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Energy infrastructure modeling for the oil sands industry: Current situation
Applied Energy 181 (2016) 435–445
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Energy infrastructure modeling for the oil sands industry: Current situation Edoardo Filippo Lazzaroni a,⇑, Mohamed Elsholkami a, Itai Arbiv a, Emanuele Martelli b, Ali Elkamel a, Michael Fowler a a b
Department of Chemical Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario N2L 3G1, Canada Department of Energy, Politecnico di Milano, Via Lambruschini 4, Milano 20156, Italy
h i g h l i g h t s A simulation-based modelling of energy demands of oil sands operations is proposed. Aspen simulations used to simulate delayed coking-based upgrading of bitumen. The energy infrastructure is simulated using Aspen Plus achieving self-sufficiency. Various scenarios affecting energy demand intensities are investigated. Energy and CO2 emission intensities of integrated SAGD/upgrading are estimated.
a r t i c l e
i n f o
Article history: Received 25 January 2016 Received in revised form 16 July 2016 Accepted 13 August 2016
Keywords: Oil sands SAGD Synthetic crude Life cycle assessment CO2 emissions Process modeling Process simulation Aspen plus
a b s t r a c t In this study, the total energy requirements associated with the production of bitumen from oil sands and its upgrading to synthetic crude oil (SCO) are modeled and quantified. The production scheme considered is based on the commercially applied steam assisted gravity drainage (SAGD) for bitumen extraction and delayed coking for bitumen upgrading. In addition, the model quantifies the greenhouse gas (GHG) emissions associated with the production of energy required for these operations from technologies utilized in the currently existing oil sands energy infrastructure. The model is based on fundamental engineering principles, and Aspen HYSYS and Aspen Plus simulations. The energy demand results are expressed in terms of heat, power, hydrogen, and process fuel consumption rates for SAGD extraction and bitumen upgrading. Based on the model’s output, a range of overall energy and emission intensity factors are estimated for a bitumen production rate of 112,500 BPD (or 93,272 BPD of SCO), which were determined to be 262.5–368.5 MJ/GJSCO and 14.17–19.84 gCO2/MJSCO, respectively. The results of the model indicate that the majority of GHG emissions are generated during SAGD extraction (up to 60% of total emissions) due to the combustion of natural gas for steam production, and the steam-to-oil ratio is a major parameter affecting total GHG emissions. The developed model can be utilized as a tool to predict the energy demand requirements for integrated SAGD/upgrading projects under different operating conditions, and provides guidance on the feasibility of lowering GHG emissions associated with their operation. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction The worldwide oil demand is on the rise and is expected to reach levels of 105 million barrels per day by 2030, and with the limited conventional oil resources, focus has shifted towards unconventional oil resources, such as the oil sands in western Canada. Unconventional oil resources (i.e. extra heavy oil and bitumen) account for approximately one third of the world’s oil ⇑ Corresponding author. E-mail address: [email protected] (E.F. Lazzaroni). http://dx.doi.org/10.1016/j.apenergy.2016.08.072 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.
reserves. The Canadian Oil Sands is the third largest crude oil proven reserves in the world, which amount to proven reserves of about 168 billion barrels constituting approximately 97% of Canada’s total oil reserves [1]. As of 2012, Alberta produced 1.9 million barrels per day of raw bitumen, which is projected to increase to 3.8 million barrels per day by 2022 [2]. Given the vast size of oil sands resources in Alberta, it is considered to be one of the leading sources of fossil energy for North American markets. The oil sands are a mixture of bitumen, sand, clay and water, from which bitumen is extracted. The bitumen extracted is diluted by solvents to reduce its viscosity for further transportation, which
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is then sold as commercial bitumen or sent to upgrading facilities to produce refinable crude (i.e. SCO). Upgrading reduces the carbon-to-hydrogen ratio of the bitumen and removes undesirable compounds from the hydrocarbons, such as sulphur, nitrogen, and heavy metals. Upgrading operations can be integrated with bitumen extraction processes, and they are typically comprised of hydrogen-based or/and thermal-based cracking processes. The two prominent bitumen extraction processes are mining and insitu, with the latter being more economically and environmentally preferable and will account to approximately two-thirds of future oil sands production capacity. In-situ methods are employed for extracting deep bitumen deposits, which account to approximately 70% of the available bitumen resources. In-situ methods, such as SAGD, rely on the use of steam, solvents or thermal energy to enhance the flow of the viscous bitumen, which is then pumped to the surface. In 2012, SAGD production exceeded surface mining, becoming the market leader in Alberta’s oil sands production. Because of the reliability and the maturity of the SAGD technology, this percentage is expected to grow in the future. Being characterized as heavy and viscous crude, bitumen requires significant amounts of energy for its extraction, upgrading and transportation. The energy consumed is in the form of steam, hydrogen, electricity, and process fuel, which is almost entirely produced from fossil fuels, particularly natural gas (i.e. combined heat and power, once-through steam generators (OTSGs), and steam methane reforming). This as a results causes the generation of significant GHG emissions, which led to making the oil sands industry the largest contributor to the growth of GHG emissions in Canada [3]. In 2007, the government of Alberta introduced reduction objectives of 12% on CO2 emissions for all plants emitting more than 0.1 Mt CO2/year. New plants have a reduction target of 2% from the fourth year of operation, which increases by 2% annually up to 12%. The facilities can improve their performance or can pay carbon taxes for emissions beyond the imposed target [4]. This, along with the low price of natural gas, motivated energy producers to explore lower carbon fuels, such as natural gas, and carbon mitigation options. Carbon Capture and Storage (CCS) is a viable strategy to mitigate GHG emissions, and is receiving increasing attention by the scientific community and governments. It has significant potential in reducing the CO2 mitigation costs when integrated with large fossil fuel based energy producers. Further benefits can be observed from utilizing the CO2-concenrated streams, for example, in enhanced oil or coal bed methane natural gas recovery. This is particularly true for the province of Alberta, including the Western Canadian Sedimentary Basement where the majority of oil sands are located, as its geological formations are suitable for these value-added applications and underground storage sinks. Moreover, based on the Alberta Geological Survey major oil sands producers and CO2 emitters in the province are located in close proximity to the available CO2 sinks, which is a major factor that contributes to the favorability of utilizing CCS. Despite the operational start of some large scale CCS projects in Alberta [5,6], there is currently some concerns regarding the economic effectiveness of this technology [7]. The further development of GHG mitigation strategies that are based on CCS technologies for oil sands operations requires adequate tools to estimate their total energy demand requirements and the associated GHG emissions. Moreover, these tools can be of assistance to industries and policy makers in the energy planning required to achieve higher levels of SCO and bitumen production. The demand for energy commodities (i.e. electricity, steam, heat and hydrogen) is tied to the forecasted increase expected for bitumen and SCO production, and the increase for their requirements will require the establishment and commissioning of additional energy production units in order to sustain the required
bitumen and SCO production levels. The development of the energy infrastructure of the oil sands industry must take into account the increasingly CO2-contrained environment, which requires a quantification of the magnitude of emissions associated with energy production. Several studies in the literature have been proposed for the modeling of the energy demands and their associated GHG emissions for oil sands operations, as well as studies that utilize these estimates of energy demands for the energy design planning for oil sands operations [8–22]. Charpentier et al. [8] developed a life cycle-based model, referred to as the GHOST model, which estimates the life cycle (total direct and indirect) emissions of multiple oil sands producers for the production of bitumen and SCO. Their model was utilized to estimate a range of emission intensity factors based on confidential operating data of existing oil sands projects. Ordorica Garcia et al. [9] developed a mathematical model to estimate the energy demands of integrated SAGD/upgrading and mining/upgrading operations based on yield data available from industrial reports of currently operating facilities. Betancourt et al. [10] later developed a mathematical optimization model that uses the energy demand estimates provided by Ordorica Garcia et al. [9] in order to determine the optimal oil sands production routes and their corresponding energy infrastructure with the goal of minimizing total costs subject to CO2 emission constraints. Giacchetta et al. [11] utilize energy demand assessment and GHG intensity data to conduct economic and environmental evaluation and optimization of industrial scale SAGD projects. Similarly, several studies [12–16] have utilized models that assess energy requirements of oil sands operations in order to investigate the feasibility of incorporating alternative energy production technologies and carbon mitigation options, such as nuclear energy, gasification of alternative fuels (e.g. coal, petroleum coke, bitumen, etc.), and production technologies integrated with carbon capture and sequestration. Nimana et al. developed a model, which is referred to as the fundamental engineering principles based model for the estimation of greenhouse gases in the oil sands (FUNNEL-GHG-OS), for the estimation of the energy demand requirements and GHG life cycle emissions associated with bitumen extraction [17], upgrading and refining [18], and for the life cycle assessment of bitumen derived transportation fuels (i.e. gasoline, diesel and jet fuel) [19]. The spreadsheet model allows the user to change default parameters to input user data in order to estimate the energy demand requirements of project-specific operations. Two studies [20,21] prepared for the Alberta Energy Research Institute analyze the GHG emissions specific to crude oil production operations in North America, including the oil sands industry. A lot of different petroleum types, extraction technologies and reservoir locations are analyzed and compared. The GREET model developed by Argonne National laboratory [22] calculates the emissions associated with a variety of processes, including bitumen extraction and upgrading to SCO. The model’s output results are expressed in terms of specific emissions (i.e. amount of CO2/MJ of SCO produced). The methodology and various parameters used in the estimation of energy demands and GHG emissions are not disclosed. Within the above context it can be realized that the development of models for the estimation of energy demands and GHG emissions of oil sands operations is important for future planning in the industry. The models developed so far in the literature can provide adequate estimates of these parameters. Even though it is possible to construct several oil sands production pathways using these models, most of them do not provide a specific methodology to estimate the specific energy consumption of bitumen extraction and upgrading operations. Therefore, it is not possible to estimate the energy consumption and GHG emissions for a specific project, as they cannot be modified to accommodate differ-
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ent projects. Moreover, the modeling of the energy infrastructure in these models is based on parameters for the efficiency and the emissions of the energy facilities used, which do not adequately describe the behavior of the energy infrastructure. To the authors’ knowledge, there are no models available in the literature that estimates the energy requirements and the associated GHG emissions of oil sands operations by conducting process simulations. This paper presents a detailed data intensive model to estimate a project specific energy demand requirements and their associated GHG emissions for an integrated SAGD extraction and upgrading process in oil sands. The energy demands for the SAGD extraction section are modeled based on fundamental engineering principles, while the upgrading section that is based on delayed coking is simulated using Aspen HYSYS [23]. This is integrated with an energy infrastructure that is modeled using Aspen Plus [23] to estimate the total GHG emissions generated. The model proposed was simulated for a range of operating parameters in order to illustrate its applicability for any integrated SAGD/upgrading project. The model quantifies the demands for steam, electricity, hydrogen and natural gas for given production levels of bitumen and SCO. In addition to the total energy demands, the model estimates the resulting GHG emissions and GHG emission intensity using the current energy production technologies. This model can be used to simulate current and future operations of integrated SAGD/ upgrading projects. The data generated from the model can be useful for policy makers and industrial operators as an input for energy planning models for these projects. The model also provides a quantification of CO2 emissions inventory that can be used for feasibility studies of CCS projects or other carbon abatement strategies. 2. Methodology This section outlines the methodology employed to quantify the energy demand requirements and the associated GHG emissions of an integrated SAGD/upgrading facility. The methodology is divided into two major steps, which are the modeling of the energy demand requirements of extraction and upgrading operations, and the modeling of the energy infrastructure required to meet these demands. The calculations were undertaken for a plant size of 112.5 thousand barrels of bitumen per day, which corresponds to 150 thousand barrels of dilbit per day assuming 25% dilution. This scale is representative of many SAGD extraction processes in Alberta. The SAGD energy demand requirements are calculated based on fundamental engineering principles and parameters available from existing operating facilities. The upgrading energy demand requirements are calculated by conducting Aspen HYSYS simulations of a typical upgrader plant. To accommodate a wider operational capacity, two cases representing high and low energy consumption were developed, which will be referred to as the high energy scenario (HES) and the low energy scenario (LES), respectively. Similarly, simulations have been conducted for the on-site energy infrastructure in order to meet the demand requirements of the integrated SAGD/upgrading facility and quantify the associated GHG emissions. The following lines describe the modeling procedure followed to estimate the energy demand requirements of and integrated SAGD/upgrading process, and the modeling of the energy infrastructure to obtain estimates of GHG emissions associated with providing these energy requirements. 2.1. Energy demand assessment The extraction and upgrading operations are typically carried out in series, with the product of the extraction section (i.e. diluted bitumen: dilbit) used as the feed for the upgrading process. Fig. 1 shows a
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block flow diagram of the integrated SAGD/upgrading process. The main energy inputs to the SAGD extraction process are in the form of high pressure steam and electricity. A diluent, which is typically naphtha, is used to reduce the viscosity of the produced bitumen (dilbit) to facilitate pipeline transportation. The first step of the upgrading process is the recovery of the diluent, which is recycled to the extraction process. The energy commodities consumed in the upgrading stage are electricity, steam, heat and hydrogen. The main product from the upgrading stage is SCO. Byproducts from the extraction and upgrading stages are produced gas, offgas, and petroleum coke (petcoke). The produced gas and offgas are used as input for natural gas-based energy infrastructure.
