Oil Sands Energy Intensity Assessment Using Facility-Level Data

Jun 27, 2015 - ABSTRACT: The energy intensity and fugitive emissions of oil sands extraction are modeled using detailed public data sets to provide mo...
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Oil Sands Energy Intensity Assessment Using Facility-Level Data Jacob G. Englander,† Adam R. Brandt,*,† Amgad Elgowainy,‡ Hao Cai,‡ Jeongwoo Han,‡ Sonia Yeh,§ and Michael Q. Wang‡ †

Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Green Earth Sciences Building, Room 065, Stanford, California 94305-2220, United States ‡ Systems Assessment Group, Energy Systems Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States § Institute of Transportation Studies, University of California, Davis, 1605 Tilia Street, Davis, California 95616, United States S Supporting Information *

ABSTRACT: The energy intensity and fugitive emissions of oil sands extraction are modeled using detailed public data sets to provide more accurate estimates of energy use. Facility-level energy consumption and environmental emission data are collected on a monthly basis for 24 operating oil sands projects (7 mining projects and 17 in situ projects) over the periods of 2005−2012 (for mining projects) and 2009−2012 (for in situ projects). This is the most detailed data set used to date for greenhouse gas (GHG) assessment from the oil sands and relies entirely on data from government data sets. Monthly facility-level data are aggregated into four pathways, depending upon the mode of primary extraction (i.e., mining or in situ) and the type of product exported [i.e., bitumen or synthetic crude oil (SCO)]. Large variability is found among pathways and between projects within each pathway. Energy intensity ranges from 0.1 GJ/GJ of bitumen for mining projects to 0.4 GJ/GJ of SCO for in situ projects. Month-to-month variability (p10−p90) ranges from −15 to +15% for mining to SCO pathways and from −15 to +11% for in situ to bitumen pathways. These four pathways are developed to implement in the greenhouse gas, regulated emissions, and energy in transportation (GREET) life-cycle model.

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INTRODUCTION The Canadian oil sands have grown rapidly in importance over the past 15 years, with production increasing from 540 to 2087 thousand barrels per day (kbbl/day).1,2 Oil sands operations have faced scrutiny because of higher energy inputs for extraction than conventional oil recovery operations, land-use impacts, and water-quality concerns.3−19 The importance of examining these life-cycle impacts of oil production are heightened by regulations, such as the California Low Carbon Fuel Standard (LCFS) and the European Union Fuel Quality Directive (FQD). These regulations require an understanding of the energy and greenhouse gas (GHG) intensity of liquid hydrocarbon fuel feedstocks on a well-to-wheels (WTW) or fuel-cycle basis.3,20,21 These regulations increasingly focus on different hydrocarbon feedstocks and their relative impacts.22 The intent of this analysis is to provide clarity on the energy inputs required to produce bitumen from the Alberta oil sands using operator-reported data.1,2 Leveraging of data reported by operators to government agencies is a step forward for the transparent analysis of oil sands GHG intensity. Given recent controversy over the GHG intensity of oil sands operations (e.g., Keystone XL pipeline debate), this approach is to be preferred over proprietary models or models using private data sources. Hydrocarbons can be extracted from oil sands in three ways: surface mining, thermal in situ production, and primary production. Reservoirs near the surface are typically mined using shovel and truck extraction methods. Deeper resources are generally extracted using thermal in situ methods. These include cyclic steam stimulation (CSS) and steam-assisted © 2015 American Chemical Society

gravity drainage (SAGD). CSS uses one vertical well, where steam is injected for a period and bitumen is subsequently produced from the same well. SAGD uses two horizontal wells; steam is injected into the top well to warm the bitumen, reducing the viscosity and allowing it to flow by gravity to the lower horizontal well, where it is drawn to the surface. A small fraction of bitumen is extracted using primary production methods, where sand is allowed to be co-produced with the oil (this is also known as cold production).23 After extraction, there are multiple pathways from which bitumen is refined into products. The most important pathways are upgrading of bitumen to pipeline-ready synthetic crude oil (SCO) and mixing of bitumen with diluent (dilbit), so that it can flow to refineries for processing. A variety of models have quantified the energy intensity of liquid fuel production; however, few have detailed distinct pathways for oil sands production operations.22−27 To date, life-cycle assessment (LCA) analyses of GHG and energy intensity of the oil sands have faced methodological challenges.3,11,23−29 These assessments typically relied on non-public, operator-supplied data, which are often not representative, sometimes covering only a short period of time or a subset of operating facilities.23,28,30 In addition, previous studies often aggregate oil sands production into two broad pathways: surface mining and in situ production. For example, the greenhouse gases, regulated emissions, and energy Received: January 27, 2015 Revised: June 25, 2015 Published: June 27, 2015 5204

