Advanced microfluidic framework for understanding of fluid-flow in porous media : microfabrication, imaging, and deep-learning
Keywords: Energy Resources Engineering.

My research with the microfluidic Reservoir-on-a-Chip (ROC) platform has produced multiple engineering science contributions toward investigating the fundamental mechanisms that dictate transport through subsurface porous media. Microfluidic devices, better known as micromodels, are devices with a connected porous network that allows the direct visualization of complex fluid flow dynamics occurring under transient conditions. The porous pattern of micromodel in my study is analogous to that of natural reservoir rock (i.e. sandstone or carbonate). The micro-pattern is etched in a crystalline silicon wafer with the DRIE (deep reactive ion etching) technique which offers a large aspect ratio (i.e. pore throat-to-body ratio), with more realistic and well-defined structures. Consequently, investigating fluid flow through representative pore network patterns and material in the micromodels have been greatly beneficial to petroleum, geologic, and environmental engineering field. Micromodel studies are based on the direct observation of the pore-scale fluid structures, the visualization of the flow field, and the characterization of matrix-fluid and fluid-fluid interactions. I implemented various methodologies that enable the real-time monitoring of events occurring in a micromodel by integrating them with high-resolution microscopy and laser-induced fluorescence. My research improves petrochemical and geophysical characteristics of transports in micromodels through the development of new micro-fabrication processes, new experimental frameworks, imaging, and novel image processing algorithms. First, my research addresses greater realism in pore structure and visualization of micromodels for the characterization of single and multiphase flows. I optimized dual-etching fabrication and improved 3D structural realism of carbonate-like flow networks inside the micromodel. I applied the micro-particle image velocimetry (micro-PIV). The micro-PIV provides insights into the fluid dynamics within microfluidic channels and relevant fluid velocities controlled predominantly by changes in pore width and depth. Compared with conventional single-depth micromodels, micro-PIV and fluid desaturation pattern prove that the dual-depth carbonate micromodel is a better representation of pore geometry showing more realistic fluid flow and capillary entry pressures. Second, I demonstrated, for the first time, that micromodels monitored using advanced spectral imaging enables real-time and in-situ quantification of the local viscosity of non-Newtonian viscoelastic polyacrylamide EOR polymers. This, in turn, paves the way to validate computational fluid dynamics models for viscoelastic fluids. Third, novel deep-learning algorithms (convolutional neural networks) were applied to the micromodel images for the automated analysis of surface properties. With proper training of deep-learning architectures on high-quality image datasets, I proved that deep-learning has a great potential to serve as a quick and automated image analysis tool for surface wettability determination with an accuracy larger than 95%. Forth, I established an in-house micro-fabrication procedure using a Direct-Write-Lithography technique for the rapid prototyping of new microfluidic designs. I worked on optimizing the micromodel channel design to make the micromodel more suitable for direct visualization of micro-pore scale mixing dynamics between precipitant and oil phase, which may cause asphaltene aggregation and their agglomerations. Furthermore, confocal microscopy enables the 3D reconstruction of asphaltene agglomerates; it reveals the size and size distribution of asphaltene aggregates as a function of flocculation time.