Stochastic Modeling of the Oil Sands Operations under Greenhouse Gas Emission Restrictions and Water Management

Abstract

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.

Energy consumption and greenhouse gas emissions in the recovery and extraction of crude bitumen from Canada’s oil sands

“…Similarly [13][14][15][16] study the carbon reduction technologies from economic aspect but do not detail the estimation of GHG emissions from specific oil sand projects. Other studies [17][18][19] answer different questions and do not suffice for calculating project-specific energy consumption and emissions. None of these studies give access to the operating parameters.…”

Optimized integration of renewable energy technologies into Alberta’s oil sands industry

“…In a later study they investigated the effect of varying key environmental and operational parameters on the oil sands operations. These are CO 2 capture levels, natural gas prices, and steam-to-oil ratios (SOR) (Betancourt-Torcat et al, 2013). The CO 2 emission targets are expected to become increasingly stringent in order to satisfy emissions policies set by the government of Alberta.…”

“…In the sequential approach the existence of a mix of energy infrastructure influences investment decisions in the following investigated period. The preference of the model to Table 4 Key techno-economic parameters (Ordorica-Garcia et al, 2007;Ordorica-Garcia et al, 2008;Betancourt-Torcat et al, 2011;Betancourt-Torcat et al, 2012a;Betancourt-Torcat et al, 2013).…”

Design multiperiod optimization model for the electricity sector under uncertainty – A case study of the Emirate of Abu Dhabi

“…This type of distribution is the most widely known and the standard for many probability problems [44,45]. The associated uncertainty is considered in the present model using a discrete distribution of the random parameter (i.e., natural gas price) with a finite number ''S'' of possible Table 1 List of power plant technologies.…”