Applications of Data-driven Classification and Connectivity Quantification Methods in High-resolution Image Analysis

Abstract

Microstructure of a material determines the transport, chemical and mechanical properties. Geological materials and geomaterials are imaged using microscopy tools. The microscopy images are analyzed to better understand the microstructural topology and morphology. Image segmentation is an essential step prior to the microstructural analysis. In this study, we trained a Random Forest classifier to relate certain features corresponding to each pixel and its neighboring pixels in a scanning electron microscopy (SEM) image of shale to a specific component type; thereby developing a methodology to segment SEM images of shale samples into 4 component types, namely, pore/crack, organic/kerogen, matrix and pyrite. We evaluate the generalization capability of the Machine Learning-assisted image-segmentation (MLIS) method by using SEM maps from Wolfcamp and Barnett shale formations. The two formations differ in topology, morphology and distribution of the four components.

The MLIS method is also implemented to classify rock and different fluid phases in micro-CT scans of carbonate core sample undergoing water alternating gas injection with a goal to quantify the three-dimensional fluid connectivity. The three-dimensional connectivity of the fluid phases in porous media plays a crucial role in governing the fluid transport, displacement, and recovery. Accurate three-dimensional quantification of the fluid phase connectivity following each fluid injection stage will lead to better understanding of the efficacy and efficiency of the fluid injection strategies. Two metrics for measuring the connectivity in 3D show robust performance; one uses fast marching method to quantify average time required for a monotonically advancing wave to travel between any two pixels and the other uses two-point probability function to approximate the average distance between any two connected pixels belonging to the same fluid phase. The two connectivity metrics are applied on the three-dimensional (3D) CT scans of one water-wet Ketton whole-core sample subjected to WAG injection to quantify the evolution of the three-dimensional connectivity of the three fluid phases (oil, water, and gas).