It is generally acknowledged that the head grade of the world’s copper resources is declining. Accordingly, there is an increase in the volume of resource material being mined and processed to maintain copper production. This has led to increases in cost base accompanied by reductions in Life of Mine (LOM) Net Present Value (NPV). The higher tonnages of material mined are driven by increased quantities of lower grade material required to satisfy the short term mine model. In turn, this has brought an increased focus on sampling and assay methods, since any error associated with estimating the grade of each block and determining its correct destination serves to magnify the losses that will occur.
Presently most open-pit copper mining operations use blasthole sample assays to feed their shortterm model. The problematic nature of blasthole sampling is well documented (Engström, 2017), particularly the poor timeliness of the data and the significant errors that are inherent to the technique. Methods to improve grade control, such as drilling a separate grade control grid, have been proposed but come with their own cost, logistical, and accuracy shortcomings.
A recent development in grade estimation is the Pulsed Fast and Thermal Neutron Activation (PFTNA) downhole logging probe. Deployed on an efficient semi-autonomous platform, PFTNA is being aggressively adopted in the Australian iron ore industry. The technology provides in-situ elemental analysis of the geological formation, delivering high accuracy, high efficiency, near realtime data from a sizeable measurement volume.
In this paper we consider the application of the technology to copper. Whilst there are several operational components to such a value proposition, we focus on the benefits of improved gradeestimation. A simulated copper mine is used to model the quantum of improvement expected from a reduced grade-estimation error, which reduces the likelihood of misallocating material to the wrong destination, which in turn increases NPV.