Production optimization technique, as a crucial step in the closed-loop reservoir management (CLRM), aims to achieve optimal development efficiency by adjusting development schemes (e.g., well-controls) with the aid of optimization methods. However, due to the unbearable computational burden brought by full-scale reservoir simulation, few optimizers can obtain satisfactory solution(s) within limited simulation calls, especially when the problem dimension is very high. This phenomenon is common in many real-world scenarios, which is also referred to as the “curse of dimensionality”. To address this issue, a novel divide-and-conquer (DAC) optimization paradigm is proposed for production optimization problems. Specifically, given a large-scale production optimization problem, it can be decomposed into a number of simpler subproblems with low dimensions. Then, to overcome the computationally expensive issue, multiple data-driven surrogates are built for the subproblems. Finally, all the subproblem surrogates are optimized cooperatively using a reuse strategy of subproblem samples. From the perspective of production optimization, the joint scheme optimization of the original problem is turned into cooperatively optimizing the schemes involved in multiple subproblems. Interestingly, the obtained subproblems always correspond to multiple flow units with weak flow interferences caused by some obstruction factors (e.g., low-permeability channel and vertical barrier layer). This indicates that the DAC method can not only serve as an optimization enhancement technique but also can be employed as an auxiliary means of connectivity analysis. In return, many connectivity analysis methods such as flow diagnostics that require fewer simulation calls can serve as the decomposition tool. More importantly, the superior flexibility of the proposed DAC-based expensive optimization framework allows it to incorporate a wide variety of state-of-the-art surrogate-assisted evolutionary solvers. In this paper, the differential evolution (DE) and two advanced surrogate-assisted evolutionary solvers are implemented under the proposed paradigm. The experimental results conducted on two 100-dimensional benchmark functions and two production optimization tasks verified the effectiveness of the proposed method.

生产优化技术作为闭环油藏管理（CLRM）中的关键步骤，旨在借助优化方法调整开发方案（如井控）来实现最佳开发效率。然而，由于全尺寸油藏模拟带来难以承受的计算负担，很少有优化器能够在有限的模拟调用内获得令人满意的解决方案，尤其是在问题维度非常高的情况下。这种现象在许多现实世界的场景中很常见，也被称为“维度灾难”。为了解决这个问题，针对生产优化问题提出了一种新的分而治之 (DAC) 优化范式。具体来说，给定一个大规模生产优化问题，这表明DAC方法不仅可以作为优化增强技术，还可以作为连通性分析的辅助手段。作为回报，许多连通性分析方法（例如需要较少模拟调用的流量诊断）可以用作分解工具。更重要的是，所提出的基于 DAC 的昂贵优化框架的卓越灵活性使其能够结合各种最先进的代理辅助进化求解器。在本文中，差分进化（DE）和两个先进的代理辅助进化求解器在所提出的范式下实现。在两个 100 维基准函数和两个生产优化任务上进行的实验结果验证了所提方法的有效性。许多连通性分析方法（例如需要较少模拟调用的流量诊断）可以用作分解工具。更重要的是，所提出的基于 DAC 的昂贵优化框架的卓越灵活性使其能够结合各种最先进的代理辅助进化求解器。在本文中，差分进化（DE）和两个先进的代理辅助进化求解器在所提出的范式下实现。在两个 100 维基准函数和两个生产优化任务上进行的实验结果验证了所提方法的有效性。许多连通性分析方法（例如需要较少模拟调用的流量诊断）可以用作分解工具。更重要的是，所提出的基于 DAC 的昂贵优化框架的卓越灵活性使其能够结合各种最先进的代理辅助进化求解器。在本文中，差分进化（DE）和两个先进的代理辅助进化求解器在所提出的范式下实现。在两个 100 维基准函数和两个生产优化任务上进行的实验结果验证了所提方法的有效性。所提议的基于 DAC 的昂贵优化框架的卓越灵活性使其能够结合各种最先进的代理辅助进化求解器。在本文中，差分进化（DE）和两个先进的代理辅助进化求解器在所提出的范式下实现。在两个 100 维基准函数和两个生产优化任务上进行的实验结果验证了所提方法的有效性。所提议的基于 DAC 的昂贵优化框架的卓越灵活性使其能够结合各种最先进的代理辅助进化求解器。在本文中，差分进化（DE）和两个先进的代理辅助进化求解器在所提出的范式下实现。在两个 100 维基准函数和两个生产优化任务上进行的实验结果验证了所提方法的有效性。