Dynamic programming (DP) is an effective method for solving multi-stage decision-making problems and has been extensively applied to the optimization of reservoir operations. However, the method is limited by the dimensionality problem; hence, it cannot be directly applied to optimizing the operations of a reservoir system with more than three reservoirs at current computing power. The progressive optimality algorithm (POA) is a classic variant of DP and has been widely used for optimizing multi-reservoir operations. Despite the use of a static variable decoupling strategy to ease DP's dimensionality problem, the POA's performance is reduced by an inherent drawback of the blind search of the static variable decoupling strategy and the dimensionality problem in two-stage solutions. To enhance the POA's performance, we propose an improved version of the POA known as the dipole optimization procedure (DOP) for optimizing cascade reservoir operations. In the improved algorithm, we use a dynamic variable decoupling strategy to obtain search directions that are more targeted than those of the POA so as to improve the quality of the solutions. The dynamic decoupling of variables was achieved by constructing and solving a dipole optimization problem using a DP model developed in this study. Moreover, a perturbation mechanism was introduced to address the POA's dimensionality problem so that the algorithm can be extended to larger reservoir systems. The results of the simulation of a hypothetical four-reservoir system and a real-world cascade reservoir system showed the superiority of the DOP over the POA and other five available alternatives. The comparison was based on the quality of solutions and solving efficiency. The results indicate that the DOP is rational and feasible and has the potential to be applied to optimizing the operation of large-scale multi-reservoir systems with numerous reservoirs.

动态规划（Dynamic Programming，DP）是解决多阶段决策问题的有效方法，已广泛应用于油藏作业优化。但是，该方法受到维度问题的限制；因此，它不能直接应用于以当前计算能力优化具有三个以上水库的水库系统的操作。渐进优化算法 (POA) 是 DP 的经典变体，已广泛用于优化多油藏作业。尽管使用静态变量解耦策略来缓解 DP 的维度问题，但由于静态变量解耦策略的盲搜索和两阶段解决方案中的维度问题的固有缺陷，POA 的性能降低了。为了提高 POA 的绩效，我们提出了一种改进的 POA 版本，称为偶极子优化程序 (DOP)，用于优化梯级油藏作业。在改进的算法中，我们使用动态变量解耦策略来获得比POA更有针对性的搜索方向，从而提高解的质量。变量的动态解耦是通过使用本研究中开发的 DP 模型构建和解决偶极子优化问题来实现的。此外，引入了一种扰动机制来解决 POA 的维数问题，以便该算法可以扩展到更大的水库系统。假设的四油藏系统和现实世界的梯级油藏系统的模拟结果表明，DOP 优于 POA 和其他五种可用替代方案。比较是基于解决方案的质量和解决效率。结果表明，DOP合理可行，具有应用于优化多储层大型多储层系统运行的潜力。