Title: A large scale network model to obtain interwell formation characteristics

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.

DOI: 10.1504/IJOGCT.2017.083856

International Journal of Oil, Gas and Coal Technology, 2017 Vol.15 No.1, pp.1 - 24

Received: 07 Aug 2015
Accepted: 14 Jan 2016
Published online: 25 Apr 2017 *

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