Characterizing Variability in Oil Sands Upgrading Greenhouse Gas

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Environmental and Carbon Dioxide Issues

Characterizing Variability in Oil Sands Upgrading Greenhouse Gas Emissions Intensity Diana M. Pacheco, Joule A. Bergerson, Anton Alvarez-Majmutov, Jinwen Chen, and Heather L MacLean Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.9b01518 • Publication Date (Web): 20 Aug 2019 Downloaded from pubs.acs.org on August 28, 2019

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Energy & Fuels

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Characterizing Variability in Oil Sands

2

Upgrading Greenhouse Gas Emissions

3

Intensity

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Diana M. Pacheco,† Joule A. Bergerson,‡ Anton Alvarez-Majmutov,¶ Jinwen

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Chen,¶ and Heather L. MacLean*,†,§

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†

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Street, Toronto, Ontario, Canada M5S 1A4

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‡

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Engineering Research and Education, Schulich School of Engineering University

Department of Civil & Mineral Engineering, University of Toronto, 35 St. George

Department of Chemical and Petroleum Engineering, Centre for Environmental

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of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4

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¶

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Canada T9G 1A8

13

§

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Toronto, Toronto, Ontario, Canada M5S 1A4

Natural Resources Canada, CanmetENERGY, One Oil Patch Drive, Devon, AB,

Department of Chemical Engineering and Applied Chemistry, University of

15 16 17 18 19

ACS Paragon Plus Environment

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Abstract

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A better understanding of the greenhouse gas (GHG) emissions intensity of

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upgrading oil sands bitumen is needed to inform industry and government

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decisions related to climate change mitigation. We develop an enhanced version

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of the Oil Sands Technologies for Upgrading Model (OSTUM 2.0), a life cycle-

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based model that estimates energy use and GHG intensities of upgrading

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technologies at the process unit level. We apply OSTUM 2.0 to commercial

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upgrading technologies operating in Alberta, Canada, and propose baseline

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estimates and ranges of direct and indirect GHG intensities for delayed coking

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based- (DC), hydroconversion based- (HC), and combined hydroconversion and

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fluid coking based upgrading (HC/FC). We identify potential drivers of variability in

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upgrading GHG intensity. These include: the application of different upgrading

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technologies (up to 36% variation in baseline GHG intensities), variation in the

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properties of upgrading products (up to 91% variation in baseline GHG intensities,

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a factor not previously modeled in the literature), and changes in the energy

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efficiency of operations and use of by-products as fuels (up to 45% variation in

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baseline GHG intensities). OSTUM 2.0 improves upon life cycle-based literature

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by facilitating more detailed modeling, assessment, and comparison of the GHG

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intensity of commercial upgrading schemes, using consistent boundaries,

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assumptions, public data sources, and calculation methods. The systems-level

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modeling approach allows comprehensive characterization of upgrading GHG

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emissions and drivers of variability. Our findings suggest that modeling different


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technology configurations, product properties, and fuel mixes can increase the

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representativeness of upgrading GHG intensity estimates.

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Introduction

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Oil sands are a mixture of sand, water, clay, and bitumen, with 165 billion barrels

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of recoverable oil in 20161 in Alberta, Canada.2 Bitumen is a form of extra-heavy

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crude oil (API gravity below 10° and viscosity above 10,000 centipoise3) that is

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too viscous to flow without being diluted or upgraded.4 It contains a large fraction

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of complex high-molecular weight hydrocarbon molecules and more impurities

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than in conventional crude oil.5 Many refineries are not engineered to handle this

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heavy feedstock. Therefore, 43% of Canada’s total bitumen production in 2017

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was transformed into a lighter, sweeter, higher-value synthetic crude oil (SCO),

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before being shipped to the downstream sector.6 Upgrading is the process by

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which bitumen is transformed into SCO by fractionation and thermochemical

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treatment, improving the quality of the bitumen by increasing its hydrogen content

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and reducing its viscosity and impurities.7 The rest of the bitumen production was

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diluted, typically with natural gas condensate, and sold directly to market as a

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heavy sour blend.

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The Canadian upgrading industry primarily uses three technology schemes

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(delayed coking based upgrading (DC), hydroconversion based upgrading (HC),

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and combined hydroconversion and fluid coking based upgrading (HC/FC)) to

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produce light sweet and heavy sour SCO products. Coking is a thermal process

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whereby heavy fractions of bitumen are decomposed into lighter ones, producing


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coke as a by-product.7 Hydroconversion breaks up the heavy bitumen

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components through adding hydrogen in the presence of a catalyst and elevated

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pressures (7-15 MPa) and temperatures (>400 °C).7 A combination of these two

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technologies is also used in specific scenarios. Each upgrader produces one or

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more commercial SCO products, which have specific product properties, by

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blending different oil fractions. There are currently five bitumen upgrading

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operations in Alberta and one in Saskatchewan, producing a range of commercial

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SCO products that vary in response to market conditions (see Table S1 in

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Supporting Information, SI). Three of these (Suncor’s Base and Millennium,

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Syncrude’s Mildred Lake, and CNRL’s Horizon) are integrated operations located

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adjacent to the bitumen production plants. Shell’s Scotford, North West

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Redwater’s Sturgeon, and Husky’s Lloydminster are stand-alone upgraders that

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receive bitumen (and heavy oil in the case of Lloydminster) through pipeline from

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off-site production plants.

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The industry has faced multiple challenges in a global market experiencing an

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oversupply of oil and low oil prices. The industry’s upgrading capacity is not

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expected to rise proportionately with bitumen production growth7 due to the

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inherently challenging economics of upgrading (considerable capital and

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operation costs, and dependence on the price differential between the input -

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diluted bitumen- and the product SCO). However, it is projected that SCO

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production will increase by 20% in the period 2017–2027.6

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Upgrading is an energy and greenhouse gas (GHG)-intensive process that

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generated 23% of GHG emissions from oil sands operations and 2% of Canada’s


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total emissions in 2016.8 As fuel regulations9,10,11,12,13,14 are implemented in

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different jurisdictions to regulate the GHG emissions intensities of transportation

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fuels, industry and government continue to seek and invest in developing new

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technologies and strategies to reduce emissions associated with upgrading

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operations.15 An accurate quantification of the GHG intensities of current and

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emerging upgrading technologies on a life cycle basis, and the characterization of

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their variability and sources of emissions, are key analyses to assist with

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compliance with regulations, to form a baseline by which mitigation strategies can

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be assessed, and to identify where opportunities exist to reduce GHG emissions.

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In our previous work, Pacheco et al.,16 we reported the development of the Oil

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Sands Technologies for Upgrading Model (OSTUM), a life cycle-based model.

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We characterize OSTUM as life cycle-based because it estimates not just direct

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(on-site generated) but as well indirect (off-site generated) energy use and GHG

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emissions intensities associated with oil sands upgrading technologies producing

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SCOs. Essentially OSTUM estimates energy use and GHG emissions associated

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with the upstream activities related to fuel (e.g., natural gas) and electricity inputs

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to the upgrader. OSTUM could be straightforwardly incorporated into a full well-

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to-wheel (WTW) model, but OSTUM itself is focused solely on the upgrading

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stage and does not include upstream (oil sands production/extraction) or

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downstream (refining) activities involved in the life cycle of transportation fuel

108

production from bitumen.

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OSTUM operates at the process unit level using public data. In Pacheco et al.,16

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the model was demonstrated by applying it to a DC-based upgrading facility

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producing a single light sweet SCO blend. OSTUM was developed after a

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knowledge gap was identified: studies and models17,18,19,20,21,22,23 reporting

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upgrading GHG intensities on a life cycle basis lacked detail to characterize

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different upgrading technologies and their sources of emissions, while chemical

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process modeling studies and models24,25,26 (mainly using Aspen HYSYS®)

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characterized upgrading emissions intensities but lacked a life cycle perspective.

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More recently, Lazzaroni et al.27 reported the energy requirements and CO2

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emissions of an integrated extraction and upgrading facility but lacked a life cycle

120

approach.

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Other sources periodically publish energy use and GHG emissions data reported

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by commercial oil sands operators, but most of the upgrading data is aggregated

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with the bitumen recovery and extraction stages. The Alberta Energy Regulator

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(AER) publishes monthly submissions by operators of production, supplies,

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dispositions and inventory of oils sands products and energy inputs in its

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Statistical Report ST39.28 Environment and Climate Change Canada (ECCC)29

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publishes an inventory of GHG emissions data reported by Canadian facilities,

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including oil sands operators. However, the integration of the data from different

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production stages limits specific insights about upgrading.

