A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

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.

Automated summary

The study introduces a Surrogate Assisted Quantum-behaved Algorithm to address the challenges in optimizing well placement in the oil and gas industry. By utilizing various metaheuristic optimization techniques, the proposed approach demonstrates superior performance in two complex reservoirs, providing a better net present value and resolving the issue of inconsistency seen in other optimization methods.