Case studies of predictive uncertainty quantification for geothermal models. (December 2021)

• Record Type: Journal Article
• Authors: Omagbon, Jericho; Doherty, John; Yeh, Angus; Colina, Racquel; O'Sullivan, John; McDowell, Julian; Nicholson, Ruanui; Maclaren, Oliver J.; O'Sullivan, Michael
• Is Part Of: Geothermics. Volume 97(2021)
• Publication Date: 2021-12
• Subjects: Hydrogeology -- Periodicals; Geothermal resources -- Periodicals; Énergie géothermique -- Périodiques; GEOTHERMAL ENGINEERING; GEOTHERMAL ENERGY; GEOTHERMAL EXPLORATION; Geothermal resources; Hydrogeology; Periodicals; Electronic journals; 621.44
• DOI: 10.1016/j.geothermics.2021.102263
• Languages: English
• ISSNs: 0375-6505

Abstract

Highlights: A linear and a nonlinear method for uncertainty quantification are discussed. Both methods are applied to a model of Leyte (Philippines) geothermal field. The linear method is much computationally much faster but requires smooth dependence of future predictions on model parameters. The nonlinear, sampling based, method is applied to Ohaaki (New Zealand) and BacMan (Philippines). The nonlinear, sampling based, method suffers from many model failures. 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.