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

Edyta Kuk, Jerzy Stopa, Michał Kuk, Damian Janiga and Paweł Wojnarowski
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Edyta Kuk: Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 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

Energies, 2022, vol. 15, issue 9, 1-22

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.

Keywords: optimal control; auto-adaptive decision tree; artificial intelligence; machine learning; sequential model-based algorithm configuration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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