1 Stochastic Joint Inversion Modeling Algorithm of Geothermal Prospects

A stochastic joint inverse algorithm is applied to prospect evaluation. The goal is to predict fluid flow and temperature distributions in the subsurface that are 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 conceptual model descriptive of structural (geology), parametric (permeability) and hydrothermal (saturation, temperature) characteristic of the system. Aspects of this model are allowed to vary during the inversion process using (a priori) estimates of conceptual model uncertainty and 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 and provides a posteriori estimates of model uncertainty. We are testing the use of sensitivity analyses to better identify critical uncertain variables, lower order surrogate models to streamline computational costs, and value of information analyses to better assess optimal use of related data. 1