The Basin and Range Dixie Valley Geothermal Wellfield, Nevada, USA-A test bed for developing an Enhanced Geothermal System exploration favorability methodology

Quantitative geostatistical analysis of the geoscience data was conducted, among other factors, to address the question of whether the baseline geoscience data could be used to predict EGS favorability without the advantage of existing well temperature data. Classification and Regression Tree (CART) was one of a number of geostatistical methods applied to the baseline geoscience data and it provided the most promising results. In CART, the response variable (RV) is predicted while using explanatory variables (EVs). The geoscience parameters (EVs) considered in the CART analysis included temperature, Vp, resistivity from magnetotellurics, Coulomb Stress Change (CSC), dilatational strain (from CSC modeling), vertical stress, lithology based on geologic analysis, lithology based on gravity-magnetic modeling (G-M lithology) and the presence or absence of a fault. Temperature increases with depth in the DVGW. Vertical stress also increases with depth and it was deemed as a redundant EV. As such, a CART sensitivity analysis was applied to the baseline data set with and without vertical stress being considered as an EV to determine the effect of removing vertical stress and to evaluate with subsets of EVs that could be predictive of key EGS parameters. R2-values ranging from 0.611 to 0.841 were obtained for the RVs: temperature, lithology, productive vs. non-productive hydrothermal cells and expected EGS favorable cells (the response variables) using both cross-section and well data and not considering vertical stress. However, these CART results were not used in the generation of the favorability maps because this is the first analysis of its kind that the authors are aware of and more testing at other sites needs to be done; the raw total baseline data set described above was considered the most appropriate for this study.