Mine Plan Risk Assessment and Grade Uncertainty Characterization Using Geostatistical Conditional Simulation: Gold Mine Case Study - SME Annual Meeting 2025

For gold mines, orebody uncertainty is one of the leading causes of differences between planned and actual production. Geostatistical conditional simulations create multiple equally probable orebody block model realizations which are conditional to the current drilling information and demonstrate the sampled grade distributions and spatial statistics. These simulated block models are key inputs for a stochastic evaluation to show the likely range of grades and cash flows to be delivered by a mine plan. The stochastic evaluation outputs can be used to compare different mine plans in terms of expected values and risks to planned grades, production, costs, Net Present Value (NPV) and other plan metrics. For the McLaughlin gold mine, risk profiles are demonstrated to show how different mine, cut-off and stockpile strategies can reduce risk and improve expected value. The strategies considered were effective in improving expected NPV and reducing or eliminating tonnage uncertainty, but the strategies had little impact on reducing the uncertainty of NPV’s or mill grades. The stochastic evaluation process is also used to compare the risk impact of orebody uncertainty against the risk of not achieving planned mining tonnages or vertical advance rates. Risks for plans created with and without stochastic mine plan optimization are also compared.