Water flooding flowing area identification for oil reservoirs based on the method of streamline clustering artificial intelligence

Abstract

Abstract For the case of carbonate reservoir water flooding development, the flow field identification method based on streamline modeling result was proposed. The Ocean for Petrel platform was used to build the plug-in that exported the streamline data, and the subsequent data was processed and clustered through Python programming, to display the flow field with different water flooding efficiencies at different time in the reservoir. We used density peak clustering as primary streamline cluster algorithm, and Silhouette algorithm as the cluster validation algorithm to select reasonable cluster number, and the results of different clustering algorithms were compared. The results showed that the density peak clustering algorithm could provide better identified capacity and higher Silhouette coefficient than K-means, hierachical clustering and spectral clustering algorithms when clustering coefficients are the same. Based on the results of streamline clustering method, the reservoir engineers can easily identify the flow area with quantification treatment, the inefficient water injection channels and area with developing potential in reservoirs can be identified. Meanwhile, streamlines between the same injector and producer can be subdivided to describe driving capacity distribution in water phase, providing useful information for the decision making of water flooding optimization, well pattern adjustment and deep profile modification.