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Figure 1. System boundary for the M + SCO (left) and IS + Bit (right) pathways.2 (see Tables S2−S6 of the Supporting Information for input data sources and characteristics). These data sets provide detailed energy consumption data on a project-by-project basis, reported monthly. We include 24 projects that are classified as either mining or thermal in situ projects (7 and 17 projects, respectively). We include all projects that produced commercial quantities of bitumen during the study period. Primary production is not included in this study because no data were reported on energy consumption for these projects (see the Supporting Information for more detail on data handling, data availability, and sources of data uncertainty).2 Data for mining and in situ projects are from the time periods of 2005−2012 and 2009−2012, respectively. In situ project data in this study begin in 2009 when detailed energy consumption data for in situ projects began to be reported. These extended time periods allow for the assessment of operation variability over time. Any recent changes are of interest because they can be placed within the context of the significant long-term changes in energy use over time within the industry.11 It should be noted that these data are reported values to regulators, and there is likely underlying uncertainty in the data, distinct from variability studied below. For example, uncertainty could be introduced through misreporting of information, measurement uncertainty, incomplete reporting requirements, or assumptions about composition and energy contents of consumed fuels. We discuss these possible sources of uncertainty in more detail in the Supporting Information but note that assessing the importance and prevalence of these sources would require access to currently unavailable proprietary data sets. System Boundary and Functional Unit. Our system boundary includes direct consumption and flaring of all primary fuels and electricity at production sites (e.g., in gigajoules of fuel consumed/ gigajoule of hydrocarbon output). Emissions associated with tailings ponds and fugitive emissions associated with crude bitumen batteries from in situ production are also included. Emissions from tailings ponds were determined from the literature, while emissions from crude bitumen batteries were calculated from reported data (see the Supporting Information for further discussion). We do not include loss in off-site electrical generation, because this would be modeled externally within the larger GREET model. Additionally, we do not include energy used to transport bitumen from a stand-alone mine to a stand-alone upgrader, embodied in infrastructure build up or in capital equipment, such as wells, trucks, or upgraders, because of the lack of available data. Figure 1 illustrates the study system boundaries. The functional unit in this study is 1 GJ of hydrocarbon output at the project output gate (i.e., before transport to a refinery). All results are presented on a lower heating value (LHV) basis. Dependent upon the pathway, the hydrocarbon output is SCO or raw bitumen.

use in transportation (GREET) model (i.e., up to version 1_2014) provided energy consumption for these two pathways. These challenges have prompted a need for further research quantifying energy consumption from oil sands operations. To address these challenges, we assemble a large data set from public sources, which include all operating oil sands projects. This data set relies on transparent, publicly available operator data that are reported as part of regulatory requirements, generally on a monthly basis. Also, including all operating oil sands projects allows us to better understand the drivers of variability in energy intensities of the industry as a whole. These data are used to generate energy intensities for four oil sands production pathways. These pathways are developed to replace the previously existing oil sands pathways for the GREET model. Previous work using similar data examined the historical trends in energy intensity within the aggregated oil sands industry. This paper, in contrast, builds project-level intensity metrics over recent years and sums these project-level results into four representative pathways for use in the GREET life-cycle GHG intensity model. See the Supporting Information for further discussion of the key improvements and increased utility of this work.14 By modeling four pathways, we allow for a greater understanding of the range of energy intensities for oil sands production. In this study, we include four oil sands pathways: (1) surface mining to bitumen, (2) surface mining to SCO, (3) in situ to bitumen, and (4) in situ to SCO. Calculating the energy intensities of our four oil sands pathways, along with the recent improvement within GREET in U.S. refinery modeling [which allows for estimations of energy and GHG intensity based on feedstock parameters, such as American Petroleum Institute (API) gravity and sulfur content], will enable improved estimates of the life-cycle energy use of U.S. petroleum products generated from oil sands feedstocks.18,31 The result of these combined efforts, which provides a detailed treatment of the life-cycle GHG impacts of these oil sands pathways, can be found in the study by Cai et al.32 Also, using these pathways in conjunction with planned capacity additions, the GHG implications of future oil sands output can be explored.