2.1.1. SAGD extraction A major energy commodity required for bitumen extraction using the SAGD technology is high pressure steam. This technology requires two parallel horizontal wells to be drilled from the surface for continuous steam injection and fluid production. The steam injected through the top well and forms a steam chamber, in which the bitumen is heated to reduce its viscosity. The heated bitumen and the condensed water form an emulsion that is artificially lifted to the surface. The key parameter required to measure the steam requirement for a specific well is the steam to oil ratio (SOR). This parameter expresses the average volume of steam required to produce a unit volume of bitumen. The SOR can be expressed as an instantaneous ratio (iSOR) or a cumulative ratio over the life of the project (cSOR). SAGD extraction processes with a low value of iSOR are less energy intensive compared to those with a high iSOR values. The steam injected in the wells is produced at a high pressure in order to overcome the pressure drops in the pipes and to maintain the target pressure in the steam chamber. The SAGD steam considered is pure saturated steam at 100 bar and 311 °C. These values are typical for a SAGD extraction plant with electric submersible pumps [24]. Most plants operate in an iSOR range of 2–3 [25–30]. The lower bound is assumed to be the iSOR for the LES, and the upper bound is assumed for the HES. The total steam demand required for SAGD extraction can be calculated as illustrated in Eq. (1), where SDext is the total SAGD extraction steam, and BIT prod the total bitumen production.
SDext ¼ BIT prod iSOR
ð1Þ
In a SAGD extraction facility, the process units that consume electricity are bottom-hole pumps, surface circulating pumps and compressors of the vapor recovery unit. In SAGD extraction facilities, the water treatment technology utilized in the process has a significant effect on the total electricity consumption. The two most commonly used water treatment technologies in SAGD extraction facilities are evaporators and warm lime softening (WLS). Due to the presence of additional compressors, evaporators normally consume more electricity than WLS [31,32]. The total electricity demand in a SAGD extraction facility is proportional to the value of iSOR [17]. Based on the total electricity consumption of various operating SAGD extraction facilities and their average bitumen production levels [25–30], an electricity intensity ratio was obtained which is expressed in units of kW h/bbl of bitumen and is referred to as the electricity-to-oil ratio (ELOR). Values of the ELOR were obtained in the range of 7.5–14 kW h/bbl, and the lower and upper bounds were used to represent the LES and HES, respectively. The value of 7.5 corresponds to a facility with iSOR of 2 and that only incorporates WLS for water treatment. An ELOR value of 14 was taken to represent a facility with an iSOR of 3, and that incorporates both evaporators and WLS. The total electricity demand required for SAGD extraction can be calculated as illustrated in Eq. (2), where ELDext is the total SAGD extraction electricity consumed.
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Fig. 1. Energy vectors in the SAGD extraction of bitumen and upgrading to SCO.
ELDext ¼ BIT prod ELOR
ð2Þ
During bitumen recovery there is a significant amount of gas produced [33] which may be used as fuel provided it has been properly desulphurized. The production of gas is due to the dissolution of non-condensable species in the water-bitumen emulsion and the evaporation of light fractions in the separation processes. The amount of dissolved gas is quantified by the gas-to-oil ratio (GOR) parameter [34]. The total amount of produced gas recovered from the SAGD extraction process, which is the sum of the solution gas and the vapors recovered, is represented by the produced gasto-oil ratio (PGOR). According to the Canada’s Oil Sands Innovation Alliance (COSIA) [35], a reasonable assumption for the value of the PGOR is twice the value of the GOR. For the SAGD extraction facility modeled in this study, a value of 10 m3 of gas/m3 of bitumen was considered for the PGOR. The value of PGOR is a design parameter associated with the investigated SAGD project, and is not affected by the LES or HES. The total produced gas (SPGext ) can be calculated as follows.
SPGext ¼ BIT prod PGOR
ð3Þ
2.1.2. Upgrading The upgrading route of bitumen considered in this study is based on thermal cracking. The process units incorporated include the atmospheric and vacuum distillation columns (ADU and VDU), delayed cokers (DC) and hydrotreaters (HTR). The majority of bitumen upgrading capacity currently operating in the oil sands industry takes place through this upgrading route [36]. The upgrading process was simulated using Aspen HYSYS [23], from which the energy demands (i.e. electricity, hydrogen, steam and heat) are obtained. The ADU is used for diluent recovery and diesel production, while the VDU is used for maximum recovery of liquid products. The cracking is simulated with a delayed coker unit and a fractionator. The model includes a custom hydrotreater for each liquid fraction. The process flow diagram of the upgrading process simulated is shown in Fig. 2. Dilbit from tankage enters the process at ambient temperature at the ADU, which consists of two atmospheric columns that separate the diluent and a portion of diesel from the bitumen [37]. The atmospheric topped bitumen is then fed to the VDU, which uses pressures in the range of 5 kPa to maximize the recovery of liquid fractions while preventing hydrocarbon cracking. A DC unit is then used to thermally crack the remaining heavy ends and produce lighter liquid fractions, as well as a vapor and a solid fraction. The remaining liquid fractions are mixed and sent to hydrotreaters to produce a sweetened SCO product. Pressure swing adsorption is used to produce a pure hydrogen stream from the hydrotreater purge gas, which is then recycled into the hydrotreater system. The model uses pseudo components to simulate distillation columns, which in Aspen HYSYS are referred to as hypothetical components and are defined based on total boiling points of liquid
fractions [38]. Temperature control across the column is achieved by a number of pumparounds designed to produce a specified temperature at a given process stream. The DC unit is simulated using the Aspen HYSYS Delayed Coker with a downstream column for fractional separation. Each hydrotreater consists of two or more hydroprocessor bed units, a separator for sour gas and liquid fractions, and hydrogen recycle system. The design of each HTR section is dependent on oil gravity, sulfur content, and olefin content of the feedstock. Temperature control across each bed is done by cooling the recycle gas streams and mixing them as a quench prior to each stage [39]. This is necessary as the reactions in the hydrotreatment process are highly exothermic. The feed to each process unit (fractionating, cracking, desulphurization, olefin saturation, etc.) must be maintained at a specified temperature specific to each unit, from which the heating requirements are estimated. Since heat integration is included in the model, the heat demand should be provided entirely from outside battery limits. Because steam is used for different purposes across the process, the steam input specifications vary among process units. For example, columns require steam to improve distillation efficiency, while the DC uses steam to avoid the coke deposition into the furnace [40]. The columns utilize steam at 20 bar and 400 °C, while the delayed cokers requires steam at a higher temperature in order to avoid heat loss (510 °C). Electricity usage varies among process units. The electricity demand of the ADU is relatively low as it is limited to fluid pumping. The VDU uses more electricity in order to run the vacuum system. In the DC, electricity consumption includes the motor drive for the hydraulic decoking pump, which is linearly correlated to coke production [40]. The HTRs have the highest consumption of electricity, due to the presence of compressors for the hydrogen make up and recycle streams, as well as the high pressures required to be produced by the inlet pumps. The hydrogen demand of the upgrading process is due to the requirement of hydrotreating. The presence of higher sulphur, nitrogen and aromatic content in heavier feeds necessitates more severe operating conditions in the hydrotreatment system. This results in a higher hydrogen requirements for hydrotreating feed streams such as diesel and gasoil. Meanwhile, the naphtha hydrotreater requires significantly more hydrogen than a standard naphtha hydrotreater due to the high olefinic content resulting from the coking process [41]. 2.2. Energy infrastructure model The energy infrastructure modeled in this study is based on currently existing energy production facilities in the oil sands industry [24–30]. Currently, the fuel used for energy production is mostly natural gas. The electricity can be bought from the grid or produced from natural gas combined heat and power plants that also cogenerate steam. Most of the steam for the SAGD extraction process is produced with once-through steam generators (OTSG) fuel by natural gas. The hydrogen for the upgrading operations is pro-
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439
Fig. 2. Simplified process flow diagram of the upgrading process.