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There are few studies30,31,32,33,34 that examine the variability in life cycle GHG

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intensities of oil sands operations published by literature models and studies, but

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none analyze the variability of the upgrading stage in detail. Englander et al.35


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examined historical trends in GHG intensities of oil sands extraction, upgrading,

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and refining utilizing AER (ST39)28 data, providing insights on the trend in

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emissions intensities reduction of the industry over time. However, given the

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aggregation of the AER data, the analysis was not focused on upgrading. Sleep

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et al.36 characterized the historical variability of GHG emissions associated with

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mining and upgrading operations using data from AER and other sources. The

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study used historical project-specific data in which the mining and upgrading

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stages are integrated, and therefore the analysis and insights were not specific to

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the upgrading stage. A better understanding of the variability in upgrading energy

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use and GHG intensities and its sources is relevant for the oil sands industry to

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help meet its GHG emissions targets and caps (e.g.,37,38,39); for policy makers

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developing emissions legislation, and for technology developers and researchers

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working on quantification and/or reduction of emissions.

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The objective of this paper is to improve our understanding of upgrading energy

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use and GHG intensity, characterize the variability of upgrading GHG intensity,

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and elucidate potential drivers of this variability. In this paper we document

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enhancements to OSTUM since Pacheco et al.,16 including improvements to the

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DC module, the development of HC and HC/FC modules, and the development of

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the capacity to evaluate the GHG intensity of SCO products in accordance with

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their levels of processing and product quality properties. We propose baseline

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estimates and ranges of direct and indirect GHG intensities for the three

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upgrading technologies studied. Additionally, we explore potential drivers of

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variability in upgrading GHG intensity by analyzing the impact of the use of


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different upgrading configurations/technologies on upgrading GHG intensity; the

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impact of co-producing SCO blends with contrasting properties through a single

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technology; and the impact of changes in the energy inputs of the upgrader.

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Finally, we evaluate OSTUM’s GHG emissions intensity and energy use

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estimates through comparison with those reported in literature.

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Methods

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OSTUM. OSTUM is a model built in spreadsheet-based software (i.e., Excel) that

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employs a process-based life cycle approach to estimate the direct and indirect

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energy use and GHG emissions intensities of oil sands upgrading technologies

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currently utilized in Alberta. The model is originally documented in Pacheco et

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al.16 OSTUM’s flexibility allows a user to customize a wide variety of upgrading

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scenarios with different upgrading technologies, diluted bitumen (dilbit)

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feedstocks, process units’ operating conditions (e.g., temperature, pressure),

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different fuel mixes and electricity generation technologies. For each upgrading

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technology, OSTUM models all the energy-intensive process units, on-site utility

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plants (steam generation in boilers, cogeneration of electricity and steam, and

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hydrogen production in a steam methane reformer, SMR), and the import of

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electricity from the grid (if cogeneration is not employed). OSTUM’s input

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parameters are informed by public data from literature on upgrading and refining

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of bitumen and heavy oils (mainly from

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estimates yields of intermediate and final products, energy consumed by each

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process unit and utility plant, and corresponding GHG emissions (emissions per

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unit of time) and GHG emissions intensities (emissions per unit of feed or

26,28,40,41,42,43,44,45,46,47,48


). The model

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product). Model outputs are direct and indirect GHG intensities, broken down by

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type of energy input (natural gas, fuel gas, coke, and off-site electricity), by the

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uses of the energy inputs (generation of heat, steam, hydrogen, cogenerated

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electricity and steam), and by process unit. The GHGs accounted for are carbon

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dioxide (CO2), methane (CH4), and nitrous oxide (N2O), which are aggregated

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into carbon dioxide equivalent (CO2e) using 100-year49 global warming potentials.

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The bitumen upgrading process modeled in OSTUM is described on SI, and

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Scheme S1 presents a simplified process flow diagram of the process and

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OSTUM’s structure. For more information on OSTUM see Pacheco et al.16

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OSTUM Enhancements. The major enhancements to OSTUM are the

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improvement of the DC module to model the simultaneous production of light

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sweet and medium to heavy sour SCO blends (the original OSTUM could only

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simulate the production of a single light sweet SCO blend), the addition of a HC

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module with multi-product modeling capabilities and a HC/FC module producing a

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single light sweet SCO blend (as is the case in commercial operations), and the

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addition of the capacity to estimate the properties of intermediate and final

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products based on their levels of processing. OSTUM 2.0 is the result of these

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improvements, and to our knowledge these novel attributes aren’t included in

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other life cycle-based models that include upgrading. Detailed descriptions of the

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upgrading technologies simulated in OSTUM 2.0 (DC, HC, HC/FC) are in SI.

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Table S2 in SI summarizes data and modelling techniques employed in OSTUM

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2.0 to estimate the product yields, energy use, GHG intensities, and product

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properties of each process unit simulated in the three upgrading technologies.


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Enhancement of the DC Module. The largest commercial DC operation (owned

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by Suncor) produces light sweet SCO blends, ultra-low sulfur diesel (ULSD), and

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medium to heavy sour SCOs. Market conditions and demand impact the

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upgrader’s product slate. Therefore, OSTUM 2.0’s DC module (see Scheme S2 in

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SI) has been modified from that presented in Pacheco et al.16 and is the first

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simulation of a commercial multi-product DC operation based entirely on public

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data. We aim to investigate the impact on upgrading GHG intensity of

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simultaneously producing a light sweet SCO blend, ULSD, and three medium to

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heavy sour SCO blends. Scheme S2 shows the intermediate crude fractions and

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final product streams that are blended into each SCO blend modeled. A

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description of OSTUM 2.0’s flexibility in modeling the yields of SCO blends and

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their quality properties in the DC multi-product upgrading scheme is presented in

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SI.

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The estimation of the energy use (utilities: natural gas, steam, electricity use) of

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DC’s process units is reported in Pacheco et al.16 and summarized in Table S2 in

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SI. OSTUM 2.0 makes use of the co-product treatment method of allocation from

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life cycle assessment (LCA) to partition the energy consumed by the upgrader

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among the various SCO blends at the process unit level. Three different

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allocation methods are available in OSTUM: mass-based, energy-based, and

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hydrogen content-based (see SI for details). The estimation of GHG intensities

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using the energy allocated to each DC product is reported in Pacheco et al.16 and

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summarized in Table S2 in SI.


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Structure of the HC and HC/FC Modules. The structure of OSTUM 2.0’s HC

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module (Scheme S3 in SI) reflects the operation of Shell’s Scotford upgrader.

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OSTUM 2.0 models the simultaneous production of three SCO blends from HC:

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two light sweet SCO blends and one heavy sour SCO blend (a capability not

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previously developed in the life cycle literature, which has only modeled single-

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product HC facilities). Scheme S3 shows the intermediate crude fractions and

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final product streams blended into each SCO product. A description of OSTUM

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2.0’s modeling approach of SCO blend yields and composition for the HC multi-

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product upgrading scheme is also presented in SI.

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OSTUM 2.0’s HC/FC module (see Scheme S4 in SI) reflects the operation of the

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largest commercial upgrading facility, which is operated by Syncrude Canada.

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The facility (and OSTUM 2.0’s HC/FC module) uses a combination of upgrading

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technologies in its conversion stage and produces only one light sweet SCO

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blend. This blend is a mixture of hydrotreated naphtha, hydrotreated light gas oil

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(LGO), and hydrotreated heavy gas oil (HGO).50 Therefore, all intermediate virgin

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and cracked distillates in the HC/FC module are hydrotreated and blended into a

240

single SCO product.

241 242

OSTUM 2.0’s exclusive use of public information and data from literature may

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result in our estimates of product yields, properties, and GHG intensities to differ

244

from real world data (see Results and Discussion for a discussion of these

245

differences).

