A Monte Carlo-based framework for assessing the value of information and development risk in geothermal exploration

The exploration and development of geothermal energy resources carries considerable financial risk. Due to the cost of drilling, there is often large uncertainty in the prediction of resource potential as well as challenges in optimizing well placement. In this paper, we propose a comprehensive Bayesian framework that accounts for high degrees of geologic uncertainty. Although Bayesian inference methods for prediction and uncertainty quantification are well-established, limitations exist, such as incorporating model realism and reducing the computational burden of simulating a large number of forward models. Using a case study problem, we demonstrate how to turn geologic understanding into a prior probability model for a basin-scale extensional geothermal system. We then use the proposed Bayesian framework, called Bayesian Evidential Learning, to generate posterior temperature predictions constrained to a temperature well without any explicit model inversion. In this approach, the relationship between data and prediction variables is learned by Canonical Correlation Analysis of a training set of models generated by Monte Carlo simulation. Sensitivity analysis results show that temperature in a geothermal target area is most sensitive to the bulk permeability of the basement and basin rock as well as the basal heat flux.