well placement optimization using differential evolution algorithm

determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. it is a computationally intensive task due to the large number of simulation runs required. therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum number of function evaluations. in this study, the differential evolution (de) algorithm is applied for the determination of optimal well locations. de is a stochastic optimization algorithm that uses a population of solutions which evolve through generations to reach the global optimum. to investigate the performance of this algorithm, three example cases are considered which vary in dimension and complexity of the reservoir model. for each case, both de algorithm and the widely used genetic algorithm (ga) are applied to maximize a modified net present value (mnpv) as the objective function. it is shown that de outperforms ga in all cases considered, though the relative advantage of the de vary from case to case. these results are very promising and demonstrate the applicability of de for this challenging problem.