Life Cycle Greenhouse Gas Emissions of Current Oil Sands

Article pubs.acs.org/est

Life Cycle Greenhouse Gas Emissions of Current Oil Sands Technologies: Surface Mining and In Situ Applications Joule A. Bergerson,*,† Oyeshola Kofoworola,‡ Alex D. Charpentier,‡ Sylvia Sleep,‡ and Heather L. MacLean‡,§ †

Chemical and Petroleum Engineering, Institute for Sustainable Energy, Environment and Economy, Centre for Environmental Engineering Research and Education, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4 ‡ Department of Civil Engineering, University of Toronto, 35 St George Street, Toronto, Ontario, Canada M5S 1A4 § Department of Chemical Engineering & Applied Chemistry, School of Public Policy and Governance, University of Toronto, 35 St George Street, Toronto, Ontario, Canada M5S 1A4 S Supporting Information *

ABSTRACT: Life cycle greenhouse gas (GHG) emissions associated with two major recovery and extraction processes currently utilized in Alberta’s oil sands, surface mining and in situ, are quantified. Process modules are developed and integrated into a life cycle model-GHOST (GreenHouse gas emissions of current Oil Sands Technologies) developed in prior work. Recovery and extraction of bitumen through surface mining and in situ processes result in 3−9 and 9−16 g CO2eq/MJ bitumen, respectively; upgrading emissions are an additional 6−17 g CO2eq/MJ synthetic crude oil (SCO) (all results are on a HHV basis). Although a high degree of variability exists in well-to-wheel emissions due to differences in technologies employed, operating conditions, and product characteristics, the surface mining dilbit and the in situ SCO pathways have the lowest and highest emissions, 88 and 120 g CO2eq/MJ reformulated gasoline. Through the use of improved data obtained from operating oil sands projects, we present ranges of emissions that overlap with emissions in literature for conventional crude oil. An increased focus is recommended in policy discussions on understanding interproject variability of emissions of both oil sands and conventional crudes, as this has not been adequately represented in previous studies.

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INTRODUCTION The recovery, extraction, and processing of bitumen, the extremely viscous oil present in Canada’s oil sands reserves, is receiving attention due to the industry’s rapid expansion coupled with mounting environmental concerns. The increasing use of oil sands products to satisfy transportation fuel demand1 in addition to recent Alberta2 and California3 Low Carbon Fuel Standard (LCFS) regulations are resulting in challenges to the industry and a focus on the greenhouse gas (GHG) emissions resulting from their operations. The production of synthetic crude oil (SCO) and nonupgraded bitumen has been forecast by one source to more than double in the next decade,1 while the GHG emissions from oil sands operations are projected to quadruple by 2020.4 Surface mining and in situ are the two main categories of bitumen recovery and extraction techniques. Surface mining involves removing the oil sands material with shovels and trucks and separating the bitumen by using a hot water process. Surface mining contributed 53% (140,000 m3/d) of total crude bitumen produced from Alberta in 2010.5 In situ techniques are employed for deeper deposits and usually involve injecting steam into the oil sands reservoir, thus heating the bitumen so it becomes less viscous and can be pumped from the reservoir. Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam © 2012 American Chemical Society

Stimulation (CSS) are the major in situ techniques employed in the industry and contributed 35% (89,000 m3/d) of total crude bitumen produced from Alberta in 2010.5 Following extraction using any of the techniques, the bitumen is either upgraded to SCO (most often the case for mined bitumen) or diluted before being transported by pipeline to refineries and refined to marketable fuels.1 For additional information on production technologies see ref 6. Oil sands-derived fuels are generally reported to have higher production-related GHG emissions than conventional crude oil-derived fuels.7 However, well-to-wheel (WTW) GHG emissions (those associated with fuel production and fuel combustion in a vehicle) in published studies as reviewed in ref 7 are not sufficient to characterize the emissions performance of the industry due to their reliance on the limited (and often low quality) publicly available data (discussed in ref 8), inconsistency in boundary definition, and limited transparency of the majority of studies. While additional studies9−12 of oil sands GHG emissions have been published since,7 these remain Received: Revised: Accepted: Published: 7865

February 24, 2012 May 30, 2012 June 5, 2012 June 5, 2012 dx.doi.org/10.1021/es300718h | Environ. Sci. Technol. 2012, 46, 7865−7874

Environmental Science & Technology

Article

Table 1. GHOST Model Input Inventory: Ranges for Surface Mining Bitumen Recovery and Extraction, Upgrading, and Transportd Surface Mining Recovery and Extraction electricity used by the process (kWh/m3 bitumen) diesel (L/m3 bitumen) flared hydrocarbon emissions (kg CO2eq/m3 bitumen) fugitive methane emissions (kg CO2eq/m3 bitumen) 1 - No Cogeneration Case efficiency - boiler ηB natural gas input (m3/m3 bitumen) 2 - Cogeneration Case efficiency - gas turbine ηGT efficiency - HRSG exhaust heat recovery ηHR efficiency - HRSG direct firing duct burners ηDB total electricity produced (kWh/m3 bitumen)

range

example scenario

50−100 7−15 0−15 3−24

60 10 2 10

80%−85% 20−80

80% 50

30%−35% 50%−60% 95% 240−2,400

30% 50% 95% 1,200 ranges

example scenario

Upgrading

delayed coking

hydrocracking

delayed coking

hydrocracking

SCO/bitumen ratio (m3 SCO/m3 bitumen) electricity used by the process (kWh/m3 SCO) coproduced process gas used by the process (m3/m3 SCO) hydrogen used by the process (m3/m3 SCO) make-up diluentb (L/m3 SCO) flared hydrocarbons emissions (kg CO2e/m3 SCO) fugitive methane emissions (kg CO2e/m3 SCO) 1- No Cogeneration Case efficiency - boiler ηB total gas used by the processc (m3/m3 SCO) 2 - Cogeneration Case efficiency - gas turbine ηGT efficiency - HRSG exhaust heat recovery ηHR efficiency - HRSG direct firing duct burners ηDB total electricity produced (kWh/m3SCO) Transport

0.78−0.90 40−70 55−115 65−200a

0.95−1.05 85−130 25−115 75−200a

0.85 55 70 80

1.03 100 55 90 20 6.5 1

5−30 5−10 0−2

80%

80%−85% 95−115

105

55−115

30% 50% 95%

30%−35% 50%−60% 95% 220−2,200 ranges

electricity required for pipeline pumping (Wh/(m3·km))

