Reconstruction of multi-scale heterogeneous porous media and their flow prediction,

ABSTRACT Fluid flow in, and the thermal, electrical, and acoustic properties of porous media are determined primarily by the geometry and topology of the pore system. Here we illustrate a stochastic 3D space reconstruction model that uses thin section images as its main input. The approach involves a third-order Markov mesh that creates the reconstruction in a single scan; and overcomes the computational issues normally associated with Markov chain methods. The technique is capable of generating realistic "pore architecture models" (PAMs), and examples are presented for a range of fairly homogenous rock samples. PAMs, or tomography models, serve as input for another suite of analysis techniques that we call pore analysis tools (PATs). PATs allow us to quantify important characteristics of the pore system, such as the pore-size distribution, and the pore connectivity. Using PATs, we are able to extract well-characterized network models that can be used for simulating two-and three-phase fluid flow. The aim of this paper is to explore how we can use PAMs/PATs to gain additional understanding of the characteristics of porous media which contain multiple scales of pores. When such materials possess pore systems that have an extreme range of pore sizes, we are able to overcome (to some extent) the difficulties of the multiple scales by creating and analysing multiple reconstructions based on different-resolution input images