Geothermal Play Fairway Analysis, Part 2: GIS methodology

Authors:
raw_author_name: Jacob DeAngelo
raw_affiliation_string: U.S. Geological Survey, 350 N. Akron Rd. P.O. Box 158 Moffett Field, CA 94035, United States
raw_author_name: John W. Shervais
raw_affiliation_string: Utah State University, Logan, UT, United States
raw_author_name: Jonathan M. Glen
raw_affiliation_string: U.S. Geological Survey, 350 N. Akron Rd. P.O. Box 158 Moffett Field, CA 94035, United States
raw_author_name: Sabodh Garg
raw_affiliation_string: Geologica Geothermal Group, Inc, 9920 Pacific Heights Blvd, Suite 150, San Diego, CA 92121, United States
raw_author_name: Patrick F. Dobson
raw_affiliation_string: Lawrence Berkeley National Laboratory, Berkeley, CA, United States
raw_author_name: Eric Sonnenthal
raw_affiliation_string: Lawrence Berkeley National Laboratory, Berkeley, CA, United States
raw_author_name: Drew L. Siler
raw_affiliation_string: Geologica Geothermal Group, Inc, 9920 Pacific Heights Blvd, Suite 150, San Diego, CA 92121, United States; U.S. Geological Survey, 350 N. Akron Rd. P.O. Box 158 Moffett Field, CA 94035, United States
raw_author_name: James P. Evans
raw_affiliation_string: Utah State University, Logan, UT, United States

Abstract: Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.