An innovative image-based workflow for quantitative pore structure characterization in reservoir rocks

Abstract

Pore connectivity is a key parameter to affect reservoir quality (e.g. permeability). Routinely, this parameter is assessed with special core analysis (i.e. mercury injection capillary pressure analysis). In this paper, an innovative image-based pore structure workflow is proposed to quantitatively delineate the connectivity of pore space of reservoir rocks by using widely available thin section images. By borrowing the concept of mercury injection capillary pressure (MICP), this workflow is able to generate pseudo MICP curves that can be used to quantitatively characterize pore throat/body size distribution and pore connectivity of pore networks. This workflow consists of four steps: (1) HSV threshold-based pore identification; (2) K-D tree structure-based pore segmentation; (3) enhanced watershed-driven pore throat detection, and (4) pore throat distribution generation derived with a media-axis-maximal-circle algorithm. The pseudo MICP curves generated by the workflow can be used to quantitatively assess the variations in pore size, pore throat size, pore geometrical complexity as well as the density of pores of reservoir rocks. Two connectivity indices derived from pseudo MICP curves are introduced to evaluate the connectivity of pore network of reservoir rocks. This workflow provides an innovative and efficient tool for geologists and reservoir engineers to perform quantitative reservoir rock quality evaluation by utilizing widely-available thin section images.