Authors: Pitcher, PA; Hocking, RJ

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Pitcher, PA & Hocking, RJ 2022, 'Assessing the risk of a sublevel cave–block cave transition using dynamic decision tree analysis overlain with Monte Carlo analysis', in Y Potvin (ed.), Caving 2022: Fifth International Conference on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 821-830,

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Depleting global copper resources and declining discovery rates are driving a step-change in the copper mining industry as open pit mines transition underground and exploration for new deposits moves deeper. The need to exploit deeper, larger and lower grade copper deposits has resulted in the emergence of a technically challenging underground mass mining method called block caving. To improve shareholder returns, mining companies rising expectations for block cave mines are currently pushing the limits of existing knowledge and positioning larger and deeper projects in uncharted territory from a geotechnical risk and cost perspective. This paper addresses unconsidered geotechnical risks in discounted cash flow (DCF) analysis by introducing Monte Carlo simulation and decision tree analysis into a DCF model for the feasibility stage Carrapateena block cave expansion, currently being advanced by OZ Minerals. As a brownfield block cave development project expected to transition from sublevel caving to block caving, the ability to pivot the production strategy in response to downside risks encountered during block cave development and ramp-up was the focus of this paper. Dynamic modelling of geotechnical uncertainty was conducted to provide insight into how uncertainty can be resolved through time, as management decisions are made in response to uncertain events during the development and ramp-up of a block cave project. A dynamic DCF model was created to help assess the risks around the schedule, including the ramp-up and the undercutting phase of the project, while also using a decision tree to determine the best decision for the project overall. The results from the simulation demonstrated an average recovery of approximately AUD 20 million to the valuation for Carrapateena and increased the minimum incremental net present value to be greater than zero, confirming based on the model assumptions and probability distributions that OZ Minerals should decide to pursue the expansion. An additional simulation, focusing on the value of the decision tree itself, indicated that management decisions could minimise the downside risks of delays by up to 15% on the base NPV, supporting the use of decision trees in DCF analysis.

Keywords: block caving, Monte Carlo analysis, decision tree analysis, mine planning

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