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Scenario Reduction of Realizations Using Fast Marching Method in Robust Well Placement Optimization of Injectors
Natural Resources Research ( IF 5.4 ) Pub Date : 2021-02-24 , DOI: 10.1007/s11053-021-09833-5
Reza Yousefzadeh , Mohammad Sharifi , Yousef Rafiei , Mohammad Ahmadi

Choosing a representative subset of realizations can reduce significantly the number of simulations and the computational cost associated with optimization under geological uncertainty. Methods that use dynamic criteria, such as full flow simulators, can choose effectively representative realizations and reduce the number of simulations during optimization. However, these methods are expensive computationally. This study aims at investigating the effect of using diffusive time of flight (DTOF) as a feature to select a representative subset of realizations for well placement optimization under uncertainty. The proposed approach is based on the calculation of DTOF for pressure propagation in a reservoir; it was calculated using the fast marching method (FMM). To determine reservoir connectivity, a threshold was used for a number of grid blocks at specific time intervals; it can be used as a measure to evaluate and select the realizations. The proposed methodology was utilized to optimize the location of vertical injection wells on three numerical models, which are three dimensional with equally probable realizations. The optimization results of the proposed method were compared with the results of optimization using the full set and the subset realizations obtained using the K-means clustering method. In this study, 10 realizations were selected from the full set as the representative subset. The selection was based on K-means clustering and FMM. Results show that the FMM-based approach outperforms the clustering method and it can capture the uncertainty range with only a small subset of realizations with a much lower computational burden compared to the full set.



中文翻译:

稳健优化喷油器中使用快速行进方法实现的方案减少

选择具有代表性的实现子集可以显着减少模拟次数以及与地质不确定性条件下优化相关的计算成本。使用动态标准的方法(例如全流仿真器)可以有效地选择具有代表性的实现,并在优化过程中减少仿真次数。但是,这些方法在计算上很昂贵。这项研究旨在调查使用扩散飞行时间(DTOF)作为特征来选择不确定性下的井位优化实现的代表性子集的效果。所提出的方法基于用于储层中压力传播的DTOF的计算。它是使用快速行进方法(FMM)计算的。为了确定油藏连通性,在特定时间间隔将阈值用于多个网格块;它可以用作评估和选择实现的度量。所提出的方法被用于在三个数值模型上优化垂直注入井的位置,这三个数值模型是具有相同可能性的三维模型。将该方法的优化结果与使用全套的优化结果以及使用K-means聚类方法获得的子集实现进行了比较。在这项研究中,从全套中选择10个实现作为代表子集。该选择基于K均值聚类和FMM。

更新日期:2021-02-24
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