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Parametric probabilistic, improved Kriging Variance and simulation methods of mineral resources classification
Owusu, Solomon Kwabena Ansah
Owusu, Solomon Kwabena Ansah
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2020
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Abstract
Uncertainty with regards to estimated grades and tons of a mineral deposit demands risk assessment in order to mitigate investment risks and build the confidence of investors and other stakeholders on the success of a project. Classification of uncertainties associated with resource estimation is a major challenge in the mining business because their negative impacts lead to unreliable production schedules and unpredictable cash flows. The various standard codes for public disclosure provide guidelines and recommendations for the classification of mineral resources and reserves but they lack the provision of details, for example, geological and geostatistical information needed for each category of the mineral resources and reserve. Another problem is the dependency of the parameters used to generate the resource classification categories on the assumptions made by the Qualified Person (QP). The research work described in this dissertation focuses on the development of simple resource classification methods to address the complexity challenges that the industry professionals currently experience. Also, the work has introduced a classification process with minimized or no QP dependency or influence. Again, the uniformity and consistency challenges associated with resource classification due to the application of different QP assumptions are addresses with proposed uniform frameworks. Unlike the traditional Kriging Variance (KV) method, the proposed production adjusted KV approach corrects the error variance for quarterly and annual production volumes before applying the 90% confidence interval around the mean grade of each block. Similarly, the simulation variance and the e-type mean for each block are used in the simulation approach. Generally, the application of the proposed methods and the traditional methods on a one-bench copper deposit and a gold deposit produced close results. Due to the consistency of the proposed methods, effective project economics, reliable investment decisions and investor confidence will be strengthened in future resource classification results, after the industry professionals adopt the new methods.
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