In oil sands mining, due to limitations in lease areas, the pit phase development and the construction of in-pit and ex-pit tailings containment dykes are carried out simultaneously. The primary research objectives are to formulate, implement and verify a theoretical optimisation framework based on Mixed Integer Linear Goal Programming (MILGP) formulations to simultaneously schedule production and dyke material. The numerical modelling approach used in implementing the framework is presented. A case study was compared with industry standard software, Whittle. In contrast, the MILGP model scheduled for more ore in the early years contributing to an increased net present value.

The primary purpose of oil sands mine planning and waste management is to provide ore from the mine pit to the processing plant while containing the tailings in an efficient manner in-pit. Incorporating waste management in the mine plan is essential to maximize the economic potential of the mineral reserve and minimize waste management costs. However, spatial variability such as grade uncertainty results in ore tonnage variations, which leads to fluctuations in the quantity of ore to be processed and waste to be managed. This paper investigates the application of a stochastic mixed integer linear programming (SMILP) on oil sands mine planning to integrate bitumen grade uncertainty and waste management. Sequential Gaussian simulation is employed to quantitatively model the spatial variability of bitumen grade in the oil sands deposit. Multiple simulated orebody models are used as inputs for the SMILP model to generate optimal mine plans in the presence of grade uncertainty. The results demonstrate that the SMILP schedule generates 14% and 17% improvements in net present value compared to the E-type and ordinary kriging schedules, respectively. These results indicate that the SMILP model is a robust tool for optimizing stochastic integrated oil sands production schedules and waste management.

In oil sands mining, the production schedule must be integrated simultaneously with in-pit and ex-pit dike construction scheduling. Any extra mined ore is stockpiled for a limited duration, and the topmost layer of the overburden is used for land reclamation. An uncertainty-based mathematical programming model is developed based on mixed integer linear goal programming for oil sands production scheduling and waste management. The model aims to maximize the net present value (NPV) while meeting all required production and technical constraints. The reclamation strategy for the stockpiled ore and the destination of dike materials is determined to minimize costs. The model uses kriged estimates with a variance penalty scheme to minimize the financial risk from grade uncertainty associated with the production schedule. The uncertainty-based model is implemented for an oil sands mine case study with two scenarios. An integrated mine plan with a waste management and stockpiling strategy is generated by Scenario 1 that maximizes the NPV of the operation and minimizes dike construction and reclamation cost. Scenario 2 uses the variance penalty scheme to estimate the production schedule financial risk from grade uncertainty.

In oil sands mining, bitumen and fines contents are used to predict ore processability. However, experimental results show that certain solid fractions known as Organic Rich Solids (ORS) negatively affect the overall bitumen recovery. A conceptual mine planning framework based on a goal programming model for oil sands production scheduling and waste management is presented. Bitumen recovery is additionally adjusted based on the ORS content. The model features automated production targeting (APT) and limited duration stockpiling constraints that optimize the annual production capacities. The model is implemented with two scenarios. Scenario 1 uses processing recovery calculated based on Alberta Energy Regulatory requirements while Scenario 2 uses processing recovery additionally adjusted based on ORS content. Results for Scenario 1 show a 3.46% overestimation of net present value compared to Scenario 2. The APT constraints provide planners a robust and efficient technique for determining annual production tonnages with minimum periodic variations.