Incorporating cut-off grade optimization and stockpiling into oil sands production scheduling and waste management.

Abstract

In achieving maximum benefit in oil sands mining, the long-term production schedule should have the time and sequence of removing ore, dyke material and waste from the final pit limit. An optimum cut-off grade profile and stockpiling will ensure the segregation between these materials meet economic and regulatory requirements. In-pit waste management strategy for oil sands mining requires dyke construction to occur simultaneously with the advancement of mining operations. This research seeks to determine: 1) the optimum life of mine cut-off grade profile and its corresponding tonnages; 2) the time and sequence for removal of ore, dyke material and waste to maximize NPV; 3) the dyke material schedule for dyke construction to minimize construction costs; and 4) the associated impacts of stockpiling and stockpile reclamation with limited time duration. Cut-off grade optimization was used to generate an optimum grade schedule which specifies the cut-off grade, duration of mining of the grade and tonnage mined during the mine life. A heuristic framework, referred to as the Integrated Cut-Off Grade Optimization (ICOGO) model was developed in this research. It generates an optimum cut-off grade policy and a schedule for mining ore and waste, as well as overburden, interburden and tailings coarse sand dyke material for long-term production planning. Subsequently, a mathematical programming framework based on Mixed Integer Linear Goal Programming (MILGP) model was developed to generate a detailed production schedule for removal of ore, waste and dyke materials from the final pit limit. Stockpiling scenarios investigated during the study include: i) no stockpiling; ii) stockpiling and reclaiming at the end of mine life; and iii) stockpiling for one year or two years prior to reclamation. The developed models were applied to two oil sands case studies to maximize the Net Present Value (NPV) of the operations. In both case studies, the NPV generated by the ICOGO model for one year stockpiling scenario was higher than other stockpiling scenarios. For the MILGP the NPV generated for the two year stockpiling scenario was higher than the one year stockpiling scenario. In comparison, whereas the ICOGO model solved the optimization problem faster, the MILGP model results provide detailed mining-cut extraction sequencing for mining.