A large scale network model to obtain interwell formation characteristics. (2017)

• Record Type: Journal Article
• Authors: Gherabati, S. Amin; Hughes, Richard G.; White, Christopher D.; Zhang, Hongchao
• Is Part Of: International journal of oil, gas and coal technology. Volume 15:Number 1(2017)
• Page Start: 1
• Page End: 24
• Publication Date: 2017
• Subjects: waterflooding -- interwell characterisation -- network model -- conductance; Petroleum as fuel -- Periodicals; Gas as fuel -- Periodicals; Coal -- Periodicals; Biomass energy -- Periodicals; Hydrogen as fuel -- Periodicals; 662.6
• Languages: English
• ISSNs: 1753-3309

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]