15−65

85

400−4,000 example scenario

1,100

2,000

40

a

No consensus on hydrogen requirement upper bound in data collected. bThe diluent input to the upgrader is not totally recovered; makeup diluent must be purchased (or produced) so that the amount of diluent shipped back to the bitumen extraction plant equals the diluent demand. cTotal gas for steam generation = natural gas + process gas; if the amount of process gas is sufficient, there is no purchased natural gas. dHRSG: heat recovery steam generator; SCO: synthetic crude oil.

pathways: surface mining and CSS. The current paper 1) delineates study boundaries, describes GHOST, and how it is utilized in this study; 2) develops and integrates into GHOST modules for surface mining and CSS pathways; 3) quantifies WTR emissions for the pathways studied; 4) quantifies WTW emissions for all pathways in GHOST (surface mining, CSS, and SAGD) and compares results with literature; and 5) discusses policy implications.

reliant on publicly available data for most stages of oil sands’ production or are meta-analyses of prior studies. To better inform environmental policies, and for improved management of emissions from the industry, there is a need for WTW emissions estimates for oil sands pathways that employ a consistent method and utilize operating data from current projects. To address the above, the GreenHouse gas emissions of current Oil Sands Technologies model − GHOST − was developed.8 It is the first oil sands life cycle-based model to be based on confidential data from a set of operating projects, and the model’s development was informed by industry, government, and academic technical experts. GHOST accounts for the GHG emissions associated with the recovery, extraction, dilution, transportation, and upgrading of bitumen, quantifying emissions associated with the activities up to the refinery entrance gate, termed well-to-refinery entrance gate (WTR). We applied GHOST to estimate the WTR GHG emissions associated with the production of gasoline from one of the primary in situ bitumen extraction processes utilized by the oil sands industry, SAGD.8 The current analysis provides a more complete characterization of oil sands pathways’ GHG emissions by modeling additional major bitumen production

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METHOD GHOST Model. GHOST, a spreadsheet-based model, utilizes process-based life cycle methods to quantify the WTR GHG emissions associated with the production of SCO and diluted bitumen from currently operating oil sands technologies. Details of GHOST are presented in ref 8, and key aspects summarized in this section and the Supporting Information. GHOST allows a user to choose among different recovery and extraction technologies (i.e., SAGD, CSS, and surface mining), as well as whether or not the produced bitumen will be upgraded, and, if so, whether through delayed coking or hydrocracking (two of the major upgrading technologies). Other options that can be user-specified include the following: a) each life cycle stage’s input parameters (e.g., steam-to-oil 7866

dx.doi.org/10.1021/es300718h | Environ. Sci. Technol. 2012, 46, 7865−7874


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For the pathways without upgrading (dilbit and synbit) a transport distance of 3,000 km is assumed (bitumen extraction to refineries in Petroleum Administration for Defense District II, which is comprised of Midwest U.S. states). For the pathways with upgrading (SCO), a 500 km distance is assumed between extraction and upgrading (approximately the distance from Ft. McMurray to Edmonton), and a 2,500 km distance is assumed between upgrading and refining (approximately the distance from Edmonton to Chicago). For each pathway, GHOST is run for the ranges of parameters compiled for each of the WTR life cycle stages. Ranges of outputs resulting from utilizing all ‘low’ and all ‘high’ values for the input parameters in GHOST are generated. Although the probability that a project will operate at all low (or high) values in practice is low, the resulting ranges indicate the variability in emissions obtainable due to interproject differences in parameters (e.g., reservoir and operating conditions, technology choices). Correlation between parameters is also possible, and one should not exclude the possibility that emissions could fall outside of the ranges presented. A sensitivity analysis, examining the impact on WTR GHG emissions of variations in input parameters is also completed. The small number of surface mining projects limited the type of uncertainty analysis that could feasibly be undertaken. Well-to-Wheel Analyses. For the WTW modeling, the WTR results from GHOST are combined with relevant downstream activities (refining, fuel delivery, vehicle refueling, and vehicle use). The transformation of diluted bitumen or SCO to final products occurs in a refinery and therefore is an essential component of the analysis. Refining estimates are taken from TIAX,10 a consulting study contracted by the Alberta Government, while fuel delivery, refueling, and use stage estimates are taken from GHGenius (3.14b),14 a Government of Canada WTW model. We acknowledge that there are issues in combining aspects of the models; however, it is critical to carry through the analysis to present WTW results to provide context, for comparison with prior studies, and as well to highlight areas for future research. We investigate the models’ boundaries and methods and took steps to avoid double-counting, as well as provide relevant caveats for interpreting the results. Additional details and justification for these assumptions are provided in the Supporting Information. Ranges representing low and high emissions estimates are propagated throughout the WTW analyses by combining the refining, fuel delivery, and refueling emissions from TIAX10 and GHGenius,14 with the ranges from GHOST. A common lightduty vehicle (see the Supporting Information for vehicle characteristics) using a common fuel (reformulated gasoline/ RFG) is assumed across all pathways so differences in WTW results only reflect differences in well-to-tank (WTT) emissions. Results for each of the WTR life cycle stages are presented on the basis of the relevant products at those stages (e.g., 1 MJ HHV bitumen, diluted bitumen or SCO), and then total WTR results are presented on the basis of 1 MJ HHV dilbit, synbit, or SCO, depending on the pathway. WTW results are presented on the basis of 1 MJ HHV RFG. All results are on a HHV basis. The GHOST/TIAX/GHGenius results for the surface mining and SAGD pathways (see ref 8 for SAGD WTR results) are compared with literature values for these pathways, as well as literature values for conventional crude oil pathways.

