Rocky Mountain (63rd Annual) and Cordilleran (107th Annual) Joint Meeting (18–20 May 2011)

Paper No. 17
Presentation Time: 8:00 AM-6:00 PM

EVALUATING THE GEOTHERMAL POTENTIAL OF THE RIO GRANDE RIFT USING SPATIAL-STATISTICAL METHODS


KELSAY, Travis E.1, FAIRLEY, Jerry P.2, HINDS, Jennifer J.3, POLLYEA, Ryan M.4, OSTERLOH, Jessica J.5 and WAGNER, Alex W.5, (1)Department of Geological Sciences, University of Idaho, MINES 220, Moscow, ID 83844, (2)Department of Geological Sciences, University of Idaho, Moscow, ID 83844-3022, (3)Department of Geological Sciences, University of Idaho, Moscow, ID 83844, (4)Department of Geology and Environmental Geosciences, Northern Illinois University, 1425 W. Lincoln Highway, Davis Hall 312, DeKalb, IL 60115, (5)Department of Environmental Sciences, University of Idaho, Moscow, ID 83844, kels8638@vandals.uidaho.edu

The high heat flow associated with the Rio Grande Rift region makes it a feasible target for the development of geothermal resources. Additionally, a thick blanket of moderately- to unconsolidated sediments fills the rift valley. These low thermal conductivity materials tend to slow heat losses and increase the geothermal potential that is associated with areas of high heat flow. Several factors have contributed to the geothermal potential of this region being under-utilized including: low population density, relatively low density of data upon which to base assessments of geothermal potential, underdeveloped infrastructure, and long distances to market. As part of an assessment for the USDOE’s National Geothermal Student Competition, we are using a Geographic Information System (GIS) as a decision-making tool to target potential regional-scale geothermal resources in the Rio Grande Rift province in southern Colorado and northern New Mexico, with particular focus in and around the San Luis and Espanola Basins. Several high-priority potential geothermal resource maps are being constructed, which identify the most promising targets for development (i.e., targets of highest quality, lowest uncertainty, and least assumed cost for infrastructural development) along with estimates of the uncertainty of the proposed targets. We expected these results will form an outline for future assessments in other locations.