Development of a Synthetic Rock Mass for Repository Geomechanics

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Canada's Nuclear Waste Management Organization (NWMO) and the Swedish Nuclear Fuel and Waste Management Company (SKB) investigate approaches for managing used nuclear fuel and disposing of all radioactive wastes in their respective countries. NWMO is also responsible for taking the Deep Geological Repository project for low- and intermediate-level waste through the regulatory approvals process. With each nuclear accident, there is mounting pressure to move the solutions for nuclear waste disposal forward. NWMO and SKB rely on each other to share the development of the technology required to implement the disposal solution. However, the geological mediums that will be used by SKB and NWMO are different, and hence, the technology must be modified and adapted for these very different geological conditions. Canada cannot afford to conduct nuclear research at this scale alone. The challenges are too great and time required too long. The research proposed in this CRD will support NWMO's and SKB's mandate, and Canada will benefit from the sharing of the technology needed for implementing the disposal solution. The requirements placed on our ability to forecast the rock mass thermal-hydro-mechanical response containing these repositories far exceed the traditional design expectations for underground civil or mining projects. Consequently, there is a significant reliance on numerical modelling and prediction. This research will advance our numerical modelling approach utilizing the concepts embedded in the synthetic rock mass and will demonstrate the validity of the approach utilizing the results from laboratory and field testing programs that have been carried out at by SKB and NWMO over the past 10 years. The results from this work will be used to advance our ability to address many of the long-term issues associated with nuclear waste repositories and establish protocols for underground design and construction of nuclear waste repositories that are compatible with the Observational Method including monitoring and optimization.