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.

为了提高采收率，降低开发成本，可以通过动态调整油藏开发方案，实现石油资源的经济高效开发。作为闭环油藏管理（CLRM）的重要组成部分，生产优化已引起越来越多的研究兴趣。近年来，已经提出了许多复杂的生产优化技术。值得注意的是，几乎所有这些现有方法都独立地优化了多个不同的问题，而忽略了它们之间潜在的协同作用。但是，很少有现实世界中的问题是孤立存在的。计算智能社区中的许多研究表明，可以利用多个不同优化任务之间的潜在相似性来实现知识转移，从而显着提高整体优化性能。考虑到这一点，引入了一种新颖的多任务优化方法，称为多因素进化算法（MFEA），以解决本研究中的生产优化问题。在多任务环境中，不同的生产优化问题被视为多个不同的任务，因此可以以多任务的方式解决给定的问题。据我们所知，这是知识转移在油藏生产优化中的第一个鼓舞性应用。不幸的是，由于没有任何关于任务间相似性的知识，MFEA采用的预先指定的传输强度参数可能会导致某些不相关问题的性能下降。这种现象也称为负转移。为了解决这个问题，本研究提出了一种新型的自适应多因子进化算法（SA-MFEA）。传输强度参数是基于一种新颖的任务间相似性测量机制在线估算的。估计较高的转移强度可以大大提高具有高度相关性的问题之间的正向转移，而采用低转移强度的值可以有效地抑制具有低相似性的不同问题之间的负向转移。最后，