246


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Estimation of Product Yields in the HC and HC/FC Modules. In both modules,

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the estimation of the product yields from the first upgrading stage (diluent

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recovery and vacuum distillation) and the third upgrading stage (secondary

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upgrading or hydroprocessing) are reported in Pacheco et al.16 and summarized

251

in Table S2. Product yields from the second upgrading stage (conversion or

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primary upgrading) are approximated using a kinetic model by Sanchez et al.51 for

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the ebullated-bed hydroconverter, which is common to HC and HC/FC. To

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increase

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hydroconverter’s unconverted residuum to extinction in the HC module. In the

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HC/FC module, unconverted residuum from the ebullated-bed hydroconverter is

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fed to the fluid coker reactor by default. Empirical correlations reported by Fahim

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et al.52 based on data compiled by Maples44 are used to approximate the product

259

yields of the fluid coker reactor.

conversion,

OSTUM

2.0

allows

the

option

of

recycling

the

260 261

Estimation of Energy Use and GHG Intensities in the HC and HC/FC

262

Modules. The requirements of fuel (natural gas, fuel gas and/or coke), electricity,

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and steam of each process unit and utility plant are calculated as described in

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Pacheco et al.16 and as summarized in Table S2 in SI. The hydrogen

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requirements of the ebullated-bed hydroconverter are approximated using an

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empirical correlation developed by Danial-Fortain et al.53 and a hydrogen mass

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balance. The approximation of the hydrogen requirements of the hydrotreaters

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has been previously reported in Pacheco et al.16 In the HC module, the energy

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consumed by the process units is allocated among the three SCO blends


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following the allocation procedure previously described in SI for the DC module.

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In the HC/FC module, no allocation procedure is necessary because only one

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SCO blend is produced. GHG intensities in both modules are estimated as

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reported in Pacheco et al.16 and summarized in Table S2.

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Estimation of Product Properties in All Modules. To estimate the properties of

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intermediate crude fractions and final product streams, OSTUM 2.0 utilizes

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different methods at each upgrading stage. The first upgrading stage, diluent

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recovery and vacuum distillation, is common to the three upgrading technologies.

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The properties of the crude fractions resulting from this first stage are

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approximated using assay data published by Crudemonitor50 and hydrogen

280

content correlations by Goossens54 and Choudhary et al.55 The second upgrading

281

stage (conversion) varies among the different technologies. The main product

282

properties from DC’s conversion stage (the delayed coker unit’s products) are

283

estimated using guidelines by Gary et al.,43 and correlations of Maples44 reported

284

by

285

hydroconverter’s products), the main properties are estimated applying a kinetic

286

model from Yui and Sanford.57 The HC/FC’s fluid coker products’ properties are

287

approximated using correlations reported by Fahim et al.52 based on data

288

compiled by Maples.44 The third upgrading stage (hydrotreating) is also common

289

to all technologies. The main properties of the hydrotreated product streams are

290

approximated by applying kinetic models by Yui,58 Yui and Sanford,59,57 and

291

correlations by Yui.60 Once the properties of the product streams are

292

approximated, the properties of the SCO blends can be tuned by adjusting the

Ancheyta.56

For

HC’s

conversion

stage


products

(ebullated-bed

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volumetric fractions of the blends’ components. A more detailed description of the

294

literature used to model the main properties of the intermediate and final product

295

streams is in SI.

296

Blending of Crude Fractions. The volumetric shrinkage expected with the

297

blending of different crude fractions is calculated using a shrinkage equation

298

developed by the API.61 The methods and the calculation of a blend’s specific

299

gravity and API are in SI. The mass additive properties of a blend (e.g., sulfur,

300

nitrogen, hydrogen and aromatics contents) are approximated by applying a

301

linear mixing rule in which the value of each component’s property is multiplied by

302

a weighting factor (e.g., mass fraction) of that component in the mixture.62

303

Scenario Analyses. Scenario analyses are developed to obtain ranges of

304

upgrading GHG intensities for each of the upgrading technologies in OSTUM 2.0.

305

Base, low, and high GHG emissions scenarios are developed for each

306

technology, informed by public literature on theoretical and actual oil sands

307

upgrading operations.

308

Base GHG Emissions Scenarios. The dilbit feedstock in all base GHG

309

emissions scenarios (“base scenarios” hereinafter) is Borealis Heavy Blend

310

(BHB), selected from a set of publicly available dilbit assays50 for the similarity of

311

the properties of its crude fractions to those of public Athabasca bitumen

312

assays.63 The energy inputs to all base scenarios comprise off-site produced

313

natural gas, fractions of the on-site production of fuel gas and coke (in the case of

314

DC and HC/FC) to produce heat, steam, hydrogen, and electricity cogenerated


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on-site (complemented with grid electricity in the cases of HC and HC/FC). AER

316

data28 are used to guide the values of the input parameters indicating the

317

upgrader’s capacity, fraction of electricity supply cogenerated, and fractions of

318

fuel gas and coke production used as fuel. The medians of the data reported to

319

AER by oil sands operators after their latest known modification to their upgrading

320

operations to the end of 2016 are used to inform the base scenarios (median of

321

Suncor’s data for the period 2008-2016 informed the DC base scenario, Shell’s

322

data for 2011-2016 informed the HC base scenario, and Syncrude’s data for

323

2007-2016 informed the HC/FC base scenario). Fuel gas and coke not used are

324

assumed to be sold and stockpiled, respectively. The arithmetic average of a

325

range of literature values (mainly from

326

most of the process units’ energy use factors (energy requirements per unit of

327

feedstock to the process unit). A single literature source is only used where its

328

value is more consistent with the scenario assumptions than using the average.

329

The lower heating value (LHV) of all fuels is used. Energy use is allocated only to

330

product streams (e.g., hydrotreated products, virgin/cracked distillates) as by-

331

products (fuel gas, coke, sulfur) are assumed to be waste streams and no energy

332

is allocated to them. Full specifications of each base scenario and references are

333

in Tables S3 to S5 of SI.

334

To identify potential drivers of variability in upgrading GHG intensity, we analyze

335

the effect of upgrader technology and configuration, and of product quality

336

properties on upgrading emissions. In the former, we model and evaluate the

337

GHG emissions that each type of upgrading technology produces per unit of total

42,43,44,45,46,47,48,64


) is used as input value for

15

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 49

338

product produced (regardless of quality) under base scenario conditions. In the

339

latter, we model and assess the GHGs allocated to each SCO blend (produced

340

by a single technology) in proportion to its processing and improvement in quality,

341

under base scenario assumptions. We focus our analysis on the technologies that

342

simultaneously produce SCO blends with contrasting properties, DC and HC.

343

These technologies produce light sweet SCO blends alongside medium to heavy

344

sour SCO products. In DC’s base scenario, a light sweet SCO blend is modeled

345

with properties similar to the historical 5-year average reported by Crudemonitor50

346

for Suncor Synthetic A blend (OSA50). ULSD with properties similar to those

347

reported by Suncor65 is also modeled. The heavy sour SCO blends in DC’s base

348

scenario are modeled after Suncor Synthetic H blend (OSH50) and Suncor

349

Custom Cracked blend (OCC40). The historical 5-year average of product

350

properties data published by Crudemonitor50 is used to guide the properties of the

351

blend similar to OSH. Typical properties of OCC proposed by Gray40 inform the

352

modeling of this blend in OSTUM 2.0. A medium sour SCO blend is also

353

modeled, composed of all remaining DC product streams. It is known that Suncor

354

produces additional SCO products, but no public information was found on their

355

quality properties. Table S6 in SI presents the product quality properties proposed

356

in the literature for each DC-derived SCO product that were used to inform their

357

modeling in DC’s base scenario.

358

In HC’s base scenario, two light sweet SCO blends are modeled with properties

359

similar to the historical 5-year average values reported by Crudemonitor50 for

360

Shell’s Premium Albian Synthetic blend (PAS) and Shell Synthetic Light (SSX).


16

Page 17 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

361

The properties of HC’s heavy sour SCO blend are informed by the historical 5-

362

year average reported for Shell’s Albian Heavy Synthetic (AHS50). Table S6

363

presents Crudemonitor’s 5-year averages used to inform the modeling of HC’s

364

SCO blends. Since no public information was found about the Scotford

365

Upgrader’s product distribution by SCO blend, two HC base scenarios are

366

developed, each with different yields of the three SCO blends. In the first base

367

scenario (HC base scenario 1), the HC upgrader produces equal amounts of

368

each SCO blend (i.e., each SCO blend’s volumetric yield is close to ⅓). In the

369

second HC base scenario (HC base scenario 2), ½ of the upgrader’s SCO

370

production is comprised of one of the light sweet SCO blends, ¼ is another light

371

sweet SCO blend, and the remaining ¼ of the production is heavy sour SCO.

