Statistically Enhanced Model of In Situ Oil Sands Extraction

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Statistically Enhanced Model of In Situ Oil Sands Extraction Operations: An Evaluation of Variability in Greenhouse Gas Emissions Andrea Orellana, Ian J. Laurenzi, Heather MacLean, and Joule A. Bergerson Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04498 • Publication Date (Web): 12 Dec 2017 Downloaded from http://pubs.acs.org on December 21, 2017

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Environmental Science & Technology

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Statistically Enhanced Model of In Situ Oil Sands

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Extraction Operations: An Evaluation of Variability

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in Greenhouse Gas Emissions

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Andrea Orellanaβ, Ian J. Laurenziδ, Heather L. MacLeanγ and Joule A. Bergerson*,β

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β

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Dr NW, Calgary, AB T2N 1N4, Canada

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δ

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3059, USA

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γ

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Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University

ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, NJ 08801-

Departments of Civil Engineering, Chemical Engineering and Applied Chemistry, School of

Public Policy and Governance, University of Toronto, Toronto, Ontario, Canada M5S 1A4

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TOC Art

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ABSTRACT

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Greenhouse gas (GHG) emissions associated with extraction of bitumen from oil sands can

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vary from project to project and over time. However, the nature and magnitude of this variability

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have yet to be incorporated into life cycle studies. We present a statistically enhanced life cycle

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based model (GHOST-SE) for assessing variability of GHG emissions associated with the

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extraction of bitumen using in situ techniques in Alberta, Canada. It employs publicly-available,

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company-reported operating data, facilitating assessment of inter- and intra-project variability as

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well as the time evolution of GHG emissions from commercial in situ oil sands projects. We

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estimate the median GHG emissions associated with bitumen production via cyclic steam

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stimulation (CSS) to be 77 kg CO2eq/bbl bitumen (80% CI: 61 – 109 kg CO2eq/bbl), and via

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steam assisted gravity drainage (SAGD) to be 68 kg CO2eq/bbl bitumen (80% CI: 49 – 102 kg

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CO2eq/bbl). We also show that the median emissions intensity of Alberta’s CSS and SAGD

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projects have been relatively stable from 2000 to 2013, despite greater than six-fold growth in

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production. Variability between projects is the single largest source of variability (driven in part

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by reservoir characteristics) but intra-project variability (e.g., startups, interruptions), is also

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important and must be considered in order to inform research or policy priorities.

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INTRODUCTION

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The bitumen in Alberta’s oil sands make up 97% of Canada’s oil reserves, which are the third

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largest proven reserves in the world (approximately 168 billion barrels) following only those of

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Saudi Arabia and Venezuela [1]. Canadian bitumen production was 2.3 million bbl/day in 2014

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and has been predicted to increase to 3.7 million bbl/day by 2030 [2]. More than half of

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Alberta’s bitumen is produced using in situ recovery techniques, among which Steam Assisted

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Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) are the most common. Steam is

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employed in these techniques to heat the bitumen – and thereby reduce its viscosity, so that it

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may be produced from the subsurface reservoir. Once extracted from sand and water, bitumen

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must be either diluted or processed (“upgraded”) before transporting it to refineries. Steam

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generation, on-surface activities (e.g., water treatment) as well as upgrading operations result in

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greenhouse gas (GHG) emissions. The result is generally higher GHG emissions than

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“conventional” crudes on average [3], although there is significant overlap in carbon intensity.

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Moreover, for in situ oil sands production, there isadded complexity to how emissions are

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estimated for different crudesThis has in turn motivated the development of improved models to

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make these assessments.

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Confounding the issue of GHG emissions from oil sands is the variability in emissions from

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project to project as well as over time. Variability is differentiated from uncertainty in that it

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reflects systematic differences between processes, locations or in time while uncertainty is

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associated with lack of data or an incomplete understanding. There are several sources of

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variability in GHG emissions of in situ oil sands operations. Emissions may vary due to

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differences in production practices and reservoir properties (inter-project variability). They may

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also vary due to differences in the maturity of the operating field and operating conditions (intra4 ACS Paragon Plus Environment

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project variability) [3, 4]. For example, SAGD may require different amounts of energy –

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resulting in different GHG emissions – at startup, at steady state and at the end of the project’s

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life.

