Cockrell School of Engineering
The University of Texas at Austin


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Graduate Seminar - Dr. Martin Blunt


Monday, December 11, 2017


03:00pm - 04:00pm


CPE 2.204


Speaker:  Dr. Martin Blunt, Professor in the Department of Earth Science and Engineering at Imperial College London

Title of Seminar: “Peering into the pore space: using X-rays to see inside rock and understand fluid flow processes"

Abstract: The use of three-dimensional X-ray imaging has transformed our understanding of fluid flow processes in porous rocks. This is important for many industrial applications, including CO2 storage, contaminant transport, oil recovery and the exploitation of unconventional resources.  The latest developments in imaging and related modelling will be reviewed with a presentation of recent work at Imperial College on wettability characterization and dynamic imaging. The scientific emphasis will be on flow regimes: under what circumstances do we see different qualitative types of behavior as one phase displaces another in a porous medium.  Dr. Blunt will show how this is affected by pore structure, flow rate and wettability and how this then controls oil recovery, or CO2 storage security.

Biography:   Martin Blunt joined Imperial in June 1999 as a Professor of Petroleum Engineering. He served as Head of the Department of Earth Science and Engineering from 2006-2011. Previous to this he was Associate Professor of Petroleum Engineering at Stanford University in California. Before joining Stanford in 1992, he was a research reservoir engineer with BP in Sunbury-on-Thames. He holds MA and PhD (1988) degrees in theoretical physics from Cambridge University. Professor Blunt's research interests are in multiphase flow in porous media with applications to geological carbon storage, oil and gas recovery, and contaminant transport and clean-up in polluted aquifers. He performs experimental, theoretical and numerical research into many aspects of flow and transport in porous systems, including pore-scale modelling of displacement processes, and large-scale simulation using streamline-based methods.