Download or read book An Integrated Risk Evaluation Model for Mineral Deposits written by Grant Nicholas and published by . This book was released on 2014 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: The core asset of most mining companies is its mineral resources and reserves. The company produces ore from its reserves, which is a subset of its mineral resources associated with varying levels of geoscientific confidence and uncertainty. One of the key evaluation challenges is to distil technical complexity into a financial model that is usually designed to focus only on one or two key valuation indicators, such as net present value (NPV) or internal rate of return (IRR). The driver behind this research was whether conventional evaluation techniques for mineral projects can evaluate accurately both the spatial and temporal characteristics of project risks in financial terms, due to their inherent nature to understate the true variance, and under-value or over-value the actual NPV. How can conventional evaluation methods be compared to an innovative, integrated evaluation technique that quantifies the non-linear impacts of spatial resource variables on production constraints in financial terms, measured at the appropriate temporal scale? To answer these questions, this research focused on developing an innovative risk evaluation methodology for two different diamond deposits and one gold deposit to incorporate spatial, non-spatial and financial data across the evaluation pipeline. The author developed an integrated evaluation modelling (IEM) framework based on a unique bottom-up methodology that follows every estimation block through the mining and processing value chain, i.e., it accurately captures the spatial variability of all relevant value chain variables in the ground and their correlated impacts on production constraints such as grade, density and processing characteristics. This variability is propagated through the processing value chain at a mining block (or selective mining unit, "SMU") scale. The IEM approach revealed differences in NPV between a 'bottom-up' (or Local) evaluation method and a 'top-down' (or Global) evaluation method - see Figure 1. While the actual NPV for the virtual ore body (VBod) was CAD 2.1 million, the figure shows that the local evaluation method (bottom-up) more closely approximated the actual NPV of the project than the global (top-down) evaluation method, which materially over-estimated the NPV. The author demonstrated that cash flow constituents derived from annual estimates in a top-down approach will not correctly reflect the asymmetries due to operational variability on a local, daily basis. The 'bottom-up' evaluation method represented a more accurate way of deriving annual cash flow estimates needed to make decisions on projects by accumulating the appropriate values from a bottom-up approach, i.e. daily, monthly, quarterly then derive annual estimates for NPV forecasts. The two main advantages of the IEM methodology are that firstly, it accurately reproduces the spatial resource characteristics of blocks at the appropriate temporal scale; and secondly, direct linkages are created between the resource-reserve-financial models within a single software environment. This allows multiple scenarios to be rapidly assessed for a mineral project and the cost/benefits of implementing risk mitigation strategies to be easily evaluated. This research also quantifies the financial impact of managerial flexibilities by evaluating selected hedging strategies that simultaneously consider production and economic uncertainties within an IEM framework. All modelled outputs are calculated in NPV terms using a modified DCF approach. The importance of linkages within an IEM framework is validated between unsystematic risks, with respect to key resource-to-reserve stochastic variables, and systematic risks considering the impact of foreign exchange rates. The author concludes that the greater the variability of key systematic and unsystematic variables, the more the mine has to consider flexibility in its mining and processing schedules and management hedging strategies; but the real costs of attaining that flexibility needs to be evaluated using an IEM framework. The confidence in a NPV estimate for complex mineral projects cannot easily be quantified using any closed-form analytical or mathematical solution. Complex, non-linear relationships between resources, reserves, financial and economic parameters requires a simulation model based on an IEM framework to provide a robust solution.