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Several peer-reviewed life cycle assessments (LCAs) have attempted to quantify the GHG

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emissions associated with transportation fuel products derived from oil sands [5, 6, 7, 8] and

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several government agencies have released public tools that include oil sands pathways among a

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suite of fuel pathways [9, 10, 11]. However, these studies have generally focused on the

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development of a single “point estimate” for the life cycle GHG emissions associated with fuels

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derived from oil sands. Ranges of GHG emissions associated with alternative oil sands

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production technologies were first estimated with the “Greenhouse gas emissions of current oil

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sands technologies” (GHOST) model [3, 12], which employed confidential operator data.

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However, the GHOST model did not quantify the distributions of GHG emissions associated

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with these technologies, nor did it assess the representativeness of the ranges. A few subsequent

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studies have attempted to better understand the variability and uncertainty associated with oil

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sands pathways [13, 14, 15]. However, no study has comprehensively addressed the statistical

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attributes of the ranges of GHG emissions estimates that may result from different oil sands

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extraction pathways, or the temporal nature of GHG emissions over a project’s lifetime.

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In this paper, we report the findings of a study of the variability associated with in situ oil

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sands extraction as estimated by a statistically-enhanced version of the aforementioned GHOST

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model (GHOST-SE). This research improves the statistical representation of operating data and

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input parameters using publicly-available data sets and improved process calculations, and

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generates project-specific distributions of GHG emissions associated with in situ oil sands

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extraction (i.e., the extraction of raw bitumen). We then apply the model to explore inter- and 5 ACS Paragon Plus Environment


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intra-project variability, including the nature and magnitude of the variability, historical GHG

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emissions trends and the effects of operating parameters. When combined with robust estimates

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of GHG emissions associated with pipeline transportation, refining, and the combustion of fuels

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refined from oil sands (60% to 80% of the life cycle GHG emissions [3]), our results may be

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utilized to assess the life cycle GHG emissions associated with oil sands, facilitating comparison

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with other energy sources on a life cycle basis.

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METHODS

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Our analysis of the variability associated with in situ oil sands extraction was conducted by

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improving the GHOST model. The new statistically-enhanced model, GHOST-SE, is

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distinguished from GHOST by the following features:

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1. Use of publicly-available operating data from the Alberta Energy Regulator [16].

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These data include production, steam generation, and electricity usage time series

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throughout major oil sands project lifetimes. Data are disaggregated on a per project

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and per month basis over the life of each project,

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2. Improved process calculations, related primarily to surface facility activities, such as optional cogeneration, and

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3. Monte Carlo (MC) simulation to representatively select operating data and parameters

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for the purposes of generating statistically-meaningful ranges of GHG emissions

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associated with in situ oil sands extraction.

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Similar to the original GHOST model, the improved model takes a life cycle approach (i.e.,

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includes both onsite as well as indirect GHG emissions. For example, indirect emissions such as

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offsite electricity generation, natural gas production are included. This analysis focuses on in situ 6 ACS Paragon Plus Environment

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techniques at the extraction stage. Emissions are reported in kg CO2 equivalent per barrel of

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undiluted bitumen, with CO2 equivalency defined by the 5th Assessment Report of the IPCC

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(AR5) for a 100-year time horizon [17]. A flowchart describing the boundaries of the model are

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provided in Figure S1 of the Supplemental Information (SI).

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Operating Data

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Alberta’s Responsible Energy Development Act [18], requires in situ oil sands project

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operators to report monthly data, including production and steam injection volumes, electricity

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supply and demand, and volumes of associated gas flared and vented. This publicly available

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data [19] replaces the confidential data utilized in the original GHOST model. Inventory data not

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reported on a monthly basis are obtained from the Alberta Energy Regulator’s (AER) Statistical

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Reports ST53: Alberta Crude Bitumen In Situ Production [20] from January 1992 to April 2014

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and the annual In Situ Performance Presentations [21]. The data from in situ annual

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presentations and ST53s are collected for all operating projects reported from the beginning of in

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situ operations in 1985 to 2014. Operating parameters such as the boiler feedwater (BFW)

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temperature, pressure and solution gas composition are collected from each project’s

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Environmental Impact Assessment (EIA), available at the Alberta Government Library [22].

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Provincial electricity grid emission intensities for the province, reported hourly by the Alberta

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Electric System Operator (AESO) [23] are converted into monthly intensity values by

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aggregating hourly data available from 2011-2014.

