Numerical estimation of rock properties and textural facies classification of core samples using X-Ray Computed Tomography images

The use of X-Ray Computed Tomography scanners to better characterize rock properties behavior at micro-scale is becoming increasingly common in oil industry. In this paper, we propose a new approach based on modeling X-Ray Computed Tomography images in terms of 2D textures in order to predict rock properties and classify main textures along core samples. First, we implement a parametric model of textures based on a multi-scale analysis to extract main representative textural descriptors. Then, we use Kohonen unsupervised classification technique to find main representative textures and classify core samples images. In addition, we simulate several rock properties such as porosity, density, formation factor and volume of clay along cores using a neural network system. Finally, we compare our simulation results with experimental real data and discuss main advantages and limitations of our approach.