duced by steam methane reformers. The simulation of the energy infrastructure was conducted with Aspen Plus [23]. The flowsheet of the energy infrastructure modeled along with the energy vectors is shown in Fig. 3. 2.2.1. Once through steam generators (OTSGs) The steam for the SAGD process is generated by once-through steam generators (OTSGs) and natural gas combined heat and power plants. Due to the use of WLS for water softening, the boiler feed water (BFW) contains enough hardness to hinder the use of drum evaporators. It is required to maintain a steam quality of around 80% in order to avoid mineral deposition and fouling in downstream pipes [42]. After the OTSGs, a flash vessel is used to
separate the water in order to produce pure steam for SAGD extraction. The blowdown water is used to preheat the BFW coming from the central processing facility. The inlet and outlet feed temperatures of the preheater are 166 °C and 200 °C, respectively. The burners of the OTSGs were modeled as adiabatic units, and the stoichiometry of the combustion reactions is maintained such that there is 3% oxygen in dry fumes. The exhaust gases produced at adiabatic flame temperature enter a cooler, which represents the shell of the OTSGs. The water enters a heater, which represents the tube side of the OTSGs, and exits at a 78% steam quality. The heat exchange between the heater and the cooler occurs with maximum energy losses equal to 2% of the fuel’s lower heating value (0.5% due to unburnt fuel, 0.5% due to radiation losses, and 1%
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Fig. 3. Flowsheet of the energy infrastructure model.
due to convective heat transfer losses). The minimum temperature approach has been set to 25 °C. 2.2.2. Natural gas combined heat and power The cogeneration system consists of gas turbines with oncethrough heat recovery steam generators (OT-HRSGs). The OTHRSGs operate along with the OTSGs to provide the total steam requirements of the SAGD extraction and upgrading operations. Similar to OTSGs, the OT-HRSGs produce steam with 78% quality. In order to increase the steam production from the OT-HRSGs, the exhaust gases from the gas turbines are post-fired with duct firing in order to achieve a temperature of 815 °C. Part of the produced gas is used as an input to the post-firing system, and the remaining is used for the OTSGs. This temperature value was obtained from the operating data of the Deltak OT-HRSG of the McKay River cogeneration plant [43]. The BFW preheater for OTHRSGs was simulated similar to that of the OTSGs. The pinch point temperature has been set to 25 °C, which is a knowledge based design parameter indicated by Nord et al. [44]. The cogeneration unit is installed on-site and provides electricity for both bitumen extraction and upgrading. In integrated SAGD/ upgrading plants, it is usually preferred to modularize the combined heat and power plants with small to medium sized units. This permits more flexibility in meeting the energy demand requirement of variable bitumen production levels. The gas turbine selected for the simulation is the 47.5 MWe SGT-800 model from Siemens [45]. The performance parameters of the turbine were simulated with the software GT-PRO by Thermoflow [46] in both ISO and working conditions. The results are shown in the Table 3. An equivalent gas turbine was simulated in Aspen Plus in order to obtain the composition of the flue gases, which is required to provide the input to the heat exchanger of the OT-HRSG. The OT-HRSG consists of an adiabatic combustion chamber and a heat exchanger, which cools the fumes to the turbine outlet temperature (TOT). The heat recovered is used to generate steam (see Table 1). 2.2.3. Steam methane reforming The technology used for hydrogen generation is steam methane reforming (Fig. 5), from which steam is coproduced. The model of the steam reforming consists of a pre-reforming unit, a steam
reformer unit, two stages of water gas shift (WGS) reactors, and a pressure swing adsorber (PSA) for hydrogen purification. The pre-reforming unit, which uses highly active nickel-based catalysts, was modeled as an equilibrium adiabatic reactor. According to Sperle et al. [47]. The assumption of chemical equilibrium provides a good approximation of the actual operating conditions of the pre-reformer catalyst. The pre-reformer is installed to reduce the thermal load of the steam reformer, to reduce the steam-tocarbon ratio, and to increase the overall efficiency of the process [48] (see Fig. 4). The steam reformer, was simulated as a full equilibrium reactor operating with inlet and outlet temperatures of 650 and 900 °C, respectively, a pressure of 38.5 bar, and a steam-to-carbon ratio of 2.7. The heat required for the endothermic reactions is provided by several gas burners located on the boiler’s wall. The syngas from the steam reformer is cooled down and sent to a two-stage WGS unit [49–51]. The high temperature shift reactor (HTS) uses iron oxide/chromium oxide catalysts that are active at a temperature range of 300–450 °C. The low temperature shift reactor (LTS) uses Cu-ZnO-Al2O3 catalysts that operate at a temperature range of 190–250 °C [52]. The inlet and outlet temperatures are 400 and 465.8 °C, respectively, for the HTS, and 200 and 242.1 °C, respectively, for the LTS. The WGS reactor was modeled as an adiabatic reactor with selective equilibrium for the shift reaction, as modeled by Martelli et al. in [53]. It is preferred to use natural gas as a fuel for the catalytic steam reforming due to additional problems related to the use of offgas [54].
Table 1 ISO and working conditions for SIEMENS SGT-800. Parameter
Unit
ISO conditions
Working conditions
Power Efficiency Air mass flow rate Fuel mass flow rate Flue gas mass flow TIT Turbine outlet (TOT)
MW % kg/s kg/s kg/s °C °C
46.43 37.54 128.5 2.5 131.1 1287.8 551.82
48.93 37.36 134.35 2.65 137 1292.9 540.8
Annual average temperature 0 °C, Aspiration pressure 5 mbar, Exhaust pressure drop 25 mbar.
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441
Fig. 4. Flowsheet of the steam methane reformer.
Following the WGS, the syngas is cooled down and sent to the purification units. After separating the water, the syngas is sent to a PSA unit. The PSA has been modelled as a simple separator block set to recover 90% of hydrogen with 99.99% purity. This block has been used based on the assumption that hydrogen purity and recovery are not sensitive to the changes in pressure and composition of the feed [55]. The hydrogen produced is sent to the upgrading facility, while the PSA offgas is sent to the burners of the steam reformer reactor. The burners of the terrace wall were modeled with an adiabatic unit. The stoichiometry is controlled such that there is 3% O2 in dry fumes. Heat from the exhaust gases is used for the steam reformer reactor, as well as for preheating BFW. Similarly to the approach provided by Martelli at al. [49] reforming combined cycles were integrated, in which the waste heat made available by the gas coolers is efficiently recovered by economizing, evaporating and superheating steam at 20 bar, 400 °C. The upgrading section requires steam both to enhance stripping efficiency in the columns and for the delayed coking process. Some of this steam requirement is provided by the steam methane reforming unit. The temperature of the feed water is 205 °C, which is achieved due to the heat integration incorporated. The heat demand for upgrading is provided by furnaces, which were modeled as adiabatic reactors. The fuel feed is the offgas from the PSA unit, and the stoichiometry is controlled such that there is 3% O2 in dry fumes. The exhaust gases are then cooled to 400 °C, considering a minimum temperature approach of 100 °C. The
fumes are then discharged at 150 °C, and the heat recovered is used to preheat the combustion air to achieve a temperature of 290 °C. Part of the fuel used in the furnaces is obtained from offgas generated from upgrading operations. The remaining offgas is used in steam generators and burners of the steam reformer unit. The input parameters used for the simulation are summarized in Table 2. These include input parameters for the OTSGs, natural gas cogeneration units, and steam methane reforming plants. 2.3. Functional units After obtaining the total energy demand requirements and generated GHG emissions, the results obtained are expressed in terms of specific energy (e.g. MJ/GJSCO) and GHG emission intensity factors (e.g. gCO2/MJSCO). The emission factor parameters incorporated in the calculations of total emissions from natural gas equipment imported from the GREET model [22]. An additional parameter that is introduced is the net external energy ratio (NEER). The NEER is a parameter introduced by Brandt and Dale in their analysis [56] to express the profitability of the exploitation of a certain resource. This parameter divides the total output of energy (SCO, electricity and produced gas) with the external input (natural gas only). The purpose of using the NEER is to provide a measure of the contribution of an energy source or project (e.g. an integrated SAGD/upgrading project) to the total energy supply of society. In other words, in order for an energy source or system
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Table 2 Parameters used for the energy infrastructure model.
Table 4 Performance of the energy infrastructure.
Parameter
Unit
Value
Energy flux
LES
HES
OTSGs/OT-HRSGs Temperature water preheat inlet Pressure water preheater inlet Minimum Temperature Approach water preheater Steam pressure Quality of steam before separator Minimum Temperature Approach OTSG Losses for unburnt/radiation/convective OT- HRSG Heat Losses OT-HRSG Postfiring Temperature
°C bar °C bar % °C % of LHV % °C
166 135 10 100 78 25 0.5/0.5/1 2 815
Bitumen extracted (MW) SCO produced (MW)
8881.7 7363.7
8881.7 7363.7
Gas turbines installed SAGD Steam cogenerated (% of the total) Electric power produced (MW) Electric power consumption (MW) Electric power exported (MW) Electric power exported (MW Primary Energy)
2 20.2 97.9 61.7 36.2 90.5
3 20.1 146.8 99.2 47.6 118.9
Natural gas steam reformer Inlet pressure Steam to Methane Ratio Pre reformer feed temperature Steam reformer feed temperature Steam reformer outlet temperature HT/LT WGS syngas input temperature HT/LT WGS reactor approach temperatures Reformers/WGS reactors pressure drops Condenser/PSA inlet temperature Combustion air preheat Minimum approach temperature HT/LT coolers Exhaust gases outlet temperature Heat losses from heat exchangers Pressure drops in heat exchangers gas side PSA hydrogen split fraction PSA hydrogen quality Steam export pressure Steam export temperature
Gas Gas Gas Gas Gas
688.3 93.5 560.8 76.2 127.5
688.3 93.5 687.4 93.3 0.89
bar Mol/Mol °C °C °C °C °C bar °C °C °C °C % % % % bar °C
38,5 2,7 500 650 900 400/200 10/10 1/0.5 40 425 100/25 215 0.5 0.05 0.9 1 20 400
Natural gas consumed (MW) Natural gas consumed (MJ/GJSCO)
1372.5 186.4
2026.1 274.5
Total Total Total Total
1933.3 262.5 1372.5 186.4
2713.5 368.5 2026.1 286.83
5.52
3.69
Upgrading steam generators Water inlet temperature Outlet temperature Coker/Columns steam Steam pressure Minimum temperature approach
Cogeneration unit
19.71–29.5
°C °C Bar °C
205 510/400 20 20
Electricity SAGD Electricity upgrading Electricity export Postfiring
produced (MW) produced (MJ/GJSCO) consumed (MW) consumed (MJ/GJSCO) exported (MW)
primary energy consumption (MW) primary energy consumption (MJ/GJSCO) external primary energy consumption (MW) external primary energy consumption (MJ/GJSCO)
NEER (GJ produced /GJ consumed)
Table 5 CO2 emissions of the energy infrastructure. Unit
kg/s
g/MJBIT
g/MJSCO
5.33–9.94 3.81–4.76 5.36–7.05 5.21–7.74
0.60–1.12
0.72–1.35 0.52–0.65 0.73–0.96 0.71–1.05
48.18–71.41 14.18–17.05 7.70–9.65 21.11–26.38
5.43–8.04
6.54–9.70 1.93–2.41 1.05–1.31 2.87–3.58
2.68–4.01
0.59–0.87
Furnaces Furnace discharge temperature Fumes exhaust temperature (After air preheating)
°C °C
400 150
Burners O2 mole fraction in dry fumes
OTSGs Steam reformer Steam generators Furnaces
%
3
Emission credita
6.54–8.59
0.74–0.97
0.89–1.17
SAGD Upgrading
58.13–88.32 46.21–57.78
6.55–9.94
7.89–11.99 6.28–7.85
Total
104.35–146.11
to be useful, the total energy output must be greater than the total energy input. The NEER parameter provides a ratio of the total energy output to the counted inputs of energy that must be produced and delivered externally through existing energy supply systems. All energy flux streams are expressed in terms of primary energy. The electricity is converted in terms of primary energy considering an efficiency factor of 0.4, which is a typical value for the electricity grid in Alberta [57]. For example, according to Brandt [58], conventional oil NEER values ranges from 15 to 30 GJ/GJ. This translates to an input of 1 GJ of energy being required to produce 15–30 GJ of conventional crude.
a
14.17–19.84
Based on an emission factor of 750 gCO2/kW h.