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To represent commercial oil sands industry operations, only projects with commercial

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production of more than 10,000 bbl/day are considered, leaving pilot projects out of the analysis.

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Projects in the startup phase are excluded because they have not reached steady conditions and 7 ACS Paragon Plus Environment


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data are insufficient to characterize their operations. Details about each project are presented in

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Table S4 in SI.

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After applying the selection criterion and filtering the data, 15 operating in situ projects were

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selected for analysis. Three of these projects are CSS operations, two of which combine steam

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and electricity generation (onsite cogeneration). The other 12 are SAGD operations and half of

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these have cogeneration capacity. The 15 projects represent approximately 75% of the total in

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situ production of the province (303 million barrels out of the 405 million barrels produced in

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2013 – according to the AER’s ST98), offering a good volume-weighted representation of the

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industry. The remaining 25% of production are small facilities, new projects and pilot facilities.

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While we don’t have complete data associated with these projects, our understanding is that none

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of these projects would drastically change the industry wide emissions profile. However, we

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exclude these projects (using the criteria of < 3 years of production and/or under 10,000 bbl/day)

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as they could bias the estimates slightly up or down and we would not have the evidence to

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explain why.

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The inventory data employed by GHOST-SE are summarized in Table 1.

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Table 1. GHOST-SE input parameters for in situ bitumen extraction (SAGD and CSS). Two cases are available in the model. Case 1 employs a natural gas fired boiler for steam generation, and electricity is imported from the grid. Case 2 utilizes onsite cogeneration – a gas turbine and heat recovery steam generator – to supply steam and power. Actual in situ extraction projects feature one or the other. Input

SAGD Range

CSS Range

Distribution

Source

SOR (m3 steam/m3 bitumen) – dry (steam x=100%)

2.2 – 5.9

2.3 – 5.5

Direct Data Sampling

AER ST53 [20]

Electricity (kWh/m3 bitumen)

55 – 247

109 – 264


AER In situ progress reports [21]

Flared Hydrocarbons (kg CO2e/m3 bitumen)

0.1 – 7.6

0.2 – 6.3



Fugitive emissions (kg CO2e/m3 bitumen)

0.3 - 1.0

0.4 – 2.6



Solution gas (m3/m3 bitumen)

4.1 – 35.1

43.0 – 89.2



Produced steam quality

80%

100%

Uniform

GHOST [12]

Boiler output pressure (MPa)

5.0 – 10.0

5.0 – 10.0

Uniform

Project EIAs [22]

Produced steam enthalpy (kJ/kg)

2,460-2,470

2,460-2,470

Function of boiler pressure and temperature

Project EIAs and superheated steam tables for water [22]

Boiler feedwater temperature (°C)

105 – 202

115 – 150

Custom

Project EIAs [22]

Alberta electricity grid emission factor (g CO2e/kWh)

647 – 761

647 – 761

Custom

Alberta Electricity System Operator [23]

Lower heating value of natural gas fuel (pipeline and solution gas), MJ/m3

37.9

37.9

NA

GHOST [12]



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CASE 1. OTSG BOILER Efficiency – boiler

80% - 85%

80% - 85%

Uniform

GHOST [12]

Efficiency – gas turbine

30% - 35%

30% - 35%

Uniform

GHOST [12]

Efficiency – heat recovery

50% - 60%

50% - 60%

Uniform

GHOST [12]

Efficiency – HRSG direct firing

95%

95%

NA

GHOST [12]

Total Electricity produced (kWh/m3 bitumen)

150 – 600

97 – 199



CASE 2. COGENERATION (GT + HRSG)

162 163 164 165 166

Notes: SOR: Steam-to-Oil Ratio; OTSG = Once-Through Steam Generator; GT: Gas Turbine (electricity generator); HRSG: Heat Recovery Steam Generator; SAGD: Steam Assisted Gravity Drainage; AER: Alberta Energy Regulator; EIA: Environmental Impact Assessment; NA: not applicable as constant values are utilized.

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Process Calculation Improvements

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GHOST-SE also includes improvements to the process calculations within the model. These

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include a more complete treatment of systems where steam and electricity are cogenerated onsite

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and more transparent calculations of the energy requirements of the surface facilities. Detailed

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descriptions of the improvements are reported in Section 1.2 of SI.

