Computational Modeling of Multiphase Flow in Porous Media

We study modeling and characterization of multiphase flow in porous media with application in the areas of energy and environment. The central objective is to develop advanced computational models for multiphase flow, effectively bridging the gap between pore-scale interactions and macroscopic reservoir behavior. Using digital rock modeling and pore-scale simulation methods such as the lattice Boltzmann method (LBM), we investigate the microscale flow dynamics within geometric structures derived from X-ray tomography images of rock samples.
To learn more, please see Bakhshian et al.,  Sci. Rep., 2019; Bakhshian et al., GRL, 2020
Microfluidics Experiments
we conduct microfluidics experiments aimed at delving into the pore-scale flow behavior in porous media, with a specific emphasis on carbon and energy storage dynamics.  Combined with imaging techniques, these microfluidic experiments provide real-time visualization of multiphase flow and dynamics in micro-structures of porous media, having the potential to play an important role in characterizing and optimizing fluid flow in subsurface storage operations. Our investigations entail the utilization of etched silicon and real-rock micromodels to investigate the effect of pore-scale properties (e.g., microstructural heterogeneity, wettability) on the fluid flow dynamics in heterogenous porous media.
Environmental Monitoring

Emerging technologies such as big data analytics, wireless communication, and the Internet of Things (IoT) are transforming environmental monitoring, especially as climate change and pollution demand urgent global attention. These advancements enable continuous collection and transfer of large volumes of streaming data, posing new challenges in data management and anomaly detection. We deploy pilot- and field-scale near-surface monitoring systems that utilize multiphysics sensors and machine learning techniques to detect fluid leakage associated with geologic carbon storage.