3. Results The consumption of each energy commodity is shown in Table 3 for the SAGD extraction and upgrading operations. A range of results was obtained for each energy commodity, which corresponds to the operating range of the LES and HES. It can be observed that the results obtained reflect the basis of realistic plant
Table 3 Energy demand assessment for the SAGD extraction and upgrading process LES-HES.a
a
Unit
Heat (MW)
Steam (MW)
Electricity (MW)
Hydrogen (MW)
Produced/Off-gas (MW)
SAGD extraction ADU VDU DC Naphtha HTR Diesel HTR Gasoil HTR Hydrogen recovery Auxiliary Overall upgrading
– 134.64–168.3 58.22–73.74 58.88–73.51 6.48–8.10 44.31–55.39 24.17–30.21 0 0 347.1–433.9
927.89–1391.83 92.5–115.6 15.41–19.27 33–41.26 0 0 0 0 0 126.4–158.0
35.94–67.08 0.64–0.8 6.86–8.57 3.84–4.81 1.52–1.9 2.22–2.78 3.2–4.0 5.09–6.37 2.34–2.92 25.72–32.5
– 0 0 0 19.4–24.25 55.33–69.16 116.59–145.74 0 0 191.32–239.16
56.25 0 0 612.04 0 0 0 27.32–34.14 0 665.0–671.8
The HES increases the energy vectors by a factor of 1.25 compared with the results of the HYSYS model (which are taken for the LES).
443
24 20 16
CO2 emission intensity (g/MJ SCO)
CO2 emission intensity (g/MJ SCO)
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Cogeneration OTSGs Steam Reformer Steam generators Furnaces
12 8 4 0 LES
24 20
Upgrading SAGD
16 12 8 4 0
HES
LES
HES
Fig. 5. Specific CO2 emission (g/MJSCO) per unit.
Table 6 Values for CO2 emissions available in the literature. Stage
Unit
This work
GREET1
GHGenius2
GHOST3
FUNNEL4
JACOBS5
SAGD Upgrading Total
gCO2/MJBIT gCO2/MJSCO gCO2/MJSCO
6.6–9.9 6.3–7.9 14.2–19.8
12.2 12.1 26.8
15.2 12.2 30.6
8.4–12.3 6.8–11.1 16.9–26.9
8.0–22.9 7.2 16.8–34.8
10.6 8.3 21.1
1. Emissions depend on default values specified in the model for energy consumption. 2. Emissions depend on default values specified in the energy consumption model. 3. The iSOR considered is in the range of 2.2–3.3. 4. The SOR considered is in the range of 2.1–6.54. 5. This model considers an iSOR of 3.
operations. For example, the hydrogen consumption of gasoil HTR is the highest among all hydrotreating systems. This is due to the high content of sulphur, nitrogen, aromatics, etc., compared to the other liquid fractions, which necessitates a higher hydrogen consumption rate in order to achieve the desired product quality. The performance parameters of the energy infrastructure modeled in this work are summarized in Table 4. The total available electricity production capacity is 97.9 MW and 146.8 MW for the LES and HES, respectively. The electricity consumption rates for the LES and HES are 61.7 and 99.2, respectively, which results in power potentially available for export to the grid (i.e. LES: 36.3 MW and HES: 47.6 MW). The amount of steam produced by the cogeneration system is around 20% of the total in both scenarios. The production of gas from the processes is identical. However, due to the higher energy demand of the upgrading process, the HES consume 22.7% more gas. In the LES, part of the produced gas is exported. However, in the HES almost all of the produced gas is consumed in the upgrading operations. Even though an adequate amount of energy is available from the available produced gases, the considerable energy demands of the SAGD extraction and upgrading operations requires the utilization of significant amounts of natural gas. The results of the model shows total natural gas consumption of 1372.5 MW and 2026.1 for the LES and HES, respectively, which indicates that the HES requires up to 50% more energy from natural gas compared to LES. This difference is mainly caused by the higher steam demand from the OTSGs. The energy intensity factor (i.e. energy specific to a barrel of SCO produced) was obtained to be in the range of 262.5–368.5 MJ/GJSCO. Approximately 76.2 –93.3 MJ/GJSCO of the total energy requirements is available from produced gas, while the remaining (186.4 – 274.5 MJ/GJSCO) is obtained from come from natural gas. The NEER obtained for the LES and HES is 5.52 GJ produced/GJ consumed and 3.69 GJ produced/GJ consumed, respectively. This indicates that the specific project parameters (e.g. iSOR, ELOR, etc.) can have a significant impact on their total energy intensity.
The total emissions generated as a result of energy production from the modeled energy infrastructure are summarized in Table 5. The emissions generated from the natural gas combined heat and power plant is classified according to the energy delivered application. The CO2 emissions associated from electricity production are generated due to the gas turbine simple cycle combustion. The CO2 emissions related to the steam cogeneration are ascribable to the postfiring. However, the export of electricity can be considered as an emission credit that could be subtracted from the total emissions of CO2. This is because the export of electricity contributes to a reduction in the electricity production of Alberta’s coal based electrical grid. Emissions associated with the electricity production from natural gas based combined heat and power plants are considerably lower than those associated with electricity production from coal. The CO2 emission factor of the Alberta grid is 750 gCO2/MW h [39]. The results show that the total CO2 emissions generate are in the range of 104.35–146.11 kg/s of CO2, and the total CO2 intensity factor was determined to be in the range of 14.17–19.84 gCO2/MJSCO. As shown in Fig. 5, the OTSGs are by far the largest contributor to CO2 emissions. In both scenarios, almost 50% of the CO2 emissions are due to the generation of steam (6.54–9.70 g/MJSCO out of 14.17 – 19.84 g/MJSCO). Moreover, the OTSGs contribute more than 80% of the emissions in the SAGD extraction operations, and cogeneration producing only less than 20% of the emissions (0.72 –1.35 g/MJSCO for electricity production and 0.71–1.05 g/MJSCO for steam generation). In the upgrading process, furnaces are the largest contributors to total emissions (2.87–3.58 g/MJSCO), followed by the steam methane reformer (1.93–2.41 g/MJSCO), steam generators, (1.05–1.31 g/MJSCO) and electricity production (0.52–0.65 g/ MJSCO). Finally, 55.7–60% of the total CO2 emissions are associated with the SAGD extraction operations. As illustrated in Table 6, this model predicts emissions well within the ranges given in existing models and industry reports. Even though the values of the input parameters incorporated in the simulation of these models might vary among each other,
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which might not facilitate a conclusive direct comparison among the obtained emission factors, the variability in the results of these models can be attributed to the differences in the energy infrastructures considered. The main differences between the energy infrastructure of the proposed model and those considered in other studies are the use of natural gas combine heat and power system for the cogeneration of electricity and steam, and the consideration of steam cogenerated from the steam methane reforming unit in the upgrading area. For example, the GREET and GHGenius do not incorporate natural gas cogeneration facilities. These technological solutions, which are used in the majority of integrated SAGD/upgrading plants currently in operation in an attempt to reduce total natural gas consumption and hence total CO2 emissions, are not considered in the energy infrastructures of these studies. In the present work, the cogeneration system produces around 20% of the total steam for the SAGD extraction facility (between 187 and 280 MWth), while the steam reformer produces 72–78 % of the steam required for the upgrading operations (between 32 and 40 MWth). The contribution to steam generation of these units leads to a relevant decrease in the GHG emission. Other factors that might have contributed to the differences in emission factors is due to the differences in some of the input parameters. For example, the FUNNEL model considers an upper bound value of 6.54 on the iSOR range, which results in a considerably higher value on the upper range of emissions compared to the results of this model.
4. Conclusions A detailed data intensive model was developed to estimate the energy demands and associated GHG emissions of integrated SAGD/upgrading oil sands operations in the Athabasca region in Alberta. The results obtained from the model reveal that SAGD extraction operations are more energy and GHG-intensive than upgrading operations, accounting to 50 –60% of total energy consumption and GHG emissions. The total energy and GHG intensity factors were determined to be within the range of 262.5–368.5 MJ/ GJSCO and 14.17 – 19.84 gCO2/MJSCO, respectively. The predicted GHG emission intensity factors are well within the range of existing models and literature. The results of the model would allow the industry to investigate the feasibility of reducing emissions in each stage of the unit operations. Among the energy commodities consumed in integrated SAGD/upgrading operations in the oil sands industry, steam production is the leading source of GHG emissions, followed by hydrogen and electricity production. Steam production accounts to approximately 50% of total emissions. One of the major parameters that have a significant impact on the total energy consumption and GHG intensity factor is the SOR. This indicates that reducing the SOR can have a considerable effect on the energy efficiency and reduction of GHG emissions. The incorporation of cogeneration and the consideration of steam production from steam methane reforming plants have significant potential in lowering the net environmental impacts of oil sands operations. A major challenge that will be facing the future operations of the oil sands industry is meeting the massive energy requirements associated with the considerable expected development in the industry in a sustainable manner. This is attainable along with a reduction in GHG emissions intensity through optimal energy planning of the oil sands energy infrastructure. The proposed model can be utilized to estimate the current and future energy requirements of integrated SAGD/upgrading oil sands projects, which can be used as an input in energy planning optimization models to determine the optimal set of energy producers that is geared towards cost minimization in a CO2-constrained environment.