372

Low and High GHG Emissions Scenarios. Low and high GHG emissions

373

scenarios (“low scenarios” and “high scenarios” hereinafter) are developed for

374

each upgrading technology, representing “more efficient” and “less efficient”

375

operations, respectively. OSTUM 2.0’s input parameters varied in the low and

376

high scenarios are those associated with the energy requirements of the upgrader

377

(e.g., amount of by-products fuel gas and coke used as fuels, energy use factors).

378

It is assumed that there are no variations in the process itself, so that product

379

yields, product quality, hydrogen consumption, etc., remain unchanged.

380

Upgrading process units are designed to achieve target product yields that do not

381

vary significantly under normal operating conditions. The development of low and

382

high scenarios realistically varying the base scenarios’ product property (and

383

hydrogen consumption) assumptions is not feasible as at this time there is not


17

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 49

384

enough public data on the SCO products modeled. Therefore, the low and high

385

scenarios represent uncertainty of the baseline GHG intensity estimates only

386

related to the energy requirements and do not encompass other sources of

387

uncertainty as these cannot be realistically simulated at this time. A detailed

388

description of the low and high scenarios is in SI, including specifications in

389

Tables S7 to S9.

390

Sensitivity Analysis. Sensitivity analysis is used to explore the impact on

391

upgrading GHG intensity of changes in the efficiency of upgrading operations and

392

in the use of upgrading by-products fuel gas and coke as fuels. The sensitivity

393

analysis is performed by running OSTUM 2.0’s base scenarios for each

394

technology while varying total fuel, steam, and electricity requirements and the

395

total consumption of fuel gas and coke, one at a time. The variations are

396

performed in cumulative incremental steps of ± 20% of the base scenario values

397

until the minimum/maximum bounds of input parameter values suggested by the

398

literature are met. Therefore, the ranges of the input parameters are informed by

399

the ranges of energy requirements found in the literature for each upgrading

400

process unit. See Tables S10 to S12 of SI for additional details.

401

Comparison of OSTUM 2.0’s GHG Intensities with Literature. GHG emissions

402

data from commercial oil sands recovery, extraction and upgrading operations

403

published by ECCC29 are not directly comparable with OSTUM 2.0’s upgrading

404

GHG intensity estimates for validation purposes due to the aggregation of the

405

ECCC data. Facility-wise GHG emissions data from Shell’s Scotford stand-alone

406

upgrader29 is not representative of the HC upgrading technology because the


18

Page 19 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

407

upgrading operations of this facility are highly integrated with the adjacent

408

refinery, petrochemical, and CO2 sequestration operations. Therefore, steps are

409

taken to evaluate OSTUM 2.0’s GHG intensity results through comparisons with

410

estimates reported by relevant literature models and studies of the upgrading

411

technologies. The GHG intensities of DC, HC, and HC/FC (per unit of total

412

product produced, regardless of quality) are compared with results from

413

GHOST,21,66

414

CanmetENERGY,26,25 and FUNNEL-GHG-OS.22 These models and studies are

415

selected because they report estimates of GHG intensity specifying an upgrading

416

technology, unlike other sources that do not specify the technology.

417

With the exception of ULSD produced through DC, which is a finished

418

transportation fuel, all of the SCOs modeled are crude oil blends. As such,

419

ULSD’s base scenario GHG intensity estimated by OSTUM 2.0 is compared with

420

the GHG intensity estimated for ULSD (from oil sands dilbit) produced in deep-

421

conversion refineries (a coking refinery and a hydrocracking refinery) by the

422

PRELIM v1.2.1 model.67,68 PRELIM v1.2.1 is an open-source, life cycle-based

423

refinery model developed by our research group. Its original version is

424

documented in Abella et al.67 and enhanced versions are available online.68 For

425

the comparison of ULSD production pathways, OSTUM 2.0’s base scenario GHG

426

intensity assumes BHB as the dilbit feedstock and the inputs and assumptions as

427

described in Table S3. PRELIM v1.2.1’s baseline GHG intensities use BHB as

428

feedstock and the default values proposed by the model for all inputs and

429

assumptions. Because upgraders and refineries are similar in some processes

OSOM,24

two

studies

by


Jacobs

Consultancy,19,20

19

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 49

430

but different in others, OSTUM 2.0 and PRELIM v1.2.1’s input parameters are

431

different. Therefore, the assumptions and conditions of the low and high

432

scenarios developed in this study for an upgrading facility do not directly apply to

433

PRELIM v1.2.1’s refinery configurations. A reasonable source of variation

434

common to both production pathways is the type of dilbit feedstock processed.

435

Therefore, ranges of GHG intensities for each ULSD production pathway are

436

determined by running the respective base scenarios in OSTUM 2.0 and PRELIM

437

v1.2.1 with crude assays of Cold Lake, Western Canadian Select, and Christina

438

Dilbit Blend dilbits. These crude assays, published by Crudemonitor,50 are

439

included in both models’ feedstock databases. This comparison provides

440

preliminary insights on the magnitude of the difference in GHG intensities

441

between the two production pathways for ULSD.

442

Comparison of OSTUM 2.0’s Energy Use with Industry Data. Although AER

443

data is used to inform the values of some input parameters in OSTUM 2.0’s

444

scenarios to increase their representativeness (i.e., volume of bitumen upgraded,

445

fractions of fuel gas and coke production used as fuels, and of cogenerated

446

electricity used), a comparison of OSTUM 2.0’s energy use estimates for each

447

upgrading technology with historical AER data is still informative to evaluate

448

OSTUM 2.0’s performance. OSTUM 2.0’s energy use estimation methods are

449

entirely independent of AER’s energy use databases. AER’s ST39 database is

450

the only source of publicly available oil sands facility data that is detailed enough

451

to be constructively compared with OSTUM 2.0’s energy use estimates for

452

evaluation purposes, as several of the energy inputs to the recovery, extraction,


20

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Energy & Fuels

453

and upgrading operations that are collectively reported by AER are known to be

454

exclusively used in the upgrading stage. In addition, AER’s data present

455

significant intra- and inter-project variability over time that might be better

456

understood when compared with OSTUM 2.0’s results from scenario and

457

sensitivity analyses.

458

OSTUM 2.0’s results are compared with data reported by companies operating

459

the largest capacity of each upgrading technology. OSTUM’s DC scenario results

460

are compared with Suncor’s full dataset available (1983-2016) and that from the

461

period after its last known improvement/expansion of upgrading operations (2008-

462

16). OSTUM’s HC results are compared with Shell’s full dataset (2003-16), and

463

with those of recent operations (2011-16), and HC/FC results are compared with

464

Syncrude’s full dataset (1983-2016) and recent operations (2007-16). Several

465

years of AER’s historical energy use data are not available and a procedure

466

described in SI is followed to fill in gaps. Box plots are developed to visualize the

467

variability in the AER data and facilitate comparison with OSTUM 2.0’s results.

468

Results and Discussion

469

Base Scenario Results. Estimates of product yields, product properties, and

470

direct and indirect GHG intensities for DC, HC, and HC/FC base scenarios

471

obtained from OSTUM 2.0 are shown in Table 1. More detailed results of the

472

base scenarios (e.g., volumetric yield of products, energy use) are presented in

473

Table S13 of SI. The upgrading technology with the highest SCO yield is HC (93

474

wt%, 107 vol%), followed by HC/FC (87 wt%, 103 vol%) and DC (77 wt%, 89


21

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 49

475

vol%). HC’s hydroconverter vacuum residue conversion (80 wt%) is similar to

476

Shell Scotford’s LC-Finers and is the highest of the three upgrading

477

configurations. In HC, all unconverted residuum from the hydroconverter is

478

blended into a heavy sour SCO blend. HC/FC’s hydroconverter conversion is

479

lower (60 wt%) and similar to Syncrude’s LC-Finer. Although all unconverted

480

residuum in HC/FC is further processed in the fluid coker, a fraction of it ends up

481

stockpiled as by-product coke. DC’s SCO yield is the lowest and most impacted

482

by the generation of coke (i.e., 3.5 more kilograms of coke per cubic meter (m3) of

483

SCO produced than in the case of HC/FC).

484

All light sweet SCO blends modeled by OSTUM 2.0 share similar product

485

properties (e.g., API: 31-33°API, sulfur content: 0.1-0.2 wt%) and are consistent

486

with the ranges of properties reported by Crudemonitor50 for Alberta’s light sweet

487

SCO blends (see Table S6). ULSD is a finished fuel product and therefore its

488

properties are considerably different than the rest of the light sweet SCO blends

489

modeled. The properties of the heavy sour SCO blends fall within a range of 19-

490

21 °API and 2-3 wt% sulfur content, in accordance with the properties of the

491

partially upgraded SCOs reported by Crudemonitor50 (Suncor’s OSH and Shell’s

492

AHS) and by Gray40 (Suncor’s OCC). As it is the case in commercial upgrading

493

operations, DC’s medium sour SCO is a blend of all remaining product streams

494

not in other SCO products. No public product quality information was found to

495

validate the properties of this SCO blend.

