Prediction of permeability for porous media reconstructed using multiple-point statistics

To predict multiphase flow through geologically realistic porous media, it is necessary to have a three-dimensional (3D) representation of the pore space. We use multiple-point statistics based on two-dimensional (2D) thin sections as training images to generate geologically realistic 3D pore-space representations. Thin-section images can provide multiple-point statistics, which describe the statistical relation between multiple spatial locations and use the probability of occurrence of particular patterns. Assuming that the medium is isotropic, a 3D image can be generated that preserves typical patterns of the void space seen in the thin sections. The method is tested on Berea sandstone for which a 3D image from micro-CT (Computerized Tomography) scanning is available and shows that the use of multiple-point statistics allows the long-range connectivity of the structure to be preserved, in contrast to two-point statistics methods that tend to underestimate the connectivity. Furthermore, a high-resolution 2D thin-section image of a carbonate reservoir rock is used to reconstruct 3D structures by the proposed method. The permeabilities of the statistical images are computed using the lattice-Boltzmann method (LBM). The results are similar to the measured values, to the permeability directly computed on the micro-CT image for Berea and to predictions using analysis of the 2D images and the effective medium approximation.