The role of geological models and uncertainties in safety assessments

Abstract

Safety assessments in nuclear waste management typically include the analysis of thermo-mechanical (TM)-coupled processes. The TM behavior of the host rock is, among other aspects, dependent on the prevalent geological geometry. This study aims to evaluate the impact of uncertainties in geometry on the TM rock behavior. It is one of the very first studies aiming to bring uncertainties of structural geological models and numerical simulations together. To analyze the influence of geological geometries, a simplified model of the region around the Mont Terri rock laboratory was created. A 3D structural geological model was set up and uncertainties of the lithological contacts were quantified by means of stochastic simulations, resulting in an ensemble of 89 model realizations. These realizations were transformed to a 2D numerical model. In this numerical model, TM simulations were computed over a simulation time of 500 years, employing the Finite Element Method. To simulate a heat source of nuclear waste, the lower edge of the model was set to 100 °C. The results of these simulations show mean temperature variations of 90.89 and 92.70 °C after 500 years, with a maximum stress varying between 0.02 and 0.16 MPa of elastic shear energy density and according mean cumulative displacements ranging from 30 to 38 cm. The presented results indicate that different model geometries and differences in material properties lead to noticeable variabilities of the TM behavior of claystone. However, in this case, these variabilities would not significantly affect the integrity of the rock.

Constraining the 3-D Geometry of Fold-Thrust Belts Using Section Balancing vs. 3-D Interpolative Structural and Probabilistic Modeling

“…It would also be possible to consider uncertainties in α k and β l , leading to an even larger space of uncertainties. In our case, we consider the uncertainties in interface positions and orientations as dominant, similar to other recent work in this field (e.g., Lindsay et al, 2012;Pakyuz-Charrier et al, 2018;Bjorge et al, 2022). In addition, it would also be possible to incorporate kriging variance as a measure of interpolation uncertainty in the applied geostatistical approach (Courrioux et al, 2015).…”

Uncertainties and robustness with regard to the safety of a repository for high-level radioactive waste: introduction of a research initiative

“…However, notion should be drawn since categorizing a specific uncertainty may be difficult as it is context-dependent and the choice is conditioned both on the current state of scientific knowledge and on the application purpose (project goal) (Kiureghian and Ditlevsen 2009). Some examples of the various sources of uncertainties in the site selection process include measurement imprecision, the uncertainty of data transformation models, statistical uncertainty related to exploration, knowledge contributed by expert judgment as well as geometrical, technical, and physical knowledge (Bjorge et al 2022;Degen et al 2022;Saltelli and Tarantola 2002). While scientific activities related to site selection and management programs aim to provide a better understanding and essentially a reduction of uncertainties, the complete elimination of uncertainties is not an achievable goal.…”

Bayesian Quantification and Reduction of Uncertainties in 3D Geomechanical‐Numerical Models

“…We focus in this study on the epistemic uncertainties due to the stress magnitude data records and their uncertainties. Further epistemic model uncertainties that result for example, from rock property and its spatial variability (Ziegler, 2022), model geometry (Bjorge et al, 2022;Wellmann & Regenauer-Lieb, 2012), or the assumption of linear elasticity are not considered.…”