Application of conditional simulations to capital decisions for Ni-Sulfide and Ni-Laterite deposits

Abstract

Prior to the acquisition of data from production drilling and grade control sampling, the spatial density of data is usually insufficient to properly address issues related to short-scale variability. Grade interpolation, whether conducted through ordinary kriging or other linear or non-linear regression techniques, usually suffers from significant over-smoothing or conditional bias. Four examples presented in this paper show that conditional simulations provide a viable and powerful alternative in assessing the sensitivity of key variables that are critical to the decisions made prior to moving forward with significant capital expenditures. These variables include the selection of the most appropriate mining method and mining equipment, the optimum cut-off strategy and the short-term variability constraints on process plant feed. The results also demonstrate that conditional simulations can be used to assess the risk associated with many of the technical aspects of the project and its financial performance.