A Simulation Approach for Improving the Assessment of Closure Risks

摘要

One of the key risks to the success of a mining operation is the accuracy of the estimates of both the economic mineral and any accompanying contaminants contained within the ore reserve. Misrepresenting the mineralogical characteristics of the ore reserve can result in considerable financial investment in an operation that is in fact uneconomic and/or carries a significant environmental risk. This in turn can result in an unplanned closure of the operation with associated financial/ environmental and social legacy. One way to minimise this risk is to undertake high density resource sampling to gain a better understanding of the resource characteristics. However, such an approach is expensive, time-consuming and often impractical. The risk of not having an accurate model is typically assessed during a feasibility study utilising a Monte Carlo simulation method or some form of sensitivity analysis. Importantly, the error margins used in these types of analyses are often based on subjective or very general criteria such as 'professional, judgement' or an 'industry standard'. This presentation introduces a conditional simulation method for developing a range of mineralogical distribution models. Based broadly on the method outlined in Journel and. Kyriakidis (2004) the proposed approach develops this work further. It combines a geostatistical analysis of the available data with automated mine design software to provide a rigorous method for assessing the confidence in the mineralogical characterisation of the ore reserve. The process is based on simulating possible mine plans based on the statistical characteristics of the available resource sample data. These plans are then assessed against high resolution resource models generated using a Sequential Gaussian Simulation method. The outcome is confidence intervals for important characteristics of the ore reserve estimate relative to the actual resource. These confidence intervals are an objective and transparent measure important for assessing closure risks and represent an improvement over common practice.