H53H-02:
Micro-CT Pore Scale Study Of Flow In Porous Media: Effect Of Voxel Resolution

Friday, 19 December 2014: 1:55 PM
Saurabh Shah1, Farrel Gray2, John Crawshaw2 and Edo Boek1, (1)Imperial College London, London, SW7, United Kingdom, (2)Imperial College London, London, United Kingdom
Abstract:
In the last few years, pore scale studies have become the key to understanding the complex fluid flow processes in the fields of groundwater remediation, hydrocarbon recovery and environmental issues related to carbon storage and capture. A pore scale study is often comprised of two key procedures: 3D pore scale imaging and numerical modelling techniques. The essence of a pore scale study is to test the physics implemented in a model of complicated fluid flow processes at one scale (microscopic) and then apply the model to solve the problems associated with water resources and oil recovery at other scales (macroscopic and field). However, the process of up-scaling from the pore scale to the macroscopic scale has encountered many challenges due to both pore scale imaging and modelling techniques. Due to the technical limitations in the imaging method, there is always a compromise between the spatial (voxel) resolution and the physical volume of the sample (field of view, FOV) to be scanned by the imaging methods, specifically X-ray micro-CT (XMT) in our case

In this study, a careful analysis was done to understand the effect of voxel size, using XMT to image the 3D pore space of a variety of porous media from sandstones to carbonates scanned at different voxel resolution (4.5 μm, 6.2 μm, 8.3 μm and 10.2 μm) but keeping the scanned FOV constant for all the samples. We systematically segment the micro-CT images into three phases, the macro-pore phase, an intermediate phase (unresolved micro-pores + grains) and the grain phase and then study the effect of voxel size on the structure of the macro-pore and the intermediate phases and the fluid flow properties using lattice-Boltzmann (LB) and pore network (PN) modelling methods. We have also applied a numerical coarsening algorithm (up-scale method) to reduce the computational power and time required to accurately predict the flow properties using the LB and PN method.