energy and emissions of unconventional resources
TRANSCRIPT
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2019-01-02
Energy and Emissions of Unconventional Resources
Umeozor, Evar Chinedu
Umeozor, E. C. (2019). Energy and Emissions of Unconventional Resources (Unpublished doctoral
thesis). University of Calgary, Calgary, AB.
http://hdl.handle.net/1880/109461
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UNIVERSITY OF CALGARY
Energy and Emissions of Unconventional Resources
by
Umeozor Evar Chinedu
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
GRADUATE PROGRAM IN CHEMICAL ENGINEERING
CALGARY, ALBERTA
JANUARY, 2019
ยฉ Umeozor Evar Chinedu 2019
ii
Abstract
Unconventional petroleum resources constitute an increasing frontier of reserves additions
as conventional production declines globally. In this era of environmental conservation and
sustainability concerns, new resource development efforts confront energy, emissions, and
economic intensities. Clearer understanding of resource development choices and their
implications can be gained by quantifying these intensities through a systematic approach
which allows effective comparisons of alternative energy systems to be drawn in the
context of policy and/or business decision-making. Yet, existing assessment studies often
lack transparency or do not furnish detailed methodological descriptions of the approach
needed for transferability or validation of results in subsequent studies which evaluate
impacts of our existing and emerging energy systems design decisions. The combination
of analytical and semi-analytical modelling holds great potential to address current
methodological challenges in assessing impacts of unconventional resources development.
Focusing on shale gas and oil sands resources, this thesis presents new modelling tools and
assessment frameworks to quantify and compare impacts of operations and technologies
needed during development and recovery of these energy resources. The first part of the
contributions evaluated potential environmental impacts of flowback methane in the U.S.
and Canada to be 2347 and 1859 Mg CO2e per completion, respectively. The second part
assessed contributions of all preproduction activities to overall energy and environmental
intensities, highlighting drilling and flowback intensities as major sources. The third and
fourth contribution chapters investigated the role of innovation to improve oil sands
production and demonstrated the application of carbon dioxide utilization to mitigate
impacts of unconventional oil and gas production, respectively.
iii
Acknowledgements
I am very grateful to my supervisor, Professor Ian D. Gates, for his support, patience and
tutelage.
My appreciation to the thesis exam committee: Professor Amit Kumar, Dr. Getachew
Assefa, Dr. Hector De La Hoz Siegler, and Dr. Roman Shor, for their time and invaluable
suggestions to the final thesis.
A big thank you to my colleagues and the management staff at the Canadian Energy
Research Institute for creating a very stimulating environment for intellectual discourse
and growth.
Special thanks to my friends Marlon, Abinet, Babatunde, Experience, and Earnest for their
support mentally, emotionally and socially.
I will always appreciate my family and my dear Leeanne, for their enduring love.
iv
Dedication
To all the wonderful women in my life; for their love, care and compassion
v
Table of Contents
Approval Page ..................................................................................................................... ii
Abstract ............................................................................................................................... ii
Acknowledgements ............................................................................................................ iii Dedication .......................................................................................................................... iv Table of Contents .................................................................................................................v List of Tables .................................................................................................................... vii List of Figures and Illustrations ....................................................................................... viii
List of Symbols, Abbreviations and Nomenclature .............................................................x
INTRODUCTION ..................................................................................1 1.1 Background ................................................................................................................1
1.2 Research Questions ..................................................................................................10 1.3 Thesis Organization and Contributions ...................................................................12 1.4 References ................................................................................................................14
LITERATURE REVIEW ....................................................................16 2.1 Development of Unconventional Resources ............................................................16
2.2 Energy and Emissions of Unconventional Gas Resources ......................................26 2.3 Energy and Emissions of Unconventional Oil Resources .......................................30 2.4 Mitigating Environmental Impacts of Unconventional Resources ..........................38
2.5 What is missing in the literature? ............................................................................40 2.6 References ................................................................................................................42
ON METHANE EMISSIONS FROM SHALE GAS
DEVELOPMENT .....................................................................................................48
3.1 Introduction ..............................................................................................................49 3.2 Methods ...................................................................................................................53
3.3 Sensitivity Analysis .................................................................................................58 3.4 References ................................................................................................................70
PREDICTIVE MODELLING OF ENERGY AND EMISSIONS
FROM SHALE GAS DEVELOPMENT ..................................................................75 4.1 References ..............................................................................................................100
DESIGNING FOR INNOVATION: PROCESS AND
TECHNOLOGY CONFIGURATIONS FOR OIL SANDS PRODUCTION ........104
5.1 Introduction ............................................................................................................105
5.2 Oil Sands Production .............................................................................................110
5.3 Study Approach .....................................................................................................114 5.3.1 Mathematical programming model ...............................................................114
5.3.1.1 Objective Function ...............................................................................115 5.4 Results ....................................................................................................................118 5.5 Conclusions ............................................................................................................125 5.6 References ..............................................................................................................126
vi
ON DESIGNING CARBON DIOXIDE UTILIZATION
PATHWAYS FOR SUSTAINABILITY ................................................................131 6.1 References ..............................................................................................................148
CONCLUSIONS AND RECOMMENDATIONS ........................152
7.1 Conclusions ............................................................................................................152 7.2 Recommendations ..................................................................................................155
7.2.1 Methane Accounting .....................................................................................156
APPENDICES .................................................................................................................157
vii
List of Tables
Table 2-1: Metrics for assessment of environmental performance of CDU. .................... 40
Table 3-1: Statistical attributes of the model estimates and measurement data (Mg CO2e
per completion). ES-3days and ES-9days represent the estimates assuming 3 and
9 days flowback periods, respectively. ..................................................................... 62
Table 3-2: Mitigation costs of green completion. ............................................................ 66
Table 4-1: Descriptive statistics comparison for model and measured completions
flowback potential methane emissions. .................................................................... 93
Table 5-1: Mobility of unconventional oil resources [3, 4, 5]. ....................................... 106
Table 5-2: Simulation data based on field and reservoir modelling observations [31 โ
33]. .......................................................................................................................... 118
Table 5-3: Energy, CO2 emission, and economic intensities of the bitumen recovery
process design options. ........................................................................................... 123
Table 5-4: Pareto optimal operating configurations for oil sands production. ............... 124
Table 6-1: CDU technology configurations and energy options. ................................... 140
viii
List of Figures and Illustrations
Figure 1-1: Global energy systems transition [9]. .............................................................. 2
Figure 1-2: A modified McKelvey box and the quantity-cost interactions for
hydrocarbon deposit assessment and development [2]. .............................................. 3
Figure 1-3: Regional and global estimates of shale gas resources by various sources
[17]. ............................................................................................................................. 7
Figure 1-4: Energy return on energy investment for various fuels [10]. ............................ 9
Figure 2-1: Estimates of conventional and unconventional oil in places [6]. ................... 17
Figure 2-2: Global distribution of estimated unconventional gas resources [4]. .............. 18
Figure 2-3: Effects of resource attributes and technical innovations on EROI of
conventional and unconventional resources [9]. ....................................................... 19
Figure 2-4: Technology innovation in oil sand in situ extraction and production growth
[19]. ........................................................................................................................... 24
Figure 2-5: Factors and issues affecting unconventional resource development and
production. ................................................................................................................ 25
Figure 2-6: Historical and projected production of conventional and unconventional
gas in the United States [22]. .................................................................................... 27
Figure 2-7: A schematic of energy and material flows for a gas supply chain. ................ 28
Figure 2-8: Unconventional oil production methods, showing three main method โ
miscible displacement, chemical flooding, and thermal recovery [10]. ................... 32
Figure 2-9: Oil sands production systems and operations for surface-mining and in-situ
recovery techniques [9]. ............................................................................................ 32
Figure 2-10: Energy return losses along an oil supply chain [8]. ..................................... 37
Figure 2-11: Pathways for mitigating GHG emissions from unconventional resources. . 39
Figure 3-1: Gas production profile from a hydraulically fractured shale gas reservoir.
Time periods (I, II, and III) not drawn to scale. ........................................................ 54
Figure 3-2: Frequency of historical peak gas occurrence among hydraulically fractured
reservoirs in the five US shale plays recorded in HPDI [26]. ................................... 56
Figure 3-3: A schematic illustration of the estimation method and calculation
procedure. .................................................................................................................. 58
ix
Figure 3-4: Comparison of the model-based completion emission estimates (ES) and
reported completion emission measurements (MS) for US shale plays. ES-3days
and ES-9days represent the estimates assuming 3 and 9 days flowback periods,
respectively. The number of data samples (n) are indicated under each boxplot.
The measurement data covers shale plays within the Gulf Coast, Midcontinent,
Rocky Mountain, and Appalachian regions in the United States (see list of sources
of measurement data in Appendix A, SI: Section 5)................................................. 61
Figure 3-5: Boxplots of the potential net revenue from REC of hydraulically fractured
US and Canadian shale gas wells in 2015 with 95% capture of the flowback gas.
B=Barnett, F=Fayetteville, H=Haynesville, M=Marcellus, W=Woodford,
DU=Duvernay, MT=Montney. Low gas price=$2/Mcf, Medium gas
price=$4/Mcf, High gas price = $6/Mcf. Outliers in the figure (+) are more than
1.5 times the interquartile range. ............................................................................... 65
Figure 3-6: Impact of REC cost variability on the potential for profitability of REC
implementation across all plays (Barnett, Fayetteville, Haynesville, Marcellus,
Woodford, Duvernay, and Montney). Quartiles of net revenue are shown under
variable cost of REC and natural gas price regimes (sample size = 2,088). ............. 67
Figure 3-7: Basin-resolved sensitivity of net revenues for the low REC cost scenario at
various natural gas prices .......................................................................................... 68
Figure 3-8: Basin-resolved sensitivity of net revenues for the high REC cost scenario
at various natural gas prices. ..................................................................................... 69
Figure 5-1: Benchmark in-situ oil sands recovery process design with SAGD. ............ 111
Figure 5-2: Superstructure of in-situ oil sands recovery via steam, solvent and NCG
methods. .................................................................................................................. 111
Figure 5-3: Oil sands process and technology innovation framework for surface and
subsurface operations. ............................................................................................. 113
Figure 5-5: Process CO2 emission when electricity is supplied by either a natural gas
combined cycle plant or from the Alberta grid systems. ........................................ 120
Figure 5-6: Capitalized costs of each design at the two fluid injection conditions
(reference and reservoir conditions). ...................................................................... 121
x
List of Symbols, Abbreviations and Nomenclature
GWP Global Warming Potential
EI Energy Intensity
EROI Energy Return On Investment
Mt Mega tonne
Mg Mega gram
GHG Greenhouse Gas
NG Natural Gas
SAGD Steam-Assisted Gravity Drainage
CSS Cyclic Steam Stimulation
SOR Steam-to-Oil Ratio
SCA System Control Area
GOR Gas-to-Oil Ratio
WOR Water-to-Oil Ratio
EUR Estimated Ultimate Recovery
LCA Life-Cycle Assessment
MILP Mixed-Integer Linear Programming
CUF Carbon Utilization Factor
CEF CO2 Emission Factor
API American Petroleum Institute
Gtoe Giga tonne of oil equivalent
cP Centipoise
mD Millidarcy
1
Introduction
1.1 Background
It all started with wood. Then came peat, coal, oil, and now, gas. The course of our societal
evolution owes a lot to human inventiveness and discovery of various energy sources and their
advantageous applications. From burning wood for heat, to massive electrical power plants, the
journey towards getting the maximum amount of energy from the least amount of resource is
marked with disruptive events that continue to redefine the states of our economy, our energy
landscape, and our environment [1]. Figure 1-1 illustrates the past, present and expected future
energy system transitions [1]. In nearly all cases, the energy from these sources is harvested by
combustion. Economic and technological advancements are often tied with shifts in sources of
energy. The advent of the industrial revolution was a significant turning point in energy sources
with the use of coal for steam engines and power plants [2].
As the 20th century began, coal remained the main source of energy, but a gradual shift towards
higher energy content sources such as oil had started [3]. Towards the end of that century, the
dominance of petroleum products as the main source of energy peaked in the global economy [3].
As the level of technical know-how increased further, more efficient sources of energy, such as
natural gas, started to be tapped in commercial quantities [3]. A number of energy experts claim
that hydrogen will drive the global economy of the future, in accordance with Figure 1-1.
2
Figure 1-1: Global energy systems transition [1].
Global hydrocarbon resources can be categorized based on their geological and techno-economic
availabilities into those that can be produced with existing knowledge, using current technologies
and under prevailing market prices, and those that require improvements in one or combinations
of those three variables to be extracted beneficially [4, 5]. Rogner [3] presented a resource
classification framework, using a modified McKelvey box approach to group world hydrocarbon
resources into reserves, resources, and occurrences. As shown in Figure 1-2, the two coordinates
of the diagram represent the degree of geological assurance and the degree of economic feasibility
[3].
3
Figure 1-2: A modified McKelvey box and the quantity-cost interactions for hydrocarbon
deposit assessment and development [2].