ratio/SOR); b) one of two electricity and steam generation cases reflective of industry practice (No Cogeneration Case: utilizes a large-scale, on-site, natural gas-fired industrial boiler and electricity purchased from the Alberta grid; and Cogeneration Case: utilizes an on-site, natural gas-fired steam and electricity cogeneration facility); and c) diluent aspects (type and proportion of diluent blended with bitumen for transport, if used) and diluent transport distance. GHOST calculates emissions based on energy inputs to the processes and their respective emissions factors as well as incorporating emissions associated with the supply chains of process inputs, emissions associated with transportation, and on-site fugitive and flaring emissions. Confidential data were obtained for the surface mining pathways (the process for data collection as well as the SAGD pathway are detailed in ref 8). These data could not be obtained for the CSS pathways, so design and performance report data from CSS projects’ applications were relied upon for these pathways, and, therefore, CSS data and results are presented in the Supporting Information. Multiple methods were used to evaluate GHOST to determine that it provides robust results throughout the ranges of input parameters, technologies, and operating conditions.8 Surface Mining Pathways. In this research, a module for surface mining pathways is developed and incorporated into GHOST. Sixty-five percent of bitumen has been produced through surface mining cumulatively up to year-end 2009;13 however, there are only five operating projects, each with unique attributes (e.g., deposit characteristics, time since startup, operating procedures). In the surface mining recovery stage, major energy demands result from diesel-fueled shovels and trucks that are used to mine the oil sands. Electricity can also be used in shovels and conveyors that transport the mined bitumen to the extraction site. Extraction consists of separating the bitumen from the oil sands and requires large quantities of hot water and steam. Once the bitumen is separated, it is still too viscous to flow in a pipeline. Therefore, it is diluted with a lighter material such as natural gas condensate, naphtha, or SCO before being sent to an upgrader or directly to a refinery through a pipeline. Figures S3 and S4 (Supporting Information) illustrate the surface mining pathways. Diesel, electricity, and natural gas are key energy inputs required for quantifying emissions from bitumen recovery and extraction using surface mining. In addition, flared emissions (from the controlled combustion of associated natural gas) and fugitive emissions (from the mine face, tailings pond, and other leakages) are also accounted for in GHOST. Surface mining data were collected under nondisclosure agreements with industry companies. Ranges of all parameters are presented in Table 1. Surface Mining Module Application: Well-to-Refinery Entrance Gate GHG Emissions. GHOST is run for three refinery feedstocks: SCO, dilbit (blend of 75% bitumen and 25% diluent), and synbit (blend of 50% bitumen and 50% SCO) produced through surface mining. The diluent used in the dilbit can be natural gas condensate or naphtha. Both the No Cogeneration and the Cogeneration Cases are explored to satisfy the on-site demands for steam and electricity. In the latter case, it is assumed that the cogeneration unit meets all steam and electricity needs, and no electricity is purchased from/sold to the Alberta grid. See the Supporting Information for cogeneration calculations. 7867



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Table 2. Well-to-Refinery Entrance Gate (WTR) GHG Emissions Associated with Surface Mining Bitumen Recovery and Extraction, Upgrading, and Transportation: Ranges of Results from GHOST (g CO2eq/MJ)d direct life cycle stagea Surface Mining Recovery and Extraction (g CO2eq/MJ bitumen)

Upstream Diluent Production and Diluted Bitumen Transport to Refinery (g CO2eq/MJ diluted bitumen)b

Diluted Bitumen Transport to Upgrader (g CO2eq/MJ SCO)

Upgrading - (g CO2eq/MJ SCO)

SCO Transport to Refinery (g CO2eq/MJ SCO)

scenario No Cogeneration

process

steam generation electricity generation (grid) Cogeneration electricity and steam generation common elements diesel combustion flaring fugitive emissions Total - Surface Mining No Cogeneration Total - Surface Mining Cogeneration 3,000 km - dilbit diluent production diluent not recycled electricity for pumping stations Total - Transport 3,000 km Dilbit (Diluent Not Recycled) 3,000 km - synbit diluent production diluent not recycled electricity for pumping stations Total - Transport 3,000 km Synbit (Diluent Not Recycled) 500 km - dilbit -diluent electricity for recycled pumping stations Total - Transport 500 km Diluent Recycled delayed coking -No steam generationc Cogeneration hydrogen productionc electricity generation (grid) delayed coking electricity and Cogeneration steam generationc hydrogen productionc hydrocracking -No steam generationc Cogeneration hydrogen productionc electricity generation (grid) hydrocracking electricity and Cogeneration steam generationc hydrogen productionc common elements flaring fugitive emissions make-up diluent Total - Delayed Coking Upgrading - No Cogeneration Total - Delayed Coking Upgrading Cogeneration Total - Hydrocracking - No Cogeneration Total - Hydrocracking Upgrading Cogeneration 2,500 km electricity for pumping stations Total - Transport 2,500 km

Well-to-Refinery Entrance Gate (WTR) Results Summary Dilbit (g CO2eq/MJ dilbit) Synbit (g CO2eq/MJ synbit) SCO (g CO2eq/MJ SCO)

indirect

high

low

high

low

high

1.0 0

3.8 0

0.1 1.1

0.5 2.2

1.0 1.1

4.3 2.2

1.7

5.1

0.1

0.7

1.8

5.8

0.5 0 0.07 1.6 2.3 0 0

1.1 0.4 0.59 5.9 7.2 0 0

0.1 0 0 1.3 0.2 0.4 1.1

0.2 0 0 3.0 0.9 1.0 4.7

0.6 0 0.07 2.9 2.5 0.4 1.1

1.4 0.4 0.59 8.9 8.1 1.0 4.7

0

0

1.5

5.6

1.5

5.6

0 0

0 0

6.8 1.1

10.7 4.6

6.8 1.1

10.7 4.6

0

0

7.9

15.3

7.9

15.3

0

0

0.4

1.6

0.4

1.6

0 4.9 1.3

0 5.9 4.1

0.4 0.5

1.6 2.9

0.4 6.7

1.6 12.9

0

0

1.0

1.7

1.0

1.7

5.4

6.7

0.5

3.0

7.2

13.8

1.3

4.1

2.8 1.5

5.9 4.1

0.6

3.1

4.9

13.1

0

0

2.1

3.2

2.1

3.2

4.1

7.7

0.7

3.3

6.3

15.1

1.5

4.1

0.13 0 0 6.4

0.27 0.05 0 10.3

0 0 0.01 1.5

0 0 0.07 4.7

0.13 0 0.01 7.8

0.27 0.05 0.07 15.0

6.8

11.1

0.5

3.1

7.4

14.2

4.5 5.8

10.3 12.1

2.6 0.7

6.4 3.4

7.1 6.4

16.7 15.5

0

0

0.9

4.0

0.9

4.0

0

0

0.9

4.0

0.9

4.0

3.5 9.2 11.6

12.7 19.9 30.7

10.6

32.4

Surface Mining + Dilution + Transport 3,000 km Surface Mining + Dilution + Transport 3,000 km Surface Mining + Dilution + Transp. 500 km + Delayed Coking + Transp. 2500 km Surface Mining + Dilution + Transp. 500 km + Hydrocracking + Transp. 2500 km

