A stochastic joint inverse algorithm is applied to prospect evaluation. The goal is to predict flow and temperature in the subsurface that is most consistent with available geologic, hydrological, and geophysical data. The approach uses a modified Monte Carlo global search algorithm. Currently, the algorithm begins with an initial geologic model (e.g. permeability) with specified temperature. This model is allowed to vary during the inversion process using constraints provided by MT, temperature measurements, and surface resistivity measurements. The algorithm has been tested on a geothermal prospect located at Superstition Mountain, California and has been successful in creating a suite of models compatible with available data. A typical inversion evaluates several thousand possible models. The results also include uncertainty associated with each model and we are testing the use of value of information to assess optimal use of related data. To increase the range of possible models but keep computational effort reasonable, we test the use of sensitivity analysis combined with optimization to find optimal well locations. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

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