Abstract
Optimization of well placement is one of the main difficult factors in the development process in the oil and gas industry. The well placement optimization is high dimensional, multi-modal and discontinuous. In previous research, conventional and non-conventional optimization techniques have been applied to resolve this problem. However, gradient-free optimization techniques such as genetic algorithm and particle swarm optimization which is considered as the most efficient algorithms in this area suffer from local optima. In this article, two new metaheuristic optimization techniques, namely, crow search algorithm and firefly algorithm are applied to the well placement optimization problem and their applications to maximize the net profit value are studied. To study the performance of the firefly and crow search algorithm, Eclipse and MATLAB environment are used. The proposed techniques are compared to popular established methods for optimizing well placement. Results show that the firefly algorithm is proved to be efficient and effective compared to other established techniques. However, the standard crow search algorithm is not suited to this problem. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Highlights
Well placement optimization has been a major issue in the eld development process for a while due to its high dimensional, discontinuous and multimodal objective function
Where L9i 8 is the number of unique q function evaluations required to nd solution i such that f (q) ≥ 0.98f for trial and is the total number of function evaluations per trial. Apart from these two criteria, statistical data like standard deviation, average and minmax are data are collected in experimental trials and the results are shown in Tabs. 4 and 6. 5 trials are considered for each algorithm to calculate the mentioned criteria
Firey algorithm (FA) can perform better than particle swarm optimization (PSO), genetic algorithms (GA), and Crow search algorithm (CSA) to tackle highly nonlinear, multimodal optimization problem as FA can automatically subdivide its population into subgroups, since local attraction is stronger than long-distance attraction
Summary
Well placement optimization has been a major issue in the eld development process for a while due to its high dimensional, discontinuous and multimodal objective function. The researchers solved the problem of optimizing the well location by utilizing random neighborhood topology with PSO [7, 22] This solution provides better results and avoids local optimization. They have found that the DE presented better performance than the PSO and CMA-ES, but greater variance is observed Algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), dierential evolution (DE), and covariance matrix adaptation evolution strategy (CMA-ES) are becoming more and more popular day by day. Their eciency and eectiveness are still not promising since the methods have many limitations.
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