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Monte Carlo Simulation

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GHOST-SE employs MC simulation to generate distributions of GHG emissions for each

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project on an annual basis (e.g., kg CO2eq/bbl in 2010) as well as on a project life cycle basis

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(e.g., total kg CO2eq/total bbl since project startup). MC simulation capability is incorporated


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into GHOST-SE using the Oracle Crystal Ball® add-in for Excel®. Testing revealed that 10,000

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MC runs were sufficient to generate stable distributions of GHG emissions for in situ extraction.

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For a given project, the Alberta Energy Regulator (AER) data for a particular month are

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simultaneously selected, preserving relationships between related parameters such as bitumen

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production, injected steam, etc. In cases where a particular data field (e.g., purchased electricity)

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is absent from a project’s time series for a particular month, it is randomly selected from the rest

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of the data pool. Data not appearing in the AER data sets are not correlated with the AER data,

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and are largely modeled as uniform random variables based on data from other sources (see

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Table 1).

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Three types of MC simulations are performed by GHOST-SE:

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1. Industry-wide variability. Simulations of all CSS and SAGD operations over all years

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the projects are operating, to obtain representative distributions of the extraction GHG

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emissions for these two technologies.

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2. Inter-project variability. Simulations of each CSS and SAGD project over all years

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the projects are operating, to obtain representative distributions of the extraction GHG

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emissions for each project over its lifetime.

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3. Intra-project variability. Simulations of each CSS and SAGD project during each

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year of their life cycle to explore the time evolution of the extraction GHG emissions

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of each project over its lifetime.

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The first type of simulation may be used to assess the variability of GHG emissions resulting

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from CSS and SAGD technologies (Industry-wide variability), permitting comparison of their

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emissions using different extraction techniques, etc. Two sampling methods are employed to

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explore industry-wide variability including “historic industry emissions intensity” where 11 ACS Paragon Plus Environment


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sampling occurs across individual projects and the emission intensity from each project is

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weighted by the production level of that project and “prospective industry emissions intensity”

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where sampling occurs randomly across all projects. The former represents a snapshot of

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industry performance historically from first commercial operation to 2013 where projects that

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have been operating longer have a larger influence on industry-wide emissions. The latter treats

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all sample points as equally likely and therefore represents the distribution of possible

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performance assuming that all projects are equally weighted and could represent future

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performance rather than being more heavily influenced by older projects.

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The second type of simulation assesses variability of GHG emissions associated with each

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project (Inter-project variability). The third type of simulation assesses how variability changes

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over the lifetime of each individual project (Intra-project variability).

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Projects that began commercial production relatively recently (2007 to present) are of special

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interest for analyzing the effect of well startup operations. At initial reservoir conditions, there is

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negligible fluid mobility due to bitumen’s high viscosity. For SAGD projects, steam is injected

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into both injection and production wells to establish inter-well communication and reduce

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viscosity of the bitumen, allowing it to be produced. As a consequence of higher-than-normal

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steam demand and lower-than-normal crude production, SAGD operations have higher GHG

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emissions per barrel of bitumen at the beginning of project operations. Subsequently, steam

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requirements stabilize and the project reaches what could be considered “steady state”. At the

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end of a well’s life, the steam requirements increases to the point where it exceeds an economic

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threshold and the well is shut in. Assessment of intra-project variability facilitates comparison of

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GHG emissions during startup- and steady state operations.

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RESULTS

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Industry-wide variability

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In Figure 1 we report the distributions of GHG emissions associated with CSS (in the upper

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panel) and SAGD (in the lower panel) operations. Vertical lines represent the 10th percentile,

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median and 90th percentile of the range obtained through Monte Carlo simulation, while the red

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markers report the distribution means. Estimates of GHG emissions from the literature (e.g., [7,

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8, 9, 10, and 13]) are reported as points, for comparison. While the previous literature when

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combined shows variability in emissions, only the current analysis demonstrates that the

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distribution is positively skewed. This means that observing emissions at the lower end of the

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distribution is more likely than at the upper end of the distribution. GHOST-SE explicitly

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quantifies statistics related to the distribution. The industry median GHG emissions for CSS-

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extracted bitumen is 77 kg CO2eq/bbl, and the variability ranges from 61 to 109 kg CO2eq/bbl

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(80% confidence interval). By contrast, the industry median GHG emissions for SAGD-extracted

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bitumen is 68 kg CO2eq/bbl, and the variability ranges from 49 to 102 kg CO2eq/bbl (80%

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confidence interval). These ranges of GHG emissions are narrower than those previously

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reported (e.g., [12] and [24]).

