Investigating rock micro-structure of sandstones by pattern recognition on their X-ray images

Abstract

Analysing rock micro-structure from micro-computed tomographic images of porous media is vital to understand fluid-flow and estimating petrophysical properties like permeability. The two main approaches of analysing rock micro-structure are (1) through experiments, a time-consuming process and (2) using numerical simulations which are a part of the standard digital rock physics (DRP) workflow. The standard DRP workflow requires the micro-computed tomographic (micro-CT) images to be segmented into distinct phases (pores and minerals). Segmentation is a user-biased and manual process. It relies heavily on the user to choose a threshold(s) that distinguishes unique phases present in the rock micro-structure. Thus, introducing uncertainty in these petrophysical properties. Our approach to resolving this subjectivity and uncertainty in analysing rock micro-structure is to directly apply techniques on the micro-CT images or the greyscale images, rather than using segmented images. For this purpose, we use a pattern recognition technique namely the Grey-Level Co-occurrence Matrix (GLCM). The GLCM technique is used to calculate spatial maps that describe features present in the rock micro-structure. Calculating these spatial maps at varying length-scales by using different displacement vectors aid in analysing the grain-sizes, grain-pore interface and pore-sizes. Unlike the histograms which only preserve the frequency of intensity values that represent different features in micro-CT images, GLCM is a second-order pattern recognition technique that additionally preserves the spatial variation and occurrence of grey-level intensity values. This method of studying the rock micro-structure using greyscale images and pattern-recognition techniques provides an advantage over the conventional segmentation techniques because full-information regarding the rock micro-structure captured during micro-computed tomography is preserved and a threshold-less workflow leads to lesser user subjectivity. Lastly, the GLCM based analysis also provides a pathway for automated investigation of rock-microstructure.