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Energy infrastructure modeling for the oil sands industry: Current situation Edoardo Filippo Lazzaroni a,⇑, Mohamed Elsholkami a, Itai Arbiv a, Emanuele Martelli b, Ali Elkamel a, Michael Fowler a a b
Department of Chemical Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario N2L 3G1, Canada Department of Energy, Politecnico di Milano, Via Lambruschini 4, Milano 20156, Italy
h i g h l i g h t s A simulation-based modelling of energy demands of oil sands operations is proposed. Aspen simulations used to simulate delayed coking-based upgrading of bitumen. The energy infrastructure is simulated using Aspen Plus achieving self-sufficiency. Various scenarios affecting energy demand intensities are investigated. Energy and CO2 emission intensities of integrated SAGD/upgrading are estimated.
a r t i c l e
i n f o
Article history: Received 25 January 2016 Received in revised form 16 July 2016 Accepted 13 August 2016
Keywords: Oil sands SAGD Synthetic crude Life cycle assessment CO2 emissions Process modeling Process simulation Aspen plus
a b s t r a c t In this study, the total energy requirements associated with the production of bitumen from oil sands and its upgrading to synthetic crude oil (SCO) are modeled and quantified. The production scheme considered is based on the commercially applied steam assisted gravity drainage (SAGD) for bitumen extraction and delayed coking for bitumen upgrading. In addition, the model quantifies the greenhouse gas (GHG) emissions associated with the production of energy required for these operations from technologies utilized in the currently existing oil sands energy infrastructure. The model is based on fundamental engineering principles, and Aspen HYSYS and Aspen Plus simulations. The energy demand results are expressed in terms of heat, power, hydrogen, and process fuel consumption rates for SAGD extraction and bitumen upgrading. Based on the model’s output, a range of overall energy and emission intensity factors are estimated for a bitumen production rate of 112,500 BPD (or 93,272 BPD of SCO), which were determined to be 262.5–368.5 MJ/GJSCO and 14.17–19.84 gCO2/MJSCO, respectively. The results of the model indicate that the majority of GHG emissions are generated during SAGD extraction (up to 60% of total emissions) due to the combustion of natural gas for steam production, and the steam-to-oil ratio is a major parameter affecting total GHG emissions. The developed model can be utilized as a tool to predict the energy demand requirements for integrated SAGD/upgrading projects under different operating conditions, and provides guidance on the feasibility of lowering GHG emissions associated with their operation. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction The worldwide oil demand is on the rise and is expected to reach levels of 105 million barrels per day by 2030, and with the limited conventional oil resources, focus has shifted towards unconventional oil resources, such as the oil sands in western Canada. Unconventional oil resources (i.e. extra heavy oil and bitumen) account for approximately one third of the world’s oil ⇑ Corresponding author. E-mail address: [email protected] (E.F. Lazzaroni). http://dx.doi.org/10.1016/j.apenergy.2016.08.072 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.
reserves. The Canadian Oil Sands is the third largest crude oil proven reserves in the world, which amount to proven reserves of about 168 billion barrels constituting approximately 97% of Canada’s total oil reserves [1]. As of 2012, Alberta produced 1.9 million barrels per day of raw bitumen, which is projected to increase to 3.8 million barrels per day by 2022 [2]. Given the vast size of oil sands resources in Alberta, it is considered to be one of the leading sources of fossil energy for North American markets. The oil sands are a mixture of bitumen, sand, clay and water, from which bitumen is extracted. The bitumen extracted is diluted by solvents to reduce its viscosity for further transportation, which
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is then sold as commercial bitumen or sent to upgrading facilities to produce refinable crude (i.e. SCO). Upgrading reduces the carbon-to-hydrogen ratio of the bitumen and removes undesirable compounds from the hydrocarbons, such as sulphur, nitrogen, and heavy metals. Upgrading operations can be integrated with bitumen extraction processes, and they are typically comprised of hydrogen-based or/and thermal-based cracking processes. The two prominent bitumen extraction processes are mining and insitu, with the latter being more economically and environmentally preferable and will account to approximately two-thirds of future oil sands production capacity. In-situ methods are employed for extracting deep bitumen deposits, which account to approximately 70% of the available bitumen resources. In-situ methods, such as SAGD, rely on the use of steam, solvents or thermal energy to enhance the flow of the viscous bitumen, which is then pumped to the surface. In 2012, SAGD production exceeded surface mining, becoming the market leader in Alberta’s oil sands production. Because of the reliability and the maturity of the SAGD technology, this percentage is expected to grow in the future. Being characterized as heavy and viscous crude, bitumen requires significant amounts of energy for its extraction, upgrading and transportation. The energy consumed is in the form of steam, hydrogen, electricity, and process fuel, which is almost entirely produced from fossil fuels, particularly natural gas (i.e. combined heat and power, once-through steam generators (OTSGs), and steam methane reforming). This as a results causes the generation of significant GHG emissions, which led to making the oil sands industry the largest contributor to the growth of GHG emissions in Canada [3]. In 2007, the government of Alberta introduced reduction objectives of 12% on CO2 emissions for all plants emitting more than 0.1 Mt CO2/year. New plants have a reduction target of 2% from the fourth year of operation, which increases by 2% annually up to 12%. The facilities can improve their performance or can pay carbon taxes for emissions beyond the imposed target [4]. This, along with the low price of natural gas, motivated energy producers to explore lower carbon fuels, such as natural gas, and carbon mitigation options. Carbon Capture and Storage (CCS) is a viable strategy to mitigate GHG emissions, and is receiving increasing attention by the scientific community and governments. It has significant potential in reducing the CO2 mitigation costs when integrated with large fossil fuel based energy producers. Further benefits can be observed from utilizing the CO2-concenrated streams, for example, in enhanced oil or coal bed methane natural gas recovery. This is particularly true for the province of Alberta, including the Western Canadian Sedimentary Basement where the majority of oil sands are located, as its geological formations are suitable for these value-added applications and underground storage sinks. Moreover, based on the Alberta Geological Survey major oil sands producers and CO2 emitters in the province are located in close proximity to the available CO2 sinks, which is a major factor that contributes to the favorability of utilizing CCS. Despite the operational start of some large scale CCS projects in Alberta [5,6], there is currently some concerns regarding the economic effectiveness of this technology [7]. The further development of GHG mitigation strategies that are based on CCS technologies for oil sands operations requires adequate tools to estimate their total energy demand requirements and the associated GHG emissions. Moreover, these tools can be of assistance to industries and policy makers in the energy planning required to achieve higher levels of SCO and bitumen production. The demand for energy commodities (i.e. electricity, steam, heat and hydrogen) is tied to the forecasted increase expected for bitumen and SCO production, and the increase for their requirements will require the establishment and commissioning of additional energy production units in order to sustain the required
bitumen and SCO production levels. The development of the energy infrastructure of the oil sands industry must take into account the increasingly CO2-contrained environment, which requires a quantification of the magnitude of emissions associated with energy production. Several studies in the literature have been proposed for the modeling of the energy demands and their associated GHG emissions for oil sands operations, as well as studies that utilize these estimates of energy demands for the energy design planning for oil sands operations [8–22]. Charpentier et al. [8] developed a life cycle-based model, referred to as the GHOST model, which estimates the life cycle (total direct and indirect) emissions of multiple oil sands producers for the production of bitumen and SCO. Their model was utilized to estimate a range of emission intensity factors based on confidential operating data of existing oil sands projects. Ordorica Garcia et al. [9] developed a mathematical model to estimate the energy demands of integrated SAGD/upgrading and mining/upgrading operations based on yield data available from industrial reports of currently operating facilities. Betancourt et al. [10] later developed a mathematical optimization model that uses the energy demand estimates provided by Ordorica Garcia et al. [9] in order to determine the optimal oil sands production routes and their corresponding energy infrastructure with the goal of minimizing total costs subject to CO2 emission constraints. Giacchetta et al. [11] utilize energy demand assessment and GHG intensity data to conduct economic and environmental evaluation and optimization of industrial scale SAGD projects. Similarly, several studies [12–16] have utilized models that assess energy requirements of oil sands operations in order to investigate the feasibility of incorporating alternative energy production technologies and carbon mitigation options, such as nuclear energy, gasification of alternative fuels (e.g. coal, petroleum coke, bitumen, etc.), and production technologies integrated with carbon capture and sequestration. Nimana et al. developed a model, which is referred to as the fundamental engineering principles based model for the estimation of greenhouse gases in the oil sands (FUNNEL-GHG-OS), for the estimation of the energy demand requirements and GHG life cycle emissions associated with bitumen extraction [17], upgrading and refining [18], and for the life cycle assessment of bitumen derived transportation fuels (i.e. gasoline, diesel and jet fuel) [19]. The spreadsheet model allows the user to change default parameters to input user data in order to estimate the energy demand requirements of project-specific operations. Two studies [20,21] prepared for the Alberta Energy Research Institute analyze the GHG emissions specific to crude oil production operations in North America, including the oil sands industry. A lot of different petroleum types, extraction technologies and reservoir locations are analyzed and compared. The GREET model developed by Argonne National laboratory [22] calculates the emissions associated with a variety of processes, including bitumen extraction and upgrading to SCO. The model’s output results are expressed in terms of specific emissions (i.e. amount of CO2/MJ of SCO produced). The methodology and various parameters used in the estimation of energy demands and GHG emissions are not disclosed. Within the above context it can be realized that the development of models for the estimation of energy demands and GHG emissions of oil sands operations is important for future planning in the industry. The models developed so far in the literature can provide adequate estimates of these parameters. Even though it is possible to construct several oil sands production pathways using these models, most of them do not provide a specific methodology to estimate the specific energy consumption of bitumen extraction and upgrading operations. Therefore, it is not possible to estimate the energy consumption and GHG emissions for a specific project, as they cannot be modified to accommodate differ-
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ent projects. Moreover, the modeling of the energy infrastructure in these models is based on parameters for the efficiency and the emissions of the energy facilities used, which do not adequately describe the behavior of the energy infrastructure. To the authors’ knowledge, there are no models available in the literature that estimates the energy requirements and the associated GHG emissions of oil sands operations by conducting process simulations. This paper presents a detailed data intensive model to estimate a project specific energy demand requirements and their associated GHG emissions for an integrated SAGD extraction and upgrading process in oil sands. The energy demands for the SAGD extraction section are modeled based on fundamental engineering principles, while the upgrading section that is based on delayed coking is simulated using Aspen HYSYS [23]. This is integrated with an energy infrastructure that is modeled using Aspen Plus [23] to estimate the total GHG emissions generated. The model proposed was simulated for a range of operating parameters in order to illustrate its applicability for any integrated SAGD/upgrading project. The model quantifies the demands for steam, electricity, hydrogen and natural gas for given production levels of bitumen and SCO. In addition to the total energy demands, the model estimates the resulting GHG emissions and GHG emission intensity using the current energy production technologies. This model can be used to simulate current and future operations of integrated SAGD/ upgrading projects. The data generated from the model can be useful for policy makers and industrial operators as an input for energy planning models for these projects. The model also provides a quantification of CO2 emissions inventory that can be used for feasibility studies of CCS projects or other carbon abatement strategies. 2. Methodology This section outlines the methodology employed to quantify the energy demand requirements and the associated GHG emissions of an integrated SAGD/upgrading facility. The methodology is divided into two major steps, which are the modeling of the energy demand requirements of extraction and upgrading operations, and the modeling of the energy infrastructure required to meet these demands. The calculations were undertaken for a plant size of 112.5 thousand barrels of bitumen per day, which corresponds to 150 thousand barrels of dilbit per day assuming 25% dilution. This scale is representative of many SAGD extraction processes in Alberta. The SAGD energy demand requirements are calculated based on fundamental engineering principles and parameters available from existing operating facilities. The upgrading energy demand requirements are calculated by conducting Aspen HYSYS simulations of a typical upgrader plant. To accommodate a wider operational capacity, two cases representing high and low energy consumption were developed, which will be referred to as the high energy scenario (HES) and the low energy scenario (LES), respectively. Similarly, simulations have been conducted for the on-site energy infrastructure in order to meet the demand requirements of the integrated SAGD/upgrading facility and quantify the associated GHG emissions. The following lines describe the modeling procedure followed to estimate the energy demand requirements of and integrated SAGD/upgrading process, and the modeling of the energy infrastructure to obtain estimates of GHG emissions associated with providing these energy requirements. 2.1. Energy demand assessment The extraction and upgrading operations are typically carried out in series, with the product of the extraction section (i.e. diluted bitumen: dilbit) used as the feed for the upgrading process. Fig. 1 shows a
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block flow diagram of the integrated SAGD/upgrading process. The main energy inputs to the SAGD extraction process are in the form of high pressure steam and electricity. A diluent, which is typically naphtha, is used to reduce the viscosity of the produced bitumen (dilbit) to facilitate pipeline transportation. The first step of the upgrading process is the recovery of the diluent, which is recycled to the extraction process. The energy commodities consumed in the upgrading stage are electricity, steam, heat and hydrogen. The main product from the upgrading stage is SCO. Byproducts from the extraction and upgrading stages are produced gas, offgas, and petroleum coke (petcoke). The produced gas and offgas are used as input for natural gas-based energy infrastructure.