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Although the mean and median GHG emissions associated with SAGD are lower than those

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associated with CSS, the distribution of GHG emissions associated with SAGD is multi-modal,

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suggesting that a subset of SAGD extraction emissions (in time, by project, or both) yields

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higher emissions on average than CSS. Moreover, the variability associated with emissions from

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both extraction operations, while similar, is likewise affected by this multi-modality. Higher

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emissions may be a consequence of higher energy requirements associated with extraction from



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the reservoirs from which these projects produce. Multi-modality, by contrast, may be the result

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of the initiation of project expansions, etc.

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Ultimately, cumulative industry-wide GHG emissions for CSS and SAGD operations have

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remained relatively stable over the past fourteen years, even as there has been a dramatic

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expansion in the province’s in situ operations during that time period. Box plots detailing these

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trends for SAGD and CSS are in S10 and S11. In theory, there are many factors that could have

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driven the emissions to be larger or smaller than historic performance. Examples of factors that

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could increase emissions include changes in reservoir quality over time, or expansion of projects

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to access alternative reservoirs. Conversely, technological innovation including adoption of co-

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generation, better well placement, and incremental energy efficiency improvements. A more

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detailed analysis of these factors is required to draw conclusions regarding the degree to which

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each of these factors have influenced net industry emissions over the past 14 years.


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Figure 1. Industry-wide cumulative GHG emissions associated with in situ extraction of undiluted bitumen, in cumulative kg CO2eq/cumulative bbl bitumen for a) Cyclic Steam Stimulation (CSS) and b) Steam Assisted Gravity Drainage (SAGD). Blue vertical lines represent the 10th and 90th percentiles of the Monte Carlo GHG emissions ranges obtained from GHOST-SE, while the grey vertical line represents its median value. The light gray area displays the original GHOST range [12]. “Plus signs” represent findings of previously published studies [7, 8, 9, 10, 13].

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industry performance up to 2013. This is in contrast to the alternative method of conducting the

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Monte Carlo simulation described in the Methods section, which treats all sample points as

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equally likely and therefore represents the distribution of possible performance if a new project

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were to come online. These latter results are presented in the SI and show higher emissions for

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the mean and median, more pronounced skewness and broader distributions with heavier tails.

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The selection of method for sampling in such Monte Carlo simulations is therefore an important

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decision that can lead to different interpretations if the analysis is focused on understanding

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historic performance or the potential emissions of new projects (using the same technology).

Based on the sampling method employed, these distributions represent a snapshot of historic

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Inter-project variability:

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In Figure 2 and Figure 3, we report the distributions of the cumulative GHG emissions

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associated with individual in situ oil sands projects. The projects are grouped by extraction

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technique (CSS, SAGD) and ordered by the median project emissions (lowest value at the top).

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These distributions quantify the variability of operations over the full life of each project up to

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2013. The figure also identifies the relative size of each project and whether cogeneration is

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included (the latter is indicated by an asterisk following the project number). Variability in GHG

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emissions across the industry appears to be derived from project-specific variability, which in

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turn depends on the operations associated with each project over time. 15 ACS Paragon Plus Environment


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The distributions reported in Figure 2 and Figure 3 explain the variability illustrated for CSS

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and SAGD in Figure 1. For instance, Projects 13 and 9 drive the variability of SAGD emissions

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in Figure 1, however, these projects have less influence on the overall SAGD industry’s

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emissions than Project 2 contributes to the CSS industry’s emissions. Joint bitumen production

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asssociated with Projects 1*, 3*, 5* and 6 represents approximately half of the total production

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of all projects considered in this study (518,000 of approximately 1,043,000 bbl bitumen/day).

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For SAGD extraction, the variability in GHG emissions associated with certain projects may

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exceed the range of median GHG emissions across all projects. The median GHG emissions

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associated with SAGD projects range from 52 kg CO2eq/bbl bitumen to 172 kg CO2eq/bbl

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bitumen, whereas the full ranges of GHG emissions for Projects 9 and 13 are 144 – 249 and 130

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– 171 kg CO2eq/bbl, respectively. GHOST-SE results displayed in Figure 2 and Figure 3

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represent inter-project and intra-project variability: both types need to be considered when

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discussing ranges of GHG emissions estimates from in situ oil sands extraction as both are

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important.

