Project: In situ recovery modelling using machine learning

Category: Innovation and collaboration (resources) - mineral, extractives or petroleum exploration or operation

In situ recovery (ISR) mining involves extracting uranium by pumping lixiviant (mining solution) underground to mobilise the uranium mineral from the geology that hosts it, with minimal disruption to the surface. ISR mining will enhance safety, minimise hazards, reduce environmental impact, and cut costs, compared to open cut mining.

Boss Energy, in collaboration with WGA, introduced an innovative machine learning-based approach to identify ISR deposits, a novel application in this context. This tool aids estimating reserves and predicting future uranium extraction at exploration phase, employing faster algorithms compared to traditional methods. This innovation improves production planning and well field development techniques, while also being cost-effective. It eliminates the need for extensive deposit profiles and significant computing resources as commonly practiced in the industry.

This machine learning model helps determine how suitable a deposit is for in situ recovery. It utilises data from special tools used to explore underground such as the downhole geophysics logs, the borehole magnetic resonance tool, as well as density and neutron logs and onsite X-Ray Fluorescence data.

The WGA team have shared the findings of use of the machine learning model at the 2022 Global Uranium Conference, and the 2023 ALTA Metallurgical Conference.

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