496

The most energy-intensive technology is HC/FC (171 kJ/MJ SCO), followed by

497

HC (160 kJ/MJ SCO) and DC (112 kJ/MJ SCO). HC/FC’s use of two conversion


22

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Energy & Fuels

498

units (hydroconverter and fluid coker) demands 15% more energy per m3 of SCO

499

produced in the primary upgrading or conversion stage than in the HC

500

technology. The fluid coker also increases the steam demand of the upgrader, so

501

that HC/FC’s boilers demand 2.4 times more energy per m3 of SCO produced

502

than the boilers in HC.


23

Energy & Fuels Page 24 of 49 503 Table 1. Product yields, product properties, and direct and indirect GHG intensities for delayed coking based- (DC), hydroconversion based504 (HC), and combined hydroconversion and fluid coking based upgrading (HC/FC) – OSTUM 2.0. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Product Yields (based on ATB feed to VDU) Gases (C1-C4 + H2S + NH3) Light sweet SCO Medium/heavy sour SCO Total SCO Coke HCU residuum (not blended) Total products SCO/bitumen mass ratio Product Properties API gravity (°API) 3 Density (kg/m ) Sulfur content (wt%) Nitrogen content (wt%) H/C ratio GHG Intensities (g CO2e/MJ product) Emissions NG used in upgrading by type of processes + utilities fuel FG used in upgrading processes + utilities Coke used in upgrading processes + utilities Electricity from grid Fugitives/venting/flaring Emissions DRU by process VDU unit DCU HCU FCU N HT LGO HT HGO HT AT/AR SRU SMR Boilers Cogeneration Fugitives/venting/flaring Total

LSB ULSD HSB1 32.6 36.6 19.3 861.3 840.9 937.4 0.18 0.0014 3.15 0.07 0.0005 0.16 1.65 1.69 1.53 LSB ULSD HSB1 HSB2

DC wt% 6.1 LSB 26.9 ULSD 14.1 HSB 1 12.4 HSB 2 15.0 MSB 8.5 76.9 17.5 NA 100.5 0.8 HSB2 MSB 20.8 24.0 928.0 909.1 3.19 3.10 0.24 0.23 1.54 1.56 MSB Total SCO

HC wt% 8.1 30.2 30.4 32.8

HC/FC wt% 8.4 LSB 87.1

LSB2 31.4 868.0 0.06 0.04 1.81 HSB

93.5 NA 0.0 101.5 0.9 HSB 19.1 938.6 2.28 0.21 1.63 Total SCO

87.1 5.7 0.0 101.2 0.9 LSB 32.3 863.2 0.18 0.06 1.67 Total SCO

LSB1 LSB2 HSB

LSB1 32.0 864.6 0.08 0.05 1.81 LSB1 LSB2

4.8

4.9

1.5

1.9

2.0

3.4

9.4

9.4

5.1

7.9

7.7

3.3

3.3

1.0

1.4

1.5

2.4

3.0

3.0

1.1

2.4

2.6

2.5

2.9

1.3

2.2

2.3

2.3

NA

NA

NA

NA

0.9

0.0 0.3 1.5 1.0 0.8 NA NA 0.1 0.3 1.2 0.0 0.01 2.7 2.6 0.4 0.3 11.0

0.0 0.4 1.7 1.1 0.9 NA NA 0.0 1.5 0.0 0.0 0.01 2.5 3.0 0.5 0.4 11.5

0.0 0.3 0.7 0.5 0.4 NA NA 0.1 0.04 0.01 0.0 0.01 0.4 1.3 0.2 0.3 4.1

0.0 0.4 1.3 0.8 0.7 NA NA 0.0 0.0 0.0 0.0 0.01 0.0 2.2 0.4 0.4 5.9

0.0 0.4 1.4 0.9 0.8 NA NA 0.0 0.0 0.0 0.0 0.01 0.0 2.3 0.5 0.4 6.2

0.0 0.4 1.3 0.9 0.8 NA NA 0.1 0.4 0.4 0.0 0.01 1.5 2.3 0.4 0.4 8.5

0.5 0.1 1.4 0.9 NA 0.7 NA 0.2 0.4 1.4 0.004 0.03 5.0 0.8 2.1 0.1 13.0

0.5 0.1 1.4 0.9 NA 0.7 NA 0.1 0.7 1.2 0.004 0.03 5.0 0.8 2.1 0.1 13.1

0.1 0.1 0.8 0.5 NA 0.5 NA 0.1 0.2 0.05 0.001 0.01 2.4 0.4 1.4 0.1 6.5

0.4 0.1 1.2 0.7 NA 0.6 NA 0.1 0.4 0.9 0.003 0.02 4.1 0.7 1.9 0.1 10.8

0.8 0.2 1.3 0.6 NA 0.6 1.3 0.2 0.6 1.1 0.01 0.04 3.7 1.6 1.2 0.2 12.2


24

Page 25 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Energy & Fuels 505 DC: delayed coking based upgrading; HC: hydroconversion based upgrading; HC/FC: combined hydroconversion and fluid coking based 506 upgrading; ATB: atmospheric topped bitumen; VDU: vacuum distillation unit; C1-C4: methane, ethane, propane and butane gases; H2S: 507 hydrogen sulphide; NH3: ammonia; SCO: synthetic crude oil; LSB: light sweet SCO blend; ULSD: ultra-low sulfur diesel; HSB: heavy sour SCO 508 blend; MSB: medium sour SCO blend; HCU: hydroconverter unit; vol.: volumetric; H/C: hydrogen to carbon ratio; FG: fuel gas; NA: not 509 applicable; GHG: greenhouse gas; NG: natural gas; DRU: diluent recovery unit; DCU: delayed coker unit; FCU: fluid coker unit; N HT: naphtha 510 hydrotreater unit; LGO HT: light gas oil hydrotreater unit; HGO HT: heavy gas oil hydrotreater unit; AT/AR: amine treatment/regeneration units; 511 SRU: sulfur recovery unit; SMR: steam methane reformer.


25

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 49

512

The ranking of GHG intensity of the technologies follows the same ranking as

513

energy intensity: HC/FC, HC, and DC (12.2, 10.8 and 8.5 g CO2e/MJ of SCO,

514

respectively) (Figure 1). This also aligns with the general finding that more

515

extensive processing (and thus the consumption of more energy) results in a

516

higher quality product. However, these relationships are not linear or easily

517

predicted using simple operating data.

518

product yield (and associated economics) and GHG intensity but exploration of

519

this aspect is beyond the scope of this paper and will be the subject of future

520

work.

521

The main factor influencing HC/FC’s higher emissions intensity with respect to

522

HC is the combustion of carbon-intensive coke as fuel in the fluid coker. In HC/FC

523

and HC, the main point source of emissions is the steam methane reformer

524

(SMR; HC/FC: 30% of total emissions, HC: 38%), which produces the hydrogen

525

demanded by both technologies. In the base scenarios, HC/FC consumes 15%

526

less hydrogen than HC per m3 of SCO produced because its hydroconverter has

527

a lower residue conversion (60 wt%) than HC’s (80 wt%), and because HC/FC

528

consumes less hydrogen in the hydrotreating stage (its light sweet SCO blend

529

has, in general, higher sulfur and nitrogen contents than HC’s lights sweet

530

blends). HC/FC’s lower hydrogen consumption and the burning of coke as fuel

531

result in its total emissions from heat production in all process units (except in

532

utilities –boilers, SMR, and cogeneration) being a larger share (40%) of total

533

emissions than those associated with hydrogen production (30%). Steam

534

production in HC/FC accounts for 13% of total emissions. In terms of energy

There are also relationships between


26

Page 27 of 49

535

inputs, natural gas use generates 63% of total emissions. The scenario results

536

are discussed in the Low and High Scenario Results section.