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No correlation of variability in emissions with the use of cogeneration or size of project was

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identified for SAGD. Projects 5* and 6 have similar distributions (means and ranges) of GHG

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emissions despite the use of cogeneration in one but not the other – likely a consequence of

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differences in reservoir quality for the two projects, necessitating a higher SOR (and higher

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steam load) for Project 5*. Likewise, Projects 7*, 10, 11 and 14* have similar distributions of

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GHG emissions despite the fact that only two of the projects use cogeneration. Projects 4 and

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12* are likewise indistinguishable in terms of their GHG emissions, despite differences in the

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use of cogeneration. We know that cogeneration offers efficiency benefits over the

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nocogeneration cases, with accompanying fuel, and thus GHG, reductions. However, these 16 ACS Paragon Plus Environment

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effects are likely dwarfed by other drivers such as reservoir characteristics. This is supported by

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previous work that confirmed SOR is the single most significant parameter in determining the

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emissions [12]. In addition, other work has confirmed that reservoir characteristics (the basic

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parameters including depth and thickness of the reservoir, porosity, permeability, gamma ray

312

measurements and oil saturation) explain roughly 80% of the variability in SOR in existing

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projects [25]. There are methods of crediting emissions to surplus electricity generation sold to

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the grid that could change the measure of benefits of congeneration. This could, in turn, increase

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the impact of cogeneration on emissions estimates for in situ extraction [26]. On average, the two

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CSS projects featuring cogeneration demonstrate lower GHG emissions than the CSS project that

317

does not include congeneration. However, those two projects (1* and 3*) have higher production

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capacities than the third (2). For CSS, cogeneration may enable lower GHG emissions as part of

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higher efficiency operations for larger scale operations. However, the impact of cogeneration on

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the variability of GHG emissions could not be discerned robustly from the three projects

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considered.

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Sensitivity analyses conducted using the original GHOST model indicated that the GHG

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emissions for in situ extraction (both CSS and SAGD) are driven by the amount of steam used to

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stimulate the petroleum reservoir (mostly generated by burning natural gas): over 90% of the

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GHG emissions associated with extraction [9]. This is confirmed in the current analysis.



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Figure 2. Distributions of cumulative GHG emissions associated with CSS oil sands projects producing more than 10 kbd. Results are reported in cumulative kg of CO2 equivalent per cumulative barrel of undiluted bitumen. Vertical lines in the histogram represent the median, 10th and 90th percentiles, and means are denoted by red markers. Lighter green coloured projects have production capacities equal to or greater than 60 kbd. Darker purple coloured projects have production capacities between 10 and 60 kbd. Project 3 employs cogeneration.


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Figure 3. Distributions of cumulative GHG emissions (i.e. over the lifetime of each operating project) associated with SAGD oil sands projects. Results are reported in cumulative kg of CO2 equivalent per cumulative barrel of undiluted bitumen. Vertical lines represent the median, 10th and 90th percentiles, and means are denoted by red “x” marks. Lighter 19 ACS Paragon Plus Environment


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green coloured projects have production capacities equal to or greater than 60 kbd. Darker purple coloured projects have production capacities between 10 and 60 kbd. Asterisks indicate projects that employ cogeneration.

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Intra-project variability:

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To explore the impact of temporal variability, MC simulations of each of the individual

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projects were conducted for each year of their operation. The distributions of GHG emissions for

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each project (CSS and SAGD) were then evaluated as time series. Time series of distributions of

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GHG emissions associated with three projects are illustrated in Figure 4 as examples; time series

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of the remaining twelve projects are presented in S12 to S23 of SI.

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349 350 351 352 353

Figure 4. Time evolution of the variability of extraction GHG emissions for SAGD Projects 8*, 6 and 9. Emissions are reported as annual kg CO2e per annual bbl of undiluted bitumen. Distribution means are reported in red, and medians are reported in gray. Blue lines denote 10th and 90th percentiles.

354 355

Project 8* is a SAGD project that has a cogeneration system that supplies steam and electricity

356

to the project. Variability in GHG emissions tends to be constant in the latter years of operation,

357

but project startup affects the variability of GHG emissions in the early years of operation. When

358

annual GHG emissions ranges are weighted by annual bitumen production, the influence of early

359

variability in emissions upon the project lifetime variability diminishes over time. Therefore, the

360

distribution of cumulative GHG emissions reported in Figure 3 for Project 8* is approximately



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361

the same as the distribution of emissions associated with operations in 2013. This temporal

362

behavior is also observed for Projects 4, 5*, 10, 11, 12 and 13*.

