Monday, 15 December 2014: 1:55 PM
Jie Liu1,2, Gerald Pereira3, Reem Freij-Ayoub4 and Klaus Regenauer-Lieb2,4, (1)Sun Yat-Sen University, Guangzhou, China, (2)University of Western Australia, Crawley, Australia, (3)CSIRO Mathematics, Informatics and Statistics, Melbourne, Australia, (4)CSIRO Energy Flagship, Perth, Australia
Microtomography provides detailed 3D internal structures of rocks in micro- to tens of nano-meter resolution and is quickly turning into a new technology for studying petrophysical properties of materials. An important step is the upscaling of these properties as micron or sub-micron resolution can only be done on the sample-scale of millimeters or even less than a millimeter. We present here a recently developed computational workflow for the analysis of microstructures including the upscaling of material properties. Computations of properties are first performed using conventional material science simulations at micro to nano-scale. The subsequent upscaling of these properties is done by a novel renormalization procedure based on percolation theory. We have tested the workflow using different rock samples, biological and food science materials. We have also applied the technique on high-resolution time-lapse synchrotron CT scans.

In this contribution we focus on the computational challenges that arise from the big data problem of analyzing petrophysical properties and its subsequent upscaling. We discuss the following challenges:

1) Characterization of microtomography for extremely large data sets – our current capability.

2) Computational fluid dynamics simulations at pore-scale for permeability estimation – methods, computing cost and accuracy.

3) Solid mechanical computations at pore-scale for estimating elasto-plastic properties – computational stability, cost, and efficiency.

4) Extracting critical exponents from derivative models for scaling laws – models, finite element meshing, and accuracy.

Significant progress in each of these challenges is necessary to transform microtomography from the current research problem into a robust computational big data tool for multi-scale scientific and engineering problems.