A holistic review on artificial intelligence techniques for well placement optimization problem. (March 2020) - Islam, Jahedul; Vasant, Pandian M.; Negash, Berihun Mamo; Laruccia, Moacyr Bartholomeu; Myint, Myo; Watada, Junzo
Highlights: Well placement optimization is one of the major challenging factor from optimizations point of view as high dimensional optimization problem. Stochastic algorithms - such as Particle swarm optimization, and Differential Evolution have been implemented in the well placement optimization. The review of natures inspired meta heuristic techniques, and their application to maximize the economic factors have been investigated. The future endeavor, exploiting multiple optimization processes for developing efficient Proxy model is expected. Abstract: Well placement optimization is one of the major challenging factors in the field development process of oil and gas industry. The objective function of well placement optimization is considered as high dimensional, discontinuous and multi-model. Over the last decade, both gradient-based and gradient-free optimization methods have been implemented to tackle this problem. Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. These optimization techniques are implemented as stand-alone or as hybrid form to maximize the economic factors. In this paper, several nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors are reviewed. Newly developed optimization algorithms are very efficient and favorable when compared to other established optimization algorithms and in all cases, it has been noticed that hybridization of two or more algorithms works better than stand-alone algorithms. Furthermore, none of the single optimization techniques can be established as being universally superior which aligns with no free lunch theorem. For future endeavor, combining optimization methods and exploiting multiple optimization processes for faster convergence and developing efficient proxy model is expected.