2.1.1. SAGD extraction A major energy commodity required for bitumen extraction using the SAGD technology is high pressure steam. This technology requires two parallel horizontal wells to be drilled from the surface for continuous steam injection and fluid production. The steam injected through the top well and forms a steam chamber, in which the bitumen is heated to reduce its viscosity. The heated bitumen and the condensed water form an emulsion that is artificially lifted to the surface. The key parameter required to measure the steam requirement for a specific well is the steam to oil ratio (SOR). This parameter expresses the average volume of steam required to produce a unit volume of bitumen. The SOR can be expressed as an instantaneous ratio (iSOR) or a cumulative ratio over the life of the project (cSOR). SAGD extraction processes with a low value of iSOR are less energy intensive compared to those with a high iSOR values. The steam injected in the wells is produced at a high pressure in order to overcome the pressure drops in the pipes and to maintain the target pressure in the steam chamber. The SAGD steam considered is pure saturated steam at 100 bar and 311 °C. These values are typical for a SAGD extraction plant with electric submersible pumps [24]. Most plants operate in an iSOR range of 2–3 [25–30]. The lower bound is assumed to be the iSOR for the LES, and the upper bound is assumed for the HES. The total steam demand required for SAGD extraction can be calculated as illustrated in Eq. (1), where SDext is the total SAGD extraction steam, and BIT prod the total bitumen production.
SDext ¼ BIT prod iSOR
ð1Þ
In a SAGD extraction facility, the process units that consume electricity are bottom-hole pumps, surface circulating pumps and compressors of the vapor recovery unit. In SAGD extraction facilities, the water treatment technology utilized in the process has a significant effect on the total electricity consumption. The two most commonly used water treatment technologies in SAGD extraction facilities are evaporators and warm lime softening (WLS). Due to the presence of additional compressors, evaporators normally consume more electricity than WLS [31,32]. The total electricity demand in a SAGD extraction facility is proportional to the value of iSOR [17]. Based on the total electricity consumption of various operating SAGD extraction facilities and their average bitumen production levels [25–30], an electricity intensity ratio was obtained which is expressed in units of kW h/bbl of bitumen and is referred to as the electricity-to-oil ratio (ELOR). Values of the ELOR were obtained in the range of 7.5–14 kW h/bbl, and the lower and upper bounds were used to represent the LES and HES, respectively. The value of 7.5 corresponds to a facility with iSOR of 2 and that only incorporates WLS for water treatment. An ELOR value of 14 was taken to represent a facility with an iSOR of 3, and that incorporates both evaporators and WLS. The total electricity demand required for SAGD extraction can be calculated as illustrated in Eq. (2), where ELDext is the total SAGD extraction electricity consumed.
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Fig. 1. Energy vectors in the SAGD extraction of bitumen and upgrading to SCO.
ELDext ¼ BIT prod ELOR
ð2Þ
During bitumen recovery there is a significant amount of gas produced [33] which may be used as fuel provided it has been properly desulphurized. The production of gas is due to the dissolution of non-condensable species in the water-bitumen emulsion and the evaporation of light fractions in the separation processes. The amount of dissolved gas is quantified by the gas-to-oil ratio (GOR) parameter [34]. The total amount of produced gas recovered from the SAGD extraction process, which is the sum of the solution gas and the vapors recovered, is represented by the produced gasto-oil ratio (PGOR). According to the Canada’s Oil Sands Innovation Alliance (COSIA) [35], a reasonable assumption for the value of the PGOR is twice the value of the GOR. For the SAGD extraction facility modeled in this study, a value of 10 m3 of gas/m3 of bitumen was considered for the PGOR. The value of PGOR is a design parameter associated with the investigated SAGD project, and is not affected by the LES or HES. The total produced gas (SPGext ) can be calculated as follows.
SPGext ¼ BIT prod PGOR
ð3Þ
2.1.2. Upgrading The upgrading route of bitumen considered in this study is based on thermal cracking. The process units incorporated include the atmospheric and vacuum distillation columns (ADU and VDU), delayed cokers (DC) and hydrotreaters (HTR). The majority of bitumen upgrading capacity currently operating in the oil sands industry takes place through this upgrading route [36]. The upgrading process was simulated using Aspen HYSYS [23], from which the energy demands (i.e. electricity, hydrogen, steam and heat) are obtained. The ADU is used for diluent recovery and diesel production, while the VDU is used for maximum recovery of liquid products. The cracking is simulated with a delayed coker unit and a fractionator. The model includes a custom hydrotreater for each liquid fraction. The process flow diagram of the upgrading process simulated is shown in Fig. 2. Dilbit from tankage enters the process at ambient temperature at the ADU, which consists of two atmospheric columns that separate the diluent and a portion of diesel from the bitumen [37]. The atmospheric topped bitumen is then fed to the VDU, which uses pressures in the range of 5 kPa to maximize the recovery of liquid fractions while preventing hydrocarbon cracking. A DC unit is then used to thermally crack the remaining heavy ends and produce lighter liquid fractions, as well as a vapor and a solid fraction. The remaining liquid fractions are mixed and sent to hydrotreaters to produce a sweetened SCO product. Pressure swing adsorption is used to produce a pure hydrogen stream from the hydrotreater purge gas, which is then recycled into the hydrotreater system. The model uses pseudo components to simulate distillation columns, which in Aspen HYSYS are referred to as hypothetical components and are defined based on total boiling points of liquid
fractions [38]. Temperature control across the column is achieved by a number of pumparounds designed to produce a specified temperature at a given process stream. The DC unit is simulated using the Aspen HYSYS Delayed Coker with a downstream column for fractional separation. Each hydrotreater consists of two or more hydroprocessor bed units, a separator for sour gas and liquid fractions, and hydrogen recycle system. The design of each HTR section is dependent on oil gravity, sulfur content, and olefin content of the feedstock. Temperature control across each bed is done by cooling the recycle gas streams and mixing them as a quench prior to each stage [39]. This is necessary as the reactions in the hydrotreatment process are highly exothermic. The feed to each process unit (fractionating, cracking, desulphurization, olefin saturation, etc.) must be maintained at a specified temperature specific to each unit, from which the heating requirements are estimated. Since heat integration is included in the model, the heat demand should be provided entirely from outside battery limits. Because steam is used for different purposes across the process, the steam input specifications vary among process units. For example, columns require steam to improve distillation efficiency, while the DC uses steam to avoid the coke deposition into the furnace [40]. The columns utilize steam at 20 bar and 400 °C, while the delayed cokers requires steam at a higher temperature in order to avoid heat loss (510 °C). Electricity usage varies among process units. The electricity demand of the ADU is relatively low as it is limited to fluid pumping. The VDU uses more electricity in order to run the vacuum system. In the DC, electricity consumption includes the motor drive for the hydraulic decoking pump, which is linearly correlated to coke production [40]. The HTRs have the highest consumption of electricity, due to the presence of compressors for the hydrogen make up and recycle streams, as well as the high pressures required to be produced by the inlet pumps. The hydrogen demand of the upgrading process is due to the requirement of hydrotreating. The presence of higher sulphur, nitrogen and aromatic content in heavier feeds necessitates more severe operating conditions in the hydrotreatment system. This results in a higher hydrogen requirements for hydrotreating feed streams such as diesel and gasoil. Meanwhile, the naphtha hydrotreater requires significantly more hydrogen than a standard naphtha hydrotreater due to the high olefinic content resulting from the coking process [41]. 2.2. Energy infrastructure model The energy infrastructure modeled in this study is based on currently existing energy production facilities in the oil sands industry [24–30]. Currently, the fuel used for energy production is mostly natural gas. The electricity can be bought from the grid or produced from natural gas combined heat and power plants that also cogenerate steam. Most of the steam for the SAGD extraction process is produced with once-through steam generators (OTSG) fuel by natural gas. The hydrogen for the upgrading operations is pro-
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439
Fig. 2. Simplified process flow diagram of the upgrading process.
