A Large Scale Network Model To Obtain Interwell Formation Characteristics

Abstract

Abstract Limited data availability and poor data quality make it difficult to characterize many reservoirs. For reservoirs that have undergone waterflooding, production and injection data is a reliable source of information from which injector to producer connections can be inferred. In this research, we use well locations and injection and production rate data to develop a reservoir-scale network model. The coarse network model approach is fast and efficient since it solves for a relatively small number of unknowns and is less underdetermined than correlation-based methods. A Voronoi mesh divides the reservoir into a number of node volumes each of which contains a well. Bonds connect each of the nodes with conductance values that must be inferred from the rate data. An inverse problem is written where the mean-squared difference between the simulated and actual production data is minimized and the conductance values between each node are the unknowns. A derivative free optimization algorithm is utilized to minimize the objective function. The application of this work is primarily for secondary and tertiary floods with limited geological data. The solution parameters are directly proportional to formation properties. In addition, they help to evaluate the degree of sweep between wells. This approach has been successfully tested for different synthetic permeability distribution cases. The main advantages of the proposed method are: It can model changes in flow pattern caused by adding new wells or shutting-in producers.It uses conventional history matching methods to solve a simplified inverse problem using only production and injection data. It uses a small number of nodes and converges to a better posed solution than statistical approaches. Convergence to a solution for higher frequency data only decreases the speed of the method slightly.The degree of injector to producer interaction is not fixed and can vary over time. Thus, the technique captures more of the physical relationships between well pairs and features that influence the dynamic behavior of the reservoir than previous correlation-based methods.