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Joint Optimization of Well Locations and Operational Conditions Using a New Hybrid Algorithm
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 78th EAGE Conference and Exhibition 2016, May 2016, Volume 2016, p.1 - 5
Abstract
Since optimal well locations and operational conditions are dependent on each other, these variables have been recently optimized together. Particle swarm optimization (PSO) algorithm is a global optimization method, which is computationally less attractive. Ensemble based optimization (EnOpt) method is a gradient based optimization method, which converges fast but is susceptible to get trapped into a local optima. In this paper, we propose a new hybrid algorithm PSO-EnOpt for the joint optimization problem. By combining PSO and EnOpt algorithms, PSO-EnOpt can take the advantages of the both algorithms. In the PSO-EnOpt algorithm, PSO locates the optimizing vector near global optima. Then, EnOpt finds a global solution with fast converge rates. Therefore, PSO-EnOpt can have faster converge rates compare to the PSO algorithm. Also, it can provide more stable results than EnOpt algorithm due to the global search ability of PSO. We apply the proposed algorithm to determine an optimal injection well location, injection rates, and producing bottomhole pressures. PSO-EnOpt shows superior performance compare to other preexisting algorithms. The proposed algorithm can be applicable to real field development problems and help decision makers to make rapid and optimal decisions.