Abstract

Engineered “smart” materials such as stimuli-responsive microspheres hold the promise to alleviate a barrier to scaling sustainable subsurface energy technologies. Unlike most engineering disciplines, inadequate control of fluid flow paths dominates the financial uncertainty of commercial-scale subsurface energy projects. In this talk, Dr. Hawkins will illustrate the impact of these uncertainties on heat transfer and introduce a promising solution. His talk will begin with an exploration of the potential for co-producing hydrogen gas and geothermal fluid. Then, he will present a field-informed thermal-hydraulic simulation that quantifies the effect of extreme flow channeling in discrete rock fractures on production well temperature over time. The talk will conclude with a “sneak peek” into the development of an “active” tracer invented to solve the “short-circuit” problem specifically in the context of geothermal reservoir engineering.

Bio

Dr. Hawkins is an Assistant Professor in the School of Civil and Environmental Engineering and Earth Sciences at Clemson University and a Visiting Professor in the Smith School of Chemical and Biomolecular Engineering at Cornell University. His multi-disciplinary research program includes a unique combination of materials synthesis, geophysics and soft matter physics, multi-scale lab/field experimentation, and physics-informed machine learning. His latest research investigates active matter transport in extreme environments, particularly in the context of “next generation” tracer technologies. His past and current research has been successfully funded in the context of geothermal reservoir engineering with novel insights relevant to subsurface hydrogen, nuclear waste disposal, carbon sequestration, and in-situ mining of critical minerals. Novel tracers of interest include engineered nanoparticles, reactive tracers for temperature and area reporting, DNA/microbial tracers, and “active” tracers for controlling subsurface fluid flow paths. He has also developed novel machine learning tools and predictive models for fluid flow in heterogeneous permeability fields.

Peer-Reviewd Reasearch Paper(s)