The issue of long-term resources was raised in the 1970s and led to the RNR development programme, but was ultimately set aside in the face of apparently abundant uranium resources. In addition to the I-Tésé work carried out using the GRUS (system dynamics) tool on the match between demand for the nuclear fleet and fissile resources (uranium and plutonium), economics thesis work has been carried out on this issue of long-term resources ("Disponibilité à long terme des ressources mondiales d’uranium " thesis A. Monnet 2013-2016). This research is now being pursued from the angle of mining production: How can uranium mining production be increased tenfold by 2100? This new approach has been made possible through collaboration with Orano's Mining BU (R&D Mining) and supervision within the 398 GRNE doctoral school by Damien Goetz, a professor at the geosciences centre of Mines Paris PSL.
There are two main questions concerning known deposits and those yet to be discovered:
- How do the production and reserves of known deposits depend on possible mining improvements?
- How can we increase exploration to not only renew but also increase reserves in order to meet fast-growing production demand?
For uranium production at deposit scale, the relationships between resources, reserves, annual capacity, mining technology, cost (CAPEX, OPEX), yield, cut-off grade, etc. will need to be determined.
For exploration at regional or global level, the aim will be to see whether new techniques, particularly in relation to new types of deposit or production technologies, can be envisaged and the consequences for the development of resources and reserves.
Modelling the development of reserves and production over the long term, using a system dynamics tool, should make it possible to determine the key parameters for ensuring the production-demand balance.
The major difficulty in the work envisaged is linked to the time scale chosen. Projections over the long term, 2100, are necessarily highly uncertain and depend on technological breakthroughs that are difficult to anticipate. System dynamics enables a scenario-based approach to simulate different futures.