There is an increasing awareness of the sensitivities in conditional simulation to sample and assay quality, geological interpretations and structural controls, and variogram parameters such as the nugget effect. A wide range of algorithms and applications has been proposed, presented and discussed (ISGSM 1999, held in Perth, Australia, is the most recent international meeting to focus solely on conditional simulation and its applications for the mining industry). <...>

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.

Detailed spatial models generated by conditional simulation provide a powerful tool for case-specific optimization of sampling designs. The entire process of sampling, estimation, and decision can be simulated on such a model by a Monte-Carlo approach. Optimization can be based on economic functions or on decision quality constraints rather than simple minimization of estimation variance.