A study of absolute permeability dependence on pore-scale characteristics of carbonate reservoirs using artificial intelligence. (23rd September 2008)

• Record Type: Journal Article
• Authors: Basbug, Basar; Karpyn, Zuleima T.
• Is Part Of: International journal of oil, gas and coal technology. Volume 1:Number 4(2008)
• Page Start: 382
• Page End: 398
• Publication Date: 2008-09-23
• Subjects: artificial neural networks -- ANNs -- absolute permeability dependence -- pore-scale characteristics -- carbonates -- carbonate reservoirs -- porosity -- specific surface area -- irreducible water saturation -- soft computing; 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

An artificial neural network (ANN) was designed and tested in the present study to examine the correlation between permeability estimations and porous medium properties, such as porosity, specific surface area, and irreducible water saturation. The network developed in this work uses soft computing techniques to investigate the absolute permeability dependence on pore-scale characteristics of carbonate reservoirs. The present study indicates that ANN generated permeability values are consistent with those obtained from core analysis. Results from this study confirm the complex relationship among permeability, porosity, specific surface area and irreducible water saturation of carbonate reservoirs, and suggest that variations in specific surface area affect the magnitude of irreducible water saturations, thus creating an apparent dependence of permeability on irreducible water saturation. Additional observations support a direct relationship between porosity and permeability, and an inverse relationship between specific surface area and permeability. [Received: March 5, 2008; Accepted: May 16, 2008]