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|Title:||Incorporating grade uncertainty in oil sands mine planning and waste management using stochastic programming|
|Keywords:||uncertainty-based oil sands mine planning;production scheduling optimization;stochastic programming;sequential Gaussian simulation;waste management;grade uncertainty;ordinary kriging|
|Abstract:||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 in maximizing the economic potential of the mineral resource and minimizing 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. If grade uncertainty is not incorporated in oil sands mine planning, it may lead to under- or over-design of the waste management system required to support the mining operation, resulting in lost opportunities. Grade uncertainties have profound impact on the Net Present Value (NPV) of the mining project as it may induce large differences between the actual and expected production targets. Thus, the primary research objectives are to develop, implement and verify an integrated oil sands mine planning optimization framework using Stochastic Mixed Integer Linear Programming (SMILP) to integrate the related domains of bitumen grade uncertainty and waste management. The SMILP model determines the order and time of extraction of ore, dyke material and waste that maximizes the Net Present Value (NPV), minimizes waste management cost, and minimizes the geological risk cost of the mining operation. Sequential Gaussian Simulation (SGS) 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. MATLAB programming platform was chosen for the SMILP framework implementation. A large-scale optimization solver, IBM CPLEX, is used for this research. To validate the SMILP model, an oil sands case study was implemented based on SGS realizations block models to generate a stochastic integrated production schedule (SMILP schedule), and the results compared with a conventional production scheduling approach based on Ordinary Kriging block model (OK schedule) and E-type block model (E-type schedule). The E-type block model is the average block model of the SGS realizations. In comparison, the SMILP schedule which was based on the developed SMILP framework generated an uncertainty-based integrated production schedule and waste management plan with better financial profitability compared to the OK and E-type schedules. Additional numerical experiments and analysis was done by applying the three schedule results to each of three randomly selected realizations. The corresponding SMILP schedules generated from the realizations were consistently uniform and smooth compared to similar OK and E-type realization schedules. The SMILP framework accounts for geological risk by placing higher penalties for ore grade and ore tonnage deviations from production targets in the early years of mine life to defer production deviations to later years when more geological information becomes available to update the block model and mine plan. By deferring geological risk to later years, the risk of not reaching production targets in the earlier years is minimized, thus creating a smoother and stable production schedule. The results demonstrate that the SMILP schedule generates 14% and 17% improvements in NPV compared to the E-type and OK schedules respectively. These results prove that the SMILP model is a useful tool for optimizing stochastic integrated oil sands production schedules whilst taking into account grade uncertainty.|
|Appears in Collections:||Natural Resources Engineering - Master's Theses|
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|210402M_MASc_Thesis_Obinna Mbadozie.pdf||4.92 MB||Adobe PDF||View/Open|
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