Assessing subsurface uncertainties during the screening of large areas for a deep geological repository for high-level radioactive waste

In the current phase of the German Site Selection Procedure for a deep geological repository, large areas of Germany’s subsurface are being screened in order to identify the most-suited regions. A key screening tool for this purpose is the preliminary safety assessment, which allows a gradual reduction of these vast areas to a series of relatively small high-potential regions.

The portfolio of potential host rocks includes claystone, rock salt and crystalline rocks. These geological formations can exhibit significantly different properties and key subsurface uncertainties relevant to a geological repository.

Additionally, in alignment with the regulations, only existing subsurface data are used during this screening phase, i.e. data acquired for other purposes, such as hydrocarbon, ore and potash exploration, geothermal energy etc. Hence, the available dataset is heterogeneous, both in terms of data types and in terms of geographical distribution.

The estimation of the subsurface uncertainties is relevant for the robustness evaluation during the safety assessment. Consequently, the uncertainty estimation concept includes methods for different scales: screening methods applicable to large areas and detailed methods applicable to the relatively small high-potential regions.

For large areas with heterogeneous data, a semi-quantitative method for a consistent and efficient assessment of the subsurface uncertainties was developed. This method estimates a “degree of confidence”, which represents the reliability of given statements concerning the interpretation of the subsurface. The “degree of confidence” can be estimated for each given area from the combination of data quality and quantity, on one the hand, and the geological complexity, on the other. For the smaller, high-potential areas, quantitative methods of estimation of the uncertainties are used. These can include statistical and geostatistical approaches.