For fossil energy, the idea of occurrence or resources in place captures the varieties of global
hydrocarbon deposits [3, 6]. Measured occurrences have the best geological certainty and techno-
economic viability [3]. In this progression, the measured occurrences are derived from indicated
resources, which come from inferred resource deposits as yet to be explored areas [3]. Indicated
resources are those whose locations are known, and they can be recovered by using enhanced
recovery methods [3]. Inferred resources can be produced through further extraction from the
margins of identified fields [3]. The combination of the measured, indicated, and inferred
occurrences constitutes โproved reservesโ โ which can mostly be produced economically with
existing technologies [3]. The interplay between technology advances/breakthroughs and higher
4
market prices (including price expectations) shift previously unworkable and/or uneconomic
resources into the reserves zone, whereas unfavourable market conditions can keep technically
producible resources out of the market until technology improvements make further reduction of
their production costs possible. Additionally, hydrocarbon resources may also be classified into
conventional and unconventional resources based on the method of developing and producing the
resource.
Generally, unconventional oil and gas reserves cannot be commercially extracted with primary
recovery methods โ that is, using natural reservoir production mechanisms โ due to a combination
of technical (low permeability, high viscosity) and economic (negligible primary recovery rates)
reasons. In other words, unconventional hydrocarbons are resources that are known to be not
producible with (historically) conventional recovery techniques [3, 6]. However, conventional oil
and gas can be described with more flexibility because both primary and enhanced recovery
methods can be combined to improve their recovery rates. Consequently, with known geological
abundance of hydrocarbons, quantification of the amount that can be available to the society at
every point in time depends on the level of technological know-how of the era and the dynamics
of valuation of the resources.
Under dwindling conventional reserves, the desire for self-sufficiency, energy security, and
economic forces motivate development of unconventional resources [4]. Energy experts assert that
unconventional hydrocarbon resources will become increasingly crucial for satisfying current and
future global energy demands [2, 7]. Unfortunately, the nature of the resources and the peculiarities
of the terrains of their deposition make their extraction often more cost, energy, and emissions
5
intensive relative to most conventional resources. However, given the huge global reserves of
unconventional heavy oil, it can be a major source of economic growth and energy security if the
resource development and production capability overcome the various challenges to realizing these
benefits [4]. Unconventional fossil fuel deposits may be characterized as being relatively immobile
either due to high viscosity or very low permeability. They occur naturally in gaseous, liquid, and
solid states.
Unconventional resources can be classified into various types, including [8]:
โข Natural gas hydrates โ natural gas trapped in structures of ice,
โข Coal bed methane โ natural gas trapped in coal deposits,
โข Shale gas/Tight gas โ natural gas found dry or in association with oil in very low or
extremely low permeability reservoir rocks (sandstone/limestone),
โข Shale oil/Tight oil โ oil in oil-bearing shale rock,
โข Extra heavy oil โ oil with high viscosity,
โข Oil sand โ a source of extra heavy oil found naturally in mixture with sands,
โข Oil shale โ rock containing a bituminous substance which yields kerogen.
The challenge to develop and produce unconventional oil and gas resources necessitates the
application of technologies and processes that are often different from those used for conventional
resources [4]. In many cases, higher technological complexity required for unconventional
resources come with rising costs until technological learning enables cost reductions to be
achievable [9]. Majean and Hope [10] found that the rate of technological learning is a critical
parameter having an immediate effect on the supply costs of unconventional resources.
6
Irrespective of the cost implications of new technologies and processes, they help to unlock
resources that may have been impracticable to produce with existing techniques and tools.
Consequently, technological innovation and favourable economics are critical to develop these
resources [10]. An emerging technology might take time to be improved or modified to the point
where it performs well enough and is cost competitive enough to be deployable in commercial
scale resource extraction ventures. When/if that happens, adoption could be quick, and other low-
performing, high cost rivals can be eliminated from the market. Therefore, knowledge of the
economic, energetic and environmental performance thresholds which drive massive adoption of
a new technology is usually sought [8].
Technologies to extract methane from natural gas hydrates are still a subject of various studies by
researchers [3, 8]. Estimates of their in-place volumes are so enormous that only 1% of the
estimated volume would be larger than the known global natural gas reserves, but not much is
known about the actual quantity that exists and their future techno-economic recoverability [3].
Essentially, three basic methods can be used to recover methane from gas hydrates:
depressurisation, thermal injection, and inhibitor injection [8]. However, none of them can
currently yield commercial quantities in a cost effective and energy efficient way [3]. For coal-bed
methane, the estimates of the in-place volume have been recently reported as 39.2 Tm3 [5]. Global
estimates of in-place tight-formation gas has been put at 54.2 Tm3, while the quantity of shale gas
is estimated to be about 193.2 Tm3 [5]. Figure 1-3 compares various estimates of recoverable shale
gas resources. Coal bed methane, tight gas, and shale gas, all require hydraulic fracturing and/or
horizontal wells to be extracted. Fracturing operations involves high pressure pumping of a fluid
into the wells to produce fractures in the formation [8]. When combined with horizontal wells,
7
more of the deposits are exposed to the wells than with vertical wells, thereby allowing for higher
production and greater overall recovery of the gas in place. Similarly, shale oil/tight oil reservoirs
are hydraulically fractured for production.
Figure 1-3: Regional and global estimates of shale gas resources by various sources [11].
Oil shale does not need hydraulic fracturing but is exploited through surface or in-situ retorting,
where the oil shale is heated in the absence of oxygen or combusted directly to about 500oC to
pyrolyze the kerogen to oil. With oil-in-place estimates in the range of 450 to 2510 Gtoe, oil shales
have the highest resource potential among unconventional oil resources, but all developments face
significant technical, economic, and environmental challenges [3].
Heavy oil, extra heavy oil and oil sands bitumen are closely related, but are generally distinguished
based on their viscosities and API gravities at reservoir conditions. The API gravity for heavy oils
range between 10-25oAPI and viscosities above 10,000 cP. Extra heavy oil and oil sands bitumen
8
have a 7-10oAPI gravity range, and are not able to flow under normal reservoir conditions [3].
Therefore, they are produced by using heat to reduce the viscosity of the resource and mobilize it
for recovery. Bitumen and heavy-oil constitute about 5.6 trillion barrels of resources occurring in
more than 70 countries world-wide [5]. However, the recoverable portion of the unconventional
resource occurrences depends on the level of technological know-how, the direction of change in
the global energy system, and the dynamics of the energy market [3].
Production of unconventional oil and gas often require significant energy inputs due to the
temperature or pressure requirements of the processes. Energy is required to extract and process a
primary energy resource to forms that can be used directly by the society [4]. Unconventional oil
with high viscosity can require significant amount of heat to be produced, while oil and gas from
low permeability reservoirs require hydraulic fracturing at high pressure pumping of a lot of
fracturing fluid. As a result, the ratio between energy used and energy produced (energy return on
investment, EROI) from unconventional oil and gas is relatively small in comparison to
conventional oil and gas [4]. The International Energy Agency estimates that the EROI of
conventional oil and natural gas production is equal to about 17 on average [8]. Hall et al. [12]
reported the EROI of various fuels and argued that EROI of energy resources generally decline
over time because earlier discoveries and developments of the resources often focus on the best
(โsweet-spotโ) geological deposits. Then over time, the best deposits are exhausted and the less
desirable deposits have to be tapped.
9
Figure 1-4: Energy return on energy investment for various fuels [12].
As can be observed from Figure 1-4, coal has a higher EROI than the other fuels. However, it is
also very carbon intensive which makes it less attractive from environmental conservation
perspective. In essence, the greenhouse gas (GHG) emissions that go with the fuel combustion for
energy needs of the resource recovery processes is dependent upon the primary energy source and
prime-mover efficiency. GHG emissions accompanying unconventional oil and gas production
follow the energy requirements of the recovery method and may also be more severe with some
energy sources than others [8]. Consequently, production of unconventional resources poses more
environmental challenges, even though the products might be less readily usable than the
conventional energy sources, thus, requiring additional treatments [3]. For instance, oil sands
production is relatively energy intensive using current commercial systems, yet the product โ
bitumen โ still has to be further diluted with solvents or upgraded in order to transport to refineries.
However, unconventional gas production does not require as much energy as unconventional oil,
but may require more water for reservoir fracturing. Estimates of shale gas EROI has been reported
to range between 10 and 120, while average SAGD EROI is between 4 and 10 [4, 13].
10
1.2 Research Questions
The literature review highlights important gaps in current understanding of quantification and
assessment tools for energy, environmental and economic impacts of unconventional resources
development which drive the subject matter of this thesis project. With a focus on shale gas and
oil sands resources, this thesis aims to address the following issues:
A. Shale gas development technique is the main source of intensity gap relative to
conventional gas. Yet, estimates of environmental and economic impacts of shale gas
development remain controversial; with most studies reporting disparate results on the
quantity of methane emissions particularly due to completions operations. Can we develop
a method to quantify methane emissions during well completions that can be
validated with real data? What would be the mitigation cost if the completions
emission were to be avoided? Would it be economical to implement a mitigation
strategy?
B. Although methane emissions during well completions have been the major focus of studies
investigating climate impacts of shale gas development, there remains other activities and
events occurring during shale gas development whose contribution to overall global
warming impacts have not been properly investigated nor quantified. Can we develop a
more complete modelling workflow covering the activities and events which influence
energy and environmental impacts of shale gas development? Can the modelling
guide us to understand the relative contributions of operations at each development
11
stage to overall energy use, energy returns, and GHG emissions from both energy
requirements and direct releases of methane?
C. Energy returns and GHG emissions from both conventional and unconventional oil
production is a well-researched topic. For this reason, literature is replete with studies
reporting both direct and life-cycle GHG emissions of oil sands production from surface
mining and in situ recovery methods using existing commercial technologies. While it is
known that oil sands has become the major source of Canadian oil production although
being more energy and emissions intensive compared to conventional oils, yet, not much
is known about the role of emerging oil sands process and technology innovations to
improve operational performance. Can we develop a method to assess innovations in oil
sands recovery operations on the bases of overall impacts on energy use, emissions
and production costs? Using the developed assessment method, how does
performances of emerging oil sands production processes and technologies compare
to each other? What is the most promising process design based on the quantified
performance?
D. In the face a growing global energy demand driving the increasing production of
unconventional resources despite their higher energy and environmental intensities; what
are the mitigation options for climate impacts of anthropogenic GHG emissions? Can
carbon dioxide utilization serve as a climate policy strategy to mitigate global
warming impacts of energy-derived CO2 emissions?
12
1.3 Thesis Organization and Contributions
An overarching theme of this thesis is the development and formalisation of methodological
frameworks by combination of physical parameters and analytical modelling for energy,
environmental and economic impacts evaluation of unconventional fossil fuels along with the
evaluation of pathways for GHG emission mitigation in order to achieve environmental
conservation and sustainability goals. For this purpose, I focus specifically on shale gas and oil
sands development, in addition to investigating the potential of carbon dioxide utilization (CDU)
to provide mitigation benefits through useful application of CO2 emissions arising from
unconventional resource development. Modelling tools and assessment metrics are developed and
applied to shale gas, oil sands and carbon-products development. Energy impacts are captured in
terms of efficiencies of the process and technology options and environmental impact is assessed
in terms of direct energy and non-energy greenhouse gas emissions. Economic impacts are
considered with respect to mitigation and capitalised costs of processes and technologies
deployable in the resource development operations. Chapter two contains literature review on
energy, environmental and economic impacts of unconventional resources, with emphasis on shale
gas and oil sands development, production, processing, transport, and the potential for mitigation
of climate impacts of final combustion. The thesis contributions corresponding to the research
questions are presented from Chapter three through Chapter six.
Chapter three presents an economic and environmental impacts assessment of shale gas
development considering flowback methane emissions and the cost of mitigation using green
completion technology (so-called reduced emissions completion - REC) to reducing methane
emissions during shale gas development. A new method for quantifying the emission is developed
13
and, for the first time, validated using actual field measurement data. This allowed cost of REC
implementation to be evaluated under various gas prices and gas handling scenarios. The entire
workflow is presented along with the modelling equations. This work reconciles the controversy
and settles the debate on the amount of potential methane releases during the well completion stage
of shale gas development.
Chapter four extends the understanding from the method presented in Chapter three by improving
the modelling technique and widening coverage of the analyses to consider all preproduction
activities and events during shale gas development. For the first time, complete modelling
workflow accounting energy requirements and GHG emissions during drilling, mud circulation,
mud gas release, hydraulic fracturing and well completion is presented. Quantifying energy use
and emissions enables determination of preproduction energy return on energy invested for shale
gas development. Additionally, contribution of each of the development stages to overall energy
use and GHG emissions is also observed. Understanding the contributions of each activity/event
to overall impacts during development can help direct focus to where efficiency improvement
and/or impacts mitigation are most needed.
Chapter five presents a novel mixed-integer based mathematical programming model to
simultaneously assess energy, emissions and economic intensities of emerging oil sands
production processes and technologies. The modelling approach derives from a proposed oil sands
innovation framework and understanding of the operational practices of the industry. On the basis
of a multi-criteria performance metric, three emerging oil sands production process designs are
compared relative to a benchmark in situ production technique (SAGD) considering both surface
14
and subsurface operational requirements. Energy, GHG emission and economic intensities of the
emerging production configurations determined. Evaluating and comparing impacts of alternative
designs facilitates understanding of the role of innovation for unconventional oil production as
conventional resources decline in the face of a growing global energy demand.