20 18 16 GHG Emissions Intensity g CO2e/MJ of SCO

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

14.8

14.3

14 12

11.2

10

9.7 8.6

8 6.3

6 4

11

12

8

2 0 DC

537 538 539 540 541 542 543 544 545 546 547

HC

HC/FC

Figure 1. Upgrading GHG intensities reported by OSTUM 2.0 for delayed coking based-, hydroconversion based- and combined hydroconversion and fluid coking based upgrading technologies. Notes: bars represent base scenarios’ GHG intensity results; high-low lines represent high and low scenarios’ GHG intensities results. Abbreviations: DC: delayed coking based upgrading; HC: hydroconversion based upgrading; HC/FC: combined hydroconversion and fluid coking based upgrading; SCO: synthetic crude oil.

548

Two HC base scenarios were developed with different yields of SCO blends. The

549

GHG intensities calculated by OSTUM 2.0 for both HC’s base scenarios (both the

550

emissions generated for the total SCO produced and those allocated to each of

551

the three SCO blends) are similar (average 4% difference). Therefore, only the

552

results of the HC base scenario 1 (where the HC upgrader produces equal

553

amounts of each SCO blend; i.e., volumetric yield of each SCO blend is close to


27

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 49

554

⅓) are shown. Results for the HC base scenario 2, where the HC upgrader

555

produces more of a particular light sweet SCO blend than of the other two SCO

556

blends, are in Table S14 and Figure S1 of SI. In HC, emissions from hydrogen

557

production (38% of total emissions) are greater than all emissions from heat

558

production in all process units (34% of total emissions; not accounting for heat

559

production in utilities). Cogeneration’s emissions account for another 18% of HC’s

560

total emissions. Natural gas is the most common fuel used, generating 73% of

561

total emissions.

562

HC/FC and HC consume hydrogen in their conversion units and in the

563

hydrotreating stage of most of their SCO products while DC only consumes

564

hydrogen in the hydrotreating stage of its light sweet SCO blends (which account

565

for 41 wt% of total yield in the base scenario). The implications of this

566

fundamental difference between the technologies are that the energy demand of

567

DC’s SMR is significantly lower (65% and 59% lower than the energy demanded

568

by HC and HC/FC’s SMRs, respectively) and emissions from hydrogen

569

production represent only 17% of DC’s total emissions. Heat generation in

570

process heaters is the largest source of emissions (46% of total emissions) in DC.

571

Steam generation (28% of total emissions) is the second most relevant source of

572

emissions. DC’s largest point sources of emissions are boilers as coke is used

573

as fuel in some of them. Natural gas is also the most common fuel used in DC

574

systems (41% of total emissions).

575

In general, the light sweet SCO blends produced through DC and HC have the

576

same sources of emissions described above for each of their production


28

Page 29 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

577

technologies overall. Exceptions to this are the GHG intensities of DC’s light

578

sweet SCO blend and ULSD, which are more affected by hydrogen production

579

emissions (which represent 22%-25% of their total emissions). Emissions from

580

DC’s medium to heavy sour SCOs are mainly associated with the burning of fuels

581

to generate heat and steam (47% and 35% of total emissions on average,

582

respectively).

583

produced by atmospheric and vacuum distillation and thermal cracking,

584

processes in which heat and steam requirements are dominant sources of

585

emissions. Emissions from HC’s heavy sour SCO blend result primarily from the

586

production of hydrogen (37% of total emissions intensity - due to most of the

587

blend’s components being processed by the hydroconverter and hydrotreaters)

588

and process heat (31% of total emissions intensity).

589

Low and High Scenario Results. The ranges of GHG intensities obtained for

590

each upgrading technology for the low and high scenarios, representing more

591

efficient and less efficient operations, are shown in Figure 1. Table S15 and S16

592

in SI present detailed results for the scenarios. DC’s low and high scenario GHG

593

intensities are 26% lower and 32% higher than the base scenario intensity.

594

Variations in the use of coke and natural gas as fuels for heat and steam

595

generation and cogeneration drive these changes. In HC and HC/FC, low and

596

high GHG scenario intensities are 20% lower (both technologies) and 33% and

597

21% higher, respectively. Changes in the demands for natural gas for heat and

598

steam generation, and cogeneration/grid electricity drive the changes in

599

emissions intensity.

These SCO blends are composed of crude fractions mostly


29

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 49

600

Shell’s historical data reported to AER (fraction of fuel gas used and fraction of

601

cogenerated electricity used) vary more than the historical data reported by

602

Syncrude (which also reports a constant historical use of coke). We use

603

measures of these variations to inform the low and high scenarios of each

604

technology (see Methods). As a result, the high scenario for HC deviates more

605

from its base scenario than in the case of HC/FC and therefore, the former

606

generates a wider range of GHG intensity. Real world variations in the GHG

607

intensities of these technologies are expected to be larger because the scenario

608

analysis does not take into account other likely sources of variability (e.g., product

609

quality).

610

Drivers of Variability in Upgrading GHG Intensity. The analysis of the baseline

611

GHG intensities of the three main upgrading technologies commercially operating

612

in Alberta allows the identification of potential drivers of variability in upgrading

613

GHG intensity. The first driver is the type of technology applied to upgrade

614

bitumen. There is a variation of 36% between the lowest (DC) and highest

615

(HC/FC) base scenario GHG intensities attributable to differences in technological

616

approaches to produce one MJ of SCO, regardless of the quality of the

617

product(s). A second driver of variability is associated with differences in

618

properties of SCO blends. Some commercial upgrading facilities in Alberta using

619

DC and HC simultaneously produce light sweet and heavy sour SCO products.

620

Figure 2 presents the GHG intensities of these products for OSTUM 2.0’s

621

scenarios using hydrogen content as the basis for allocation (GHG intensities of


30

Page 31 of 49

622

each SCO blend are similar when using any of the three allocation methods – see

623

Figure S2 in SI).

20

(a) Delayed Coking Based Upgrading

18

GHG Emissions Intensity g CO2e/MJ of Product

16 14.2

14 12 10

6

2

8.8

8.7

8.2

8 5.8

11.0

4

3.9

3.6 2.5

4.1

5.9

6.2

DC-HSB2

DC-MSB

0 DC-HSB1

624 20

DC-LSB

(b) Hydroconversion Based Upgrading

18

17.2

17.3

16 GHG Emissions Intensity g CO2e/MJ of Product

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

14 12 10.5

10

10.6

8.8

8 6

13.0

13.1

HC-LSB1

HC-LSB2

4.9 4

6.5

2 0 HC-HSB

625 626 627 628 629 630 631

Figure 2. Variations in GHG emission intensities of co-products produced through (a) delayed coking based upgrading and (b) hydroconversion based upgrading technologies in OSTUM 2.0. Notes: bars represent base scenario GHG intensity results; high-low lines represent high and low scenarios’ GHG intensities results. Abbreviations: DC: delayed coking-based


31

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 49

632 633 634

upgrading; HC: hydroconversion-based upgrading; HSB: heavy sour SCO blend; MSB: medium sour SCO blend; LSB: light sweet SCO blend.

635

There is a variation in baseline GHG intensities of up to 91% in DC co-products

636

due to differences in product properties (between the GHG intensity of the light

637

sweet SCO blend with properties similar to OSA and the GHG intensity of the

638

heavy sour blend similar to OSH). A variation of up to 68% is found in the

639

baseline GHG intensities of HC co-products (between the GHG intensity of the

640

light sweet SCO blend similar to PAS and the GHG intensity of the heavy sour

641

blend similar to AHS).

642

A third driver of variability, which is associated with changes in upgrader energy

643

inputs, is identified through the sensitivity analysis. Variations in the total natural

644

gas requirements of the upgraders have the greatest impact on the GHG

645

intensities of all SCO products (Figure S3 in SI). There is an average variation of

646

45% in GHG intensities for all SCO products when the upgrader’s natural gas

647

requirements vary by ± 20-40% of the base scenario requirements. Variations in

648

the upgrader’s steam requirements result in an average variation of 20% for the

649

GHG intensities of all SCO products when steam requirements vary ± 40% of the

650

base scenario requirements. Variations in the amount of coke burned in DC and

651

HC/FC has the third greatest impact on GHG intensities. Coke’s impact on DC-

652

derived products’ GHG intensities is greater (14% average difference) than on

653

HC/FC-derived SCO (4% difference). This is because DC burns 2.5 times more

654

coke per m3 of SCO produced than HC/FC, and because the range of variation in

655

coke use is slightly greater in DC than in HC/FC (percentage of coke production


32

Page 33 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

656

used as fuel varies between 0-15% and 13%-25%, respectively). While a

657

narrower range of variation in HC/FC’s coke use is suggested in the literature69

658

for the operation of a fluid coker (a variation controlled by the technology and

659

process performance), the wider range in DC’s coke consumption reflects its use

660

as an alternative fuel in place of natural gas and other fuels in boilers, where

661

there may be greater fluctuations depending on operational decisions.