363

Project 6 illustrates an alternative time evolution of a project, with distinct characteristics that

364

affect variability. In this case, initial variability in emissions (years 2003 – 2008) affects the

365

cumulative variability to the extent that the variability in emissions associated with the most

366

recent year for which data were available (2013) is different from the cumulative variability of

367

the project (Figure 2 and Figure 3). In this case, the distributions of GHG emissions in early

368

years are multi-modal. Variability in GHG emissions does not seem to decrease monotonically

369

over time, and does not appear to be associated with any particular distribution. This behavior is

370

a consequence of the project’s production prolificacy and the effect of temporal discontinuities in

371

project development. This is akin to multiple “startup effects” as new wells impact the variability

372

of steam-to-oil ratios (SORs) within a project and consequently, the variability of associated

373

GHG emissions. Most projects have gone through different phases of expansion in their

374

production capacity, with consequent startup effects in later years. The time evolutions of the

375

variability in GHG emissions associated with Projects 7, 9, 14* and 15 share characteristics

376

similar to those of Project 6, although the effect in Project 6 is the most pronounced.

377

That said, bimodal variability in GHG emissions is observed for individual projects despite the

378

absence of startup effects (e.g., Projects 2, 3* and 14*). For these projects, we could not attribute

379

the variability to any particular input parameter (e.g., SOR). We hypothesize that this variability

380

is associated with heterogeneity in the reservoirs from which these projects produce bitumen.

381

In Figure 4 we also illustrate the range of GHG emissions associated with Project 9, which has

382

been commercially operating since 2007 and has the largest range of emissions of all in situ

383

projects considered in this study. Our estimates of GHG emissions for this project are


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384

significantly higher than the previously reported GHOST range of emissions. This project’s

385

behavior confirms the presence of outliers and the influence of project specificities (either

386

reservoir conditions and/or production strategies) on GHG emissions, that cannot be attributed to

387

the industry as a whole.

388

GHG emissions associated with the more recent projects (Projects 12* and 15) seem to exhibit

389

bimodal probability distributions. This may be a consequence of variable steam generation and

390

bitumen extraction during the initiation of the projects. Estimates of GHG emissions associated

391

with recently-initiated projects are calculated with fewer months of data that result in these data

392

being more heavily weighted towards the more variable operations. As projects age, data

393

progressively are more heavily weighted to the steady-state operations, in turn masking the peaks

394

in steam demand at startup relative to bitumen production.

395 396

Sensitivity:

397

Insofar as the AER data sets are not comprehensive, certain parameters were retained from the

398

original GHOST model. Of these, the heating value of natural gas purchased from pipelines and

399

the efficiency of HRSGs (direct fired) had the greatest impact on the results. The dependency of

400

the distribution of GHG emissions associated with Project 1 is illustrated in Figure S24. For this

401

and most projects, either (a) decreasing the LHV from 37.9 to 33.7 MJ/m3 or (b) decreasing the

402

default efficiency from 95% to 82.5% (the default boiler efficiency for non-cogen systems)

403

results in an approximately 10% increase in the estimate of the average GHG emissions

404

associated with an in situ oil sands project. Reduction of either parameter also increases the

405

width of the distributions.

406



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407 408

DISCUSSION

409

This analysis improves the previously developed GHOST model with more detailed

410

calculations and the incorporation of publically available input data for all commercial in situ

411

projects. A Monte Carlo simulation is conducted to derive insights about industry-wide trends,

412

inter- and intra-project variability. Arguably, the most significant insight from this analysis is

413

that the use of a statistically enhanced model, GHOST-SE, allows for a better representation of

414

the GHG emissions from oil sands operations. This study confirms that the original GHOST

415

range of emissions (based on proprietary operating data) is generally a good representation of the

416

overall industry-wide range of emissions, but fails to provide information about the probability

417

distribution of emissions to effectively inform decision makers. For example, the positive

418

skewness of the GHG emissions estimates for both CSS and SAGD indicate that studies that

419

have implied a uniform distribution have likely overestimated industry wide estimates. The use

420

of project specific data reported by the AER on a monthly basis throughout the lifetime of each

421

in situ project increases transparency and confirms the calculation of project specific emissions

422

estimates.