Chapter six investigates mitigation options for reducing global warming impact of more fossil
fuel consumption. The role of carbon dioxide utilization (CDU) as a sustainable climate mitigation
strategy through CO2 capture, storage, sequestration, or conversion is discussed. Various pathways
for potential CDU development including production of synthetic fuels, chemicals and polymer
materials are investigated to ascertain their individual processing energy and net environmental
impacts. Understanding the energy requirements and net environmental impacts of CDU systems
can inform on its merits as a mitigation strategy as the world aims to control global warming with
more, higher intensity, unconventional resources composing the energy mix. Lastly, Chapter
seven draws conclusions from the findings of these studies and provides recommendations for
future and further research directions.
1.4 References
[1] Dunn, S. (2002). Hydrogen futures: toward a sustainable energy system. International journal
of hydrogen energy, 27(3), 235-264.
[2] World Petroleum Council. โGuide: unconventional oil.โ (2014): 1-80.
[3] Rogner, Hans-Holger. "An assessment of world hydrocarbon resources." Annual review of
energy and the environment 22.1 (1997): 217-262.
15
[4] Nduagu, Experience I., and Ian D. Gates. "Unconventional heavy oil growth and global
greenhouse gas emissions." Environmental Science & Technology 49.14 (2015): 8824-8832.
[5] Hein, F. J. (2017). Geology of bitumen and heavy oil: An overview. Journal of Petroleum
Science and Engineering, 154, 551-563.
[6] Le, M. T. (2018). An assessment of the potential for the development of the shale gas industry
in countries outside of North America. Heliyon, 4(2), e00516.
[7] Wang, K., Vredenburg, H., Wang, J., Xiong, Y., & Feng, L. (2017). Energy return on
investment of Canadian oil sands extraction from 2009 to 2015. Energies, 10(5), 614.
[8] International Energy Agency (IEA). โUnconventional oil and gas production.โ Energy
Technology Systems Analysis Programme. P02 (2010).
[9] Organization of Petroleum Exporting Countries (OPEC). World oil outlook: oil supply and
demand outlook to 2040, 2015.
[10] Mรฉjean, Aurรฉlie, and Chris Hope. "Modelling the costs of non-conventional oil: A case study
of Canadian bitumen." Energy Policy 36.11 (2008): 4205-4216.
[11] McGlade, C., Speirs, J., & Sorrell, S. (2013). Methods of estimating shale gas resourcesโ
Comparison, evaluation and implications. Energy, 59, 116-125.
[12] Hall, C. A., Lambert, J. G., & Balogh, S. B. (2014). EROI of different fuels and the
implications for society. Energy policy, 64, 141-152.
[13] Sell, B., Murphy, D., & Hall, C. A. (2011). Energy return on energy invested for tight gas
wells in the Appalachian Basin, United States of America. Sustainability, 3(10), 1986-2008
16
Literature Review
2.1 Development of Unconventional Resources
During the early days of the oil industry, various parts of the world were familiar with natural
seepages of oil for many years, but the oil and gas industry as we know it today started in 1859
when Edwin Drakeโs well in Titusville in Pennsylvania, USA, struck oil [1]. Drakeโs innovation
was to drill using cast-iron piping to protect the wellbore, and oil sprouted out of the bore under
reservoir pressure. Although the quantity of oil that can be recovered is limited, flow to surface
under natural reservoir pressure or pumping (primary production) is in the domain of conventional
oil and cold production of heavy oil (with and without sand). However, things are changing as the
world is no longer as it used to be during Drakeโs time. High quality and abundant conventional
oil and gas are not ubiquitous.
The worldโs population continues to increase. Based on recent estimates, the global population is
expected to become 9 billion by 2040 โ changing from 7.2 billion in 2014 [2]. Global energy
demand is also anticipated to increase by 50% over the same period, a large share of which is
predicted to be from oil and gas consumption [2]. Despite significant efficiency improvements, a
larger population generally implies greater energy needed, but the availability of cheaper fossil-
based energy resources is not unlimited [3]. As conventional resources are depleted, reserve
additions from unconventional resources are often from difficult formations with economic
viability and technological feasibility challenges, thus, often calling for significant technological
innovations [4]. We have witnessed steady declines of various conventional energy reserves but
the overall supply and demand of fossil-based energy are higher in our generation than ever before
17
[5]. Most of that supply growth comes from unconventional oil and gas [2, 6]. Recent estimates
expect the share of unconventional oil in the global crude oil production to grow to 15% by 2035,
as technology advances in the upstream sectors make more fossil energy-resource easily available
and economic [7]. Figure 2-1 compares recent estimates of conventional and unconventional oils
indicating the huge availability of unconventional oil. One estimate of the global occurrences of
conventional and unconventional resources put them at 613.4 and 21935.8 Gtoe, respectively [3].
Figure 2-2 shows global technically recoverable unconventional gas estimates. However, resource
in place estimates are often riddled with uncertainties which should be a cause for caution when
working with these numbers [4].
Figure 2-1: Estimates of conventional and unconventional oil in places [6].
18
Figure 2-2: Global distribution of estimated unconventional gas resources [4].
Energy returns of energy resources generally decline over time because earlier discoveries and
developments of the resources often focus on the best (โsweet-spotโ) geological deposits [8]. Then
over time, the best deposits are exhausted and the less desirable deposits have to be accessed.
Wang et al. [9] evaluated energy efficiencies of oil sands processes in comparison to conventional
oil and observed that the EROI (the ratio of energy produced to energy invested in the production)
of oil sands has shown continued improvement as technical innovation of the industry improved,
even as conventional oil efficiencies depreciate with depletion of sweet spot conventional resource
deposits. Figure 2-3 compares EROI of other unconventional resources to the results observed by
Wang et al. [9]. Brandt et al. [10] also recorded increasing efficiency of oil sands production and
predicted a growing dominance of unconventional resources as geopolitical concerns and
dwindling conventional production become more pronounced.
19
Figure 2-3: Effects of resource attributes and technical innovations on EROI of conventional
and unconventional resources [9].
However, this seemingly natural progression in resource extraction efficiencies, from declining
EROI of commercial resources (including conventional and unconventional) as the less desirable
deposits start to be developed, can be shifted by the effect of incremental or disruptive
technological innovation. The advent of shale gas is an example of where directional drilling and
hydraulic fracturing disruptive innovations altered the unconventional gas resources development
landscape, resulting in abundant gas reserves. On the other hand, incremental technological
improvements can enhance the efficiencies in producing newly added commercial resources
(reserve additions) thereby creating a temporary impression of increasing efficiencies that may
vanish as the less desirable deposits of the resource start to be developed. For instance, the majority
of the operating oil sands assets in Alberta are currently producing from some of the best geological
formations, so that energy returns improvements reported by [9] and [10] can be ascribed to
20
incremental process and technological innovations which might not necessarily significantly
improve performance as development moves to the less attractive oil sands geologies.
Under a carbon-constrained environment, unconventional resource development must overcome a
unique set of conditions in order to be viable. Technological innovations that facilitate cheaper
recovery of more resources without serious environmental impacts will be necessary for long-term
fossil energy-resource availability. Such technologies impact costs through improvements in
efficiency, management, and productivity, which all translates to better environmental
performance [1]. Over the past 20 years, technology-enabled efficiency improvements reduced the
energy intensity of oil sands production by almost 40% [1]. Nduagu and Gates [7] highlighted
three major factors that influence technology choices and adoption as: the energy penalty, emission
intensity and the economic costs. For individual players and companies in the energy sector,
innovation in business models is also crucial for their resource development activities and social
acceptance [16].
Most disruptive innovations have mostly come through transformations of existing business
models. That was the case with shale oil, which has become an important and cost-effective
unconventional resource with about 535,000 barrels per day produced in the USA in 2011, and
forecasted to grow to between 1.2 to 4 million barrels per day by 2035 [1]. Before the model for
developing shale formations was perfected in the USA, petroleum projects took years to complete.
Some offshore conventional oil projects took close to a decade and billions of dollars to get the oil
flowing. These days with horizontal drilling and hydraulic fracturing, projects are completed in
weeks, not years. And most of the projects cost a few millions of dollars to be ready [17].
21
Recent estimates of the worldwide shale oil resources range from 330 to 1,465 billion barrels [1]
which suggests that there are now enough proven reserves to last many years. The same can also
be said about shale gas. In recent times, this flood of unconventional oil and gas has been said to
be a major contributor to the current low prices in various regions and countries and is also
stimulating developments in various sectors of the global economy [1]. Between the years 2004 to
2008, growth in demand for gas for electric power generation resulted in very high prices for gas
until shale gas reserves became abundant. Therefore, the abundance and availability of
unconventional fossil fuels contributes to stability of the energy market by balancing the forces of
supply and demand to minimize price volatilities [6]. For instance, the ease with which shale
production can be ramped up or down is considered as a useful tool to control supply more quickly
restoring market balance within a shorter time period [17]. In addition, estimates of future oil prices
that include unconventional oil have reported prices to be around $60 per barrel whereas estimates
based on only conventional oil go as high as $200 per barrel [1].
Furthermore, market dynamics affect the amount of resource that can be produced: fluctuating
prices can render previously uneconomic resources profitable or lead to the adoption of higher cost
technologies that offer higher recoverability. Higher prices tend to accelerate production and
influence technology change [3]. Rogner [3] posited that most investments in unconventional oil
production have happened during periods of high oil price expectations. In the absence of higher
oil prices, technological advancements that lead to more attractiveness of unconventional
resources can drive investments in the sector. The cumulative effect of higher prices and
technology innovation is increased supply of the resource.
22
Essentially, price-induced increase of resource development and production happens when the
technology needed is available but costly whereas technology-induced increase of resource supply
may come in the form of technological advancement or breakthroughs. Advancements improve
recovery rates from known reservoirs whereas breakthroughs unlock prior unproducible resource
deposits with potentially attractive development incentives. With all the fossil resources that can
last humankind for many centuries, resource availability issues are unlikely to be responsible for
a transition to a different energy future. Hence, responsible development of fossil energy resources
calls for economic, efficiency, and environmental considerations [3].
Apart from the techno-economic forces, there are also political and social contexts to
unconventional resources development. Oil and gas producers have to be licensed and regulated
to operate. The regulatory policy may nurture or hamper developments in the sector. The
Government of Alberta facilitated the development of the oil sands sector through an aggressive
investment in research and development under the Alberta Oil Sands Technology and Research
Authority (AOSTRA) in 1974 [18]. This enabled, by de-risking field testing, various technologies
to be developed and deployed for bitumen recovery, creating the path for the industry to be where
it is today. Figure 2-4 shows technology innovation timeline and productivity of in-situ oil sands
extraction in Alberta.
Moreover, the Alberta Governmentโs requirement of the industry to recycle 90% of water usage
of SAGD process led to the wide adoption of technologies that allowed that to happen. Regardless
of the economic benefits and opportunities that come with the operations of an oil and gas business
within a given locality, much of the environmental footprints of their activities remain a source of
23
conflicts with the public. Environmental impacts of resource development often attract the interests
of activists and the media. If government responds with regulations that push for higher carbon
prices/taxes, it may stimulate investment and direct technical change to methods and devices that
ameliorate the environmental impacts. Alternatively, it could lead to a shift to other energy sources
to the detriment of developing unconventional resources [6]. For this reason, public trust is
essential for sustained operation by a producer โ since government decisions might aim to balance
public perceptions and the need to attract investments for resource development. Public opposition
could also result in delay to embark on a new development project or the complete cancellation of
an ongoing one [5]. Therefore, getting the right policy mix is very important for unconventional
resources development.
24
Fig
ure
2-4
: T
ech
nolo
gy i
nn
ovati
on
in
oil
san
d i
n s
itu
extr
act
ion
an
d p
rod
uct
ion
gro
wth
[19].
25
Figure 2-5 summarizes the relevant issues and factors that can act in support or against the
development and production of unconventional resources. As the dearth of conventional fossil
fuels and the needs of a growing human society continue to necessitate a shift towards
unconventional resource development, adequate understanding and quantification of the roles and
impacts of these variables which affect the resource development and production would be
essential for higher productivity, lower costs, and emissions reduction. These call for a need to
adopt holistic approaches when assessing the energy, environmental, and economic issues which
affect unconventional resource development.
Figure 2-5: Factors and issues affecting unconventional resource development and
production.
26
2.2 Energy and Emissions of Unconventional Gas Resources
Natural gas burns cleaner than any other fossil fuel source of energy. Relative to coal, natural gas
has up to 60% less carbon content [11, 22]. Methane is the primary constituent of natural gas and
is also a major source of concern on the growing influence of natural gas in the energy system due
to its high potency for global warming. Until the 2000s, global gas production was mainly from
conventional oil and gas wells, and was already on decline [12]. The advent of unconventional
gas from coal seams, shale and tight formations was a game changer leading to abundant, cheaper
natural gas. This was courtesy of innovations in the resource development processes and
technologies like directional drilling and hydraulic fracturing. Current estimates of technically
recoverable in-place volumes indicate that shale is the predominant source of unconventional gas,
accounting for about 67.4% of total global estimates [26]. Figure 2-6 shows historical and
projected production of conventional and unconventional gas production in the U.S.