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Table 2. continued With the exception of the ‘Surface Mining Recovery and Extraction’ and ‘Upstream Diluent Production and Diluted Bitumen Transport to Refinery’, results are expressed on a MJ SCO basis to facilitate the aggregation of WTR results. The disaggregated results do not sum to the WTR result due to the different units used for the various life cycle stages. The Surface Mining Recovery and Extraction results can be converted to other units using the following ratios: 0.80 (g CO2eq/MJ dilbit)/(g CO2eq/MJ bitumen); 0.52 (g CO2eq/MJ synbit)/(g CO2eq/MJ bitumen); and 1.15 (g CO2eq/MJ SCO)/(g CO2eq/MJ bitumen). These conversions result in the following ranges of emissions for Surface Mining Recovery and Extraction: 2.0−7.1 g CO2eq/MJ dilbit, 1.3−4.6 g CO2eq/MJ synbit, and 2.9−10.2 g CO2eq/MJ SCO. bResults presented on/MJ dilbit or/MJ synbit bases as relevant. Dilbit volume blend: 75% bitumen and 25% diluent (naphtha or condensate). Synbit volume blend: 50% bitumen and 50% SCO. Synbit diluent production assumes an emissions factor range of 510−770 kg CO2eq/m3 SCO. Make-up diluent fuel cycle emissions accounted for in Upgrading process. cIndirect emissions from ‘Steam Generation’, ‘Electricity and Steam Generation’, and ‘Hydrogen Production’ are emissions from the natural gas supply chain and therefore reported jointly. Low-end results assume use of shallow gas; high-end results assume use of conventional natural gas from Southeastern Alberta. dSCO: synthetic crude oil. Emissions totals represent ranges of emissions and are based on summing all minimum or all maximum emissions results (including both No Cogeneration and Cogeneration Cases). Note that totals may not add due to rounding. All results are on a HHV basis. a

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RESULTS Our analysis includes nine base case oil sands pathways: surface mining, SAGD, and CSS with the WTR pathways producing SCO, dilbit, or synbit (intermediate products) and the resulting WTW pathways producing RFG and including fuel use in a light-duty vehicle. The surface mining pathways are detailed in this paper, the SAGD pathways in ref 8, and the CSS pathways modeled in this research are presented in the Supporting Information. Surface Mining: Recovery, Extraction, and Upgrading. The GHG emissions calculated by GHOST for the surface mining recovery, extraction, and upgrading stages of the life cycle are shown in Table 2. The table shows a breakdown of activities contributing to these emissions. The ranges of results characterize the input parameters’ variability among projects using surface mining technologies. Surface Mining Bitumen Recovery and Extraction. Emissions associated with surface mining bitumen range from 2.5 to 8.9 g CO2eq/MJ bitumen. Direct emissions (those occurring on-site such as combustion of natural gas) represent a larger fraction of total recovery and extraction emissions than indirect emissions (those occurring off-site such as the extraction and processing of natural gas prior to combustion). This is most pronounced in the Cogeneration Case [direct emissions of 2.3 and 7.2 g CO2eq/MJ bitumen (low and high scenarios, respectively) or 89 and 92% of total (comprised of direct and indirect) emissions vs the No Cogeneration Case: 1.6 and 5.9 g CO2eq/MJ bitumen or 54 and 67%]. The on-site combustion of natural gas for steam and electricity production accounts for over 63% of total recovery and extraction emissions for the Cogeneration Case [1.7 and 5.1 g CO2eq/ MJ bitumen (low and high)] and steam production account for over 34% (1.0 and 3.8 g CO2eq/MJ bitumen) for the No Cogeneration Case. Diesel for trucks and shovels accounts for 12−20% [0.5 and 1.1 g CO2eq/MJ bitumen (low and high)] of total emissions with the amount of diesel used depending in part on the distance between the mine and extraction sites (generally this distance increases as the number of years a project is operating increases) across all scenarios. Fugitive and flaring emissions account for between 2 and 12% of total emissions [0.07 and 1.0 g CO2eq/MJ bitumen (low and high)] across all scenarios. Use of grid electricity (50−100 kWh/MJ bitumen) results in emissions between 1.1 and 2.2 g CO2eq/MJ bitumen and accounts for considerable portions (37 and 25%) of total emissions in the No Cogeneration low and high scenarios, respectively. The ranges of emissions for recovery and extraction are slightly lower for the Cogeneration Case than

the No Cogeneration Case (2.5−8.1 and 2.9−8.9 g CO2eq/MJ bitumen, respectively). While emissions associated with on-site combustion of natural gas for electricity generation (assumed in the Cogeneration Case) are lower than those associated with the predominantly coal-based Alberta electricity grid (assumed in the No Cogeneration Case), on an energy basis, much less electricity is consumed than steam, so the net impact on system emissions is relatively small. However, our application of GHOST only considers two possible options for applying these technologies and calculating the resulting emissions. These two options assume that the amount of electricity produced perfectly matches on-site demand. Several oil sands companies generate surplus electricity and sell it to the grid. How the associated emissions are calculated can greatly affect the emissions performance of the overall project. Doluweera et al. provide an in-depth analysis of methods to treat the cogenerated electricity from oil sands projects.15 Indirect emissions from the natural gas fuel cycle and diesel production account for 0.1−0.7 g CO2eq/MJ bitumen or 3−9% and 0.1− 0.2 g CO2eq/MJ bitumen or 2−4% of total recovery and extraction emissions, respectively. Upgrading. The emissions associated with upgrading bitumen to SCO range from 6.4 to 16.7 g CO2eq/MJ SCO. Upgrading emissions are also dominated by the combustion of both natural gas and process gas (a byproduct of upgrading) for steam and electricity (in the Cogeneration Case) generation but to a lesser extent than in the recovery and extraction stages (these emissions are responsible for 2.8−7.7 g CO2eq/MJ SCO or 35−73% of total upgrading emissions across all scenarios). This is due to an additional major emissions source being present in upgrading (but not in recovery and extraction), namely hydrogen production (emissions are 1.3−4.1 g CO2eq/ MJ SCO or 17−29% of total upgrading emissions). The natural gas fuel cycle also generally accounts for a considerable fraction of upgrading emissions (up to 3.3 g CO2eq/MJ SCO or 21%). In the No Cogeneration Cases, grid electricity-based emissions can represent up to 3.2 g CO2eq/MJ SCO or 30% of emissions. Upstream Diluent Production and Transportation Stages. For the synbit pathway, the emissions (all indirect) associated with diluent (SCO) production are a considerable source of emissions [6.8 and 10.7 g CO2eq/MJ synbit (low and high scenarios)], due primarily to the GHG intensity of SCO production. The contribution from diluent production in the case of dilbit is much smaller due to the lower GHG intensity of natural gas condensate and naphtha production. Transport emissions are a function of the grid electricity required to pump the product to the upgrader and/or the refinery and are moderate emissions contributors. 7869



