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

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 a...