Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

Abstract

High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper-molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.

A Computer Program for Practical Semivariogram Modeling and Ordinary Kriging: A Case Study of Porosity Distribution in an Oil Field

“…(6)(7)(8)(9)(10)(11). For this, firstly, the grids are defined for the region that the estimation will be performed on and then these grids estimates are performed using theoretical semivariogram functions determined on the previous screen.…”

“…The techniques, developed by Krige [2] and Matheron [3] to evaluate orebody, have been disseminated out into many other fields, utilising spatial data. Its diverse disciplines include petroleum geology [4], hydrogeology [5], hydrology [6], meteorology [7], oceanography [8], geochemistry [9], metallurgy [10], geography [11,12], forestry [13], environmental control [14], landscape ecology [15], soil science and agriculture [16,17]. In petroleum industries, geostatistics is successfully applied to characterize petroleum reservoirs based on interpretations from sparse data located in space, such as reservoir thickness, porosity, permeability and seismic data [18][19][20].…”

Geometallurgy—A Route to More Resilient Mine Operations

“…In addition to estimates with too low variability, kriging may introduce a bias for variables such as metallurgical properties, which do not combine linearly [25,85]. Geometallurgical domains may be more optimally defined via conditional simulation, which provides multiple outcomes for scenario evaluation [86,87]. Such an approach provides a better opportunity to determine how NPV changes across various scenarios.…”

“…While resource models focus on modelling of the primary variables that drive metallurgical responses, building of geometallurgical models separately may involve direct modelling of response variables in the 3D block model [25]. A complication with the estimation of response variables with classic techniques based on a variogram is their non-additivity [25,86,87]. Hence caution is required if undertaking the direct modelling of response variables.…”

Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences

“…To the authors' knowledge, all studies conducted on predictive geometallurgy by mathematical geoscientists (Bye 2011;Boisvert et al 2013;Rossi and Deutsch 2014;Hosseini and Asghari 2015;Ortiz et al 2015;Deutsch et al 2016) consisted on appropriately predicting the secondary properties at each block of a mining block model, and proposing the mining and processing engineers to conduct their mine planning and plant scheduling based on those properties instead of on metal grades. The first step (Vann et al 2011) is the geometallurgical analysis of the ore body with respect to its primary properties.…”