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Apply Two-Way Cluster Analysis to Select Candidate Reservoir Models From Multiple Realizations

机译:应用双向集群分析以从多种实现中选择候选库模型

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Unconventional resource plays are not typically homogeneous; therefore the reservoirs might not be contiguous. Geostatistical based earth modeling can create hundreds of reservoir property realizations. The challenge is to select a few optimal models from these realizations for further analysis. Traditionally, candidates are selected from ranked distributions representing quantiles (e.g., P10, P50, and P90) of the reservoir volumetrics, which assumes contiguous reservoir volumes. However, such methods lack any physical geometrical information. In this paper, a two-way clustering method is used to take into account spatial property information. First, a three-dimensional (3-D) realization is transformed into a one-dimensional (1-D) array, so that each element of the array maps to one single grid cell that carries the spatial location information. Next, a matrix is created from the multiple realizations. Each column represents a single realization, and each row represents a single 3-D grid cell. Then, clustering algorithm is applied to both columns (R-mode) and rows (Q-mode). R-mode shows the similarity of different realizations, whereas Q-mode shows the clusters of different cells. Finally, connected geobodies are extracted from the clusters. This novel method was applied to a west Texas Permian basin reservoir. Domain experts can first select realization candidates from different R-mode clusters and compare the results. Then, the location and connectivity of cells from Q-mode clustering can be mapped in addition to evaluating their statistical ranking. During Q-mode cluster analysis, Euclidean distance carries the spatial property location of the cell in a distance calculation. Statistical analysis of the geobodies suggests the optimal realization, which is not necessarily the P50. Through such quantitative evaluations of all the realizations, geologists and reservoir engineers can select optimistic to pessimistic realizations to aid economic assessment of the reservoir.
机译:非传统资源扮演通常不是均匀的;因此,水库可能不连续。基于地统计的地球建模可以创造数百个水库属性实现。挑战是从这些实现中选择一些最佳模型以进行进一步分析。传统上,候选者选自排名分布,该分布代表储存量容积的量级(例如,P10,P50和P90),该分量是假设连续的储层体积。然而,这种方法缺乏任何物理几何信息。在本文中,双向聚类方法用于考虑空间属性信息。首先,将三维(3-D)实现变换为一维(1-D)阵列,使得阵列的每个元素映射到承载空间位置信息的一个网格单元。接下来,从多个实现中创建矩阵。每个列表示单个实现,并且每行表示单个3-D网格单元格。然后,将聚类算法应用于两个列(R模式)和行(Q模式)。 R-Mode显示了不同实现的相似性,而Q模式显示不同小区的簇。最后,连接的地磁从簇中提取。这种新方法适用于西德克萨斯州二叠纪盆地储层。域专家可以首先从不同的R模式集群中选择实现候选人并比较结果。然后,除了评估其统计排名之外,还可以映射来自Q模式聚类的细胞的位置和连接。在Q模式集群分析期间,欧几里德距离在距离计算中携带小区的空间属性位置。地质统计分析表明最佳实现,这不一定是P50。通过所有实现的这种定量评估,地质学家和水库工程师可以选择乐观的悲观实现,以帮助对水库进行经济评估。

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