Permeability Estimation Based on Pore Characterization and Flow Modeling From Thin-Sections Image Analysis of Grain-Dominated Carbonates

Abstract

Image analysis can provide insights regarding pore networks, including pore sizes and types, and can also be useful for permeability estimation. Permeability estimation based on image analysis is a good alternative when intact core-plugs are not available for laboratory measurement. While there is much research on permeability estimation from 2D and 3D images or models, accurately estimating permeability for carbonates, which contains highly variable pore networks, is still challenging. In this study, a method for permeability estimation based on thin-section image analysis and 2D permeability simulation was developed for grain-dominated carbonates based on semi-theoretical analysis of 2D and 3D permeability (K2D and K3D) relationships. The mathematical expression of the K2D/K3D is examined with a carbonate grainstone sample, for which both K2D and K3D were obtained through permeability simulation based on micro-CT images. The method was applied to 24 grain-dominated carbonate samples collected from different wells in West Texas and Abu Dubai having large variations in rock textures and fabrics and a broad range of permeability from 0.1 mD to 3200 mD. The representativeness of the thin sections and correct determination of the effective pores are key for accurate permeability estimation. When estimated permeability values were compared to measured values, we found that 92% of the estimated results are within a factor of ±5 of the measured values (a factor of 5 means the ratio of estimated over measured permeability equals 5; a factor of -5 means the ratio of estimated over measured permeability equals 1/5), and 46% within a factor of ±2. Two-dimensional image analysis and modeling are simpler than those in 3D, but still include the influence of pore shapes and size distribution. Therefore, this thin-section based method can be used as a quick yet reliable way for permeability estimation.

AAPG Datapages/Search and Discovery Article #90291 ©2017 AAPG Annual Convention and Exhibition, Houston, Texas, April 2-5, 2017