energy Efficiency in High-Nitrogen Underground Gas Storage," Energies, MDPI, vol. 15(9), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3413-:d:810228
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    References listed on IDEAS

    as
    1. Yin, Zhenyuan & Zhang, Shuyu & Koh, Shanice & Linga, Praveen, 2020. "Estimation of the thermal conductivity of a heterogeneous CH4-hydrate bearing sample based on particle swarm optimization," Applied Energy, Elsevier, vol. 271(C).
    2. Michał Kuk & Edyta Kuk & Damian Janiga & Paweł Wojnarowski & Jerzy Stopa, 2020. "Optimization Wells Placement Policy for Enhanced CO 2 Storage Capacity in Mature Oil Reservoirs," Energies, MDPI, vol. 13(16), pages 1-20, August.
    3. Yin, Zhenyuan & Huang, Li & Linga, Praveen, 2019. "Effect of wellbore design on the production behaviour of methane hydrate-bearing sediments induced by depressurization," Applied Energy, Elsevier, vol. 254(C).
    4. Edyta Kuk & Jerzy Stopa & Michał Kuk & Damian Janiga & Paweł Wojnarowski, 2021. "Petroleum Reservoir Control Optimization with the Use of the Auto-Adaptive Decision Trees," Energies, MDPI, vol. 14(18), pages 1-20, September.
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