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Figure 1. Well-to-wheel (WTW) greenhouse gas emissions for in situ SAGD and surface mining pathways generated employing GHOST/TIAX/ GHGenius combination and comparison with SAGD, mining and conventional crude oil literature pathways (all results are on a HHV basis). Notes. RFG: reformulated gasoline; SAGD: steam assisted gravity drainage; SCO: synthetic crude oil; Conv.: conventional crude oil. SCO, dilbit and synbit designations indicate the refinery feedstock. WTW results for example project are shown with solid colored columns. The ranges represent WTW emissions obtainable for the most and least favorable combinations of input parameters for each of the life cycle stages (with or without cogeneration). An exception is the SCO pathways’ upgrading and refining stages, which must be treated differently due to their interdependence (upgrading is prerefining) and whose combined emissions are lowest for the default levels of upgrading and refining and highest for the ‘maximum’ level of upgrading followed by ‘minimum’ refining. A common vehicle is assumed across all pathways so differences in the results only reflect differences in WTT emissions (see the Supporting Information for vehicle characteristics). In situ literature results as follows: peer reviewed literature results reported in Charpentier et al.:7 (A) SCO in GHGenius3.13a,18 (B) SCO in GREET 1.8b,19 (C) Dilbit in GHGenius 3.13a,18 (D) Dilbit in GREET 1.8b,19 and (E) Dilbit in the McCann result in Flint;20 gray literature results from Jacobs:9 (F) SCO SAGD and Delayed Coking, (G) SCO SAGD and Hydrocracking, and (H) Dilbit SAGD, and from TIAX:10 (I) SCO SAGD Bury Coke, (J) Dilbit SAGD 1, (K) Synbit SAGD 1, and (L) GREET In Situ Default result in TIAX.10 Surface mining literature results are as follows: peer reviewed literature results reported in Charpentier et al.:7 (α) SCO in Brandt and Farrell,21 (β) SCO low result in Furimsky,22 (γ) SCO high result in Furimsky,22 (δ) SCO in GHGenius 3.13a,18 (ε) SCO in GREET 1.8b,19 (ζ) SCO low result in McCann and Magee,23 (η) SCO high result in McCann and Magee,23 and (θ) SCO in the McCann result in Flint;20 gray literature results from Jacobs:9 (ι) SCO Mining and Delayed Coking, and from TIAX:10 (κ) SCO Mining Bury Coke and (λ) GREET Mining Default result in.10 Conventional oil literature results are as follows: peer reviewed literature results reported in Charpentier et al.:7 (a) Brandt and Farrell,21 (b) GHGenius 3.13a,18 (c) GREET 1.8b,19 (d) McCann and Magee,23 and (e) McCann result in Flint;20 gray literature results from Jacobs:9 (f) Arab Medium pathway, (g) Kirkuk Blend pathway, (h) Bachaquero pathway, (i) Maya pathway. (j) Mars pathway, and (k) Bonny Light pathway, and from TIAX:10 (l) Texas pathway, (m) Saudi pathway, (n) Mexico pathway, (o) Iraq pathway, (p) Venezuela pathway, and (q) Nigeria pathway (crudes refined in PADD 2 or PADD 3).

SCO/bitumen, amount of process gas produced in upgrading, and the amount of diesel consumed were also considered in the analysis. While they individually contribute small proportions to WTR emissions, they collectively can contribute a nontrivial fraction (e.g., factors other than steam and electricity generation constitute up to 3.0 g CO2eq/MJ SCO or 37% of surface mining recovery and extraction emissions) and therefore should not be neglected. The contributions from these parameters should be noted in any discussion on how to focus efforts to reduce emissions. Well-to-Wheel Results. The WTW emissions calculated with the combination of GHOST/TIAX/GHGenius (referred to as GHOST in this section) for two extraction technologies (SAGD and surface mining) are shown in Figure 1, with emissions disaggregated by life cycle stage (note that the y-axis on the figure starts at 60 g/MJ RFG as all pathways are assumed to have the same TTW emissions). The CSS results, which are presented in the Supporting Information due to limitations associated with the data set, are very similar to those

The majority of GHG emissions associated with surface mining bitumen recovery and extraction as well as upgrading result from the combustion of fossil fuels to satisfy steam and electricity demands. In the case of upgrading, hydrogen production is a third major emissions source. The fourth most important emissions source is the natural gas fuel cycle. In the case of synbit, the contribution of the diluent production is another major source. A sensitivity analysis confirmed the relative impact on WTR emissions results of these parameters, as well as the GHG emissions intensity of the electricity consumed (see the Supporting Information). For example, the natural gas required in the process can range from 20 to 80 m3/ m3 bitumen. The resulting WTR emissions can therefore vary by approximately 4 g CO2eq/MJ SCO due to the variability in this parameter alone. The physical explanation for the source of this variability in natural gas demand includes operating conditions such as boiler efficiency, level of heat integration, composition of the sand, bitumen, water recovered, etc. Other factors such as flaring and fugitive emissions, volume ratio of 7870