duced by steam methane reformers. The simulation of the energy infrastructure was conducted with Aspen Plus [23]. The flowsheet of the energy infrastructure modeled along with the energy vectors is shown in Fig. 3. 2.2.1. Once through steam generators (OTSGs) The steam for the SAGD process is generated by once-through steam generators (OTSGs) and natural gas combined heat and power plants. Due to the use of WLS for water softening, the boiler feed water (BFW) contains enough hardness to hinder the use of drum evaporators. It is required to maintain a steam quality of around 80% in order to avoid mineral deposition and fouling in downstream pipes [42]. After the OTSGs, a flash vessel is used to
separate the water in order to produce pure steam for SAGD extraction. The blowdown water is used to preheat the BFW coming from the central processing facility. The inlet and outlet feed temperatures of the preheater are 166 °C and 200 °C, respectively. The burners of the OTSGs were modeled as adiabatic units, and the stoichiometry of the combustion reactions is maintained such that there is 3% oxygen in dry fumes. The exhaust gases produced at adiabatic flame temperature enter a cooler, which represents the shell of the OTSGs. The water enters a heater, which represents the tube side of the OTSGs, and exits at a 78% steam quality. The heat exchange between the heater and the cooler occurs with maximum energy losses equal to 2% of the fuel’s lower heating value (0.5% due to unburnt fuel, 0.5% due to radiation losses, and 1%
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Fig. 3. Flowsheet of the energy infrastructure model.
due to convective heat transfer losses). The minimum temperature approach has been set to 25 °C. 2.2.2. Natural gas combined heat and power The cogeneration system consists of gas turbines with oncethrough heat recovery steam generators (OT-HRSGs). The OTHRSGs operate along with the OTSGs to provide the total steam requirements of the SAGD extraction and upgrading operations. Similar to OTSGs, the OT-HRSGs produce steam with 78% quality. In order to increase the steam production from the OT-HRSGs, the exhaust gases from the gas turbines are post-fired with duct firing in order to achieve a temperature of 815 °C. Part of the produced gas is used as an input to the post-firing system, and the remaining is used for the OTSGs. This temperature value was obtained from the operating data of the Deltak OT-HRSG of the McKay River cogeneration plant [43]. The BFW preheater for OTHRSGs was simulated similar to that of the OTSGs. The pinch point temperature has been set to 25 °C, which is a knowledge based design parameter indicated by Nord et al. [44]. The cogeneration unit is installed on-site and provides electricity for both bitumen extraction and upgrading. In integrated SAGD/ upgrading plants, it is usually preferred to modularize the combined heat and power plants with small to medium sized units. This permits more flexibility in meeting the energy demand requirement of variable bitumen production levels. The gas turbine selected for the simulation is the 47.5 MWe SGT-800 model from Siemens [45]. The performance parameters of the turbine were simulated with the software GT-PRO by Thermoflow [46] in both ISO and working conditions. The results are shown in the Table 3. An equivalent gas turbine was simulated in Aspen Plus in order to obtain the composition of the flue gases, which is required to provide the input to the heat exchanger of the OT-HRSG. The OT-HRSG consists of an adiabatic combustion chamber and a heat exchanger, which cools the fumes to the turbine outlet temperature (TOT). The heat recovered is used to generate steam (see Table 1). 2.2.3. Steam methane reforming The technology used for hydrogen generation is steam methane reforming (Fig. 5), from which steam is coproduced. The model of the steam reforming consists of a pre-reforming unit, a steam
reformer unit, two stages of water gas shift (WGS) reactors, and a pressure swing adsorber (PSA) for hydrogen purification. The pre-reforming unit, which uses highly active nickel-based catalysts, was modeled as an equilibrium adiabatic reactor. According to Sperle et al. [47]. The assumption of chemical equilibrium provides a good approximation of the actual operating conditions of the pre-reformer catalyst. The pre-reformer is installed to reduce the thermal load of the steam reformer, to reduce the steam-tocarbon ratio, and to increase the overall efficiency of the process [48] (see Fig. 4). The steam reformer, was simulated as a full equilibrium reactor operating with inlet and outlet temperatures of 650 and 900 °C, respectively, a pressure of 38.5 bar, and a steam-to-carbon ratio of 2.7. The heat required for the endothermic reactions is provided by several gas burners located on the boiler’s wall. The syngas from the steam reformer is cooled down and sent to a two-stage WGS unit [49–51]. The high temperature shift reactor (HTS) uses iron oxide/chromium oxide catalysts that are active at a temperature range of 300–450 °C. The low temperature shift reactor (LTS) uses Cu-ZnO-Al2O3 catalysts that operate at a temperature range of 190–250 °C [52]. The inlet and outlet temperatures are 400 and 465.8 °C, respectively, for the HTS, and 200 and 242.1 °C, respectively, for the LTS. The WGS reactor was modeled as an adiabatic reactor with selective equilibrium for the shift reaction, as modeled by Martelli et al. in [53]. It is preferred to use natural gas as a fuel for the catalytic steam reforming due to additional problems related to the use of offgas [54].
Table 1 ISO and working conditions for SIEMENS SGT-800. Parameter
Unit
ISO conditions
Working conditions
Power Efficiency Air mass flow rate Fuel mass flow rate Flue gas mass flow TIT Turbine outlet (TOT)
MW % kg/s kg/s kg/s °C °C
46.43 37.54 128.5 2.5 131.1 1287.8 551.82
48.93 37.36 134.35 2.65 137 1292.9 540.8
Annual average temperature 0 °C, Aspiration pressure 5 mbar, Exhaust pressure drop 25 mbar.
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441
Fig. 4. Flowsheet of the steam methane reformer.
Following the WGS, the syngas is cooled down and sent to the purification units. After separating the water, the syngas is sent to a PSA unit. The PSA has been modelled as a simple separator block set to recover 90% of hydrogen with 99.99% purity. This block has been used based on the assumption that hydrogen purity and recovery are not sensitive to the changes in pressure and composition of the feed [55]. The hydrogen produced is sent to the upgrading facility, while the PSA offgas is sent to the burners of the steam reformer reactor. The burners of the terrace wall were modeled with an adiabatic unit. The stoichiometry is controlled such that there is 3% O2 in dry fumes. Heat from the exhaust gases is used for the steam reformer reactor, as well as for preheating BFW. Similarly to the approach provided by Martelli at al. [49] reforming combined cycles were integrated, in which the waste heat made available by the gas coolers is efficiently recovered by economizing, evaporating and superheating steam at 20 bar, 400 °C. The upgrading section requires steam both to enhance stripping efficiency in the columns and for the delayed coking process. Some of this steam requirement is provided by the steam methane reforming unit. The temperature of the feed water is 205 °C, which is achieved due to the heat integration incorporated. The heat demand for upgrading is provided by furnaces, which were modeled as adiabatic reactors. The fuel feed is the offgas from the PSA unit, and the stoichiometry is controlled such that there is 3% O2 in dry fumes. The exhaust gases are then cooled to 400 °C, considering a minimum temperature approach of 100 °C. The
fumes are then discharged at 150 °C, and the heat recovered is used to preheat the combustion air to achieve a temperature of 290 °C. Part of the fuel used in the furnaces is obtained from offgas generated from upgrading operations. The remaining offgas is used in steam generators and burners of the steam reformer unit. The input parameters used for the simulation are summarized in Table 2. These include input parameters for the OTSGs, natural gas cogeneration units, and steam methane reforming plants. 2.3. Functional units After obtaining the total energy demand requirements and generated GHG emissions, the results obtained are expressed in terms of specific energy (e.g. MJ/GJSCO) and GHG emission intensity factors (e.g. gCO2/MJSCO). The emission factor parameters incorporated in the calculations of total emissions from natural gas equipment imported from the GREET model [22]. An additional parameter that is introduced is the net external energy ratio (NEER). The NEER is a parameter introduced by Brandt and Dale in their analysis [56] to express the profitability of the exploitation of a certain resource. This parameter divides the total output of energy (SCO, electricity and produced gas) with the external input (natural gas only). The purpose of using the NEER is to provide a measure of the contribution of an energy source or project (e.g. an integrated SAGD/upgrading project) to the total energy supply of society. In other words, in order for an energy source or system
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Table 2 Parameters used for the energy infrastructure model.
Table 4 Performance of the energy infrastructure.
Parameter
Unit
Value
Energy flux
LES
HES
OTSGs/OT-HRSGs Temperature water preheat inlet Pressure water preheater inlet Minimum Temperature Approach water preheater Steam pressure Quality of steam before separator Minimum Temperature Approach OTSG Losses for unburnt/radiation/convective OT- HRSG Heat Losses OT-HRSG Postfiring Temperature
°C bar °C bar % °C % of LHV % °C
166 135 10 100 78 25 0.5/0.5/1 2 815
Bitumen extracted (MW) SCO produced (MW)
8881.7 7363.7
8881.7 7363.7
Gas turbines installed SAGD Steam cogenerated (% of the total) Electric power produced (MW) Electric power consumption (MW) Electric power exported (MW) Electric power exported (MW Primary Energy)
2 20.2 97.9 61.7 36.2 90.5
3 20.1 146.8 99.2 47.6 118.9
Natural gas steam reformer Inlet pressure Steam to Methane Ratio Pre reformer feed temperature Steam reformer feed temperature Steam reformer outlet temperature HT/LT WGS syngas input temperature HT/LT WGS reactor approach temperatures Reformers/WGS reactors pressure drops Condenser/PSA inlet temperature Combustion air preheat Minimum approach temperature HT/LT coolers Exhaust gases outlet temperature Heat losses from heat exchangers Pressure drops in heat exchangers gas side PSA hydrogen split fraction PSA hydrogen quality Steam export pressure Steam export temperature
Gas Gas Gas Gas Gas
688.3 93.5 560.8 76.2 127.5
688.3 93.5 687.4 93.3 0.89
bar Mol/Mol °C °C °C °C °C bar °C °C °C °C % % % % bar °C
38,5 2,7 500 650 900 400/200 10/10 1/0.5 40 425 100/25 215 0.5 0.05 0.9 1 20 400
Natural gas consumed (MW) Natural gas consumed (MJ/GJSCO)
1372.5 186.4
2026.1 274.5
Total Total Total Total
1933.3 262.5 1372.5 186.4
2713.5 368.5 2026.1 286.83
5.52
3.69
Upgrading steam generators Water inlet temperature Outlet temperature Coker/Columns steam Steam pressure Minimum temperature approach
Cogeneration unit
19.71–29.5
°C °C Bar °C
205 510/400 20 20
Electricity SAGD Electricity upgrading Electricity export Postfiring
produced (MW) produced (MJ/GJSCO) consumed (MW) consumed (MJ/GJSCO) exported (MW)
primary energy consumption (MW) primary energy consumption (MJ/GJSCO) external primary energy consumption (MW) external primary energy consumption (MJ/GJSCO)
NEER (GJ produced /GJ consumed)
Table 5 CO2 emissions of the energy infrastructure. Unit
kg/s
g/MJBIT
g/MJSCO
5.33–9.94 3.81–4.76 5.36–7.05 5.21–7.74
0.60–1.12
0.72–1.35 0.52–0.65 0.73–0.96 0.71–1.05
48.18–71.41 14.18–17.05 7.70–9.65 21.11–26.38
5.43–8.04
6.54–9.70 1.93–2.41 1.05–1.31 2.87–3.58
2.68–4.01
0.59–0.87
Furnaces Furnace discharge temperature Fumes exhaust temperature (After air preheating)
°C °C
400 150
Burners O2 mole fraction in dry fumes
OTSGs Steam reformer Steam generators Furnaces
%
3
Emission credita
6.54–8.59
0.74–0.97
0.89–1.17
SAGD Upgrading
58.13–88.32 46.21–57.78
6.55–9.94
7.89–11.99 6.28–7.85
Total
104.35–146.11
to be useful, the total energy output must be greater than the total energy input. The NEER parameter provides a ratio of the total energy output to the counted inputs of energy that must be produced and delivered externally through existing energy supply systems. All energy flux streams are expressed in terms of primary energy. The electricity is converted in terms of primary energy considering an efficiency factor of 0.4, which is a typical value for the electricity grid in Alberta [57]. For example, according to Brandt [58], conventional oil NEER values ranges from 15 to 30 GJ/GJ. This translates to an input of 1 GJ of energy being required to produce 15–30 GJ of conventional crude.
a
14.17–19.84
Based on an emission factor of 750 gCO2/kW h.
