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A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization
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AI
DOI：10.1109/ACCESS.2022.3145244.png)
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摘要
En 中文
The oil and gas industry faces difficulties in optimizing well placement problems. These problems are multimodal, non-convex, and discontinuous in nature. Various traditional and non-traditional optimization algorithms have been developed to resolve these difficulties. Nevertheless, these techniques remain trapped in local optima and provide inconsistent performance for different reservoirs. This study thereby presents a Surrogate Assisted Quantum-behaved Algorithm to obtain a better solution for the well placement optimization problem. The proposed approach utilizes different metaheuristic optimization techniques such as the Quantum-inspired Particle Swarm Optimization and the Quantum-behaved Bat Algorithm in different implementation phases. Two complex reservoirs are used to investigate the performance of the proposed approach. A comparative study is carried out to verify the performance of the proposed approach. The result indicates that the proposed approach provides a better net present value for both complex reservoirs. Furthermore, it solves the problem of inconsistency exhibited in other methods for well placement optimization.
Keyword:
Optimization
Reservoirs
Oils
Tuning
Metaheuristics
Search problems
Heuristic algorithms
Quantum computation
well placement optimization
multimodal optimization
metaheuristic
nonlinear optimization problem
reservoir simulation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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