Authors: S. Amin Gherabati; Richard G. Hughes; Christopher D. White; Hongchao Zhang
Addresses: Bureau of Economic Geology, 10100 Burnet Rd, Austin, TX 78758, USA ' Louisiana State University, Baton Rouge, LA 70803, USA ' Tulane University, 6823 St. Charles Ave., New Orleans, LA 70118, USA ' Louisiana State University, Baton Rouge, LA 70803, USA
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]
Keywords: waterflooding; interwell characterisation; network model; conductance.
International Journal of Oil, Gas and Coal Technology, 2017 Vol.15 No.1, pp.1 - 24
Available online: 11 Apr 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article