Joint stochastic optimisation of short and long term mine production planning: method and application in a large operating gold mine

A new multistage stochastic mine production scheduling approach is developed and tested in a large operating gold mine. The approach takes short scale orebody information in the form of grade control data into account. As simulated orebodies used in stochastic long term mine planning are based on sparse exploration data while grade control data are unavailable at the time of production scheduling, the short scale information is simulated stochastically. Stage 1 of the approach simulates high density future grade control data based on exploration data and grade control in previously mined out parts of a deposit. In stage 2, the technique of conditional simulation by successive residuals enables preexisting simulated orebody models to be updated using the simulated future grade control information. Stage 3 is based on a stochastic programming mine scheduling formulation that handles jointly multiple simulated orebody models from stage 2, and accommodates both maximising net present value (NPV) and minimising deviations from expected production targets. Stage 4 includes quantification of risk in the production schedules generated, comparisons and reporting. The application at a large operating gold mine demonstrates that the proposed approach is practical, and adds value to the operation. The approach is shown to deliver additional ore (3?6 Mt more) and metal (2?6 Mg more) which matches the mines reconciliations, unlike the conventional schedule, and results in a cumulative NPV which is on average 7?7 million AUD higher than that of a stochastic schedule without the simulated grade control data. The NPV is 230 million AUD higher compared to the NPV from the actual schedule of the mine. An additional key contribution of the proposed approach is the compliance of shortto long term production schedules and performance, leading to higher probability of meeting production targets and increased productivity. Given the capital intensity of mining projects, this contribution can be critically important to mining operations.