Computer vision and unsupervised machine learning for pore scale structural analysis of fractured porous media
Abstract Understanding fluid flow in complex fractured porous media requires an accurate representation of the pore space, especially in the presence of both granular pores and fractures, which significantly differ in their geometries. The effect of such a complex fluid pathway is prominent in fractured sandstones and carbonates, which store a significant amount of energy resources. Digital core analysis allows three dimensional imaging of rock cores at high resolutions to capture essential features such as granular pores, fractures, and minerals. These features are represented by a multi modal grey level histogram. Later, segmentation techniques are applied to these images to uniquely differentiate pores (fractures and granular pores) from minerals. However, these segmentation routines are purely based on gray level intensities and hence, are incapable of automatically segregating the pore space into granular pores and fractures. This paper applies a computer vision technique called contour detection for structural analysis of the entire pore space. Since fractures are mainly considered planar features, we carry out a slice by slice analysis on 3D segmented images of fractured sandstones and carbonates. Contours of pores (granular pores and fractures) and their structural properties are read into a principal component analysis followed by a K means clustering algorithm to segregate granular pores from fractures. The exploratory analysis showed that the optimum number of principal components required for segregation is 2 for both sandstones and carbonates. The test for generalization used the above information for K means clustering in x, y, and z directions. The voxels identified as fractures and granular pores are then merged to give a 3D representation of fractures showing an accuracy greater than 89% compared to the ground truth labels. The permeability differences between reconstructed and labelled fractures for digitally fractured samples were less than 9% for all the directions. This analysis provides pathways to understand better competing fluid flow phenomena in pores and fractures and aids in the extraction of pore network models for complex fractured porous media.