This article presents a new energy model that predicts the energy infrastructure required to maintain oil production in the Canadian Oil Sands operation at minimum cost. Previous studies in this area have focused on the energy infrastructure for fixed energy demands (i.e., the production schemes that produce synthetic crude oil (SCO) and commercial diluted bitumen remained fixed in the calculation of the optimal infrastructure). The key novelty of this work is that the model searches simultaneously for the most suitable set of oil production schemes and the corresponding energy infrastructures that satisfy the total production demands under environmental constraints, namely, CO 2 emissions targets. The proposed modeling tool was validated using historical data and previous simulations of the Canadian Oil Sands operation in 2003. Likewise, the proposed model was used to study the 2020 Canadian Oil Sands operations under three different production scenarios. Also, the 2020 case study was used to show the effect of CO 2 capture constraints on the oil production schemes and the energy producers. The results show that the proposed model is a practical tool for determining the production costs of the Canadian Oil Sands operations, evaluating future production schemes and energy demand scenarios, and identifying the key parameters that affect Canadian Oil Sands operations.

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There exist several inherent uncertainties in the energy optimization modeling of Oil Sands operations. In this work, the deterministic model proposed by Betancourt-Torcat et al. in 2011 has been extended to account for parameter uncertainty in the natural gas price and steam-to-oil ratio (SOR). The new extended steady-state model considers freshwater withdrawal constraints and a new methodology to account for greenhouse gas (GHG) emissions. The problem was formulated as a single-period stochastic (MINLP). The application of the stochastic energy optimization model includes results reflecting all uncertain outcomes simultaneously and enabling optimal arrangement of the energy supply and oil producer infrastructures. The model's capabilities have been shown in the present work through two new case studies accounting for uncertainty while the deterministic case is presented as a reference. The case studies under uncertainty consider the forecasted oil production scenario for the year 2035 in an uncertain environment where the price of natural gas is volatile and the SOR unknown. The results of the stochastic model were compared with those of the deterministic model by studying the expected values of the stochastic approach and those of the deterministic solution. The results presented in this study were discussed regarding the characteristics of uncertainty of the varied fuel price and SOR parameter. The key findings of this study are that oil producers considering hydrocracking are favored over thermocracking-based schemes, and the GHG emission constraint cannot be met for SOR values higher than 2.48.