Well placement optimization is a critical part of the oil field development planning which aims to find the optimal locations of wells to maximize a traditional objective function (TOF), e.g., cumulative oil production (COP) or the net present value (NPV). However, the optimization process can be quite time-consuming since it requires iterative evaluations of the objective function and each evaluation requires one simulation run of the fluid flow in the discretized time domain and space domain. This paper examined the consistency between productivity potential value (PPV) and cumulative oil production (COP), and proposed to use PPV as the objective function, whose evaluation does not require simulation run, to improve computational efficiency. However, since PPV is a static measure of a reservoir, we use PPV only in early iterations of the well placement optimization followed by TOF in later iterations in order to capture reservoir dynamics. The use of PPV and TOF in a sequential manner is referred to as a hybrid objective function (HOF). In this work, a naturally parallelizable optimization algorithm, particle swarm optimization (PSO), where simulation runs can be conducted in batches is used as the optimizer. The effectiveness of the proposed procedure is validated based on three numerical examples including a 2D model, the PUNQ-S3 model and the Egg model. Results demonstrate the well placement optimization strategy using HOF finds comparable COP within much less simulation runs compared to the optimization using TOF. In summary, well placement optimization with the objective function defined as PPV in the first 25% iterations and TOF in the following 75% iterations is the best combination.

井位优化是油田开发计划的关键部分，旨在寻找井的最佳位置，以最大化传统目标函数（TOF），例如累计石油产量（COP）或净现值（NPV）。但是，优化过程可能非常耗时，因为它需要对目标函数进行迭代评估，并且每次评估都需要对离散时域和空间域中的流体流进行一次模拟运行。本文研究了生产力潜力值（PPV）与累计石油产量（COP）之间的一致性，并提出以PPV作为目标函数，其评估不需要模拟运行，以提高计算效率。但是，由于PPV是储层的静态度量，我们仅在井位优化的早期迭代中使用PPV，然后在后续迭代中使用TOF，以捕获储层动态。以顺序方式使用PPV和TOF称为混合目标函数（HOF）。在这项工作中，可以并行使用模拟运行的自然可并行优化算法（粒子群优化（PSO））用作优化程序。基于包括2D模型，PUNQ-S3模型和Egg模型的三个数值示例，验证了所提出程序的有效性。结果表明，与使用TOF进行优化相比，使用HOF进行的井位优化策略可在更少的模拟运行中找到可比的COP。综上所述，