A large scale network model to obtain interwell formation characteristics

Abstract

Limited data availability and poor data quality make it difficult to characterise many reservoirs. For waterflooded reservoirs, production and injection data provide information from which injector-to-producer connections can be inferred. In this research, well locations and injection and production rate data are used to develop a reservoir-scale network model. A Voronoi mesh divides the reservoir into node volumes, each of which contains a well. Bonds connect the nodes with conductance values that are inferred from the rate data. The inverse problem minimises the mean-squared difference between computed and observed production data by adjusting the conductances between nodes. A derivative free optimisation algorithm is used to minimise the mean-squared difference. This coarse network model approach is fast and efficient because it solves for a small number of unknowns and is less underdetermined than correlation-based methods. The reservoir network model has promise as a reservoir description tool because of its modest data requirements, flexibility, efficiency, interpretability, and dynamism. [Received: July 17, 2015; Accepted: January 14, 2016]