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METHODS

Data Gathering and Processing. We use energy production and consumption data reported by the Alberta Energy Regulator (AER), formerly known as the Energy Resources Conservation Board (ERCB) 5205

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Energy & Fuels Project-Level Computation and Aggregation to Pathways. First, detailed energy intensities are computed for each project by computing output-specific energy intensities (EI) for a given project EIfp,SCO =

EIfp,Bit =

Ffp PpSCO Ffp

PpBit

⎛ GJ of fuel ⎞ ⎜ ⎟ ⎝ GJ of SCO ⎠

(1)

⎛ GJ of fuel ⎞ ⎜ ⎟ ⎝ GJ of bitumen ⎠

(2)

Resulting energy intensities for M + SCO represent an aggregation of these different technologies in proportion to their production volume. Equation 3 shows the production-weighted average energy intensity calculation for the M + SCO pathway.

⎞ ⎛ PpSCO ⎟ EIfp,SCO⎜⎜ SCO ⎟ p ∈ M + SCO ⎝ ∑p ∈ M + SCO Pp ⎠ ⎛ GJ of fuel ⎞ ⎜ ⎟ ⎝ GJ of SCO ⎠

EIf,M + SCO =

where F is the process fuel consumed, P is the amount of output product produced, f is the index of fuel types consumed, p is the index of projects, and SCO or Bit is the output product produced. This is also the case for flared fuel (see the Supporting Information for more detail). Note that Bit is raw bitumen before the addition of diluent. While diluent production does require energy, in this analysis, diluent is treated as a carrier or as a substance, which is used only in its ability to transport bitumen and, thus, is not part of the system boundary for producing bitumen. These project-specific computations result in a large amount of data (24 projects over 48−96 months, with multiple fuel sources for each project). These project-specific results would be impractical to implement in LCA models, such as GREET. We therefore examine three modeling options for aggregating projects into pathways: (1) projects aggregated into a single industry-average pathway. (2) projects aggregated on the basis of the production method, creating mining and in situ pathways, and (3) projects aggregated on the basis of both the production method (e.g., mining or in situ) and output product (e.g., SCO or dilbit). Modeling option 1 is too coarse to appropriately model oil sands processes and products. While modeling option 2 provides more specificity, it does not distinguish between output hydrocarbon product quality and would require assuming that all mining processes produce SCO, while in situ processes produce dilbit (bitumen thinned by an added diluent, typically natural gas condensate).24,29,33,34 This is a useful approximation, because these are the most historically prevalent pathways. However, as refineries have become better able to accept bitumen as either dilbit or synbit (bitumen mixed with diluent or bitumen mixed with SCO), this approximation breaks down.18 In 2012, 7% of products sent from upgraders is either dilbit or synbit, and there are in situ projects that send their products to upgraders (e.g., Suncor-Firebag) or are integrated with surface-upgrading facilities (e.g., Nexen-Long Lake). Thus, modeling option 2 also does not represent oil sands production practices correctly. Modeling option 3 is used for this analysis, which aggregates projects by both the production method (mining or in situ) and output product (SCO or diluted bitumen). This results in four pathways. This allows for increased fidelity because each pathway produces more uniform products (e.g., projects that produce SCO are classified together). Consequently, the accuracy is increased when accounting for refinery energy use as a function of the refinery input product, given energetic differences in SCO and dilbit refining. The assignment of each project into one (or more) of the four pathways is given in Table S1 of the Supporting Information. Pathway Implementation. Project-specific energy intensities are aggregated to generate pathway-specific weighted fuel energy intensities, accounting for both production technology and output product. These four pathways are defined as (1) mining + SCO (M + SCO), producing SCO, (2) mining + bitumen (M + Bit), producing diluted bitumen (dilbit, synbit, or dil−synbit), (3) in situ + SCO (IS + SCO), producing SCO, and (4) in situ + bitumen (IS + Bit), producing diluted bitumen (dilbit, synbit, or dil−synbit). Approximately 90% of primary bitumen production occurs in M + SCO and IS + Bit pathways. Variation exists between output products produced at a given project (e.g., varying grades of SCO can be sold by the same project) and across projects within a pathway. Uncertainty associated with this variability is discussed below. Mining to SCO. In the case of the M + SCO pathway, upgraders produce SCO via delayed coking, fluid coking, and/or hydrocracking.