27
Figure 2-6: Historical and projected production of conventional and unconventional gas in
the United States [22].
The operations involved in unconventional gas development and production require both materials
and energy inputs which characterize overall impacts of the gas. Figure 2-7 shows a high-level
breakdown of a natural gas supply chain existing within an arbitrarily defined geography, which
is referred to here as a system control area (SCA). Gas produced at an upstream location is put
through a sequence of operations before arriving at the final consumption point along the chain. It
is the preproduction operations during unconventional gas development that constitute the major
differences in energy and environmental intensities between conventional and unconventional gas.
28
Figure 2-7: A schematic of energy and material flows for a gas supply chain.
During production, both gas categories are often fed into the same supply chain. This drives
interest to understand the impacts of unconventional gas development particularly in the face of
climate conservation goals prompting the replacement of coal sources with more gas and
renewable energy. At the same time unconventional natural gas is also displacing conventional gas
as the deposits decline across various jurisdictions world-wide. Sell et al. [13] reported that the
EROI of conventional natural gas from tight formations in the U.S. to be in range of 67 to 120
using an LCA-like approach that accounted for energy used to produce various inputs required in
the resource development in addition to the energy requirements of the development activities.
Wang et al. [14] reported that the EROI of U.S. shale gas to be in the range of 10 to 25.
Furthermore, Aucott et al. [15] reported higher estimates of EROI for Marcellus shale gas using
EUR data and fuel-use reports from industry. They considered all activities along the gas supply
chain up to the distribution, and on the basis of a 3 Bcf EUR, they estimated EROI for Marcellus
29
shale gas to range between 64 and 112 [5]. It is often challenging to compare the values for
conventional gas with those of shale gas because most of existing studies have lumped the
estimates for conventional gas along with the oil, for this reason conventional gas efficiencies are
often reported to be in the range of 8.5 to 25 [7].
In recent times, natural gas liquefaction (LNG) has also become a major feature of most gas supply
chains in various countries. Energy consumption and GHG emissions are involved when the gas
molecules are cooled to around -160oC at atmospheric pressure, resulting in a compression ratio
of about 600 at which LNG is produced [42, 43]. Liquefaction process could also be optimized
along the condensation curve for lower or higher cooling temperature and compression pressure
conditions [23, 41]. The uniqueness of individual gas supply chains in various jurisdictions,
combined with differences in assumptions, scope and boundary definitions among LCA based
studies have led to inconsistencies in estimates of energy and environmental impacts of natural gas
[20]. Branosky et al. [25] presented guidance on boundary setting to obtain more consistent
assessments and comparison of impacts of natural gas supply chain. In response, a number of
efforts have been made to present transparent and coherent assessment results. Weber and Clavin
[21] reviewed carbon footprints of conventional and unconventional gas (shale gas), reporting
estimates of 12.4โ19.5 g CO2e/MJLHV and 11.0โ21.0 g CO2e/MJLHV, respectively. However,
Hultman et al. [22] estimated GHG emission from shale gas to be 11% higher than that of
conventional gas. Sapkota et al. [23] estimated the cost and emissions of unconventional gas from
Western Canada for shipment to Europe via LNG and arrived at a range of values of 22.9โ
42.1โฏgCO2e/MJ, at a cost range of 8.9 - 12.9 ($/GJ). Kasumu et al [24] calculated GHG emissions
for exporting Canadian natural gas in the form of LNG to various countries in Asia and Europe.
30
Despite detailed breakdown of life cycle stages and clarification of assumptions, various studies
still arrive at different estimates of impacts even when analysing similar systems under common
scope definitions [20]. A primary reason for this being that the methods and data used to analyse
activities/events within each assessment stage still remain subject to the investigator. Such
methods have often relied on heuristic metrics and industry reports which can be dependent on
preferences for particular process and systems design and operational choices among individual
project proponents and operators. For a meaningful comparison of impacts, there is need for
consistency of approach and transparency on methods used by studies investigating energy and
environmental implications of unconventional gas development. To achieve this, high quality input
data is needed but more importantly, the methodology needs to be grounded in solid science and
engineering principles to assure reliability and transferability.
2.3 Energy and Emissions of Unconventional Oil Resources
Heavy oil, extra heavy oil (e.g. bitumen) and shale/tight oil are currently the major sources of
unconventional oil predominantly produced in Venezuela, Mexico, Canada, and the United States
of America [4]. These resources require enhanced recovery techniques involving hydraulic
fracturing, heating, and/or dilution due to very low permeability or very high viscosity. As shown
in Figure 2-8, there are numerous techniques for producing high viscosity oils, broadly categorized
into thermal, chemical/biochemical, and dissolution schemes [10]. Low permeability systems can
be given propped or acid fracturing treatments [27]. However, propped fracturing stimulation is
still the development method of choice for most operators and hydrocarbon reservoirs where
permeability challenge is an issue [28, 29].
31
The oil sands in Canada has the third largest proven reserves of oil in the world, with about 170
billion barrels of commercial deposits โ representing about 10% of global reserves of which 98%
is unconventional oil from the oil sands [31, 33]. In 2017, average crude bitumen production from
the oil sands was 2.8 million barrels per day, accounting for almost 3% of global crude oil
production that year [30, 32]. Total oil sands production is projected to become 3.8 million barrels
per day by 2022 [31] and 4.2 million barrels per day by 2035 [34]. Current Canadian production
mainly comes from facilities deploying recovery techniques such as in-situ based steam-assisted
gravity drainage (SAGD), cyclic steam stimulation (CSS), cold production strategies (with and
without sand), and surface-mining based hot-water processes [9, 31]. Figure 2-9 depicts the major
system operations for in-situ and surface-mining oil sands production.
32
Figure 2-8: Unconventional oil production methods, showing three main method โ miscible
displacement, chemical flooding, and thermal recovery [10].
Figure 2-9: Oil sands production systems and operations for surface-mining and in-situ
recovery techniques [9].
33
The majority of the production from oil sands utilize thermal recovery methods. This results in
high energy and GHG emission penalties of the operations. Due to high viscosities of the oil, about
20% to 25% of the energy content must be expended to produce extra-heavy oil [35]. In some
operations, the energy required for oil sands bitumen production (surface and in-situ mining) goes
close to 30% of the energy content of the produced oil [36]. In relation to CSS, the SAGD process
requires more energy but can give higher bitumen recovery of about 40-70% against 25-30% from
CSS [5, 37]. Most of the energy requirement of SAGD (more than 90%) goes into steam
generation; with one barrel of oil needing around 1 and 1.25 GJ of natural gas for steam generation
[36, 38]. The steam consumption in recovery operations is captured in the steam-to-oil ratio (SOR)
โ a good indicator of the energy and water intensities of the process.
Brandt and Unnash [39] quantified energy intensity and GHG emissions from thermal enhanced
oil recovery processes. They estimated GHG emissions to be in range 105-120 g CO2/MJ (gasoline
basis) covering the range for when electricity need is generated from natural gas or heavy oil,
whereas the range is found to be 70-120 g CO2/MJ for grid electricity without coal and when coal
is used for heating to reduce viscosity of the oil. When dealing with such estimates, it is important
to recognise that certain assumptions are required to determine the yield of gasoline (the functional
unit) from the crude oil being assessed. Product yield would depend, among other factors, on the
refinery type, refining configuration, and input feedstock blending. This has to be borne in mind
when aiming to relate results from one study to another. On the other hand, when assessment
results are reported relative to primary energy content of the resource, there could also be pitfalls
in how the EUR is quantified and utilized, particularly with respect to lifecycle activities/events
34
which occur during the preproduction stage of a development project. Often times, amortizing
early-life impacts on the basis on lifelong cumulative production estimates detracts attention from
clarifying the effects of ongoing emissions on current environmental conservation goals. It also
neglects the influence of other factors like economics on amount of the resource which is
ultimately recovered.
Brandt et al. [10] also investigated energy efficiency of oil sands extraction using historical data.
They reported historical energy returns to range from 1 to 8 GJ/GJ (on energy input to the
extraction process basis), highlighting an observed overall improvement in energy efficiency of
the oil sands industry. On the future of oil sands, they predicted a growing dominance of
unconventional resources as geopolitical concerns and dwindling conventional production become
more pronounced. More recently, Wang et al. [14] have also estimated energy returns for the oil
sands reporting separate values for surface-mining and in-situ production. They estimated the
EROI of mining and in situ oil sands to be in the ranges of 3.9-8 and 3.2-5.4, respectively. They
also compared EROI data computed for various Canadian and U.S. resources (both oil and gas)
over some periods of years, noting a decreasing trend in the efficiency of conventional oil and gas
even as unconventional resources indicated an improving efficiency over the observed years. The
authors argued that at the observed rate of efficiency improvement in the oil sands, and baring any
policy obstacles, it could catch up with the EROI of global oil and gas by 2027. However, they
cautioned that this would be contingent on continued investment and technology improvement,
under a conducive oil price environment.
35
Despite the efficiency gains, Englander et al. [40] noted that the carbon intensity of oil sands has
continued to increase due to overall industry production growths. They provided a well-to-wheel
estimate GHG emissions reporting average intensity for mining as 102 g CO2e/MJ gasoline and
for in-situ as 111 g CO2e/MJ gasoline. With these numbers they evaluated total oil sands emissions
in 2010 as 65 Mt.CO2e. Nimana et al. [31] presented a modelling workflow to quantify energy
consumption and GHG emissions in oil sands covering surface mining and in situ production. They
broke down energy consumption into three sources, including diesel, natural gas and electricity.
For surface mining they reported diesel use in the range of 4.4-7.1 MJ/GJ bitumen; natural gas use
of 52.7-86.4 MJ/GJ bitumen; and electricity need of 1.8-2.1 kWh/GJ bitumen. For in situ
production, they obtained natural gas use of 123-462.7 MJ/GJ bitumen and electricity requirement
of 1.2-3.5 kWh/GJ bitumen. With respect to the emissions, surface mining emission was presented
in a range of 4.4-7.4 g CO2e/MJ bitumen, and in situ emission as 8.0-34 g CO2e/MJ bitumen. The
authors also quantified the opportunities to improve environmental performances of mining and
SAGD up to 16-25% and 33-48%, respectively. Although the authors present a detailed workflow
of their approach, there is no clarity on the functional forms of the models governing the
assessment at each stage of the identified workflow. Besides, estimates of the surface facility
requirements would be subject to the subsurface attributes and operating configurations which are
often overlooked.
In the U.S., tight/shale oil has become the predominant source of total daily volumes, accounting
for about 6 million barrels per day, which represents around 60% of total daily production of
around 10.5 million barrels per day [32]. Brandt et al. [45] assessed energy intensity and GHG
emissions from tight oil production in the Bakken formation reporting EI of 1.505% of produced
36
crude energy content on a well-to-refinery gate basis. Relative to their estimate of U.S.
conventional crude average GHG emission of 8 gCO2e/MJ, they calculated Bakken tight oil GHG
emission to range between 2 to 28 g CO2e/MJ of crude where the endpoints of the estimates
represent the lowest and highest emitting wells, respectively. Laurenzi et al. [46] estimated GHG
emissions and water use for production of Bakken tight oil on well to wheel basis, reporting 89 g
CO2e/MJ gasoline and 90 g CO2e/MJ diesel. Obviously, majority of the emissions occur at final
fuel consumption resulting in higher values of the emission relative to other studies with more
limited scope. They estimated water use of 1.14 bbl/bbl gasoline and 1.22 bbl/bbl diesel โ with
13% of total water use being for hydraulic fracturing.
Yeh et al. [47] evaluated energy intensity and GHG emissions from Eagle Ford shale oil
production. The authors estimated production energy requirements covering drilling, extraction,
processing and operation of wells to be between 1.5-2.2% of energy content of produced oil and
gas; along with a well-to-refinery emission estimate of the range 4.3 โ 5.1 g CO2e/MJ. On
comparing their results with previous estimates of GHG emissions from conventional and
unconventional oil production of 5.9 โ 30 gCO2e/MJ, they concluded, contrary to a number of
existing studies, that unconventional oil from Eagle Ford has a lower emission impact than
conventional oil and gas. However, they did not cover major emitting events/activities along the
oil and gas supply chain, such as hydraulic fracturing fluid flowback and fugitive emissions. As
the oil is moved along the supply chain, there are efficiency losses due to energy inputs at various
points along the chain resulting in the lowest EROI value at the final consumption boundary. As
illustrated by Figure 2-10, the EROI of oil can be evaluated across various life cycle boundaries
along the supply chain [8]. For the case considered in the figure, energy returns at the oil extraction
37
boundary is less than that of processing and refining by about 40%. However, this may not be the
case in other situations considering a different resource type.
Figure 2-10: Energy return losses along an oil supply chain [8].
