Simulation of weathered profiles coupled with multivariate block-support simulation of the Puma nickel laterite deposit, Brazil

Modelling and assessing spatial variability and uncertainty of mineral deposits is critical for both capital investments in mining projects as well as operational issues once a mine is developed. However, traditional approaches for modelling geological domains and geostatistical estimation provide smoothed representations of the pertinent deposit attributes, ignore spatial variability and, thereby, can mislead downstream decisions. Spatial variability and related uncertainty in modelling mineral deposit characteristics of interest, ranging from metal content and geological boundaries to geomechanical - geotechnical rock properties, can be modelled and quantified by stochastic spatial simulations. This is demonstrated through a detailed, step- by-step application to the Puma deposit, a major nickel lateritic asset in Brazil, part of the Onça-Puma mining complex. To integrate the variability of the regolith profiles of the deposit, their thicknesses are calculated after an unwrinkling process is applied and the deposit is then jointly simulated using min/max autocorrelation factors (MAF). The realizations serve as geological boundaries within which Ni, Co, Fe, SiO2, MgO and Dry-tonnage factor (DTF) are subsequently jointly simulated directly at block support scale. The final result is a series of equally probable representations of the Puma deposit, which are used to quantify and assess the uncertainty about key aspects of the project at the Puma deposit, such as the uncertainty of the in-situ resources documented herein, and the strict control of the ore's quality that feeds the ferronickel processing plant. The framework presented shows the advantages of the MAF and direct block simulation approaches for the efficient joint simulation of spatially variant geological attributes of large deposits for industrial environments. The methods presented are general, new in the context of geotechnical and geomechanical engineering, and can assist in the modelling of spatial variability and quantification of uncertainty linked to geotechnical rock properties and pertinent lithological boundaries.