∑

(3)

Here, project-level energy intensities are weighted by SCO output, because this is the primary output of the M + SCO pathway. In Situ to Bitumen. The IS + Bit pathway is modeled similarly to the M + SCO pathway.

EIf,IS + Bit =

⎞ ⎛ PpBit ⎟ EIfp,Bit⎜⎜ Bit ⎟ ⎝ ∑p ∈ IS + Bit Pp ⎠ p ∈ IS + Bit

∑

⎛ GJ of fuel ⎞ ⎜ ⎟ ⎝ GJ of bitumen ⎠ (4)

Here, project-level energy intensities are weighted by bitumen output, because this is the primary output of the IS + Bit pathway. We do not include the volume of diluent in this weighting term. The volume of diluent blended with the raw bitumen will vary depending upon the project and grade of desired output product. Mining to Bitumen. Bitumen is exported as a minor product from the Suncor-MSV (an integrated mine and upgrader) as well as from Shell-Scotford (a stand-alone upgrader). The source of this exported bitumen is not recorded in production statistics, except as an overall output from the upgrading facility. When reporting energy use for integrated surface mining and upgrading facilities, AER data do not distinguish between energy use for surface mining and upgrading processes.1 This is not the case for Syncrude-Aurora, Albian SandsMuskeg River, and Shell-Jackpine, which are stand-alone mines (see Table S1 of the Supporting Information for further description). Instead, we model the mining portion of the M + Bit pathway using production and consumption data from the major non-integrated, stand-alone mines (i.e., Albian Sands-Muskeg River, Shell-Jackpine, and Syncrude-Aurora). A similar approach is used by the GHGenius model.35 While these projects currently do not export bitumen directly to market in significant quantities, they are the best proxy for mining energy use in a stand-alone M + Bit pathway. Equation 5 illustrates the calculation of energy use in this pathway.

EIf,M + Bit =

⎞ ⎛ PpBit ⎟ EIfp,Bit⎜⎜ Bit ⎟ ∑ P p ⎠ ⎝ p ∈ M + Bit p ∈ M + Bit

∑

⎛ GJ of fuel ⎞ ⎜ ⎟ ⎝ GJ of bitumen ⎠ (5)

As surface mining projects designed to export large quantities of raw bitumen come online (e.g., the Imperial-Kearl mine), greater fidelity in modeling this pathway will be possible. Because of data gaps, some complexity emerges in using this equation. Natural gas and electricity use are reported by all modeled mines; therefore, the equation can be used directly for these energy intensities. However, the Albian Sands-Muskeg River and ShellJackpine facilities do not report diesel use for truck fuel, although they do consume such fuel.36 To model diesel consumed, we define an energy intensity for diesel (GJ of diesel/GJ of bitumen produced) as the average of the diesel energy intensity of Suncor-MSV, SyncrudeMildred Lake, and Aurora for all mining projects (see the Supporting Information). In Situ to SCO. In situ produced bitumen is upgraded to SCO in a limited number of cases. Bitumen from the Suncor-Firebag in situ project is upgraded at the Suncor-MSV integrated mine and upgrader, and the integrated Nexen-Long Lake project produces upgraded SCO onsite and uses the asphaltene byproducts of upgrading to fuel steam generation for in situ recovery. Nexen-Long Lake energy use for bitumen production and upgrading is reported fully in AER statistics. Note that Nexen-Long Lake is a unique facility configured quite 5206