Oil shale is another unconventional resource with great potential globally. Due to differences in
the resource readiness, the emission range for oil sand and heavy oil is lower in comparison to oil
shale. The range for oil shale has been put at between 44 to 69 gCO2/MJ [36]. Additional to the
aforementioned emissions, is the fuel combustion emission of about 20 gCO2 per MJ of final
energy [36]. If a low carbon-content fuel like natural gas is used to generate steam for the heating
(retorting) requirements, the GHG emission could be reduced by up to 50% [8, 36]. On the other
hand, the GHG impact of conventional oil exploitation is significantly lower at around 4.4 and 4.7
gCO2/MJ [36]. For oil shale, it gets as high as between 232% to 892% of the conventional
production emissions [36]. Moreover, both surface mined and in-situ oil sands are still more
38
emissions intensive than conventional oil production [44]. GHG emissions pose an enormous
social and environmental challenge for the oil sands producers [5]. The emissions mostly comprise
of a lot of carbon dioxide, in addition to methane and nitrous oxide gases. Therefore, mitigating
the emissions and managing water use in the processes are becoming critical issues confronting
further developments in the industry, a reason for which a number of new processes and
technologies are being proposed to improve overall oil sands energy, environmental, and economic
intensities [1, 38].
2.4 Mitigating Environmental Impacts of Unconventional Resources
Apart from the role of efficiency improvements on mitigating emissions, capture, storage and
utilization are among other options for reducing environmental impacts of unconventional
resources development and use. In this section, the potential for carbon dioxide utilization (CDU)
to serve as mitigation strategy for the environmental impacts of anthropogenic emissions being
influenced by increasing unconventional resources production and use in the global energy mix is
explored. CDU comprises of the suite of processes and technologies for direct use of CO2 or
conversion of CO2 into useful products [47, 49]. For a CDU system to yield net environmental
benefits it must be a net-fixer of carbon or, at the least, delay immediate release of GHG emissions
thereby limiting warming impact of total emissions over a defined time period of assessment [47,
48, 49]. Figure 2-11 shows CDU pathways for reducing environmental impact of GHG emissions
from unconventional resources development.
39
Figure 2-11: Pathways for mitigating GHG emissions from unconventional resources.
Currently, to evaluate climate impacts of CDU systems, LCA is often performed to account for
total energy inputs and GHG emissions for producing products including fossil fuels, synthetic
fuels, chemicals, polymers, etc. Adequate accounting of climate impacts and benefits call for a
holistic approach considering the requirements and processing details of each product, in addition
to comparing product options on overall energetic and environmental intensity bases [47, 50, 52].
Effective assessment of options, various studies propose different metrics. Table 2-1 presents the
main assessment metrics available in the carbon utilization literature. Assen et al. [47] showed that
a major limitation in existing assessment of CDU impacts lie in their implementation of LCA
methodology. The authors made a case for the use of a holistic approach, incorporating the
essential elements of the CDU system within individual study boundaries and the evaluation of
impacts of delayed emissions via CO2 sequestration. This enables reliable comparison of CDU
alternatives.
40
Table 2-1: Metrics for assessment of environmental performance of CDU.
Sustainability Metric Definition Reference
Carbon footprint Amount of carbon dioxide emitted per unit of
product
[47], [50]
Annual carbon
dioxide reduction
Emission reduction based on the demand for the
CDU product
[50]
Net CO2 emission Difference between CO2 released and utilized by
the CDU systems
[47], [50]
CO2 storage
duration
Time period for which CO2 remains in the CDU
product
[50]
Fossil fuel energy
ratio
Ratio of the energy content of a CDU fuel to the
fossil energy required to produce it
[51]
Life-cycle
efficiency
Energy content of CDU fuel divided by its sum with
the energy input to CDU process
[51]
Carbon fixation
fraction Ratio of the net utilized CO2 to the utilized CO2 [51]
CO2 utilization
potential
Ratio of CO2 use that satisfies market demand for
the product, to the baseline CO2 emission
[52]
CO2 utilization
intensity Amount of CO2 use per unit amount of the CDU
product
[52]
CO2 emission
reduction
The net of annual CO2 emitted between an existing
and new processes, divided by annual emission with
existing process
[52]
CO2 avoided
potential
CO2 avoided (difference in emissions between old
and new methods/process/technology) divided by
the emission when using the old approach
[52]
2.5 What is missing in the literature?
The foregoing discussion highlight a number of gaps in the literature and opportunities to improve
common impacts assessment strategies as follows:
I. Although LCA is a well-established framework commonly used for assessing impacts of
energy resources, it does not prescribe a methodology for quantification of the impacts.
41
This often results in disparate and controversial estimates of impacts even with overlaps of
study boundaries. Without clarity on quantification approach, it is a pitfall to adopt or
transfer study results in subsequent assessments comparing impacts of energy systems
decisions.
II. Current assessment methods are also limited in their application of a multi-criteria
approach particularly in the face of making choices from comparisons of various
alternative decisions. This calls for new multi-objective assessment paradigms steeped in
mathematical programming techniques to simultaneously handle multiple impacts when
evaluating relative performances various decision options.
III. Following the understanding that upstream activities during shale gas development are the
main sources of differences in impacts relative to conventional gas, yet some studies equate
the impacts of both as the same. Therefore, there is a need for clarity on how such activities
influence energy, emissions and economic intensities of shale gas development.
IV. Even though oil sands is a well-researched topic given its significant contribution to
Canadian oil production, yet not much is known about the prospects and relative
performances of emerging process and technology configurations especially using a
simultaneous approach to compare impacts of alternative design choices.
V. In this era of environmental conservation goals, there is a need to identify and explore
mitigative or eradicative pathways to minimize climate impacts of increasing
unconventional resources development and extraction as global energy demand continues
to grow along with GHG emissions.
42
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48
On Methane Emissions from Shale Gas Development
This chapter has been published in the peer-reviewed journal Energy, with the following reference:
Umeozor, E. C., Jordaan, S. M., & Gates, I. D. (2018). On methane emissions from shale gas
development. Energy, 152, 594-600.
Abstract
Environmental and economic impacts of methane escaping from the natural gas supply chain
remain uncertain. Flowback emissions from hydraulically fractured natural gas wells are a key
component of emissions from unconventional gas wells. While reduced emission completions in
the United States are required by regulation, Canadaโs proposed regulation will only be
implemented in 2020 with the two highest producing provinces under exemption. To understand
potential benefits of regulations, we use predictive modelling of well-level production data of 1633
hydraulically fractured shale gas wells in five plays to estimate pre-production emissions. The
mean estimate for flowback emissions (2,34695% confidence interval of 91 Mg
CO2e/completion) fall within the 95% confidence limits of measured potential
emissions (2,566777 Mg CO2e/completion). Our results indicate that in 2015, the average
emissions per shale gas well undergoing flowback was 2,347 Mg CO2e/completion in the U.S. and
1,859 Mg CO2e/completion in Canada. Mean potential profits from controlling methane emissions
using reduced emission completions were US$17,200/well in the U.S. and US$11,200/well in
Canada.
49
3.1 Introduction
As the cleanest available fossil fuel option, natural gas is often perceived as the transitional energy
source to a potential decarbonized energy future [1, 2]. Policies across various jurisdictions appear
to favor displacement of various fossil-based systems with gas and renewables for electricity,
transportation, heating, and chemicals manufacturing [1]. Most future use of natural gas is
expected to be in power generation as coal-powered electricity is replaced with natural gas-fired
power plants [2, 3, 4]. Due to the adoption of natural gas-fired systems, development of
unconventional gas resources is expected to experience large scale expansion globally [2].
One key issue exists: methane escapes to the atmosphere from the extensive natural gas supply
chain [5]. Emissions may be released anywhere from production wells, pipelines to the processing
facility, and pipelines to the end user. The magnitude of the fugitive emissions is challenging to
quantify from this diffuse infrastructure system. Alvarez et al. [6] used a technology-based
warming potential measure to show that in a growing gas economy, with higher penetration of
natural gas technology, accurate quantification of methane emissions and its minimization from
natural gas infrastructure would be essential for achieving significant climate benefits [3, 7, 8].
Relative to coal power systems, methane emissions must not exceed 3% of total gas production
(over a 20-year warming horizon) or 10% of the production (over a 100-year warming horizon)
for gas power systems to be environmentally beneficial [39].
This need to understand emissions from natural gas production systems has been met with calls to
improve measurement along the chain [5-7,9-14]. In response, Allen et al. [9] reported direct
measurements of methane emissions at 190 natural gas sites in the United States (US). They used
50
emission factors for activities and equipment to develop national emission estimates for source
categories. McKain et al. [10] used atmospheric measurements to quantify methane emissions
from natural gas delivery and end use within a U.S. city. Frankenberg et al. [11] used spectroscopy
to determine methane emissions covering both upstream and downstream sections of a U.S. natural
gas system. Zavala-Araiza et al. [6] made key recommendations for obtaining consistent and
synchronized emission estimates from remote and direct measurements, such as: accurate source
attribution in remote measurements; accurate facility counts of all major sources in direct
measurements; and generation of emission factors from both measurement methods using
representative sampling of source categories and their frequencies. Unfortunately, implementing
these recommendations is more challenging in practice. Remote measurement studies need to
accurately extricate thermogenic methane from biogenic footprints which could account for up to
50% of the total measured gas [40, 41]. This can be complicated by variable compositions of source
apportionment tracers at different field measurement conditions [40].
Although measurement science has come of age, it can be expensive; thus, there are limits on
sample sizes and representativeness. Brandt et al. [5] identified the mitigation benefits and
complementary role of reliable methods that can rapidly identify major sources of emissions
without the need for extensive measurement campaigns. The development and application of
analytical and semi-analytical models is a growing area of interest to fill this gap [39, 41].
Nevertheless, existing estimates of completion emissions are generally derived from heuristic
methods and their representativeness has yet to be confirmed. They often rely on low sampling
sizes or are limited in their application of probabilistic risk analysis [1,7,12-14]. Up till now,
methane emission estimates based on completion operations have not been verified with actual
field measurement data. This presents a pitfall in the application of such results for understanding
51
the impacts of shale gas development and creating emission control policies. For example,
Howarth et al. [12] evaluated the methane footprint of hydraulic fracturing by using initial
production testing (IPT) data from ten wells and estimated emissions to be 30 to 50% greater than
that of conventional gas [12]. Criticisms of their methodology stems from: the inconsistency of
using IPT data from nine wells ready for full commercial production along with the potential
emissions from one well undergoing flowback to calculate average flowback emissions for all ten
wells; double counting the IPT data from the highest producing well in calculating the average
flowback emissions; using this over-estimated average as the actual emissions per well; and,
assuming complete venting of the assumed actual emission per well (thus, implying no portion of
the flowback gas is flared or captured) [15]. OโSullivan et al. [14] also used a heuristic approach
based on flowback analysis to estimate GHG emissions from about 4,000 hydraulically fractured
shale gas wells across 5 shale plays in the U.S. They estimated gas production during flowback as
ramping linearly from zero at flowback initiation to the peak gas production rate for the well at
flowback completion. However, as can be observed from actual field experience (and illustrated
in Figure 3-1), peak gas production occurs way beyond the flowback period; implying that the use
of peak gas production rate to estimate flowback emission leads to overestimate of the actual
amount of flowback methane. To the best of our knowledge, all the existing literature which
propose the estimation of emissions through proxy modelling did not present a functional
workflow of their modelling, with the relevant equations, needed to formalize their approaches.
Improved understanding of methane emissions drives the creation of control frameworks that
include both economic and environmental considerations as the natural gas industry grows [2].
Regulations in the US to address emissions from completions have been supported by studies that
52
proved either profitability or cost-effectiveness of reduced emission completions (RECs), also
referred to as green completions [13,14,16,17]. These new standards are expected to reduce 0.5
Mt of methane (11 Mt CO2eq) by 2025. In line with the US, Canada has proposed similar emissions
regulations that will be implemented commencing in 2020 [37], but with an exemption for wells
completed in the provinces of Alberta and British Columbia (the two highest natural gas producers)
[38]. Alberta and British Columbia have announced plans to reduce methane emissions from oil
and gas operations by 45% by 2025 by applying new emission standards at the design stage of new
facilities, improving measurement and reporting of methane emissions, and ensuring that existing
facilities follow regulated standards through monitoring and verification. The Natural Resources
Defence Council of the US [16] estimated that about 40% of methane emissions from US oil and
gas industry can be captured by RECs.
Here, the focus is specifically on generating a new predictive model to estimate emissions that
occur at the production well during the flowback stage of hydraulically fractured operations. We
formalize the application of predictive modelling for this purpose by presenting the relevant
equations and the workflow of our calculations so that our approach can be deployed in areas
worldwide where shale gas is becoming prominent but there is desire to understand the global
warming impact of its development and economic losses through methane leakage. Based on the
literature, emissions from flowback are the most significant source of upstream emissions
[3,12,14] being estimated to represent up to 25% of the total carbon footprint [18].
53
3.2 Methods
The development of a shale reservoir involves drilling wells and producing gas, which happens in
a sequence of steps. These steps are categorized into drilling, completion, flowback, initial
production testing, and commercial production. After hydraulic fracturing, the fracturing liquid is
cleared from the well as flowback fluid to enable future gas production [19]. Emissions escaping
as the liquid is cleared are the focus of our analysis: the methods presented here estimate the
emissions that can be captured using RECs and verify results using measurements. The approach
to estimate net revenue from requiring this technology through regulation are then described.
