The performance of streamline simulation technique to mimic the waterflooding management process in oil reservoirs

Abstract

Multiple simulation techniques are often used to predict the performance of waterflooding and to plan the infill drilling techniques via a process called waterflooding management. However, such simulation techniques usually suffer from many drawbacks and shortages. Therefore, the waterflooding process management is in an evitable need for a robust technique, which can predict the optimal water injection rates and the incremental oil production in a cost-effective manner.In this study, the streamline simulation technique (SST) was used to visualize and analyze the fluid flow patterns in order to investigate the validity of this technique for improving waterflooding management process. In addition, the feasibility of SST in waterflooding management was clearly tested by setting new injection strategies to improve the sweep efficiency via estimating the injector-producer relationship as well as the water injection efficiency. Furthermore, the advantages of streamline simulation visualization in determining the best infill drilling locations, which production wells could be converted to injection wells, and optimizing water injection rates based on monitoring and trials were investigated. The results of this research indicated that the streamline simulation technique is capable to efficiently model and predict the waterflooding process. The SST is computationally efficient and fast in terms of providing flow visualization among injectors and producers, estimating an allocation factor for injection system, and optimizing the locations of new injectors/producers’ wells to increase the sweep efficiency. In addition, the SST could provide a deep understanding with visualization to the waterflooding process and how to improve the waterflooding in oil reservoirs. In this work, through the comparison of various waterflooding scenarios, we discovered the best solution that eliminated the need for optimization algorithms with high computational requirements. This approach relies on areas exhibiting low streamline densities to enable streamlined prediction of infill well locations, thereby reducing simulator processing time. Consequently, this method represents an effective first step in the process of identifying approximate infill well locations, and its efficacy can be enhanced by integrating optimization methods.