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Figure 2. Project-level energy intensity variability and average fuel mix for projects for all available months in the mining + bitumen and mining + SCO pathways. (Top) Boxplot of month-to-month variability of total energy intensity per project. (Bottom) Average energy intensity for each project divided by the fuel mix.2

⎛ ⎞ P Bit SCO SCO ⎜ Bit SunFBBit ⎟ PSunFB = PSunMSV ⎝ PSunFB + PSunMSV ⎠

differently from others. Because of its relatively small production volume, it does not strongly affect the aggregate results for the industry. In contrast, the Suncor-MSV facility statistics do not distinguish between energy used in the mine and the upgrader (see above). To approximate the requirements of upgrading of SuncorFirebag in situ bitumen at Suncor-MSV, the following steps are performed: (1) The Suncor-MSV energy intensity for SCO/diesel is removed, because this is known to be used in mining trucks. (2) For natural gas and electricity, the energy intensity per unit of bitumen produced from the Syncrude-Aurora mine is used to adjust the energy intensity of the combined mining and upgrading facility (e.g., the natural gas intensity for Syncrude-Aurora is subtracted from the total natural gas and electricity intensity for the integrated mine and upgrader at Suncor-MSV). The remaining fuel consumed per unit of bitumen produced is an approximation of the requirements to upgrade Suncor-Firebag in situ bitumen to SCO at Suncor-MSV. This method is illustrated for natural gas (ng) in eq 6.

SCO PSunMSV

⎛ GJ of ng ⎞ ⎜ ⎟ ⎝ GJ of SCO ⎠

(7)

⎞ ⎛ PpSCO ⎟ EIfp,SCO⎜⎜ SCO ⎟ ∑ P p p ∈ IS + SCO ⎠ ⎝ p ∈ IS + SCO ⎛ GJ of fuel ⎞ ⎜ ⎟ ⎝ GJ of SCO ⎠

EIf,IS + SCO =

∑

(8)

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RESULTS Project-Level Energy Use. Project-level energy intensities in the mining pathways (M + Bit and M + SCO) are shown in Figure 2, and the energy intensities for the five largest in situ projects can be found in Figure 3 (this represents 65% of in situ production). Note that each project exhibits significant variability from observation to observation (i.e., month to month; see top panels of Figures 2 and 3). Also, projects within a given pathway can consume significantly different fuel shares (e.g., compare coke use across M + SCO projects; see bottom panel of Figure 2). Historical Trends. The overall energy intensities for each pathway represent volume-weighted average fuel intensities over the time period of analysis (2005−2012 for mining projects and 2009−2012 for in situ projects). These timedependent variations can be seen for the M + SCO and the IS + Bit pathways in panels a and b of Figure 4. Each data point

⎛ Fng,SunFB ⎞ EIng,SunFB,SCO = ⎜ Bit + EIng,SunMSV,Bit − EIng,SynAur,Bit⎟ ⎝ PSunFB ⎠ Bit PSunMSV

(GJ of SCO)

(6)

A similar approach was used in the GHGenius model to estimate stand-alone upgrading energy use.27 The resulting intensities for Suncor-Firebag and Nexen-Long Lake are combined with a production-weighted average energy intensity as used in the other pathways. 5207

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Figure 3. Project-level energy intensity variability and average fuel mix for the five largest in situ projects (representing 65% of in situ production for 2009−2012). All of these projects are included in the IS + Bit pathway. (Top) Boxplot of month-to-month variability of total energy intensity per project. (Bottom) Average energy intensity for each project divided by the fuel mix.

Figure 4. Output volume-weighted energy intensity for (a) mining + SCO pathway, 2005−2012, and (b) in situ + bitumen pathway, 2009−2012.

intensity value are shown in Figure S9 of the Supporting Information. Long-term trends for the M + SCO pathway have been found to show an overall decline in coke consumption over prior decades, because it is substituted by natural gas.11,14 Natural gas consumption appears to vary periodically, peaking

represents the volume-weighted pathway energy intensity for each fuel in a month. Figure 4a shows that the overall energy intensity ratios of the M + SCO pathway exhibit relatively stable time-dependent trends over the study period. Histograms of each energy 5208

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Figure 5. Energy intensity for four oil sands production pathways (EIf,M + Bit, EIf,M + SCO, EIf,IS + Bit, and EIf,IS + SCO).

