Robust subsurface temperature models in geothermal systems allow for more focused exploration, enhanced well targeting, and improved reservoir characterization. In the Great Basin region, existing maps of sub-surface temperature at specified depths were created through synthesis and interpolation of bottom-hole temperature (BHT) data compiled by the Southern Methodist Geothermal Lab (e.g. Google.org map products). For this approach, corrected BHTs were used in combination with site specific models of vertical thermal conductivity to calculate heat flux: this in turn was used to extrapolate temperatures at various depth slices and interpolate between points. We explore an alternative approach to evaluating the BHT dataset, using geostatistical tools in combination with advances in modelling software: this approach may provide additional, new insight into the spatial variability of the thermal regime in the Great Basin region. In contrast to previous methods used, geostatistical tools incorporate the spatial correlation structure of a dataset into the resulting model and can highlight other possible spatial relationships. In this pilot study, we use the Leapfrog Edge geostatistical software package to create a 3D statistical model of subsurface temperatures in an area of eastern Nevada that was previously studied as part of a geothermal play fairway analysis and is home to multiple geothermal power plants. We use the same well BHT dataset from the Southern Methodist Geothermal Lab to facilitate direct comparison of our results with previous sub-surface temperature models. Methods such as ordinary kriging and radial base function estimates are used to quantitatively blend the data and extrapolate to depth. We compare our results to those of the play fairway results, previous models using other software, and available field data, review how different statistical approaches affect the sub-surface model, and discuss the implications of any identified differences in modelled temperature at depth.

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