Neutron imaging for geothermal energy systems

6 March 2013 Neutron imaging for geothermal energy systems

Philip Bingham, Yarom Polsky, Lawrence Anovitz

Proceedings Volume 8661, Image Processing: Machine Vision Applications VI; 86610K (2013) https://doi.org/10.1117/12.2004617
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Geothermal systems extract heat energy from the interior of the earth using a working fluid, typically water. Three components are required for a commercially viable geothermal system: heat, fluid, and permeability. Current commercial electricity production using geothermal energy occurs where the three main components exist naturally. These are called hydrothermal systems. In the US, there is an estimated 30 GW of base load electrical power potential for hydrothermal sites. Next generation geothermal systems, named Enhanced Geothermal Systems (EGS), have an estimated potential of 4500 GW. EGSs lack in-situ fluid, permeability or both. As such, the heat exchange system must be developed or “engineered” within the rock. The envisioned method for producing permeability in the EGS reservoir is hydraulic fracturing, which is rarely practiced in the geothermal industry, and not well understood for the rocks typically present in geothermal reservoirs. High costs associated with trial and error learning in the field have led to an effort to characterize fluid flow and fracturing mechanisms in the laboratory to better understand how to design and manage EGS reservoirs. Neutron radiography has been investigated for potential use in this characterization. An environmental chamber has been developed that is suitable for reproduction of EGS pressures and temperatures and has been tested for both flow and precipitations studies with success for air/liquid interface imaging and 3D reconstruction of precipitation within the core.

Citation Download Citation

Philip Bingham, Yarom Polsky, and Lawrence Anovitz "Neutron imaging for geothermal energy systems", Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610K (6 March 2013); https://doi.org/10.1117/12.2004617

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