A Capillary Pressure-Driven Empirical Model for Permeability Estimation in Carbonate Reservoirs

DOI: 10.14800/IOGR.1348

Abstract

Permeability prediction is a crucial aspect of reservoir characterization, typically derived from core analysis. Using mercury injection test data, permeability can also be predicted. Various models have been proposed for permeability estimation, with their coefficients depending on the pore geometry, rock heterogeneity, and pore throat size. Most existing models rely on a single saturation point or parameter, such as 35% or 25% mercury saturation, or the weighted geometric mean of pore throats and porosity. This study introduces a new empirical model that combines multiple effective parameters to estimate permeability. A total of 50 carbonate samples were used to develop the model, with 20 additional samples, and log data used for verification. In this study, permeability ranges from 0.01 to 450 mD, and porosity ranges from 1% to 30%. Multiple linear regression was employed to establish a relationship between permeability, porosity, R35 (the radius corresponding to 35% mercury saturation), and Swanson's parameter (the ratio of Sb/Pcmax, where Pcmax is the capillary pressure). This model addresses potential errors in previous models by incorporating more comprehensive parameters. The model was verified using mercury injection test data from various wells and has demonstrated promising results.