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

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 c...

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Veröffentlicht in:Energies (Basel) Jg. 15; H. 9; S. 3413
Hauptverfasser: Kuk, Edyta, Stopa, Jerzy, Kuk, Michał, Janiga, Damian, Wojnarowski, Paweł
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.05.2022
Schlagworte:
Artificial intelligence
auto-adaptive decision tree
Control theory
Decision trees
Dynamic programming
Efficiency
Genetic algorithms
Geology
Hydrocarbons
machine learning
Natural gas
Numerical analysis
optimal control
Optimization algorithms
Process controls
sequential model-based algorithm configuration
ISSN:1996-1073, 1996-1073
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15093413