Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks

Accurate estimation of permeability from other data has been a challenge for many years. The aim of this study was to establish relationships between permeability, porosity and pore throat size, and apply these relationships in a predictive sense. Regression analysis was utilized to achieve a set of relationships between permeability, porosity and pore throat size for 144 carbonate samples. These relationships can be used to estimate permeability from porosity and pore throat radii. Also in this study, a fully-connected multi-layer perceptron network was used to predict permeability from porosity and pore throat radii. An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems. These characteristics have enabled artificial neural networks to be more successful in predicting permeability when compared to regression analysis.This study also indicates that pore throat radii corresponding to a mercury saturation of 50% (r50) is the best permeability predictor for carbonates with complex pore networks.