Potential emissions from this stage of shale gas development can be estimated using well-level
production profiles. Investigation of field production profiles from shale gas wells reveal a build-
up of production during the first two to three months of production, with peak production often
observed in the second or third months [20]. Gradual ramping of gas rates occurs as fluids are
produced from the shale reservoir. Initially, injected liquid is mostly produced in the flowback
fluid. On this basis, we consider an idealized shale gas production profile as depicted by Figure 3-
1 where three regimes can be identified, including: (I) flowback fluid production, (II) full
production, and (III) declining production. This profile is reasonable after considering actual
flowback profiles described in [20] and [21]. The gas flow rate builds up as the reservoir
permeability rises with removal of liquid. Focusing on the flowback regime, the total flowback
gas (๐๐,๐๐) can be obtained as the integral of the flowback gas rate (๐๐) over the flowback period
(0 to ๐ก๐๐), given by:
๐๐,๐๐ = โซ ๐๐๐๐ก๐ก๐๐0
1
54
Figure 3-1: Gas production profile from a hydraulically fractured shale gas reservoir.
Time periods (I, II, and III) not drawn to scale.
Previous estimates of flowback emissions through proxy modelling are based on either peak gas
production rate (๐๐,๐๐๐๐) or initial production testing data [12, 14, 19]. Unfortunately, determining
the functional form of the flowback profile is complex considering the multiphase nature of
flowback [22,23]. For this reason, we estimate the integral from shale gas field production rate
data by using the first occurrence of the single-phase gas production, ๐๐,๐ ๐, at its onset, ๐ก๐,๐ ๐. The
integral can be approximated by using the Newton-Cotes integration formula [24]:
๐๐๐ = โซ ๐๐๐๐ก๐ก๐๐0
โ๐ก๐,๐ ๐
2[๐๐,0 + ๐๐,๐ ๐] 2
55
The first term in the square brackets is the gas flowrate at flowback initiation, ๐๐,0, which is
typically equal to zero. Figure 3-1 suggests that the use of the peak production rate would lead to
erroneously high gas rates during the flowback period. Also, using initial production testing (IPT)
measurements would significantly overestimate the potential emissions since the purpose of IPT
is to estimate the maximum productivity of a well, which is done at conditions different than at
any other stage in the developmental or productive life of the well. During IPT, the wellhead
pressure is brought to the atmospheric pressure (no back pressure) condition to induce the
maximum pressure gradient for production. If the gas production is represented by the commonly
used Darcy equation for flows in oil and gas reservoirs, given by [23]:
๐ = โ๐๐ด(โ๐)
๐๐ฟ 3
The flowrate (๐) is directly proportional to the pressure drop (โ๐). Therefore, considering just the
bottom hole pressure (๐๐โ๐) and wellhead pressure (๐๐คโ๐) segments of the shale gas well, at every
point in the life of the well โ other than during IPT โ the pressure drop is:
โ๐ = ๐๐คโ๐ โ ๐๐โ๐ 4
However, during IPT, the pressure drop is given by:
โ๐ = ๐๐๐ก๐ โ ๐๐โ๐ 5
where ๐๐๐ก๐ is the air/atmospheric pressure at the location.
Consequently, the pressure gradient is maximum at IPT conditions, thereby producing the highest
flowrate from the well. For this reason, we argue that IPT data is an inadequate metric for
56
estimating flowback gas quantities as it leads to overestimates. Similarly, methods based on
recorded peak gas production from a well also overestimates the flowback emissions. From the
HPDI database for U.S. shale gas development activity [26], we investigated the frequency of peak
gas occurrence over the age of a well, expressed in months in Figure 3-2. It is observed that most
of the recorded peak gas production occurred in the 2nd, 3rd, and 4th months โ with majority
occurring in the 3rd month โ much after the flowback regime. This observation is also captured in
Figure 3-1.
Figure 3-2: Frequency of historical peak gas occurrence among hydraulically fractured
reservoirs in the five US shale plays recorded in HPDI [26].
57
All field data used here was obtained from the Drilling Info (HPDI) database [26] with the first
occurrence of single-phase gas production obtained from the โGas Practical Initial Productionโ
data which represents the average daily gas production for the first month in which only gas was
produced [26]. This implies that the difference between ๐ก๐,๐ ๐ and ๐ก๐๐ is less than or equal to one
month and thus the model given by Equation 2 provides an upper limit of flowback gas emissions.
Equation 2 was used to estimate gas production during the flow back period for all wells examined
here.
To arrive at our methane emissions estimates, we employ well-level data from Drilling Info that
were queried for active shale gas fields and reservoirs using the model workflow presented in
Figure 3-3. Here, wells fractured in 2015 from five shale plays in the US (Barnett, Fayetteville,
Haynesville, Marcellus, and Woodford) and from two Canadian shale gas plays are analyzed (the
Duvernay and Montney Formations). A total of 1,633 US wells and 455 Canadian wells are
reported in the database for 2015. The current New Source Performance Standard (NSPS)
requirement for green completion of hydraulically fractured shale gas wells is adopted to reflect
the existing policy environment in the US [17]. Under this policy, 95% of potential methane
emissions are considered recoverable whereas the remaining 5% are taken to be more technically
challenging to recover, and thus, can be flared. The EPAโs average methane content of flowback
gas equal to 78.8% is used. The potential GHG emission is calculated based on methane content
by applying a global warming potential (GWP) of 21. Modelling results were validated with field-
based emission measurement data by using 12 data points from EPAโs NGSTAR program [25,27]
and 37 data points from past literature studies [7,9,28-30]. For studies that report only the range of
their emission measurements, both the lower and upper values are included in the dataset.
58
3.3 Sensitivity Analysis
The sensitivity of the results to the length of the flowback period, either 3 or 9 days, is examined,
labelled as ES-3days and ES-9days, respectively. Since the recent EPA update of methane GWP
is given as a range from 28 to 36 [31], sensitivities were evaluated at both these limits as well.
There is also uncertainty on the methane content of the flowback gas since it varies across basins,
between wells within a basin, and even for a given well through time [32]. Therefore, sensitivities
at 50% and 95% methane contents at the density of 19 kg/Mcf [8,32] are also evaluated.
Figure 3-3: A schematic illustration of the estimation method and calculation procedure.
59
2.2 Net Revenues from Reduced Emissions Completions
To estimate the economic effect of regulations that require RECs, the net revenues associated with
the application of this technology are calculated. Net revenue is calculated as the difference
between the revenues of the captured gas and the costs of REC implementation on the new wells
given by:
Net Revenue = (Sales of Captured Gas) โ (Cost of REC) 6
The sensitivity of net revenue to low (US$2 per Mcf), medium (US$4 per Mcf), and high (US$6
per Mcf) natural gas price is evaluated for each shale play. The sensitivity of the results to the cost
of REC technology (low = US$5,000/completion, average = US$12,000/completion, and high =
US$65,000/completion) is also examined by using the range of costs reported in the literature
[16,33,34].
3. Results and Discussion
Figure 3-4 displays a comparison of completion emission estimates from the new model, given by
Equation 2, for all producing wells with existing measurements reported under EPAโs Natural Gas
60
STAR program [7,9,25,27,28,29,30]. Both 3- and 9-day flowback duration is applied to bound
uncertainty consistent with existing literature [14,25,35]. The relative accuracy of the new model
is assessed by comparing results to actual measurement data from shale plays within the Gulf
Coast, Midcontinent, Rocky Mountain, and Appalachian regions in the US [7,9,25,27,28,29,30].
Table 1 lists descriptive statistics of the measured and estimated flowback emissions. The results
in Figure3-4 and data listed in Table 3-1 reveal that estimates using a 3-day flowback duration
gives a more representative estimate of the measurement studies. By using the 3-day flowback
assumption, the mean estimate for flowback emissions (2,34695% confidence interval of 91 Mg
CO2e/completion) falls within the 95% confidence limits of measured potential emissions
(2,566777 Mg CO2e/completion).
61
Figure 3-4: Comparison of the model-based completion emission estimates (ES) and
reported completion emission measurements (MS) for US shale plays. ES-3days and ES-
9days represent the estimates assuming 3 and 9 days flowback periods, respectively. The
number of data samples (n) are indicated under each boxplot. The measurement data
covers shale plays within the Gulf Coast, Midcontinent, Rocky Mountain, and Appalachian
regions in the United States (see list of sources of measurement data in Appendix A, SI:
Section 5).
Methane emissions per well experience large ranges in maximum and minimum values, pointing
to the importance of extremes in analyses related to the oil and gas sector [31]. Potential emissions
across all US shale plays range up to 21,021 Mg CO2e per completion (mean of 2,347 Mg CO2e
62
per completion). For Canadian shale wells, potential emissions range up to 8,598 Mg CO2e per
completion (mean of 1,859 Mg CO2e per completion). At a lower methane content of 50%, the
mean flowback emission for the US plays is 1,489 Mg CO2e per completion whereas for the
Canadian plays it is 1,180 Mg CO2e per completion. At the higher methane content of 95%, mean
flowback emission for the US plays is 2,830 Mg CO2e per completion whereas for the Canadian
plays it is 2,241 Mg CO2e per completion.
Table 3-1: Statistical attributes of the model estimates and measurement data (Mg CO2e per
completion). ES-3days and ES-9days represent the estimates assuming 3 and 9 days
flowback periods, respectively.
Data ES-3days ES-9days MS
Sample size (n) 1,633 1,633 49
Mean 2,346 7,039 2,566
Median 1,891 5,672 939
Minimum 1 3 4
25th percentile 977 2,932 134
50th percentile 1,891 5,672 939
75th percentile 3,326 9,979 2,552
Maximum 21,021 63,062 21,739
Standard Deviation 1,883 5,650 4,464
95% CI +/-91 +/- 274 +/- 777
63
By using the updated EPAโs GWP range of 28 and 36 [36], the minimum potential emission
estimates for the US wells are 1.2 and 1.5 Mg CO2e per completion, respectively, with an average
value of 1.4 Mg CO2e per completion with maximum potential emission estimates for the same
US plays being 28,028 and 36,036 Mg CO2e per completion, respectively. For the Duvernay and
Montney Formation wells, the minimum potential emission estimates range between 0.7 and 0.9
Mg CO2e per completion (depending on the assumption for GWP), with an average value of 0.8
Mg CO2e per completion, whereas the maximum potential emission estimates range from 8,598 to
14,740 Mg CO2e per completion, respectively.
The mean potential emissions from wells drilled in the Barnett region of Texas are estimated to be
988 Mg CO2e per completion, with 95% confidence intervals (CI) of 137 Mg CO2e per completion.
For Fayetteville, the mean is 1,078 Mg CO2e per completion (95% CI of 59 Mg CO2e per
completion). Haynesville has the highest mean potential emission of 3,608 Mg CO2e per
completion (95% CI of 371 Mg CO2e per completion). The Marcellus and Woodford plays
produced mean potential emissions of 2,860 and 1,896 Mg CO2e per completion, respectively,
with 95% CIs of 128 and 207 Mg CO2e per completion, respectively. Plots of the distributions of
potential emissions within plays show various degrees of skewness with the identifiable high
emitters within each shale play (see Appendix A, SI: Section 2).
Consistent with the existing NSPS in the US, a gas handling scenario in which 95% of the potential
emissions from each well are captured while the remaining 5% are flared is examined. Figure 3-5
presents boxplots of play-level net revenues under the three natural gas prices and the average
REC implementation cost scenario. The plot of net revenues for the average REC cost case indicate
64
that under a low natural gas price regime, about 50% of US wells and 60% of Canadian wells
cannot be green completed profitably. A medium pricing environment allows about 76% of green
completed US wells and 78% of Canadian wells are profitable from sales of the captured gas. For
the high price regime, 87% of US wells and 88% of Canadian wells can make profit by using
RECs. Cumulative density plots of net revenue are also provided separately for the US and
Canadian wells in the Appendix A, SI: Section 3.
Figure 3-6 shows quartiles of net revenues for all wells (both US and Canadian) under the three
price regimes and REC implementation costs. For the low-cost REC deployment, the lowest P25
value across the gas price regimes is US$1,300 per well. For the average REC cost, both P25 and
P50 are less than zero indicating that REC is not profitable for about half of all wells. However,
the P75 and above are positive. All quartiles are positive for the medium and high gas price
scenarios. When high REC cost is applied, P25 through P75 are all negative for all the three gas
price scenarios. In fact, beyond P75, the net revenue remains negative into the fourth quartile for
all gas price regimes until P80 in the high gas price regime, P93 in the medium price regime, and
P99 in the low gas price regime. However, P100 is positive for all three gas prices, with the highest
value of US$317,600 obtained for the high gas price scenario. Basin-resolved sensitivity of net
revenue under high or low REC costs scenarios are also presented in Figures 3-7 and 3-8. A
summary table of the quartiles of net revenue is available in the Appendix A, SI: Section 4.
