Reconstruction of Pore-space Images Using Microtomography and Multiple-point Statistics

Quantitative prediction of petrophysical properties for reservoir rocks frequently employs representative microscopic models of the pore space as input. Recently digital imaging techniques such as microtomography have been used to provide void space images at the resolution of a few microns. However, this resolution may be insufficient to capture some smaller structures, particularly in carbonates. An emerging destructive focused ion beam method can provide better resolution but only on very small samples. Two-dimensional (2D) thin sections, in contrast, are easily available and they can image micro-porosity. However, they do not directly capture the three-dimensional (3D) pore space. We propose an integrated approach to combine different types of image to reconstruct porous media. In order to generate geologically realistic pore space images with appropriate connectivity, particularly for carbonate rocks, relatively low resolution microtomography and higher resolution images from thin-sections are used. 2D thin-sections provide multiple-point statistics (MPS), which describe the statistical relation between multiple spatial locations and their statistics can be used to generate 3D images at higher resolution. The reconstruction using multiple-point statistics allows the connectivity of the void space to be reproduced accurately. The statistically reconstructed images are then combined with the images measured by the microtomography to generate a realistic pore structure since some 2D thin-sections may miss macro-porosity due to their size. The integrated method is tested on carbonates for which 3D images of larger vug porosity are captured, while 2D thin sections accurately characterize small-scale structure. The integrated images have permeabilities computed using the latticeBoltzmann method (LBM) that are similar to laboratory-measured values, which indicates that the proposed method is both practical and realistic.