Table 1. Energy and Flaring Intensities (GJ of Fuel Consumed/GJ of Refinery Input) by Pathway, Mean Results from 2005 to 2012, with 10th to 90th Percentiles (p10−p90) mining + bitumen share of production (%) (2009−2012)

3.4 mean

coke SCO produced gas fuel gas fuel gas for H2 natural gas natural gas for H2 net electricity total

diluent produced gas fuel gas natural gas tailings and mine face fugitives bitumen batteries

0.02

p10 0.01

p90 0.03

0.06

0.05

0.08

−0.005 0.08

−0.008 0.06

−0.002 0.1

mean

p10

p90

0.005

in situ + bitumen

mining + SCO 56.0 Fuel Consumption mean p10 0.03 0.02 0.02 0.01 0.12 0.1 0.02 0.01 0.08 0.06 0.05 0.04 −0.003 −0.006 0.32 0.27 Flaring and Fugitives mean p10 0.003 0.002 0.002 0.0005 0.006

0.001 0

in situ + SCO

34.7 p90 0.05 0.03 0.14 0.03 0.11 0.05 0 0.37 p90 0.003

mean

5.9 p10

p90

0.03

0.02

0.03

0.17

0.15

0.20

−0.002 0.20

−0.003 0.17

−0.0001 0.22

mean

p10

p90

0.0007

0.0002

0.004 0.001

0.002

mean 0.04

p10 0.03

p90 0.06

0.002 0.08 0.04 0.23 0.02 −0.0006 0.41

0.001 0.07 0.03 0.22 0.01 −0.005 0.38

0.003 0.09 0.07 0.25 0.02 0.0047 0.45

mean 0.002 0.0001 0.004 0.03

p10 0.0017 0.00 0.002 0.006

p90 0.0021 0.0001 0.007 0.05

0.007

Aggregated Fuel Pathways. The resulting fuel intensities aggregated for each pathway EIf are presented in Figure 5, which plots mean values of EIf by pathway over the study period. The uncertainty range for each pathway represents the 10−90 percentile range for the overall energy intensity for each pathway, with each month of data representing an observation (96 observations for the mining pathways and 48 observations for the in situ pathways). As highlighted in Figure 5, there are significant differences between the four different oil sands production pathways. The mining operations are more energy-efficient than the in situ operations for both bitumen and SCO production, even though there are a higher energy inputs required during the upgrading process for mining operations than from in situ production. Table 1 presents detailed fuel-specific consumption results, along with the product shares of bitumen and SCO from mining and in situ operations. Flaring rates are also included in Table 1.

in the winter months. More recent trends include a decline in fuel gas consumption, which has coincided with an increase in fuel gas for hydrogen generation, an increase in natural gas consumption, and a slight decrease in net electricity exports. Although the production-weighted intensity values have not changed significantly, there have been some changes in the upgrading technology used. There has been a decrease in fluid coking, which has been replaced by delayed coking. This shift appears to be reflected in the decrease in energy intensity for fuel gas shown in Figure 4a. As Figure 4b demonstrates, trends for the IS + Bit pathway are less apparent. In 2012, there was a decline in natural gas consumption as well as a slight decrease in produced gas consumption. It is unclear if such trends will continue. The increase in SAGD as a share of in situ production over CSS has allowed for more efficient extraction, because SAGD typically has lower steam requirements per unit of bitumen produced, as seen in Figure 3 (see Figure S10 of the Supporting Information for further illustration). 5209

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Figure 6. Energy consumption for each pathway, including results from this study, GHGenius version 4.03a, GHOST, and Jacobs 2012.

