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

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 ec...

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Bibliographic Details
Published in:Energies (Basel) 2021-09, Vol.14 (18), p.5702
Main Authors: Kuk, Edyta, Stopa, Jerzy, Kuk, Michał, Janiga, Damian, Wojnarowski, Paweł
Format: Article
Language:English
Subjects:
Algorithms
artificial intelligence
auto-adaptive decision tree
Carbon dioxide
CCS-EOR
Control theory
Decision making
DIABLO protein
Energy demand
Hydrocarbons
machine learning
Net present value
Numerical analysis
Oil and gas production
Optimization
Parameterization
Process controls
production optimization
Reservoirs
sequential model-based algorithm configuration
Simulation
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Summary: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 CO2-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%.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14185702