Abstract
Mining operations face a decision regarding additional drilling several times during their lifetime. The two questions that always arise upon making this decision are whether more drilling is required and, if so, where the additional drill holes should be located. The method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. The MAB optimizes the best infill drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. The proposed method is applied to a long-term, multi-element stockpile, which is a part of a gold mining complex. The stockpiles in this mining complex are of particular interest due to difficult-to-meet blending requirements. In several mining periods grade targets of deleterious elements at the processing plant can only be met by using high amounts of stockpiled material. The best pattern is defined in terms of causing the most material type changes for the blocks in the stockpile. Material type changes are the driver for changes in the extraction sequence, which ultimately defines the value of a mining operation. The results of the proposed method demonstrate its practical aspects and its effectiveness towards the optimization of infill drilling schemes.
Highlights
Optimizing an infill drilling pattern can be linked to a more general problem present in many different applications such as stock markets, research effort direction, project development, and others
The results for the five-hole budget class are not as expected in the sense that it is pattern 1 and not pattern 2 that has the highest average reward. This can be explained by pattern 2 strongly outperforming pattern 1 for some simulations as the possibly ”true” stockpile, while in others pattern 2’s performance is much worse than pattern 1’s
The case study demonstrates how the method successfully quantifies the influence of geological uncertainty on the performance of an infill drilling pattern
Summary
Optimizing an infill drilling pattern can be linked to a more general problem present in many different applications such as stock markets, research effort direction, project development, and others. The value of future information is a monetary value This is hard to quantify for a mineral deposit. Boucher et al (2005) indirectly do this via a misclassification cost of material This misclassification refers to ore versus waste based on a fixed cut-off grade and one processing stream. This is usually not the case in a mining complex. Menabde et al (2007) show that it is optimal to use a variable cut-off grade that comes directly from the schedule optimization This makes it impossible to evaluate misclassification errors without rescheduling the deposit. This effect is strengthened if multiple processing streams are considered in combination with the blending of material from different sources, as in the application presented below
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