Investments in oil and gas projects are driven by critical field development decisions including well placement, which often significantly affect the projects’ economics. Due to their typically high cost and inherently insufficient data (especially in greenfields, or fields in their early stage of development), managing uncertainty is critical when optimizing a field development plan. The use of a single deterministic base case for hydrocarbon, both in place assessment and production forecasting is often misleading and leads to sub-optimal decisions. Consequently, robust field development plans require multiple geological realizations covering the range of uncertainty in reservoir properties and encompassing both multiple geological concepts and geostatistical properties distribution. Typically, an objective function such as the average net present value (NPV) or the average cumulative oil production (COP) is optimized in order to select an optimal development scenario. Nevertheless, such an assessment can be computationally prohibitive, especially when using optimization methods require hundreds, often thousands of costly simulations over a single realization, a number that significantly increases when multiple realizations are involved. This study proposes a new method for well placement optimization under uncertainty, building on map-based evolutionary optimization technique: the black hole particle swarm optimization (BHPSO). The statistical net hydrocarbon thickness (SNHCT) map is introduced to guide the BHPSO algorithm; and hence, pragmatically account for uncertainty in the process of well placement optimization. We optimize well placement on the realization corresponding to the minimum difference between its NHCT map and the SNHCT map. The SNHCT combines the average and the P90 NHCT maps; hence, assuring that the selected sweet spots for well placement are statistically the best with regard to the multiple subsurface realizations. The method is applied on the Olympus benchmark case and results are compared to two scenario reduction methods: RMfinder and k-means-k-medoids Clustering. Results show superior performance over the two methods in terms of optimality of the result and the required computational load.

对油气项目的投资受关键领域开发决策（包括油井布置）的驱动，这些决策通常会严重影响项目的经济效益。由于它们通常成本高昂，并且固有地数据不足（尤其是在未开发的土地或处于开发初期的油田），因此在优化油田开发计划时，管理不确定性至关重要。在现场评估和产量预测中使用碳氢化合物的单一确定性基础案例通常会产生误导，并导致次优决策。因此，稳健的油田开发计划需要多种地质认识，以涵盖储层物性不确定性的范围，并涵盖多种地质概念和地统计特征分布。通常，为了选择最佳的开发方案，优化了诸如平均净现值（NPV）或平均累计石油产量（COP）等目标函数。然而，这样的评估可能在计算上是令人望而却步的，特别是当使用优化方法在单个实现上需要数百个（通常是数千个）昂贵的仿真时，当涉及多个实现时，该数量会大大增加。这项研究提出了一种在不确定性下优化井位优化的新方法，该方法基于基于地图的进化优化技术：黑洞粒子群优化（BHPSO）。介绍了统计净烃厚度（SNHCT）图，以指导BHPSO算法；因此，务实地考虑了井位优化过程中的不确定性。我们在实现上优化对应于其NHCT图和SNHCT图之间的最小差异的井位。SNHCT结合了平均值图和P90 NHCT图。因此，就多个次地下实现而言，要确保所选的井位甜点在统计上是最佳的。该方法适用于Olympus基准案例，并将结果与​​两种场景减少方法进行比较：RMfinder和k-means-k-medoids聚类。在结果的最佳性和所需的计算量方面，结果显示出优于这两种方法的性能。对于多个地下实现，要确保所选的井位最佳位置在统计上是最佳的。该方法适用于Olympus基准案例，并将结果与​​两种场景减少方法进行比较：RMfinder和k-means-k-medoids聚类。在结果的最佳性和所需的计算量方面，结果显示出优于两种方法的性能。对于多个地下实现，要确保所选的井位最佳位置在统计上是最佳的。该方法适用于Olympus基准案例，并将结果与​​两种场景减少方法进行比较：RMfinder和k-means-k-medoids聚类。在结果的最佳性和所需的计算量方面，结果显示出优于这两种方法的性能。