423

The wider variability in emissions of some projects could suggest that operating decisions and

424

the learning within each individual project might be exerting a larger influence on the range of

425

GHG emissions for in situ oil sands extraction than other variables, such as reservoir

426

characteristics (reservoir conditions are different across projects, thus the variability they

427

generate will be represented by the variability across projects). Recent work by Akbilgic et al.

428

[25] suggests that the reservoir characteristics can explain a large portion of the variability in the

429

SOR (>80%) and by extension, the variability in GHG emissions. Hence, the rest of the


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430

variability must be due to facilities’ technology selection or deployment strategy. This may

431

explain why the impact of cogeneration or project scale on the variability in GHG emissions is

432

challenging to discern from our results – the variability in extraction GHG emissions associated

433

with the subsurface-driven demand for steam may dominate other factors such as technology

434

choice. Insofar as the Alberta grid has been historically coal based, GHG emissions associated

435

with purchased power have been higher than those associated with cogeneration, thereby

436

reducing net emissions. However, in recent years, the Alberta grid intensity has decreased from

437

949 kg CO2eq/MWh in 1990 to 689 kg CO2eq/MWh in 2014. If the Alberta grid continues to

438

“decarbonize”, then the GHG reductions associated with cogeneration will be reduced.

439

Although we have exclusively considered the upstream emissions of the in situ oil sands life

440

cycle in this paper, this analysis could be expanded to include other extraction methods (e.g.,

441

mining) as well as other life cycle stages (e.g., transport, refining, combustion of transportation

442

fuels). Indeed, the upstream emissions assessed in this paper are only part of total life cycle

443

emissions that need to be accounted for before alternative product pathways can be compared.

444

Once the life cycle has been accounted for and further detailed statistical analysis investigated to

445

show the role that each parameter plays in determining GHG emissions, this tool could be

446

explored for its potential use in predicting GHG emissions associated with new projects to

447

inform decisions at the design and deployment stages. Our results confirm the need to consider

448

the distributions of emissions given the variability is large, positively skewed and the inter-,

449

intra- and time evolution sources of the variability are all important. Finally, we note that a broad

450

view of oil sands GHG emissions, including their variability and uncertainty, must also consider

451

mined oil sands. We will consider these in future research.



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453 454 455

Acknowledgments

456

We wish to acknowledge EXXONMOBIL RESEARCH AND ENGINEERING COMPANY for

457

financial support.

458 459

Supporting Information

460 461

The Supporting Information includes a more detailed discussion of the modeling, data and sensitivity analysis.

462 463

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Bergerson, J.A.: Oyeshola, K.: Charpentier, A.D.; Sleep, S. and MacLean, H.L. Life cycle greenhouse gas emissions of current oil sands technologies: surface mining and in situ applications. Environ. Sci. Tech. 2012, 46 (14), 7865-7874.

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Charpentier, A.D.; Oyeshola, K; Bergerson, J.A. and MacLean, H.L., Life cycle greenhouse gas emissions of current oil sands technologies: GHOST model development and illustrative application, Environ. Sci.Tech. 2011, 45 (21), 9393-9404.

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Brandt, A.R. Variability and Uncertainty in Life Cycle Assessment Models for Greenhouse Gas Emissions from Canadian Oil Sands Production. Environ. Sci. Tech. 2011, 46, 12531261.

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Vafi, K. and Brandt, A.R. Uncertainty of Oil Field GHG Emissions Resulting from Information Gaps: a Monte Carlo Approach, Environ. Sci. Tech. 2014, 48, 10511-10518.

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Steinmann, Z.Z.N.; Hauck, M.; Karuppiah, R.; Laurenzi, I. and Huijbregts, M.A.J., A methodology for separating uncertainty and variability in the life cylce greenhouse gas emissions of coal-fueled power generation in the USA, Int. J. of Life Cycle Assess., 2014, 19 (5), 1146-1155.

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Akbilgic, O.; Zhu, D.; Gates, I. and Bergerson, J. Prediction of Steam to Oil Ratio of Steam-Assisted Gravity Drainage from Reservoir Characteristics. Energy. 2015, 93, 16631670.

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Statistically Enhanced Model of In Situ Oil Sands Extraction

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