Use of local binary pattern in texture classification of carbonate rock micro-CT images

A newly emerged technique known as Digital Rock Physics demonstrates an ability to characterize properties of the porous media. This technique is based on the imaging of rock micro-structure using a micro-CT scanner. Images are segmented based on their grayscale values to extract pore network from the solid phase. Then, rock properties are estimated using extracted pore network and numerical simulations. Porosity and absolute permeability are two essential properties that can be derived from grayscale images. These properties represent storage and flow capacity of the rock. Some rock samples, particularly carbonate rocks have complex micro-structures at several length scales. Due to limited image resolution, 3D images of carbonate rock may not have top-bottom pore connectivity. In this case, one unable to simulate fluid flow throughout the images. Therefore, permeability is computed on small image sub-volumes, where pore connectivity is revealed locally. Such approach requires long simulation runs. In this paper, a new approach is proposed to estimate permeability from 3D carbonate rock images, where pore connectivity is not revealed from top to bottom. In this approach, first a number of texture classes that represents various textures in the 3D image are identified. For each identified texture class, several sub-volumes from the 3D image are extracted. These sub-volumes are representative of the identified textures and have local pore connectivity. To simulate fluid flow through the pore network of extracted sub-volumes Lattice Boltzmann method (LBM) is used. Permeability is then calculated using Darcy's equation. After determining permeability ranges of each textural class, the 3D image is divided into sub-volumes. Then, each sub-volume is classified into one of the specified texture classes using a modified version of the texture classification algorithm, Local Binary Pattern (LBP). In this study, traditional 2D LBP texture feature vector is adopted to handle 3D volume classification. After classification, each sub-volume is assigned a permeability value that is equivalent to its texture class. Finally, the permeability of the whole 3D image is computed by simulating Darcy's flow through the all sub-volumes. Preliminary outcomes of the proposed technique indicate that it is able to closely estimate experimental results from the laboratory.