Uncertainty quantification for uranium production in mining exploitation by In Situ Recovery

• Published in: Computational geosciences 2021-06, Vol.25 (3), p.831-850
• Main Authors: Langanay, Jean, Romary, Thomas, Freulon, Xavier, Langlais, Valerie, Petit, Gwenaele, Lagneau, Vincent
• Format: Article
• Language: English
• Subjects: Approximation; Chemical properties; Chemicophysical properties; Computational fluid dynamics; Computer applications; Distribution; Earth and Environmental Science; Earth Sciences; Exploitation; Fluid flow; Geochemistry; Geology; Geostatistics; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; In situ leaching; Leaching; Mathematical analysis; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mining; Original Paper; Recovery; Sciences of the Universe; Simulation; Soil Science & Conservation; Spatial distribution; Stochasticity; Transport; Uncertainty; Uranium; Uranium ores; Variability
• ISSN: 1420-0597; 1573-1499
• DOI: 10.1007/s10596-020-10018-x

Uranium In Situ Recovery (ISR) is based on the direct leaching of the uranium ore in the deposit by a mining solution. Fluid flow and geochemical reaction in the reservoir are difficult to predict due to geological, petrophysical and geochemical uncertainties. The reactive transport simulation code used to model ISR is very sensitive to the spatial distribution of physical and chemical properties of the deposit. Stochastic geostatistical models are used to represent the uncertainty on the spatial distribution of geological properties. The direct propagation of geological uncertainties by multiple ISR mining simulations is intractable in an industrial context. The CPU time needed to perform one ISR numerical simulation is too heavy. This work presents a way to propagate geological uncertainties into uranium production uncertainties at a reduced computational cost, thanks to a scenario reduction method. A subset of geostatistical simulations is built to approximate the variability of a larger set. The selection is obtained using a proxy of reactive transport simulation. The main contribution of this work is the development of the proxy, which is based on an artificial mineral exploitation that has common properties with uraninite. It allows the discrimination of geostatistical realizations in terms of potential uranium production. Then, the ISR simulation carried out with the selected geostatistical realizations gives a good approximation of the uranium production variability over the whole set of geostatistical simulations. This approximation is then used to quantify the uncertainties on the uranium production. The proposed approach is assessed on real case studies.