Wettability Control of Multiphase Flow in Porous Media

The simultaneous flow of multiple fluid phases through porous media is central to a wide range of natural and industrial processes. A key factor influencing such flows is wettability—the tendency of the solid matrix to preferentially attract one fluid over another. Despite decades of research, the complex interplay between wettability and multiphase flow remains an active area of investigation. In this talk, I will present our recent work combining microfluidic experiments and pore-network modeling to uncover the microscale mechanisms and macroscopic implications of wettability control. I will discuss how both homogeneous and heterogeneous wettability conditions influence flow behavior.

What Will You Learn?
  • Lab on a chip technique for studying multiphase flow in porous media
  • Pore-network modelling of multiphase flow in porous media
  • Statistical physics of multiphase flow in porous media
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Webinars
Duration Time
1 hour
Level
Master
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Date & Time: May 6th at 16:00 CEST (Central European Summer Time) / (10:00 to 11:00 EDT)

Benzhong (Robin) Zhao is an Associate Professor in the Department of Civil Engineering at McMaster University, where he also serves as the Associate Chair for Graduate Studies. His research focuses on multiphase flow and transport in porous materials, with applications in climate change mitigation, renewable energy storage, and water security. Dr. Zhao earned his BASc from the University of Waterloo and both his SM and PhD from the Massachusetts Institute of Technology (MIT). His doctoral research at MIT explored the fluid mechanics of geological carbon dioxide sequestration, contributing to advancements in carbon storage technologies. He then completed a postdoctoral fellowship at the University of Toronto, where he focused on improving the efficiency of electrochemical devices for renewable energy storage. At McMaster, Dr. Zhao leads a research group that employs advanced visualization and simulation techniques to investigate fundamental phenomena in multiphase flow through porous media. His work also integrates machine learning techniques into digital rock physics, enabling more efficient and accurate characterization of subsurface materials. Dr. Zhao’s research aims to develop practical technologies for reducing carbon emissions and adapting to climate change impacts on hydrologic systems.

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