School of Earth, Energy and Environmental Sciences


Showing 1-9 of 9 Results

  • Liang Yang

    Liang Yang

    Ph.D. Student in Geological Sciences

    Current Research and Scholarly InterestsGeostatistics; Computer graphics/vision; Machine Learning

  • Timothy Yeo

    Timothy Yeo

    Ph.D. Student in Energy Resources Engineering

    Current Research and Scholarly InterestsA numerical simulation of the fully coupled fluid flow and geomechanical deformation in fractured reservoirs.

  • Wonjin Yun

    Wonjin Yun

    Ph.D. Student in Energy Resources Engineering

    Current Research and Scholarly InterestsImproved understanding of the physicochemical mechanisms controlling the interplay between oil, water,and rock during EOR processes at pore scale is vital to achieve successful applications of enhanced oil recovery (EOR). Microfluidics provides an experimental platform to probe such interplay. The present work addresses greater realism in pore structure and visualization of micromodels for characterization of single and multiphase flows.

    We describe 4 advancements, as follows, and representative results. First, we demonstrate improved 3D structural realism of pores inside etched-silicon microfluidic devices. In particular, we etch the micropores 1.5 to 21 μm width) within a carbonate pore network less deeply than the wider macropores (>21 μm width). Second, we apply micro-particle image velocimetry (μ-PIV) to so-called end-point relative permeability measurements of oil and water as well as pore-scale observationsmduring imbibition and drainage processes. The μ-PIV technique provides insights into the fluid dynamics within microfluidic channels and relevant fluid velocities controlled predominantly by changes in pore width and depth. Third, we demonstrate that micromodels may be monitored using advanced spectral imaging that enables real-time and in-situ quantification of the local viscosity of shear-thinning and viscoelastic fluids. Spectral imaging of in situ viscosity paves the way for validation and optimization of computational fluid dynamics models for non-Newtonian viscoelastic EOR polymers. Fourth, we show the application of deep-learning to the micromodel images for the automated analysis of surface properties. Specifically, understanding wettability of porous media, namely the relative affinity of the fluids for the solid, and its influence on the efficiency of wetting-phase displacement of non-wetting phase is a key factor determining multiphase flow. Hence, we want to achieve a systematic methodology to study themlarger domain of porous media that consists of a tremendous number of complex interplays between surface and reservoir fluids at the pore and pore network scale. With proper training, deep-learning has a great potential to serve as a quick and comprehensive image classification and evaluation tool.