Pore space reconstruction using multiple-point statistics

The reconstruction of porous media is of great interest in a wide variety of fields, including earth science and engineering, biology, and medicine. To predict multiphase flow through geologically realistic porous media it is necessary to have a three-dimensional (3D) representation of the pore space. Multiple-point statistics were used, based on two-dimensional (2D) thin-sections as training images, to generate 3D pore space representations. The method was borrowed from geostatistical techniques that use pixel-based representations to reproduce large-scale patterns. Thin-section images can provide multiple-point statistics, which describe the statistical relation between multiple spatial locations. 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 Fontainebleau and Berea sandstones for which 3D images from micro-CT scanning are available. The use of multiple-point statistics predicts long-range connectivity of the structures (measured by local percolation probability) better than standard two-point statistics methods. The selection of multiple-point statistics is a key issue and is discussed in detail.