Computer simulations are used to understand processes in nuclear waste disposal. The results are used to judge the safety of waste repository. Not all the information needed for such analyses, e.g. rock properties, is precisely known, contributing to uncertainty in the analysis results. We are interested in understanding the effect of the uncertainty of input quantities and of certain simplifications made during model creation on the outcome of computer simulations.

The SW-A experiment is a large-scale in situ test at the Mont Terri rock laboratory that implements a vertical hydraulic shaft-sealing system in argillaceous host rock. The response of the system and the surrounding rock to hydration is examined. The experiment objectives are to demonstrate the feasibility of installation, to investigate the saturation process, to qualify measurement and monitoring techniques, and to assess the sealing effectiveness. Recent data and experience are presented.

Understanding complex systems such as radioactive waste repositories involves the study of cross-scale coupled processes. We discuss some important concepts and their mutual interactions for interpreting such systems based on complementary model-based analyses at various scales. One goal statement is to explain the formation of drying cracks. Near-field understanding can be used to determine how detailed repository far-field models must be and can lead to more robust analysis results.

In the AREHS project, the effect of the alternation of cold and warm periods over 1 million years on the hydrogeological system in the vicinity of a repository was simulated. This was done with thermal–hydraulic–mechanical (–chemical) simulations. The simulations were implemented for generic 3D models for all three host rock formations: clay rock, salt rock and crystalline rock. In addition to the results for the generic sites, a workflow was developed that can be applied to concrete sites.

In this project, an assessment tool is developed to evaluate the applicability of AI methods in geosciences with respect to key geological activities in the site selection process. The results show that AI should not have any decision-making power when used in the site selection process. It is strongly recommended that all methods should be evaluated and validated iteratively and that the results should be made publicly available when applied to the key activities of the site selection process.