65
Figure 3-5: Boxplots of the potential net revenue from REC of hydraulically fractured US
and Canadian shale gas wells in 2015 with 95% capture of the flowback gas. B=Barnett,
F=Fayetteville, H=Haynesville, M=Marcellus, W=Woodford, DU=Duvernay,
MT=Montney. Low gas price=$2/Mcf, Medium gas price=$4/Mcf, High gas price = $6/Mcf.
Outliers in the figure (+) are more than 1.5 times the interquartile range.
Table 3-2 lists values for USA and Canada REC costs per metric ton of potential CO2 equivalent
emissions, for the three REC cost scenarios considered in this study. It is observed that the
mitigation cost is higher as REC implementation becomes more expensive. The results for Canada
are slightly higher probably due to lower shale development activities and experience relative to
the US. However, the average revenue per metric tonne of CO2 equivalent emissions is US$12.44
per ton CO2e for USA and US$12.48 per ton CO2e for Canada. Thus, there is an economic
incentive to deploy moderate-cost REC for shale gas development projects.
66
Table 3-2: Mitigation costs of green completion.
Country Mitigation Cost (US$/ton)
Low REC Cost Average REC Cost High REC Cost
US 2.13 5.11 27.69
Canada 2.69 6.46 34.97
67
Figure 3-6: Impact of REC cost variability on the potential for profitability of REC
implementation across all plays (Barnett, Fayetteville, Haynesville, Marcellus, Woodford,
Duvernay, and Montney). Quartiles of net revenue are shown under variable cost of REC
and natural gas price regimes (sample size = 2,088).
68
Figure 3-7: Basin-resolved sensitivity of net revenues for the low REC cost scenario at
various natural gas prices
69
Figure 3-8: Basin-resolved sensitivity of net revenues for the high REC cost scenario at
various natural gas prices.
4. Conclusions
A new model to estimate flowback gas emissions from hydraulically fractured wells has been
developed. The results demonstrate that the results of the new model of gas emissions based on
well-level data provides reasonable estimates of flowback emissions from hydraulically fractured
shale gas wells. The new model is validated by comparing its results with data from five US shale
plays. The model is then used to estimate emissions from Canadian shale plays in the provinces of
Alberta and British Columbia. The analyses highlight the significant benefits of capturing potential
GHG emissions from hydraulically fractured wells undergoing flowback. Such results are
particularly useful for understanding the economic implications of applying regulations that
70
require RECs in shale producing regions. Depending on natural gas prices, mean revenues of up
to 95% of flowback methane gas were captured from wells completed in 2015 is US$17,200 per
well in the U.S. and US$11,200 per well in Canada. Considering the net revenue obtained, it is
observed that it is economical to implement green completions in most cases.
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75
Predictive Modelling of Energy and Emissions from Shale Gas Development
This chapter has been published in the peer-reviewed journal Environmental Science and
Technology, with the following reference: Umeozor, EC and Gates, ID (2018). Predictive
Modeling of Energy and Emissions from Shale Gas Development. Environmental Science &
Technology 2018 52 (24), 14547-14555.
Abstract
Contributions of individual preproduction activities to overall energy use and greenhouse gas
(GHG) emissions during shale gas development are not well understood nor quantified. This paper
uses predictive modelling combining the physics of reservoir development operations with
depositional attributes of shale gas basins to account for energy requirements and GHG emissions
during shale gas well development. We focus on shale gas development from the Montney basin
in Canada and account for the energy use during drilling and fluid pumping for reservoir
stimulation, in addition to preproduction emissions arising from energy use and potential gas
releases during operations. Detailed modelling of activities and events that take place during each
stage of development is described. Relative to the hydraulic fracturing activity, we observe
significantly higher energy intensity for the well drilling and mud circulation activities. Well
completion flowback gas is found to be the predominant potential source of GHG emission. When
these results are expressed on an annual basis, consistent with the convention of most climate
policy goals and directives, environmental impacts of our growing natural gas economy are better
appreciated. Estimated likely GHG emission from new development wells in 2017 in the Montney
Formation, alone, is 2.68 million metric ton CO2e. However, on a preproduction requirements
76
basis and dependent on mean estimated ultimate recovery (EUR), energy return on invested energy
for shale gas from the Montney Formation in Canada is estimated to be about 3,400. The approach
described here can be reliably extended to areas, globally, where natural gas development is
becoming prominent.
Introduction
Ever since horizontal drilling with multistage hydraulic stimulation unlocked vast shale resources
in many areas in North America and beyond, global natural gas production has increased
tremendously resulting in abundant, cheap gas [1]. For this reason, gas-based energy technologies
have become favourable for business decision reasons including desires to curtail climate effects
of a growing global energy demand [2]. However, concerns about the actual environmental
benefits of unconventional gas in the energy mix, particularly against coal and coupled with
depleting conventional gas, have triggered a lot of scrutiny of the operational practices of natural
gas producers, especially at upstream operations where both development and production activities
occur [1, 3]. In combustion, it is known that natural gas โ whether conventional or not โ burns
cleaner than other fossil fuels with up to 50% less carbon generation [3]. Moreover, between
conventional and unconventional natural gas, it is primarily the difference in their development
techniques, occasioned by resource deposition attributes, which may translate to disparate energy,
emissions and economic impacts [4, 5, 6, 7, 8, 9]. At production, both natural gas sources can be
piped into the same supply chain [9].
77
Shale gas is a type of unconventional resource with the depositional attribute of entrapment, or
exceedingly low permeability, within pockets of petroleum reservoir rock. Shale is also deeper
underground than conventional natural gas [10]. As such, relatively greater investment and energy
input is required for development than that of conventional gas. Generally, this energy requirement
is met by fossil fuel (often diesel) combustion at the predominantly remote locations where the
resources are exploited [5]. Higher energy penalty implies more greenhouse gas (GHG) emissions,
yet there is a lack of clarity on how the resource development activities distribute energy and
emission intensities of the operation. This apparent lack of understanding of preproduction impacts
becomes amplified when the scope of analyses is reduced to an individual gas well basis, without
accounting for the annual scale of shale resource development campaigns since the shale
revolution started. As of 2013, unconventional gas contributed about 64% of total U.S. natural gas
production and is expected to climb to 70% by 2020 [11]. In Canada, unconventional gas
accounted for 51% of total gas production in 2014 and is projected to represent 80% of all gas
production by 2035 [12]. In the Canadian province of Alberta alone, a total of about two thousand
gas wells were drilled in 2015 of which over one thousand were for unconventional gas [13].
Given that global GHG emission reduction policies and targets are normally designed on the basis
of annual emissions to be reduced to particular baseline year values, better insights can be gained
on climate impacts as more gas is consumed in global energy flows by taking a more holistic and
systematic approach in the analysis of energy and environmental implications of unconventional
gas development [6, 7]. The energy requirement for drilling shale gas wells depends on a number
of factors: attributes of drilling machinery (e.g. efficiency), type and properties of formation being
drilled, and measured depth of wellbore to be developed, among others. After drilling is completed,
78
energy is still required for hydraulic fracturing โ that is, to pump fluids, including proppant, into
the reservoir to create and sustain fractures. At every stage in the development, GHG emissions
are generated as energy for drilling and fracturing operations are furnished โ often from fossil fuel
combustion to provide the mechanical drive needed to drill or pump fluids into the formation [6,
14]. Emissions could also arise from leakages of hydrocarbons and other GHGs as the drilling
operations or well completion activities expose the subsurface during development. Figure 4-1
breaks down shale gas preproduction activities into three steps, including drilling, hydraulic
fracturing, and flowback.
Figure 4-1: Preproduction operations (in dashed box) during shale gas development.
Previous reports in the literature have used reported data or heuristic approaches based on
assessments of primary energy feedstocks for development operations to gauge preproduction
emissions [14, 15, 16, 17, 18]. These approaches lead to limitations in transferability of the results
when the conditions for measurements, type of energy source, or attributes of the resource
depositions differ from one development project to another which in turn creates pitfalls for
applying emission factors arising from such studies. This study presents a predictive modelling
approach with a strong analytical background to account for energy use and GHG emissions during
shale gas development. We identify the activities and events which trigger energy-derived or direct
methane emissions. Applicability of our approach is demonstrated using data from 1,403 shale gas
wells in the Montney Formation in Western Canada. Figure 4-2 shows the Montney basin area
Drilling Fracking Flowback Production
79
covering developed wells within the provinces of British Columbia and Alberta. Well-level data
are obtained from the HPDI and GeoScout databases [19, 20]. Detailed modelling of sources and
the implementation workflow is presented to enable transferability of our method to other areas
where shale gas development activity is growing.
Figure 4-2: Study focus area showing the spread of Montney over British Columbia and
Alberta with developed wells highlighted in red.
Method
We focus on energy and methane emissions from preproduction activities during shale gas
development covering drilling, hydraulic fracturing, and flowback operations. Diesel is used as the
primary energy source for both drilling and fracturing operations. Overall preproduction emission
British Columbia Alberta
Montney Formation Wells
80
is computed as the combination of energy consumption emissions and potential direct releases of
methane during each development operation. The principal activities requiring energy input during
shale gas development include drilling, drilling mud circulation and hydraulic fracturing. The total
potential preproduction emissions can be expressed as a sum of potential direct and energy
emissions, expressed by:
๐๐ถ๐2๐๐ = ๐ท๐๐๐๐๐๐๐โ ๐ธ๐๐๐๐๐ฆ
+๐๐ข๐ ๐๐๐๐คโ ๐ธ๐๐๐๐๐ฆ
+๐๐ข๐ ๐๐๐ โ ๐ท๐๐๐๐๐ก
+ ๐ป๐ฆ๐๐๐๐ข๐๐๐ ๐๐๐๐๐ก๐ข๐๐๐๐โ ๐ธ๐๐๐๐๐ฆ
+ ๐น๐๐๐ค๐๐๐๐ ๐๐๐ โ ๐ท๐๐๐๐๐ก
(1)
Actual emissions depend on whether the potential direct methane releases are captured, flared or
vented. There is no reason to restrict gas handling to either capturing or flaring scenarios since
current regulatory requirement does not demand a strict adherence to either option [21, 22, 23].
Therefore, we estimate total preproduction emission on the basis of energy and potential
preproduction methane emissions. Flowback gas is assumed to have a volumetric methane content
of 78.8%; which agrees with recorded Montney Formation, air-free, natural gas methane
composition. Methane density of 19 kg/Mcf is used to calculate the mass, and the CO2-equivalent
emission is obtained by applying a global warming potential of 36. Model parameter values are
chosen for ease of comparison of results with previous studies. However, sensitivities of emission
estimates are evaluated by using up-to-date parameter values. The ranges of modelling input
parameter and variable values are available in the supplementary document (Appendix B, Section
SB.1). Detailed modelling of preproduction activities and events is presented individually below.
81
Drilling energy use and emission
Well drilling is a major activity in the development of shale gas. Unfortunately, existing lifecycle
impact assessment studies have not presented a systematic and elaborate approach to quantify
energy and emission impacts of the drilling operations during shale gas development. Vafi and
Brandt [24] was the first attempt to shed more light in this area through careful modelling of some
of the events during oil and gas well development. However, their work did not cover all sources
(like mud gas and completion emissions) and generally handled some of the critical variables as
time-invariant. Faezelaideh [25] used analytical modelling to investigate the forces on the
drillstring during a drilling operation. This modelling approach enables understanding of the
effects of changes in design and operational variables when treating different types of wells within
a play or among wells in various basins. The required drilling torque can be obtained for the
straight (vertical, horizontal, or inclined) and curved sections of the target wellbore design by
summing the effective and lost torque components as follows:
๐๐๐ = โ {๐ฝ๐โ๐๐(cos ๐ผ + ๐sin ๐ผ)}๐๐โ๐๐ (2)
๐๐ถ๐ = โ {๐ฝ๐โ๐๐ (sin๐ผ๐โsin๐ผ๐โ1
๐ผ๐โ๐ผ๐โ1+ ๐
cos๐ผ๐โ1โcos๐ผ๐
๐ผ๐โ๐ผ๐โ1)}๐
๐โ๐ถ๐ (3)
82
where ๐๐ and ๐ถ๐ indicate the sections of the target wellbore being developed. To estimate total
drilling energy requirement supplied by a top-drive system, if ๐ represents each section of the
drillstring (in addition to the drill bit), ๐ indicates straight sections of the wellbore to be created,
and ๐ stands for the curved parts of the wellbore, then the energy use can be expressed as:
๐ธ๐ = โ โ (๐ฝ๐คโ๐๐๐)๐,๐๐๐ (cos ๐ผ๐,๐ + ๐ sin ๐ผ๐,๐) + โ โ (๐ฝ๐คโ๐๐๐)๐,๐๐๐ (sin๐ผ๐,๐โsin๐ผ๐,๐โ1
๐ผ๐,๐โ๐ผ๐,๐โ1+
๐๐,๐cos๐ผ๐,๐โ1โcos๐ผ๐,๐
๐ผ๐,๐โ๐ผ๐,๐โ1) (4)
where ๐ is total angular displacement of Section ๐ of drillstring through the ๐/๐ segment of the
wellbore. This can be evaluated sequentially by following the entire path of the drill bit through
the wellbore, as illustrated in Figure 4-3. The energy use accounts for the rotational motion of the
drilling assembly as propelled solely by a top-drive system. Therefore, this value only represents
the useful energy requirement for the drilling operation. To evaluate the actual energy input, we
apply the efficiencies of the systems:
๐ธ๐ท = ๐ธ๐
๐๐๐๐๐ (5)
where ๐๐ is the drilling motor efficiency and ๐๐๐ is the prime-mover efficiency.