662

Changes in the volume of fuel gas used (as a substitute for natural gas) and

663

variations in the total electricity requirements of the upgrader have small impacts

664

on the GHG intensities of SCO products (average variation in GHG intensity of

665

2%). Fuel gas and natural gas generate similar levels of direct (on-site generated)

666

GHG emissions when combusted. However, when less fuel gas is used (with

667

respect to the base scenario), the volume of natural gas used increases

668

(assuming constant fuel requirements), and accordingly, indirect emissions from

669

its off-site production also increase (while by-product fuel gas has no indirect

670

emissions). Therefore, the marginal impact on upgrading emissions associated

671

with changes in fuel gas used is associated with considering/not considering

672

indirect emissions from fuel production. Decarbonizing the Alberta electricity grid

673

has minimal impact (1% reduction) on upgrading GHG emissions intensity. The

674

main reason for this small difference is that most upgrading facilities use

675

electricity generated on-site through cogeneration and only a small fraction of

676

their electricity comes from the grid.

677

Impact of the Extent of Upgrading on Well-to-Wheel GHG Intensity. The

678

portion of GHG emissions allocated to each SCO blend is proportional to the level


33

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 49

679

of processing required by each of its components. Light sweet SCO blends are

680

generally composed of hydrotreated products. Hydrotreating decreases the levels

681

of impurities in SCO blends but also increases their GHG intensities. The GHG

682

emissions allocated to medium to heavy sour SCO blends are lower because

683

they are mainly composed of virgin and cracked products from primary upgrading

684

units without any subsequent hydrotreating. These SCO products have lower

685

hydrogen contents, lower API gravities, and higher levels of impurities. The

686

hydrogen to carbon (H/C) ratio of a SCO blend may be used as an indicator of its

687

quality and GHG intensity, as discussed in SI. Medium to heavy sour SCO blends

688

generally require more processing at the refining stage and result in higher

689

refining GHG emissions than do light sweet blends (depending on refinery

690

configuration).

691

A preliminary WTW assessment of the GHG intensities of oil sands products

692

derived from the upgrading technologies and product qualities evaluated is

693

performed to provide insights on the implications that the “extent” of upgrading

694

may have throughout the full life cycle of transportation fuels (see page S65 in

695

SI). The WTW GHG intensity for a fuel derived from a production pathway where

696

a DC upgrader partially upgrades bitumen to a heavy sour SCO (similar to OSH)

697

prior to the refining stage is estimated to be 3% greater than one from a pathway

698

where the DC upgrader fully upgrades bitumen to a light sweet SCO (similar to

699

OSA) prior to refining (see SI Figure S5). In contrast, the WTW GHG intensity for

700

a fuel derived from a production pathway where a HC upgrader partially upgrades

701

bitumen to a heavy sour SCO (similar to AHS) is estimated to be 3% lower than


34

Page 35 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

702

one from a pathway where the HC upgrader fully upgrades bitumen to a light

703

sweet SCO (similar to PAS). While the above differences in WTW emissions

704

intensity appear modest, the vehicle combustion emissions (tank-to-wheel)

705

dominate WTW emissions and somewhat mask differences among the well-to-

706

tank components of the fuel production pathways. The corresponding

707

percentages when only considering the well-to-tank components of the above

708

WTW analyses increase to a 9% increase and 9% decrease, respectively.

709

The fuel derived from the HC-based light sweet SCO blend (similar to PAS) has

710

the highest WTW GHG intensity of all fuels analyzed, potentially because of HC’s

711

extensive use of hydrogen. The WTW GHG intensity of a fuel derived from a

712

HC/FC-based light sweet SCO (similar to SSP) is the second highest. A fuel

713

produced through the bitumen dilution pathway, which does not involve

714

upgrading, is estimated to have the lowest WTW GHG intensity. While

715

preliminary, these findings highlight the importance of taking into account product

716

properties when modeling the GHG performance of the upgrading and refining

717

stages, and call for comprehensive WTW assessments that incorporate this level

718

of detail.

719

Comparison of OSTUM 2.0’s GHG Intensities with Literature. The GHG

720

intensities of DC, HC, and HC/FC from OSTUM 2.0’s scenario analysis are

721

evaluated through a comparison with relevant literature models and studies

722

reporting estimates of GHG emissions of these technologies. Figure 3 (a) shows

723

that there is considerable variability in the GHG intensities reported by the

724

literature for DC (79% difference between highest and lowest estimates). OSTUM


35

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 49

725

2.0’s base scenario GHG intensity for DC (8.5 g CO2e/MJ of total SCO) is similar

726

(4% smaller) to the average of GHG intensities reported for this technology (8.8 g

727

CO2e/MJ SCO). The GHG intensities reported by the literature for HC have a

728

similar variability to DC’s (see Figure 3 (b)) with an 83% difference between the

729

highest and lowest GHG intensity estimates. OSTUM 2.0’s base scenario GHG

730

intensity for HC (10.8 g CO2e/MJ SCO) is similar (2% smaller) to the average of

731

literature values reported for HC (11.0 g CO2e/MJ SCO). There is only one

732

study24 reporting a GHG intensity estimate for HC/FC (9.8 g CO2e/MJ SCO), and

733

it is 24% lower than OSTUM 2.0’s base scenario estimate (12.2 g CO2e/MJ

734

SCO). The main reasons for this discrepancy might be that in OSTUM 2.0 it is

735

assumed that coke is used as a fuel in the fluid coker (as is the case in

736

Syncrude’s operations) while Ordorica-Garcia24 did not report any coke use.

737

Different assumptions and calculation methods also led to the study reporting

738

lower energy use of the HC/FC upgrader.

739


36

Page 37 of 49

20

(a) Delayed Coking Based Upgrading

GHG Emissions Intensity g CO2e/MJ of SCO

18 16 14 12

11.2

10 8

14.2

6.3

6

9.6

8.5

4

8.3

7.4

8.1

7.9

6.2

2

20

N N FU

(b) Hydroconversion Based Upgrading

18 GHG Emissions Intensity g CO2e/MJ of SCO

EL -G H

ER EN et

an m C

740

G -O S

G Y

12 bs co

Ja

G

Ja

H

co

bs

20

09 20

ig h H O ST

O ST

G H

O ST U

M

O SO M

Lo w

0

16 14.3

14 12 10

8.6

8 6

15.5 11.8

10.8

11.6

4

11.1

9.5

6.4

2 0 M

TU

OS

OM

OS

T OS

igh

w

Lo

GH

G

HO

H ST

Ja

co

2 bs

00

9

nm

Ca

Y

RG

NE

etE

E

NN

FU

S

-O

HG

L-G

741 20 18 GHG Emissions Intensity g CO2e/MJ of SCO

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

(c) Combined Hydroconversion and Fluid Coking Based Upgrading

16 14.8

14 12 10

9.7

8 6

12.2 9.8

4 2 0

742

OSTUM


OSOM

37

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 49

743 744 745

Figure 3. Comparison of OSTUM 2.0’s GHG intensities for (a) delayed coking based-, (b) hydroconversion based-, and (c) combined hydroconversion and fluid coking based upgrading with estimates reported in the literature.

746 747 748 749 750

Notes: OSOM’s results are published in Ordorica-Garcia et al.;24 GHOST’s results are published in Charpentier et al.21 and Bergerson et al.;66 Jacobs 2009’s results are reported in Keesom et al.;20 Jacobs 2012 results are reported in Bohm et al.;19 CanmetENERGY’s results are published in Alvarez-Majmutov et al.;26,25 and FUNNELGHG-OS’ results are published in Nimana et al.22

751

Abbreviations: SCO: synthetic crude oil.

752 753

OSTUM 2.0’s base scenario GHG intensity of ULSD produced through DC and

754

PRELIMv1.2.1’s GHG intensities of ULSD produced in a coking refinery and in a

755

hydrocracking refinery are shown in Figure S6 of SI. The GHG intensity of ULSD

756

produced in a coking refinery is 35% higher than when produced in a DC

757

upgrader when processing Borealis Heavy Blend (BHB) as dilbit feedstock, and

758

an average of 25% higher when processing other dilbit feedstocks available in the

759

models. Likewise, the GHG intensity of ULSD produced in a hydrocracking

760

refinery is 80% higher than from DC when processing BHB, and 51% higher

761

when processing other dilbits. The higher estimated GHG intensity of ULSD

762

produced by refineries may be associated with the use of process units for ULSD

763

production not employed in upgraders (e.g., hydrocracker, catalytic cracker) and

764

with the models using different calculation methods and assumptions. These

765

preliminary results suggest an in-depth assessment of the GHG intensity of

766

different ULSD production pathways is a relevant future research area.

