The Prediction of Water-oil Relative Permeability Key Points Using An Adaptive Neuro-fuzzy Inference System

• Published in: Petroleum science and technology Vol. 32; no. 16; pp. 2004 - 2019
• Main Authors: Roghanian, R., Asadolahpour, S. R., Rasaei, M. R.
• Format: Journal Article
• Language: English
• Published: Colchester Taylor & Francis 18.08.2014
• Subjects: Adaptive systems; ANFIS; Applied sciences; Artificial neural networks; Crude oil, natural gas and petroleum products; Energy; Exact sciences and technology; Fluid dynamics; Fluid flow; Fluids; Fuels; Fuzzy logic; Iranian reservoirs; key points; Permeability; Petroleum products, gas and fuels. Motor fuels, lubricants and asphalts; Processing of crude oil and oils from shales and tar sands. Processes. Equipment. Refinery and treatment units; Rock; water-oil relative permeability; key points; water-oil relative permeability; ANFIS; Iranian reservoirs
• ISSN: 1091-6466; 1532-2459
• DOI: 10.1080/10916466.2010.493913

Adaptive neuro-fuzzy inference system (ANFIS) is a powerful nonlinear, multivariable regression technique. Here ANFIS was used to identify complex relation between water-oil relative permeability key points and rock and fluid properties. Some 260 relative permeability curves from Iranian carbonate and sandstone reservoirs were used in this study. For each curve six key points (i.e., end points and the crossover points) were considered. ANFIS was then used to predict these key points from different rock and fluid properties. The results showed that very high correlation coefficients in the range of 0.8-0.92 are achievable for Kr key points. ANFIS is a very suitable tool therefore to obtain un-normalized water-oil relative permeability curves with high accuracy when the required core and fluid properties are available.