Original scientific paper
https://doi.org/10.54820/ASQF9458
Smart Optimization of Proactive Control of Petroleum Reservoir
Edyta Kuk
orcid.org/0000-0003-3298-7359
; AGH University of Science and Technology, Poland
Michał Kuk
; AGH University of Science and Technology, Poland
Damian Janiga
; AGH University of Science and Technology, Poland
Paweł Wojnarowski
; AGH University of Science and Technology, Poland
Jerzy Stopa
; AGH University of Science and Technology, Poland
Abstract
Artificial Intelligence plays an increasingly important role in many industrial applications as it has great potential for solving complex engineering problems. One of such applications is the optimization of petroleum reservoirs production. It is crucial to produce hydrocarbons efficiently as their geological resources are limited. From an economic point of view, optimization of hydrocarbon well control is an important factor as it affects the whole market. The solution proposed in this paper is based on state-of-the-art artificial intelligence methods, optimal control, and decision tree theory. The proposed idea is to apply a novel temporal clustering algorithm utilizing an autoencoder for temporal dimensionality reduction and a temporal clustering layer for cluster assignment, to cluster wells into groups depending on the production situation that occurs in the vicinity of the well, which allows reacting proactively. Then the optimal control of wells belonging to specific groups is determined using an auto-adaptive decision tree whose parameters are optimized using a novel sequential model-based algorithm configuration method. Optimization of petroleum reservoirs production translates directly into several economic benefits: reduction in operation costs, increase in the production effectiveness and increase in overall income without any extra expenditure as only control is changed.
Keywords
hydrocarbon wells production; control optimization; artificial intelligence; temporal clustering; auto-adaptive decision tree
Hrčak ID:
271554
URI
Publication date:
7.12.2021.
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