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other oil sands studies (SAGD 81-119 and surface mining 79108) is shown in Figure 1. The studies in the comparison include those in the peer reviewed and gray literature as well as WTW models (study notes/citations included in figure footnotes). In interpreting these results, readers should recognize that the boundaries (activities included/excluded), allocation methods, etc., of the other studies are not generally ‘equivalent’ to those utilized in this study. Wherever possible, assumptions were made consistent to eliminate these issues (e.g., a common TTW value of 68.1 g CO2eq/MJ RFG was applied to all pathways). A number of observations can reasonably be noted from the comparison. GHOST presents wider and generally higher ranges of emissions for most pathways then any previous study. Any study in the literature tends to report emissions that are narrower, and the majority are on the lower end of the GHOST range. GHOST is based on confidential operating data and contains broader ranges of technology performance and, in some cases, additional activities compared to literature studies (e.g., diluent fuel cycle). The low-end results in the literature for the SAGD pathway (81 g CO2eq/MJ RFG) cannot be ‘replicated’ by GHOST based on the input data we collected. Running GHOST with all input parameters set to minimize emissions, the low-end SAGD emissions are 94 g CO2eq/MJ RFG. The above observations suggest that the variability in surface mining and in situ projects’ emissions performance is not adequately represented in previous studies. The GHOST oil sands WTW emissions (88−120 g CO2eq/ MJ RFG for the full range of emissions and pathways) are also compared with results for conventional crude pathways from the literature (74−106 g CO2eq/MJ RFG). The majority of previous oil sands WTW results have been compared to only one or two relatively low point estimates for a baseline conventional North American crude typically sourced from regions where flaring is minimal (e.g., ref 7). The range of results for conventional crude pathways has been broadened significantly by two recent studies that investigated a larger set of imported crudes (and took into consideration a wider range of impacts due to location-specific factors such as reservoir depth, extraction technique, flaring procedure, etc.).9,10 Finally, emissions of conventional crudes from literature vary based on differing methods and assumptions used in those studies. As above, care must be taken in interpreting results due to differences in the studies. Generally the oil sands pathway emissions are higher than those of the conventional crude pathways, even with the broadened conventional crude range. However, the oil sands pathways for both SAGD and surface mining have the potential to have emissions within the conventional crude range (largely due to the addition to the literature of ref 9). Lower estimates for all oil sands pathways fall within the conventional range, and the surface mining dilbit range falls entirely within the conventional range. The key message is that there are large (and overlapping) ranges of potential WTW GHG emissions performance of both oil sands and conventional crudes, and, therefore, ranges rather than point estimates should be utilized to represent this performance.

of SAGD. The pathways consider the production of RFG from three intermediate products: SCO, dilbit, and synbit, the three main exports from the oil sands region. Results for an ‘Example Scenario’, consisting of a set of default values provided in GHOST (see Table 1) that are ‘representative’ of operating conditions based on our review of currently operating projects and discussion with experts, are presented along with the ranges generated through using the low and high input values for all parameters for each life cycle stage. The Example Scenario does not reflect an average of the industry nor any one project. Appropriate qualifications need to be made in interpreting the results; however, they provide a basis for discussion of the range of potential in situ and surface mining project performance. The variability shown in the range of results for each of the pathways confirms that employing one or two point estimates to represent life cycle emissions from the oil sands industry is simply not sufficient to characterize the full range of potential emissions. WTW emissions for the SCO pathways are 102−120 and 94−111 g CO2eq/MJ RFG (low-high) for SAGD and surface mining, respectively. The surface mining dilbit pathway has the lowest range of emissions (88−100 g CO2eq/MJ RFG), while SAGD’s SCO pathway has the highest. Across all Example Scenario pathways modeled by GHOST, WTW emissions are dominated by the vehicle-use phase (TTW) emissions with its contribution ranging from 64% (SAGD SCO pathway with WTT emissions of 38.9 g CO2eq/ MJ RFG) to 74% (surface mining dilbit pathway with WTT emissions of 23.9 g CO2eq/MJ RFG). Refining emissions are reported to contribute between 10.7 g CO2eq/MJ RFG or 10% (SAGD SCO pathway) to 16.4 g CO2eq/MJ RFG or 16% (surface mining synbit pathway) of WTW emissions. Upgrading and diluent components are important for the relevant pathways as upgrading can represent up to 9.8 g CO2eq/MJ RFG or 10% (surface mining SCO pathway) and the diluent fuel cycle up to 10.7 g CO2eq/MJ RFG or 10% (surface mining synbit pathway) of WTW emissions. Bitumen production contributes between 2.9 g CO2eq/MJ RFG or 3% (surface mining synbit pathway) and 13.9 g CO2eq/MJ RFG or 13% (SAGD SCO pathway) to WTW emissions. Transportation emissions while not as high are still important (∼4.1 g CO2eq/MJ RFG or 4% of WTW emissions for all scenarios) due to the relatively large transport distances and the emissions intensity. While generally in situ pathways have higher emissions than surface mining pathways, there is significant overlap among most of the pathways. The SCO pathways have the highest emissions intensities followed by synbit then dilbit. There are also different relative contributions of each life cycle stage to these emissions. For example, WTR emissions for surface mining SCO are 10.9 g CO2eq/MJ higher than they are for surface mining dilbit but refining emissions for SCO are 4.0 g CO2eq/MJ lower than for dilbit. These results suggest that the lower refining requirements (and hence, lower emissions from this stage) for SCO [due to it being a ‘prerefined’, lighter (higher API gravity), sweeter (lower sulfur) product] compared to dilbit do not fully offset the emissions associated with the additional upgrading. Further work is needed to confirm that this conclusion is robust for all scenarios. However, future studies should report intermediate products/processing assumed. A comparison of GHOST’s WTW results (in g CO2eq/MJ RFG) (SAGD 94-120 and surface mining 88-111) with those of

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DISCUSSION GHOST can be used by industry for project-specific analyses to identify key areas for process improvement; by researchers developing, or stakeholders evaluating, new technologies to benchmark their GHG emissions performance; by life cycle 7871



