Petroleum Reservoir Control Optimization with the Use of the Auto-Adaptive Decision Trees

Edyta Kuk, Jerzy Stopa, Michał Kuk, Damian Janiga and Paweł Wojnarowski
Additional contact information
Edyta Kuk: Edyta Kuk is currently with Hitachi ABB Power Grids Research, ul. Pawia 7, 31-154 Kraków, 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, 2021, vol. 14, issue 18, 1-20

Abstract: The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO 2 -EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%.

Keywords: production optimization; auto-adaptive decision tree; artificial intelligence; machine learning; sequential model-based algorithm configuration; CCS-EOR (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: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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