3. Results The consumption of each energy commodity is shown in Table 3 for the SAGD extraction and upgrading operations. A range of results was obtained for each energy commodity, which corresponds to the operating range of the LES and HES. It can be observed that the results obtained reflect the basis of realistic plant
Table 3 Energy demand assessment for the SAGD extraction and upgrading process LES-HES.a
a
Unit
Heat (MW)
Steam (MW)
Electricity (MW)
Hydrogen (MW)
Produced/Off-gas (MW)
SAGD extraction ADU VDU DC Naphtha HTR Diesel HTR Gasoil HTR Hydrogen recovery Auxiliary Overall upgrading
– 134.64–168.3 58.22–73.74 58.88–73.51 6.48–8.10 44.31–55.39 24.17–30.21 0 0 347.1–433.9
927.89–1391.83 92.5–115.6 15.41–19.27 33–41.26 0 0 0 0 0 126.4–158.0
35.94–67.08 0.64–0.8 6.86–8.57 3.84–4.81 1.52–1.9 2.22–2.78 3.2–4.0 5.09–6.37 2.34–2.92 25.72–32.5
– 0 0 0 19.4–24.25 55.33–69.16 116.59–145.74 0 0 191.32–239.16
56.25 0 0 612.04 0 0 0 27.32–34.14 0 665.0–671.8
The HES increases the energy vectors by a factor of 1.25 compared with the results of the HYSYS model (which are taken for the LES).
443
24 20 16
CO2 emission intensity (g/MJ SCO)
CO2 emission intensity (g/MJ SCO)
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Cogeneration OTSGs Steam Reformer Steam generators Furnaces
12 8 4 0 LES
24 20
Upgrading SAGD
16 12 8 4 0
HES
LES
HES
Fig. 5. Specific CO2 emission (g/MJSCO) per unit.
Table 6 Values for CO2 emissions available in the literature. Stage
Unit
This work
GREET1
GHGenius2
GHOST3
FUNNEL4
JACOBS5
SAGD Upgrading Total
gCO2/MJBIT gCO2/MJSCO gCO2/MJSCO
6.6–9.9 6.3–7.9 14.2–19.8
12.2 12.1 26.8
15.2 12.2 30.6
8.4–12.3 6.8–11.1 16.9–26.9
8.0–22.9 7.2 16.8–34.8
10.6 8.3 21.1
1. Emissions depend on default values specified in the model for energy consumption. 2. Emissions depend on default values specified in the energy consumption model. 3. The iSOR considered is in the range of 2.2–3.3. 4. The SOR considered is in the range of 2.1–6.54. 5. This model considers an iSOR of 3.
operations. For example, the hydrogen consumption of gasoil HTR is the highest among all hydrotreating systems. This is due to the high content of sulphur, nitrogen, aromatics, etc., compared to the other liquid fractions, which necessitates a higher hydrogen consumption rate in order to achieve the desired product quality. The performance parameters of the energy infrastructure modeled in this work are summarized in Table 4. The total available electricity production capacity is 97.9 MW and 146.8 MW for the LES and HES, respectively. The electricity consumption rates for the LES and HES are 61.7 and 99.2, respectively, which results in power potentially available for export to the grid (i.e. LES: 36.3 MW and HES: 47.6 MW). The amount of steam produced by the cogeneration system is around 20% of the total in both scenarios. The production of gas from the processes is identical. However, due to the higher energy demand of the upgrading process, the HES consume 22.7% more gas. In the LES, part of the produced gas is exported. However, in the HES almost all of the produced gas is consumed in the upgrading operations. Even though an adequate amount of energy is available from the available produced gases, the considerable energy demands of the SAGD extraction and upgrading operations requires the utilization of significant amounts of natural gas. The results of the model shows total natural gas consumption of 1372.5 MW and 2026.1 for the LES and HES, respectively, which indicates that the HES requires up to 50% more energy from natural gas compared to LES. This difference is mainly caused by the higher steam demand from the OTSGs. The energy intensity factor (i.e. energy specific to a barrel of SCO produced) was obtained to be in the range of 262.5–368.5 MJ/GJSCO. Approximately 76.2 –93.3 MJ/GJSCO of the total energy requirements is available from produced gas, while the remaining (186.4 – 274.5 MJ/GJSCO) is obtained from come from natural gas. The NEER obtained for the LES and HES is 5.52 GJ produced/GJ consumed and 3.69 GJ produced/GJ consumed, respectively. This indicates that the specific project parameters (e.g. iSOR, ELOR, etc.) can have a significant impact on their total energy intensity.
The total emissions generated as a result of energy production from the modeled energy infrastructure are summarized in Table 5. The emissions generated from the natural gas combined heat and power plant is classified according to the energy delivered application. The CO2 emissions associated from electricity production are generated due to the gas turbine simple cycle combustion. The CO2 emissions related to the steam cogeneration are ascribable to the postfiring. However, the export of electricity can be considered as an emission credit that could be subtracted from the total emissions of CO2. This is because the export of electricity contributes to a reduction in the electricity production of Alberta’s coal based electrical grid. Emissions associated with the electricity production from natural gas based combined heat and power plants are considerably lower than those associated with electricity production from coal. The CO2 emission factor of the Alberta grid is 750 gCO2/MW h [39]. The results show that the total CO2 emissions generate are in the range of 104.35–146.11 kg/s of CO2, and the total CO2 intensity factor was determined to be in the range of 14.17–19.84 gCO2/MJSCO. As shown in Fig. 5, the OTSGs are by far the largest contributor to CO2 emissions. In both scenarios, almost 50% of the CO2 emissions are due to the generation of steam (6.54–9.70 g/MJSCO out of 14.17 – 19.84 g/MJSCO). Moreover, the OTSGs contribute more than 80% of the emissions in the SAGD extraction operations, and cogeneration producing only less than 20% of the emissions (0.72 –1.35 g/MJSCO for electricity production and 0.71–1.05 g/MJSCO for steam generation). In the upgrading process, furnaces are the largest contributors to total emissions (2.87–3.58 g/MJSCO), followed by the steam methane reformer (1.93–2.41 g/MJSCO), steam generators, (1.05–1.31 g/MJSCO) and electricity production (0.52–0.65 g/ MJSCO). Finally, 55.7–60% of the total CO2 emissions are associated with the SAGD extraction operations. As illustrated in Table 6, this model predicts emissions well within the ranges given in existing models and industry reports. Even though the values of the input parameters incorporated in the simulation of these models might vary among each other,
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which might not facilitate a conclusive direct comparison among the obtained emission factors, the variability in the results of these models can be attributed to the differences in the energy infrastructures considered. The main differences between the energy infrastructure of the proposed model and those considered in other studies are the use of natural gas combine heat and power system for the cogeneration of electricity and steam, and the consideration of steam cogenerated from the steam methane reforming unit in the upgrading area. For example, the GREET and GHGenius do not incorporate natural gas cogeneration facilities. These technological solutions, which are used in the majority of integrated SAGD/upgrading plants currently in operation in an attempt to reduce total natural gas consumption and hence total CO2 emissions, are not considered in the energy infrastructures of these studies. In the present work, the cogeneration system produces around 20% of the total steam for the SAGD extraction facility (between 187 and 280 MWth), while the steam reformer produces 72–78 % of the steam required for the upgrading operations (between 32 and 40 MWth). The contribution to steam generation of these units leads to a relevant decrease in the GHG emission. Other factors that might have contributed to the differences in emission factors is due to the differences in some of the input parameters. For example, the FUNNEL model considers an upper bound value of 6.54 on the iSOR range, which results in a considerably higher value on the upper range of emissions compared to the results of this model.
4. Conclusions A detailed data intensive model was developed to estimate the energy demands and associated GHG emissions of integrated SAGD/upgrading oil sands operations in the Athabasca region in Alberta. The results obtained from the model reveal that SAGD extraction operations are more energy and GHG-intensive than upgrading operations, accounting to 50 –60% of total energy consumption and GHG emissions. The total energy and GHG intensity factors were determined to be within the range of 262.5–368.5 MJ/ GJSCO and 14.17 – 19.84 gCO2/MJSCO, respectively. The predicted GHG emission intensity factors are well within the range of existing models and literature. The results of the model would allow the industry to investigate the feasibility of reducing emissions in each stage of the unit operations. Among the energy commodities consumed in integrated SAGD/upgrading operations in the oil sands industry, steam production is the leading source of GHG emissions, followed by hydrogen and electricity production. Steam production accounts to approximately 50% of total emissions. One of the major parameters that have a significant impact on the total energy consumption and GHG intensity factor is the SOR. This indicates that reducing the SOR can have a considerable effect on the energy efficiency and reduction of GHG emissions. The incorporation of cogeneration and the consideration of steam production from steam methane reforming plants have significant potential in lowering the net environmental impacts of oil sands operations. A major challenge that will be facing the future operations of the oil sands industry is meeting the massive energy requirements associated with the considerable expected development in the industry in a sustainable manner. This is attainable along with a reduction in GHG emissions intensity through optimal energy planning of the oil sands energy infrastructure. The proposed model can be utilized to estimate the current and future energy requirements of integrated SAGD/upgrading oil sands projects, which can be used as an input in energy planning optimization models to determine the optimal set of energy producers that is geared towards cost minimization in a CO2-constrained environment.
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