Adam Hawkins

Bio

Adam Hawkins completed a B.S. in 2009 from the University of California, Davis, and an M.S. in 2013 from California State University, Long Beach, both in geology. In May 2017, Hawkins completed his Ph.D. in geology at Cornell University. His research has focused on transport phenomena of fluids and fluid components in bedrock fractures. His work combines small-scale field experiments with computational models and laboratory experiments in an effort to provide a comprehensive understanding of how fluids, thermal energy, and reactive/inert fluid components flow through fractures.

An Electrical Resistivity Tomography (ERT) Method for Mapping the Spatial and Temporal Distribution of Heat Exchange in a Discrete Fracture Network (DFN)

Publications

Geological activity shapes the microbiome in deep-subsurface aquifers by advection PNAS, Vol. 119, No. 25, May 2022

Inert and Adsorptive Tracer Tests for Field Measurement of Flow‐Wetted Surface Area;  American Geophysical Union, Volume 54, Issue 8, Pages 5341-5358, August 2018. (awarded the WWR Editors’ Choice Award)

Insufficient subsurface characterization threatens the growth of several sustainable energy technologies and limits responsible management of water resources. Sustainable Subsurface Technologies (SSTs) such as geothermal power, carbon sequestration, and energy storage rely on transmitting and/or constraining fluids in the intertwining network of fractures and pore space in deep geological formations. Future growth of these industries will compete with the world’s increasing consumption of groundwater. In order to grow SSTs and protect Earth’s largest freshwater resource, subsurface reservoir characterization techniques must be advanced. Hawkins plans to develop and test a reservoir characterization technique that will combine the use of heat as a tracer and time-lapse subsurface imaging via Electrical Resistivity Tomography (ERT). If successful, “ERT thermal imaging” could be used by industry and water resource managers to monitor the migration of fluids in the subsurface and subsequently predict the spatial and temporal distribution of fluid flow.