generator (OTSG) with gas lift (low and high ranges are given by SOR = 2 bbl/bbl, OTSG with mechanical lift for the low case, and SOR = 4 bbl/bbl, OTSG with gas lift for the high case). These values are fairly representative of current SAGD operations. Comparing these study results to GHGenius results shows that intensities are largely similar for three of the four pathways. One notable difference includes increased natural gas consumption for the IS + Bit pathway in GHGenius, although GHGenius uses a similar method to this analysis for determining SOR values (which is a SOR of 3.9 for CSS and 3.0 for SAGD).27 When examining the IS + Bit pathway, a major difference lies in the treatment of produced gas. We found a large volume of produced gas consumed in cyclic steam stimulation projects. GHGenius in its modeling does not account for produced gas for CSS projects, although it does aggregate produced and natural gas consumption for SAGD production.27 Another difference with GHGenius is that it does not account for electricity co-generation. The IS + SCO pathways also have notable differences, because GHGenius has a higher consumption of natural gas and electricity, while this study has a slightly higher coke consumption and flared gas rates. These differences could be due to data vintage, pathway definitions, or the choice of in situ projects modeled. As discussed in the Methods, the attribution of energy consumption of upgrading for in situ production in this work required some judgment. Also, it is also possible that in situ projects used by GHGenius for the IS + SCO pathway have different SORs and, therefore, different natural gas consumptions. This occurs because the IS + SCO pathway defined here uses the two in situ projects, which currently send bitumen to upgraders to produce SCO (Suncor-Firebag and Nexen-

Because the fuel intensities from Table 1 do not include the refinery efficiency differences between the pathway outputs (i.e., bitumen versus SCO), they are not indicative of total WTW impacts for a given oil sands production pathway.18 In general, refinery energy intensity that varies with input product quality will reduce some of the difference shown here between bitumen and SCO pathways (e.g., higher upgrading energy intensity will be somewhat offset by lower refining energy intensity for SCO).32

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DISCUSSION We compare the energy intensities provided in this report to those from the Jacobs report for the Alberta Petroleum Marketing Commission, GHGenius model version 4.03a, and the GHOST model.23,25−27 Although each of these models relies in part on operator provided data, they do not reflect the entire oil sands industry. Additionally, GHGenius was the only model that uses publicly available data that could be directly compared to this analysis. The results of this comparison can be seen in Figure 6. Tabular comparisons are presented in Table S7 of the Supporting Information. The Jacobs report, which relies on proprietary information in both its data gathering and modeling, provides energy intensity for the M + Bit and the IS + Bit pathways. High- and lowefficiency mining cases were constructed by Jacobs for different technologies. The M + Bit case from this study is on average higher than the results from Jacobs. The Jacobs results show large variability between high and low energy intensities. Our results report higher natural gas and lower electricity use when compared to the Jacobs study. The Jacobs IS + Bit results presented here are for the case where the steam to oil ratio (SOR) = 3 bbl of water as steam/bbl of oil, once-through steam 5210

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Energy & Fuels

estimates of energy intensity, fuel shares, flaring, and fugitive emission rates from oil sands operations represents an improvement over methods based on proprietary figures. The use of operator-reported data provides clarity on the energy production and consumption during bitumen production. Second, we increase the fidelity of oil sands modeling by defining four distinct production pathways rather than two aggregate production pathways implemented in previous studies. This provides a better reflection of the current state of the oil sands industry, because there are multiple products exported from oil sands producers to refineries.38−40 Third, project specificity allows us to incorporate operational variability between projects, including month-to-month variability in energy requirements, allowing for more rigorous uncertainty analysis. The result of this work can be used by scientists and policy analysts to compare the energy and greenhouse gas intensity of the Alberta oil sands to other transportation fuel pathways. These data are included in GREET model version 2014_1 developed at Argonne National Laboratory and are able to be used in directly comparing oilsands-derived crude oils to all other major transportation fuels.32

