STOCHASTIC JOINT INVERSION OF A GEOTHERMAL PROSPECT

We are developing a stochastic inverse algorithm to jointly analyze multiple geophysical and hydrological datasets for a geothermal prospect. The purpose is to improve prospect evaluation and estimate the likelihood of useful temperature and fluid flow fields at depth. We combine Bayesian inference with a Markov Chain Monte Carlo (MCMC) global search to conduct a staged inversion of the different data sets. The results consist of a detailed description of the uncertainty in the solution as well as a suite of alternative geothermal reservoir models. The method is highly flexible and capable of accommodating multiple and diverse datasets.