Abstract

Permeability is a key practical concept for many scientific and engineering endeavors. Current methods for estimation of permeability span field, laboratory, simulation, and theoretical approaches. Despite apparent simplicity of permeability measurements, the literature data are scattered, and this scatter not always can be attributed to the precision of experiment or simulation or to sample variability. In the first part of this talk, we demonstrate an excellent agreement (<1%) between experiments and simulations, where experimental results are extensive and stable, while flow is simulated from first principles, directly on three-dimensional images of the sample, and without fitting parameters. By analysis of the agreement between experiments and simulations we demonstrate a major flaw affecting many experimental measurements with the out-of-sample placement of pressure ports, including permeability estimation of geological samples (Hassler cell).

Realistic pore-scale simulations of flow frequently use discrete images (pixels in 2D or voxels in 3D) of real-life samples as inputs. Today's commonly held belief is that higher-accuracy simulations require higher-resolution images, which often result in lengthy scanning and/or simulation times. Conversely, decreasing the resolution destroys the simulation accuracy when the features of the sample (e.g., pores) are unresolved. In the second part of this talk, we present the discovery of superstructures in discrete images, which emerge from the sample's features and discrete mesh. These superstructures — and not the original features of the sample — control flow in low-resolution simulations. Consequently, decreases in resolution change the topology (flow “pathways”) and morphology (pore “shapes”) in the discrete image of the sample. Using permeability as an example, we present a new methodology to enhance the flow simulation accuracy for both low resolution X-ray Computed Tomography-imaged and computer-generated samples. The presented methodology improves extraction of quantitative information from discrete images and not limited by image dimensionality, imaging technique, or simulated processes.

Biography

Siarhei Khirevich graduated from Belarusian State University with a diploma in Radiophysics and Electronics. Hereafter he moved to Germany to pursue a doctoral degree under the supervision of Prof. Ulrich Tallarek at Universities of Magdeburg and Marburg. Khirevich remained with the group as a Post-doctoral Fellow to perform additional research relating to findings during his Ph.D. studies.  After short-term visit with Prof. Irina Ginzburg (France), Khirevich joined KAUST as a post-doctoral fellow, and currently work as a research scientist at the group of Prof. Tadeusz Patzek. His research interests include pore-scale flows from computational and experimental perspectives, X-ray computed tomography, lattice Boltzmann and random walk particle tracking methods, high-performance computing, laboratory estimation of material and transport properties, rheology, packed beds.