767

Comparison of OSTUM 2.0’s Energy Use Results with Industry Data.

768

OSTUM 2.0’s energy use results for HC from the scenario and sensitivity

769

analyses are evaluated through a comparison with energy use statistics from


38

Page 39 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

770

Shell’s Scotford HC upgrading operations (Figure S7 in SI). This is the only

771

energy use data publicly available for a commercial upgrader operating

772

independently from the bitumen recovery and extraction stages (as of end of

773

2016, the last year of data analyzed). However, Scotford’s energy use data is

774

highly impacted by the integration of its upgrader with a refinery, chemicals plant,

775

and carbon capture project (the facility is known to be one of North America’s

776

most efficient, modern and integrated hydrocarbon processing sites). As OSTUM

777

2.0’s results are representative of a stand-alone HC facility, differences are

778

expected between OSTUM 2.0’s energy use results for a HC facility and

779

Scotford’s integrated data. For example, OSTUM 2.0’s fuel gas production and

780

use estimates are below Scotford’s median, while OSTUM 2.0’s electricity and

781

natural gas requirements are above it. Potential reasons for these discrepancies

782

may be related to the boundaries set for upgrading in an integrated operation

783

such as Scotford’s (additional fuel gas-generating process units from the refining

784

operations may be considered part of the upgrader), and to the use of more

785

efficient processes (in part due to energy integration) than those assumed by the

786

refining and upgrading literature, upon which OSTUM 2.0 is based. When

787

OSTUM 2.0’s HC kinetic models are adjusted to increase fuel gas production to

788

Scotford’s levels, OSTUM 2.0’s natural gas consumption decreases to

789

approximately Scotford’s median. Similarly, when the efficiency of OSTUM 2.0’s

790

HC upgrader is increased, its natural gas use and electricity use estimates are

791

closer to the levels of natural gas and electricity consumption reported by

792

Scotford. Fuel gas production and use and electricity use present the largest

793

historical variability in Scotford’s data. In recent years (2011-16), the variability in


39

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 49

794

fuel gas production and use as fuel has increased. Shell consistently reports

795

higher monthly fuel gas consumption than production, potentially suggesting that

796

the surplus might come from the adjacent refining operations, and that the

797

integration of operations might contribute to the increase in variability of the data.

798

OSTUM 2.0’s energy use results for DC and HC/FC are evaluated by comparison

799

with energy use statistics from Suncor and Syncrude’s operations, respectively

800

(Figures S8 and S9 and discussion in SI).

801

Conclusions

802

We developed and applied OSTUM 2.0 to the main upgrading technologies in

803

commercial operation in Alberta to improve understanding of energy use and

804

GHG emissions intensity associated with upgrading oil sands bitumen to SCO.

805

For each upgrading technology, we reported baselines and ranges of GHG

806

intensities, characterized GHG-intensive energy inputs and processes and

807

identified potential drivers of variability of the GHG intensity. Finally, we compared

808

OSTUM 2.0’s energy use and GHG intensity estimates with relevant literature,

809

and analyzed variability in publicly reported industry energy use considering

810

OSTUM’s results.

811

A better understanding of variability in upgrading GHG intensity increases

812

confidence in modeling estimates and can assist stakeholders in regulating,

813

controlling, and decreasing upgrading emissions. We identified three potential

814

drivers of variability in upgrading GHG intensity. The type of upgrading

815

technology used to produce SCO can generate a variation in baseline upgrading


40

Page 41 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

816

GHG intensity of up to 36%. This improves upon our prior, less detailed, study

817

with GHOST,21 which did not find a significant difference in the GHG intensity of

818

SCO produced using DC and HC. Secondly, we showed that simulation of SCO

819

blends with contrasting quality properties co-produced by a single upgrading

820

technology can result in a variation in the baseline GHG intensity allocated to

821

each product of up to 91% in DC and 68% in HC. This modeling capability is

822

novel and expected to be valuable for stakeholders interested in estimating GHG

823

intensities for specific upgrading products and SCO. Finally, the efficiency of the

824

upgrader and the use of by-product coke as fuel were found to have considerable

825

effects on upgrading GHG intensity, with variations in natural gas requirements

826

having the most effect. This result has potentially important implications as the

827

efficiency of operations and the use of less GHG-intensive fuels may be the most

828

feasible routes for emission reductions in the near-term. The aforementioned

829

drivers of variability should be taken into account when modeling the GHG

830

emissions of upgrading processes.

831

OSTUM 2.0 improves upon previous life cycle-based studies that include

832

upgrading through more detailed modeling of the upgrading stage, allowing the

833

systematic

834

technologies, and the characterization of potential drivers of variability using

835

consistent boundaries, assumptions, and calculation methods. OSTUM 2.0 can

836

simulate a broader range of commercial upgrading technologies (DC, HC,

837

HC/FC), operating schemes (e.g., simultaneous production of SCO blends),

838

energy inputs, and product properties than those presented in literature.

evaluation

and

comparison

of

GHG


intensities

of

different

41

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 49

839

OSTUM 2.0 has several limitations. The estimation methods for the upgrader’s

840

energy use and product yields are not directly linked and therefore these must

841

both be accurate to reasonably estimate the GHG intensity. Another limitation is

842

related to the lack of publicly available data and that OSTUM 2.0 has not been

843

validated, although its results have been compared with literature and limited

844

industry data, useful steps until more complete data are available. It is important

845

to recognize that OSTUM 2.0’s ability to make accurate predictions needs to be

846

validated using actual upgrader data as it becomes available. Future work to

847

improve OSTUM 2.0 includes its incorporation into a WTW model, potentially as

848

part of a low carbon fuel standard, which could facilitate the exploration of trade-

849

offs in GHG intensity associated with the extent of upgrading versus refining and

850

product yields (and associated economics). Future developments also include

851

addition of emerging upgrading technologies and assessment of non-GHG

852

environmental impacts. OSTUM 2.0 has been developed in spreadsheet-based

853

software to facilitate its accessibility in potential open-source applications,

854

including in supporting low carbon fuel standards. While it is not currently publicly

855

available, our aim is to make future versions open-source.

856

The research presented in this paper advances insights about the GHG

857

emissions of upgrading generally. It helps to better discern emissions associated

858

with different technologies and processing options as well as the sources of

859

variability seen in operating facilities today. OSTUM 2.0 has the potential to assist

860

technology developers, the oil sands industry and policy makers in meeting its

861

GHG emissions targets and caps.


42

Page 43 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

862 863

ASSOCIATED CONTENT

864

Supporting Information. A file is available free of charge containing additional

865

descriptions of the upgrading technologies modeled in OSTUM 2.0, data and

866

methodology employed to develop the model, input parameters and assumptions

867

used in the scenario and sensitivity analyses, as well as additional results and

868

discussion.

869 870 871

AUTHOR INFORMATION

872

Corresponding Author

873

*Address: Dept. of Civil Engineering, University of Toronto, 35 St. George St.,

874

Toronto, Ontario, Canada, M5S 1A4; email: [email protected];

875

phone: +1 (416) 946-5056; fax: +1 (416) 978-6813.

876

Author Contributions

877

The manuscript was written through contributions of all authors. All authors have

878

contributed equally and given approval to the final version of the manuscript.

879

Notes

880

The authors declare no competing financial interest.

881 882

ACKNOWLEDGMENT

883

We thank the Natural Sciences and Engineering Research Council of Canada


43

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 49

884

(NSERC) and Mexico’s National Council for Science and Technology

885

(CONACYT) for financial support, and an oil sands company that provided

886

feedback to improve the results and insights of this study. Dr. Kavan Motazedi,

887

Dr. Sylvia Sleep and Zainab Dadashi from University of Calgary, and Dr. Yaser

888

Khojasteh Salkuyeh from University of Toronto provided feedback to improve this

889

research. We also thank three anonymous reviewers for the insightful comments

890

that improved this paper.

891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922


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923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968

Energy & Fuels

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Characterizing Variability in Oil Sands Upgrading Greenhouse Gas

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