Case studies of predictive uncertainty quantification for geothermal models

Abstract We present case studies on two methods for quantifying uncertainty in predictions for highly parameterised geothermal models. One method is fully linear, while the other is nonlinear as it requires sampling model parameters and additional simulations but can handle non-differentiable quantities of interest. We test both methods on a Leyte (Philippines) model and find similar results, though the linear method is computationally cheaper. We then consider models for BacMan (Philippines) and Ohaaki (New Zealand). Here the quantity of interest does not satisfy smoothness requirements, and we apply only the simulation-based method. This method achieves reasonable results but at the cost of many rejected samples.