EPJ Nuclear Sci. Technol.
Volume 2, 2016
|Number of page(s)||12|
|Published online||08 April 2016|
Statistical model of global uranium resources and long-term availability
French Alternative Energies and Atomic Energy Commission, I-tésé, CEA/DEN, Université Paris Saclay, 91191
2 Université Montpellier 1–UFR d’Économie–CREDEN (Art-Dev UMR CNRS 5281), Avenue Raymond Dugrand, CS 79606, 34960 Montpellier, France
⁎ e-mail: firstname.lastname@example.org
Received in final form: 5 January 2016
Accepted: 19 January 2016
Published online: 8 April 2016
Most recent studies on the long-term supply of uranium make simplistic assumptions on the available resources and their production costs. Some consider the whole uranium quantities in the Earth's crust and then estimate the production costs based on the ore grade only, disregarding the size of ore bodies and the mining techniques. Other studies consider the resources reported by countries for a given cost category, disregarding undiscovered or unreported quantities. In both cases, the resource estimations are sorted following a cost merit order. In this paper, we describe a methodology based on “geological environments”. It provides a more detailed resource estimation and it is more flexible regarding cost modelling. The global uranium resource estimation introduced in this paper results from the sum of independent resource estimations from different geological environments. A geological environment is defined by its own geographical boundaries, resource dispersion (average grade and size of ore bodies and their variance), and cost function. With this definition, uranium resources are considered within ore bodies. The deposit breakdown of resources is modelled using a bivariate statistical approach where size and grade are the two random variables. This makes resource estimates possible for individual projects. Adding up all geological environments provides a repartition of all Earth's crust resources in which ore bodies are sorted by size and grade. This subset-based estimation is convenient to model specific cost structures.
© A. Monnet et al., published by EDP Sciences, 2015
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