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projects although their potential for emissions reductions is lower. These include adjusting operating conditions (e.g., boiler feedwater temperature), using higher efficiency equipment, optimizing operating conditions, selecting a diluent with low associated GHG emissions, on-site heat integration, and minimizing transportation distances. Companies have deployed these to varying extents to date. Policy Implications. GHG emissions regulations in Canada, the U.S., and other jurisdictions have the potential to impact the competitiveness of the oil sands industry.2,3 Current perception is that the production of transportation fuels from the oil sands is more GHG-intensive than production of fuels from conventional crude. While overall our results support this, we also show that, on a WTW basis, the lower emitting oil sands cases can outperform higher emitting conventional crude cases. The wide range of potential emissions intensities for both oil sands and conventional crudes suggests that treating all oil sands (or all surface mining or all in situ) or all conventional crudes as having the same emissions may lead to unintended consequences. In addition, the emissions associated with all of the petroleum sources will continue to change over time (e.g., a transition to heavier conventional oil, technology improvements, deteriorating reservoir conditions as the oil sands resource is further developed). Jurisdictions implementing LCFSs have elevated the importance of getting life cycle carbon intensity values for fuel pathways “right”. Most prominent is California’s LCFS that requires a reduction in the average fuel carbon intensity of the State’s transportation fuels of at least 10% by 2020 which will be complicated by the overlapping ranges of emissions associated with oil sands and conventional crude pathways. WTW emissions for pathways vary by more than ±10% (e.g., there are differences of 28%, 27%, and 23% between the lower and upper WTW emissions results for SAGD, surface mining, and conventional crude, respectively). GHG emissions of the oil sands should be put into perspective considering the full set of WTW activities and, more broadly, emissions from all sectors of the economy. Vehicle operation (i.e., fuel combustion) comprises 64−74% of WTW emissions in our oil sands pathways. Even a 50% reduction in WTT emissions from, for example, the SAGD synbit pathway would only reduce WTW emissions by 16− 26%. Tackling emissions from the personal transportation sector requires a transition to low carbon fuels/energy carriers, increasingly efficient vehicles, and changes in consumer/driver behavior. Actions to lower GHG intensity must be prioritized by efficiency ($/t CO2eq reduced) and effectiveness. Policies that attempt to incorporate life cycle emissions of oil sands pathways would be improved by employing more sophisticated tools and reporting requirements, which will assist policymakers to better understand factors influencing variability and uncertainty across and within projects. Recommended improvements include the following: 1) clearer and more consistent analysis boundaries (both physical and temporal), e.g., a project might have a high SOR because it is at start-up with only the first few wells operating or it might have been operating at a high SOR for several years but is not shutdown because the project is still economically viable; 2) reporting of key operating conditions not typically specified in previous studies, e.g., intermediate products/which pathway is being pursued and type/amount of diluent used; and 3) stating the verification process used and trigger periodic updates as

analysts for further examination of oil sands pathways; and by government to inform policies. The emissions results presented are the first that are based on confidential operating data from oil sands projects, and the observations reinforce the need for an integrated model to evaluate a range of technologies, pathways, and products using a consistent set of assumptions. Potential for GHG Emissions Intensity Improvement. Our sensitivity analyses reveal that for all pathways, emissions associated with steam and electricity generation are the largest contributors to emissions (e.g., responsible for 71−99% of recovery and extraction emissions across all pathways) and the most sensitive parameters overall, followed by emissions associated with hydrogen production for upgrading when this activity is included. These results indicate that the ranges of emissions calculated are driven much more by interproject variability rather than uncertainty in the data or modeling methods; however, further work is required to quantify the specific contribution of each.16 The best performing (lowest GHG intensity) in situ pathways tend to have low SORs (close to 2) and electricity requirements (∼45 kWh/m3 bitumen) and use a high proportion of solution gas (dissolved gas in produced bitumen) (∼12 m3/m3 bitumen). The latter reduces solution gas-related flare or fugitive emissions and lowers indirect emissions associated with natural gas. The best performing surface mining projects have low natural gas, diesel, and electricity requirements (∼20 m3, ∼7 L, and ∼50 kWh/m3 bitumen, respectively). Lowering SORs of in situ projects is a good place to begin to reduce emissions, given SOR’s direct link with on-site natural gas combustion. While a project’s SOR is largely dictated by geological conditions in the reservoir, there is a role for operators to improve performance through better well configuration, scheduling the shut-down of underperforming wells, etc. Many current SAGD projects have SORs close to 2, but no project has been below 2 for any sustained period of time. The steam required by in situ projects can be reduced further through the use of solvents, in situ combustion, or electrothermal technologies, but new technologies such as these also have the potential for unintended consequences and therefore require further assessment. Reducing emissions in surface mining projects is generally more difficult than for in situ projects because there are more sources of emissions, and these sources tend to be dispersed across the project site. As for in situ projects, natural gas use in surface mining projects is also the largest source of emissions but to a lesser extent. Emissions from electricity production and diesel production and combustion, as well as fugitive emissions, play a larger role in surface mining pathways. This implies that most emissions reduction opportunities for these pathways tend to be incremental and will have lesser impact individually (e.g., management of transport logistics on-site). Another opportunity to reduce emissions would be to replace or supplement natural gas with lower carbon fuels. Nuclear, geothermal, biomass, and wind have all been considered to varying extents to provide some or all of the energy and hydrogen requirements in the oil sands. However, each of these energy sources poses its own challenges so there is no obvious replacement for natural gas in the near term. Carbon capture and storage could be applied to oil sands projects, although cost and other factors must be considered.17 In the near-term, it will likely be more feasible to implement incremental process improvements, which are possible for all 7872



