Enhanced Decision-Making in Gas Lift Optimization through Deep Neural Network-based Multi-Objective Approaches and Feasible Operating Regions

Decision-making flexibility can be a key challenge in optimizing oil well production through a gas lift process. In this work, we introduce a multi-objective optimization strategy facilitated by deep neural networks (DNNs) as surrogate models to lessen the computational burden. Together with a likel...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2024, Vol.58 (14), p.301-306
Main Authors: Rebello, Carine Menezes, Jäschke, Johannes, Nogueira, Idelfonso B.R.
Format: Article
Language:English
Subjects:
feasible operation region
Gas lift system
multi-objective optimization
optimality
surogate models
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Summary:Decision-making flexibility can be a key challenge in optimizing oil well production through a gas lift process. In this work, we introduce a multi-objective optimization strategy facilitated by deep neural networks (DNNs) as surrogate models to lessen the computational burden. Together with a likelihood test, we build a feasible operating region (FOR) using points from particle swarm optimization. Thus providing a tool for the refined process operation. We also subdivide the pareto region into constraint-compliant sub-regions, amplifying operational flexibility and identifying optimal settings. An optimality analysis is included to validate the results and assure the reliability of the surrogate-based optimization. This framework generates an operational map that can be instrumental for real-time process monitoring. Importantly, these computational tools can support the quality of real-time decisions in system operation by providing nuanced, data-driven insights into trade-offs and optimal conditions.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2024.08.353