Long Lake), while GHGenius uses pathway average in situ consumption values. Another difference between this study and GHGenius is the larger energy use for the M + SCO pathway in this analysis. The reasons for this discrepancy are not clear. The discrepancy appears to be similar in magnitude to the energy used for H2 production in the form of natural gas and fuel gas (noted in AER statistics as gas used for “further processing”). The results of the comparison to the GHOST model are also shown in Figure 6. Reported GHOST processes do not align in all cases with pathway definitions in this analysis.25,26 We therefore create GHOST cases for the comparison shown in Figure 6 as follows: (1) GHOST mining figures are compared to our M + Bit pathway, as defined above. (2) GHOST-delayed coking and hydrocracking are aggregated on a volume-weighted basis to compare to our M + SCO pathway with a weighting of 78% to coking and 22% to hydrocracking (representing the shares of these technologies in the outputs of this pathway over the modeled time period). (3) GHOST SAGD case is compared in the table to our IS + Bit pathway. For the GHOST mining and upgrading cases, we combine the GHOST mining results with the GHOST upgrading results, adjusting mining energy consumption for the volumetric gain/ loss associated with upgrading (m3 of SCO/m3 of bitumen).26 Note that reported GHOST cases with co-generation have very large electricity outputs, representing a “total potential” for cogeneration. It is unclear how these co-generation cases can be compared to our study results; thus, instead, we use results from co-generation for a “base co-generation” case, where only enough power as needed on site is generated via cogeneration.37 A few differences exist between GHOST and the results of this study. The overall uncertainty ranges for GHOST are wider than those for this study. One reason for this may be that our study calculates energy intensities using volume-weighted average time series, while GHOST calculates results for a limited number of projects. Reported energy use in hydrocracking-based upgrading is significantly larger than those from the GHOST model. GHOST does not include coke consumption for the M + SCO pathway, while reported fuel consumption does show coke consumption by upgraders. The IS + Bit pathway in this analysis has higher energy intensity than the GHOST SAGD pathway. This difference is due to the inclusion of CSS in this analysis, which is not modeled in GHOST. Among thermal in situ methods, CSS tends to have higher energy intensity than SAGD because of generally higher SORs (see the Supporting Information). Reported statistics show that SAGD accounts for 53% and CSS accounts for 47% of the IS + Bit pathway; therefore, GHOST excludes a significant source of thermal IS bitumen. Also, flaring and fugitive gas emissions for SAGD are larger in this analysis than in GHOST. This is likely because our study includes data on flaring and venting emissions from crude bitumen batteries, using data reported in AER ST-60B (see the Supporting Information), while the GHOST model does not include this emission source. In summary, differences between the GHOST model and this analysis appear to result from the broader and more comprehensive data set used in this analysis (i.e., GHOST focuses on modeling fewer projects).

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ASSOCIATED CONTENT

S Supporting Information *

Developments from Brandt et al. and Englander et al., included projects, data, in situ projects, energy contents of fuels, project level intensities, results, limitations of analysis, and references (PDF). The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/ acs.energyfuels.5b00175.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors appreciate the useful feedback and suggestions provided by Don O’Connor, (S&T)2 Consultants, Inc., Joule Bergerson, University of Calgary, and Heather MacLean, University of Toronto. The authors thank the Bioenergy Technologies Office and the Vehicle Technologies Office of The Energy Efficiency and Renewable Energy Office of U.S. Department of Energy (DOE) for Awards 3F-30822 (to Jacob G. Englander and Adam R. Brandt) and 3F-30841 (Sonia Yeh).

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REFERENCES

(1) Energy Resources Conservation Board (ERCB). ST39: Alberta Mineable Oil Sands Plant Statistics Monthly Supplement; ERCB: Calgary, Alberta, Canada, 2015. (2) Energy Resources Conservation Board (ERCB). ST53: Alberta Crude Bitumen ProductionMonthly Statistics; ERCB: Calgary, Alberta, Canada, 2015. (3) 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. (4) Yeh, S.; Zhou, A.; Hogan, S. Spatial and Temporal Analysis of Land Use Disturbance and Greenhouse Gas Emissions from Canadian Oil Sands Production; Argonne National Laboratory: Argonne, IL, 2014. (5) Parajulee, A.; Wania, F. Evaluating officially reported polycyclic aromatic hydrocarbon emissions in the Athabasca oil sands region with

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CONCLUSION This study improves upon previous studies of the energy intensity of the oil sands in multiple ways. First, the use of publicly available and transparent data to generate detailed 5211

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Article

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