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2007_139.cfm&leg_type=Regs&isbncln=9780779738151 (accessed month day, year). (3) Final Statement of Reasons. State of California Air Resources Board: Sacramento, CA, 2009. http://www.arb.ca.gove/regact/2009/ lcfs09/lcfsfsor.pdf (accessed month day, year). (4) Turning the Corner: Detailed Emissions and Economic Modelling Annex 4: The Environment Canada Reference Case to 2020; Environment Canada: Ottawa, ON, 2008. http://www.ec.gc.ca/doc/viragecorner/2008-03/571/Annex4_eng.htm (accessed January 2, 2009). (5) Alberta’s Energy Reserves 2010 and Supply/Demand Outlook 2011−2020; Serial Publication ST98−2011; ISSN 1910-4235; Energy Resources Conservation Board: Calgary, AB, 2011. http://www.ercb. ca/docs/products/STs/st98_current.pdf (accessed October 15, 2010). (6) Oil Sands Technology Roadmap, Unlocking the Potential; Alberta Chamber of Resources: Edmonton, AB, 2004. http://www.arc.ab.ca/ documents/Oil%20Sands%20Technology%20Roadmap.pdf (accessed September 4, 2006). (7) Charpentier, A. D.; Bergerson, J. A.; MacLean, H. L. Understanding the Canadian oil sands industry’s greenhouse gas emissions. Environ. Res. Lett. 2009, 4, 1−11. (8) Charpentier, A. D.; Kofoworola, O. F.; Bergerson, J. A.; MacLean, H. L. Life Cycle Greenhouse Gas Emissions of Current Oil Sands Technologies: GHOST Model Development and Illustrative Application. Environ. Sci. Technol. 2011, 45, 9393−9404. (9) Life Cycle Assessment Comparison of North American and Imported Crudes; Jacobs Consultancy and Life Cycle Assoc. for the Alberta Energy Research Institute: Chicago, IL, 2009. http://eipa.alberta.ca/ media/39640/life%20cycle%20analysis%20jacobs%20final%20report. pdf (accessed month day, year). (10) Comparison of North American and Imported Crude Oil Lifecycle GHG emissions; TIAX LLC for the Alberta Energy Research Institute: Cupertino, CA, 2009. http://eipa.alberta.ca/media/39643/ life%20cycle%20analysis%20tiax%20final%20report.pdf (accessed month day, year). (11) Growth in the Canadian Oil Sands: Finding the New Balance; IHS CERA Inc.: Cambridge, MA, 2009. http://www.cera.com/aspx/cda/ public1/news/pressReleases/pressReleaseDetails.aspx?CID=10329 (accessed February 10, 2010). (12) Brandt, A. R. Variability and Uncertainty in Life Cycle Assessment Models for Greenhouse Gas Emissions from Canadian Oil Sands Production. Environ. Sci. Technol. 2012, 46 (2), 1253−1261. (13) Dunbar, R. B. Canada’s Oil Sands _ A World-Scale Hydrocarbon Resource; Strategy West Inc.: Calgary, AB, 2010. http://www.strategywest.com/downloads/StratWest_OilSands_2010. pdf (accessed month day, year). (14) Natural Resources Canada. GHGenius Version 3.14b; Natural Resources Canada: Ottawa, ON, 2009. http://www.ghgenius.ca. (15) Doluweera, G. H.; Jordaan, S. M.; Moore, M. C.; Keith, D. W.; Bergerson, J. A. Evaluating the Role of Cogeneration for Carbon Management in Alberta. Energy Policy 2011, 39, 7963−7974. (16) Stratton, R. W.; Wong, H. M.; Hileman, J. I. Quantifying Variability in Life Cycle Greenhouse Gas Inventories of Alternative Middle Distillate Transportation Fuels. Environ. Sci. Technol. 2012, 45, 4637−4644. (17) Bergerson, J. A.; Keith, D. The truth about dirty oil: Is CCS the answer? Environ. Sci. Technol. 2010, 44 (16), 6010−6015. (18) Natural Resources Canada. GHGenius Version 3.13a; Natural Resources Canada: Ottawa, ON, 2008. http://www.ghgenius.ca. (19) Wang, M. Greenhouse Gas, Regulated Emissions, and Energy Use in Transportation (GREET) Model, Version 1.8b; Center for Transportation Research, Argonne National Laboratory: Argonne, IL, 2008. http://greet.es.anl.gov (accessed February 1, 2008). (20) Flint, L. Bitumen & Very Heavy Crude Upgrading TechnologyA Review of Long Term R&D Opportunities; LENEF Consulting Ltd: Calgary, AB, 2004. http://www.ptac.org/links/dl/osdfnlreport.pdf (accessed October 7, 2009). (21) Brandt, A. R.; Farrell, A. E. Scraping the bottom of the barrel: greenhouse gas emission consequences of a transition to low-quality and synthetic petroleum resources. Clim. Change 2007, 84, 241−263.

performance changes (e.g., extreme events like outages, a switch of supplier). While the current research has enhanced our understanding of the GHG emissions performance of oil sands pathways, study limitations that could be addressed through future work include the following: 1) GHOST could be expanded to include refining, product transport and vehicle use activities and could link product characteristics directly with GHG intensities; 2) several input parameters could be improved through additional data collection and economics could be incorporated into the analysis to compare the cost-effectiveness of GHG mitigation opportunities throughout the full life cycle; the combinations of the high/low input data to generate ranges could be improved through availability of larger data sets, which would facilitate the use of more sophisticated analytical methods; 3) the processes of collecting and ‘sanitizing’ confidential data as implemented in this work were laborious but could be streamlined by employing a third-party organization; 4) finally, further distinction between uncertainty and variability in emissions ranges will help to determine incentives/policies required to move projects to the lower end of emissions ranges and beyond. The focus on oil sands GHG emissions must be integrated into a more comprehensive view of reducing emissions across the entire economy and expanding this focus to other environmental impacts such as those on air, land, and water. Policies such as LCFSs and a focus on reducing oil sands operations emissions alone is an initial step but will not be sufficient to achieve meaningful long-term environmental policy goals.

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ASSOCIATED CONTENT

S Supporting Information *

Details on methods, emissions, technology systems, sensitivity analysis, and cyclic steam stimulation methods and results. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 403-220-5265. E-mail: [email protected]. Notes

Disclosure: Any opinions, findings, and recommendations expressed in this material are those of the authors. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Alberta Innovates-Energy and Environment Solutions, Natural Resources Canada, Carbon Management Canada, AUTO21 NCEs, and the Oil Sands Industry Consortium for financial support and insights helpful to the research. We also thank David Keith (Harvard University) for helpful feedback on the research.

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REFERENCES

(1) Canada’s Energy Future, Infrastructural changes and Challenges to 2020; National Energy Board: Edmonton, AB, October 2009. http:// www.neb.gc.ca/clf-nsi/rnrgynfmtn/nrgyrprt/nrgyftr/2009/ nfrstrctrchngchllng2010/nfrstrctrchngchllng2010-eng.pdf (accessed month day, year). (2) Climate Change and Emissions Management Act − Specified Gas Emitters Regulation. Alberta Regulation 139/2007; Alberta Government: Edmonton, AB, 2007. http://www.qp.alberta.ca/574.cfm?page= 7873



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(22) Furimsky, E. Emissions of carbon dioxide from tar sands plants in Canada. Energy Fuels 2003, 17, 1541−1548. (23) McCann, T.; Magee, P. Crude oil greenhouse gas life cycle analysis helps assign values for CO2 emissions trading. Oil Gas J. 1999, 97 (8), 38−43.

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Life Cycle Greenhouse Gas Emissions of Current Oil Sands

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