Abstracts Category : Other

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

by Hosseini Navid Seyed

In achieving maximum benefit in oil sands mining, the long-term production schedule shouldhave the time and sequence of removing ore, dyke material and waste from the final pit limit. Anoptimum cut-off grade profile and stockpiling will ensure the segregation between thesematerials meet economic and regulatory requirements. In-pit waste management strategy for oilsands mining requires dyke construction to occur simultaneously with the advancement ofmining operations. This research seeks to determine: 1) the optimum life of mine cut-off gradeprofile and its corresponding tonnages; 2) the time and sequence for removal of ore, dykematerial and waste to maximize NPV; 3) the dyke material schedule for dyke construction tominimize construction costs; and 4) the associated impacts of stockpiling and stockpilereclamation with limited time duration. Cut-off grade optimization was used to generate an optimum grade schedule which specifies thecut-off grade, duration of mining of the grade and tonnage mined during the mine life. Aheuristic framework, referred to as the Integrated Cut-Off Grade Optimization (ICOGO) modelwas developed in this research. It generates an optimum cut-off grade policy and a schedule formining ore and waste, as well as overburden, interburden and tailings coarse sand dyke materialfor long-term production planning. Subsequently, a mathematical programming frameworkbased on Mixed Integer Linear Goal Programming (MILGP) model was developed to generate adetailed production schedule for removal of ore, waste and dyke materials from the final pitlimit. 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 PresentValue (NPV) of the operations. In both case studies, the NPV generated by the ICOGO modelfor one year stockpiling scenario was higher than other stockpiling scenarios. For the MILGP theNPV generated for the two year stockpiling scenario was higher than the one year stockpilingscenario. In comparison, whereas the ICOGO model solved the optimization problem faster, theMILGP model results provide detailed mining-cut extraction sequencing for mining.