Conditional simulation methods to determine optimum drill hole spacing / Методы условного моделирования для определения оптимального расстояния между скважинами

Resource evaluation drilling is usually on a regular pattern; the pattern spacing should be optimised to maximise profit from mining, accounting for the cost of drilling and the value of additional information from increased density of drilling, and also to reduce risk in mined ore tonnes and grades to an acceptable level. Conditional simulation methods for determining optimum drilling spacing are more powerful than traditional methods, and simulation methods can take into account local variability in grade. Mining profit functions can consider profit and density of evaluation drilling so that profit can be maximised. A conditional  simulation method, here termed the simulation-estimation method, consists of the following: making conditional simulations of the orebody using the evaluation drilling; sampling these realisations to produce sets of artificial drill holes; estimating resources using the artificial drill holes; and comparing estimated resources with the realisation from which the respective artificial drill holes were taken. The effectiveness of the simulation-estimation method is demonstrated in a case study with the exhaustive Walker Lake dataset in which predicted results, using a sampled dataset, are similar to those from the exhaustive dataset. The simulation-estimation method is applied to a mining case study of a Western Australian bedded iron ore deposit and shows that estimation errors are higher for the impurities SiO2, Al2O3, and P, rather than Fe. A mining profit function suggests that the preferred resource evaluation drill hole spacing to maximise mining profit is a spacing of 40 × 40 m, which has a higher expected profit than spacings of 10 × 10 m, 20 × 20 m and 80 × 80 m. Charts of drill hole spacing versus expected average error allow a spacing to be selected that has the maximum allowable level of average error. Conditional simulation methods can also be used for quantitative resource classification that considers mining production rates and selectivity, as well as for highlighting periods of high risk in mine plans due to high uncertainty in ore grade. <...>