Flow Field Description and Simplification Based on Principal Component Analysis Downscaling and Clustering Algorithms

Abstract

The flow field obtained from streamline simulation reflects key properties of the reservoir, such as the distribution of the remaining oil and the location of channels. However, in the three-dimensional streamline field, the advantages of streamline simulation are limited. Because numerous streamlines interfere with each other and distribute in a sophisticated way, it is really difficult to infer the connectivity between wells and the flow characteristics of the reservoir. To make a more effective and visualizable description of the flow field, the three-dimensional streamline field has to be simplified. In this paper, principal component analysis (PCA) is applied to parameterize the streamline attributes and reduce the dimensionality of the flow field. After dimension reduction, the principal components of the streamline field can be analyzed by the clustering method. In the clustering procedure, the mainstream lines are selected according to the clustering center, thereby intuitively illustrating the properties of the reservoir. Through experimental verification, the proposed method can characterize the streamlines of the flow field more efficiently and reflect the inter-well connectivity more clearly than the commercial numerical simulator.