Another major risk that has been extensively identified and studied from a technical perspective corresponds to the geological risk. The geological model for a mineral deposit is developed from the limited information obtained from techniques such as exploration drilling, channel samples or from outcrops, amongst others. It’s well known that the estimates in a geological model, such as those commonly used in the estimation of resources and reserves, are an approximate representation of the mineral grade distribution. This leads to a high incertitude regarding the distribution and variability of mineral grades within the deposit, commonly referred to as geological uncertainty. Several studies have shown that a large percentage of projects do not meet production targets due to the differences between the mineral grade they had in the model with respect to the grade obtained in reality, this being a result of the geological uncertainty previously mentioned.
Another risk factor affecting all mining operations is the risk of over and under excavation of blocks, which results in dilution, operational losses of material, and possible geomechanical instability. In most cases, a mining operation assumes a dilution rate based on historical values, previous experiences or similar operations. However, this dilution rate can differ greatly in reality, compromising the profitability of the operation.
From a financial point of view there are large risks related to the uncertainty in the price of metals and raw materials, operating costs, as well as taxes and regulations. In a similar way we could list many more risks that arise from all the uncertainties that are present in a mining operation.
The question we have to ask ourselves is how to face this uncertainty. We can identify three working methodologies to deal with this uncertainty. The first option is to ignore the uncertainty, which is the traditional methodology along with the ensuing risks associated with that. The second option is to reduce uncertainty, which can be done up to some extent and can come at a great cost. This option can be considered for certain types of uncertainties, but in certain cases, such as geological uncertainties, the deposit is only fully known once it is extracted. The third option is to live with uncertainty and consider it in decision-making using stochastic methods. In this case, uncertainty is accounted for in the model and strong decisions have to be made, understanding that uncertainty will be present at the moment of making decisions.
Stochastic solutions perform better than their deterministic counterparts in the presence of uncertainty. The challenge is to correctly model uncertainty and develop efficient stochastic methods. It’s in this direction that we must all work in the industry, academia and technology companies to be able to provide us with the tools that will allow us to reduce the risks associated with mining activities.
Luis Montiel Petro, PhD.