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GEOPHIRES (GEOthermal energy for the Production of Heat and Electricity (“IR”) Economically Simulated) is a software tool that combines reservoir, wellbore, and power plant models with capital and operating cost correlations and financial levelized cost models to assess the technical and economic performance of Enhanced Geothermal Systems (EGS). It is an upgrade and expansion of the “MIT-EGS” program used in the 2006 “Future of Geothermal Energy” study. GEOPHIRES includes updated cost correlations for well drilling and completion, resource exploration, and Organic Rankine Cycle (ORC) and flash power plants. It also has new power plant efficiency correlations based on AspenPlus and MATLAB simulations. The structure of GEOPHIRES enables feasibility studies of using geothermal resources not only for electricity generation but also for direct-use heating, and combined heat and power (CHP) applications. Full documentation on GEOPHIRES is provided in the supplementary material. Using GEOPHIRES, the levelized cost of electricity (LCOE) and the levelized cost of heat (LCOH) have been estimated for 3 cases of resource grade (low-, medium-, and high-grade resource corresponding to a geothermal gradient of 30, 50, and 70 °C/km) in combination with 3 levels of technological maturity (today's, mid-term, and commercially mature technology corresponding to a productivity of 30, 50, and 70 kg/s per production well and thermal drawdown rate of 2%, 1.5%, and 1%). The results for the LCOE range from 4.6 to 57 ¢/kWhe and for the LCOH from 3.5 to 14 $/MMBTU (1.2 to 4.8 ¢/kWhth). The results for the base-case scenario (medium-grade resource and mid-term technology) are 11 ¢/kWhe and 5 $/MMBTU (1.7 ¢/kWhth), respectively. To account for parameter uncertainty, a sensitivity analysis has been included. The results for the LCOE and LCOH have been compared with values found in literature for EGS as well as other energy technologies. The key findings suggest that given today's technology maturity, electricity and direct-use heat from EGS are not economically competitive under current market conditions with other energy technologies. However, with moderate technological improvements, electricity from EGS is predicted to become cost-effective with respect to other renewable and non-renewable energy sources for medium- and high-grade geothermal resources. Direct-use heat from EGS is calculated to become cost-effective even for low-grade resources. This emphasizes that EGS for direct-use heat may not be neglected in future EGS development.

The goal of this study was to characterize the uncertainty associated with the cost of drilling and completion of geothermal wells. Previous research and publications have produced correlations for the average cost of geothermal wells as a function of well depth. This project develops this concept further by using a probabilistic approach to evaluate the distribution of geothermal well costs for a range of well depths. The well cost uncertainty was characterized by identifying the main cost components of geothermal wells and quantifying the probability distributions of the key variables controlling these costs. These probability distributions were determined based on the detailed cost records of U.S. geothermal wells drilled or designed from 2009 to 2013 as well as cost data from drilling equipment manufacturers and vendors. Probability distributions of the key variables were examined to find statistically significant correlations between them. Lastly, the previously determined probability distributions of individual cost components and the correlations between them were input into WellCost Lite, a predictive geothermal drilling cost model, using the Monte Carlo method. This approach allowed us to generate the overall well cost probability distributions for 8,000-15,000 ft. (2,400-4,600 m) geothermal wells. We have shown that the median geothermal well cost increases exponentially with depth. Deep wells typically have higher cost uncertainty and more positively-skewed cost probability distributions. The correlations presented in this paper can be used to determine the economic feasibility of geothermal energy systems, assess the project risk, and facilitate investment decisions.