Self-adaptive Multifactorial Evolutionary Algorithm for Multitasking Production Optimization

Abstract The economic and efficient development of petroleum resources can be realized by dynamically adjusting the reservoir development scheme for the sake of higher recovery efficiency with low development cost. As an important part of the closed-loop reservoir management (CLRM), production optimization has gained increasing research interests. A number of sophisticated production optimization techniques have been proposed in the recent years. It is noteworthy that almost all of these existing methods optimize multiple distinct problems independently and neglect the latent synergies among them. However, seldom real-world problems exist in isolation. A number of studies in the community of computational intelligence demonstrated that the latent similarities among multiple distinct optimization tasks can be utilized to achieve knowledge transfer and thus significantly improve the overall optimization performance. With this in mind, a novel multitasking optimization method named multifactorial evolutionary algorithm (MFEA) is introduced to solve production optimization problems in this study. Different production optimization problems are seen as multiple distinct tasks in a multi-tasking environment thus the given problems can be solved in a multi-tasking manner. To the best of our knowledge, this is the first inspirational application of knowledge transfer to reservoir production optimization. Unfortunately, without any prior knowledge about inter-task similarity, a prespecified transfer intensity parameter adopted by the MFEA can potentially lead to performance slowdowns on some unrelated problems. This phenomenon is also known as the negative transfer. To address this issue, a novel self-adaptive multifactorial evolutionary algorithm (SA-MFEA) is proposed in this study. The transfer intensity parameter is estimated online based on a novel inter-task similarity measurement mechanism. The positive transfer between the problems with high degree of relatedness can be greatly boosted by estimating a higher transfer intensity, while the negative transfer between the distinct problems with low similarity can be effectively curbed by adopting a low value of transfer intensity. At last, the efficacy of the proposed method is experimentally verified on a synthetic multitasking problem with three distinct benchmark functions and two multitasking production optimization problems with distinct component reservoir models.