83
Figure 4-3: Workflow for calculating the drilling energy requirement.
Obtaining this result enables us to quantify the actual CO2 emissions from energy use based on
carbon content of the input energy source:
๐๐ถ๐2๐,๐ = ๐๐ถ๐2๐ธ๐ท (6)
84
Figure 4-4: Schematic of drilling arrangement with vertical, curved and horizontal sections,
showing mud circulation (not drawn to scale).
Mud circulation energy use and emission
Another aspect of the drilling operation involves pumping of drilling mud to provide balance,
lubrication and cooling at the cutting edge of the driller [26]. Mud circulation has been reported as
a major source of GHG emission arising from the pumping energy requirements [24]. Generally,
the drilling operation is conducted in either one of underbalanced or overbalanced condition;
underbalanced is where the mud pressure is lower than that of the formation and overbalanced is
the opposite [27]. Here, we consider an overbalanced drilling operation, which is common
practice.
Mud Flow
Pump
Casing
Drill String
Bit
85
Figure 4-4 illustrates mud circulation in a simplified drilling setup. Vafi and Brandt [24] gave an
elaborate discussion on drilling mud circulation dynamics in terms of mud differential pressure;
considering frictional, dynamic, discharge, and hydrostatic elements of the overall pressure drop.
However, their model did not demonstrate the dynamics of the differential pressures as drilling
progresses through the various segments of the wellbore being developed. Given that the
hydrostatic component in the model is zero, and without a downhole motor in the drilling
assembly, the pressure differential can be expressed in terms of frictional and dynamic losses [24]:
โ๐๐๐ข๐๐ = โ๐๐๐๐๐๐ก๐๐๐ + โ๐๐๐ฆ๐๐๐๐๐ (7)
The total losses occurring over the course of the drilling operations can be computed consecutively
following segments of the drilling assembly through the wellbore. Frictional losses are computed
for each drillstring segment as it penetrates through the subsurface, covering both flows of the mud
within the pipe and through the annular area between the pipe and wellbore contact. Dynamic
losses at the drill bit is computed for each bit used and through its coverage of measured depth of
the wellbore. Consequently, the total energy required for mud circulation is then:
๐ธ๐ = โ โ โ๐๐๐๐๐๐โ๐ก๐๐๐๐ (8)
where ๐ and ๐ are indexes for sections of the drillstring and wellbore segments, respectively. To
calculate the actual primary energy input, we apply efficiencies of pump and prime-mover, and
the energy emission is calculated by multiplying with carbon content of fuel:
86
๐ธ๐ =๐ธ๐
๐๐๐๐๐ (9)
๐๐ถ๐2๐,๐ = ๐๐ถ๐2๐ธ๐ (10)
Apart from energy used for drilling mud circulation, mud gas is released whenever a gas bearing
zone is encroached. As drilling cuts through reservoir pay zone, entrapped gas and cuttings are
entrained to the surface by the mud. Emission at this stage is primarily from released mud gas
which gets vented. This mud gas volume (๐๐) can be estimated from the relationship:
๐๐ =๐๐๐
2๐ฟ๐๐ง๐(1โ๐๐)
4๐ต๐ (11)
where ๐ฟ๐๐ง is well length within the pay zone, ๐ is reservoir porosity, ๐๐ is liquid saturation, and
๐ต๐is the gas formation volume factor. The HPDI database contains information on gas-to-oil and
water-to-oil ratios from which the liquid saturation can be calculated from:
๐๐ =๐ค๐๐+1
๐๐๐+๐ค๐๐+1 (12)
At this point, we can define GHG content of formation gas based on formation gas compositions
for individual wells or shale basins. Considering GHG components of the raw gas (๐), potential
GHG emissions can be estimated from:
87
๐๐ถ๐2๐,๐ = โ ๐บ๐๐๐๐๐๐๐๐๐ (13)
where ๐๐ is the composition of GHG component ๐ in the gas and ๐ is the gas density. For our
analysis, only methane content of the gas is accounted for using an average methane content of
78.8% and global warming potential of 36 in line with updated IPCC methane climate warming
potency. We further bracket these estimates in the sensitivity analyses using reported ranges of
shale gas methane content of 45-95% and published range of methane GWP of 21-36 to ease
comparison with past studies [22].
Hydraulic fracturing energy use and emission
Energy is required to pump fluids into the reservoir to create fractures. The fractures enhance
hydrocarbon flow in the formation by connecting the reservoir and the wellbore [10]. Energy
emission is the primary emission source at this stage, and it depends on the type of energy source
being used. The field profile of the typical reservoir stimulation operation indicates that the
fracturing fluid is injected from the surface at a specific rate via perforations in the well casing and
then into the formation [10]. The reservoir pressure builds up to the formation breakdown pressure
at which the targeted shale rocks start to break. Hydraulic fracturing is a complex process
influenced by a number of factors, including: injection rate, fracturing fluid, wellbore dimensions,
state of stress, and reservoir rock properties, among others [28]. The pressure needed for hydraulic
fracturing derives from the bottomhole pressure, given as [24]:
๐๐๐๐๐ = ๐๐ ๐ข๐๐๐๐๐ + ๐โ๐๐๐ โ ๐๐๐๐๐๐ก๐๐๐ (14)
88
where ๐๐ ๐ข๐๐๐๐๐ is fracturing treatment pressure applied at the surface by the pump system, ๐โ๐๐๐is
the hydrostatic pressure due to the fluid column in the wellbore, and ๐๐๐๐๐๐ก๐๐๐ account for all
frictional losses [10, 28]. After rock breakdown is achieved or in residence of natural fractures in
the formation, the net fracturing pressure, which is responsible for propagating fractures in the
reservoir rock can be expressed as the bottomhole pressure less of the closure stress (or fracture
reopening pressure) [10, 28]:
๐๐๐๐๐ = ๐๐ ๐ข๐๐๐๐๐ + ๐โ๐๐๐ โ ๐๐๐๐๐๐ก๐๐๐ โ ๐๐๐๐๐ ๐ข๐๐ (15)
Analysis of fracturing pressure demonstrates that it follows a power dependence with treatment
time, as reported by [29, 30]:
๐๐๐๐๐ = ๐(๐ก โ ๐ก๐)๐ (16)
The pump work needed to achieve this can be determined based on thermodynamic relations:
โ๐ป = โ๐ + ๐โ๐ + ๐โ๐ (17)
โ๐ = ๐ โ๐ (18)
89
Considering โ๐ is relatively unchanged for an incompressible fracturing fluid and only the flow
work is provided by the pump (i.e. โ๐ป and ๐ are zero), the input rate of pumping energy which
supplies the flow work needed for hydraulic fracturing becomes:
๐๐ธ๐๐๐๐
๐๐ก= ๐โ๐ (19)
where ๐ is the volumetric injection rate and โ๐ is defined with respect to the reference pressure
by:
โ๐ = ๐๐๐๐๐ โ ๐๐๐๐ (20)
๐๐๐๐ = ๐โ๐๐๐ (21)
For a given injection rate, the energy use for fracturing operations can be obtained from the integral
of the input rate of pumping energy given by:
๐ธ๐๐๐๐ =๐๐
๐+1(๐ก โ ๐ก๐)
๐+1 โ ๐๐๐๐๐(๐ก โ ๐ก๐) (22)
However, the flow of fracturing fluid through the well suffers frictional losses which must be
included and thus, the total energy requirement is the sum of the fracturing energy input and losses:
๐ธโ = ๐ธ๐๐๐๐ + ๐ธ๐๐๐๐ (23)
90
where ๐ธ๐๐๐๐ is the frictional losses as fracturing fluid pressure drops along the well. Determination
of frictional pressure losses for Newtonian and non-Newtonian fluids are elaborately treated for
different flow regimes by [26]. Our model incorporates the equations for both laminar and
turbulent flow regimes. To evaluate total energy losses, the number of fracturing stages have to be
accounted for in the model โ given that each stage is located at a unique measured depth โ as
follows:
๐ธ๐๐๐๐ = โ ๐๐โ๐๐โ๐ก๐๐ (24)
where ๐ represents each fracturing stage along the horizontal section of the well. Given that ๐ธโ
represents the ideal energy requirement, the actual energy input considering pumping and prime-
mover efficiencies (๐๐, ๐๐๐), depending on the type of energy source, is then:
๐ธ๐ป = ๐ธโ
๐๐๐๐๐ (25)
Then, the emissions for the fracturing operation is obtained by using the emission intensity of the
input fuel:
๐๐ถ๐2๐,โ = ๐๐ถ๐2๐ธ๐ป (26)
91
Flowback emission
Methane leakage during flowback operations occur as fracturing fluid is cleared from a shale gas
reservoir to the surface in the absence of an arrangement to capture the flowback gas [31]. Reduced
emission completion (REC) technologies, otherwise called green completions, are used by some
operators to recover flowback gas for use or sales. Although not all injected fluid is recovered in
most cases due to leak-off, the flowback regime covers the period from initiation until all fracturing
fluid has been removed or the production of liquid levels off [4]. Umeozor et al. [4] used field data
and flowback analysis to describe the three regimes of the lifetime of a shale gas well. Based on
the observed flowback profile, we propose that the flowback rate from a well can be represented
by the equation:
๐๐๐ = ๐๐,๐๐๐๐(1 โ ๐โ๐ โ ๐๐โ๐) (27)
where ๐๐,๐๐๐๐ is the peak gas rate from the well and ๐ is a parameter that characterizes the shape
of the flowback profile of the gas well. Therefore, ๐ can be related to the flowback duration and
peak gas value, and takes values between 0 and 1. To evaluate potential emissions from flowback
(๐๐๐), we integrate the equation over the flowback regime to obtain:
๐๐๐ = ๐๐,๐๐๐๐[(๐ โ 2) + (๐ + 2)๐โ๐] (28)
Relative initial production (IP) based models, peak gas production data is easily available and the
historical range of its values within a basin or play can be used to bracket potential emissions from
92
new well developments. IP based models also require more data inputs which may introduce more
uncertainty in estimation results [4]. Peak gas data for North American shale plays can be found
in the Drilling Info database [19]. The parameter ๐ can be calibrated for a given shale gas well due
to differences in the attributes of each shale gas reservoir/basin. Further details on the parameter
estimation can be found in the supplementary material (Appendix B, Section SB.2). Calibrated
values for individual shale basins range from 0.6 to 1. However, for the generality of wells
considered in this study, a representative value of parameter ๐ is equal to 0.75.
Results
Figure 4-5 compares modelled flowback gas estimate to actual field measurement data. The mean
value of estimated potential emission is 4810 Mg CO2e (ยฑ 190 Mg CO2e at 95% CI), which is
within 95% confidence limits of actual field measurements of potential flowback emissions. Table
4-1 lists descriptive statistics of the model along with those of measurement data. The results
indicate good agreement and capability of the model to capture the range of variability in measured
potential emissions. High standard deviations in both results reflect discrepancies in the emissions
from a few high-emitters and a majority of wells which do not release as much emissions. To
further explore predictiveness of the model, the data and model estimates are visualized on a parity
plot in Figure 4-6 and uncertainty is evaluated based on the relative error to be 5.2%. An important
use of the flowback model is that it requires only one variable input; which is the anticipated peak
gas production from the well. Therefore, information on the range of historical peak gas volume
at any shale gas basin can be used to bracket estimates of potential methane emissions during
93
development. Such knowledge would be useful for decision making on the gas handling scenario
to deploy for either economic or regulatory reasons.
Figure 4-5: Comparison of proposed flowback gas model results with actual field
measurements.
Table 4-1: Descriptive statistics comparison for model and measured completions flowback
potential methane emissions.
Method Mean Median Std Min Max P25 P75 95% CI
Estimated (Mg CO2e) 4810 4070 3530 3 32970 2100 6490 4810ยฑ190
Measured (Mg CO2e) 4400 1610 7650 7 37270 230 4490 4400ยฑ2200
94
Figure 4-6: Comparison of modelled emission estimates to the data, with an inset parity line.
To understand the contribution of each preproduction activity and event to overall development
potential emissions, a breakdown of direct and energy emissions is presented in Figure 4-7. As can
be observed from the results, completions flowback gas is a major potential source of
preproduction GHG emissions, accounting for 4,810 Mg CO2e per well. It must be mentioned that
this represents the potential emission which can be avoided, reduced, or released; depending on
the jurisdictional regulatory requirements or the gas handling decisions of the operator. The next
95
main source of emissions is the well drilling activity. We have subdivided the entire drilling
operations into circulation of drilling mud and the actual rotary drilling activity powered by a top-
drive system. Both the mud pump and rotary driver are assumed to be powered by diesel prime
mover. A diesel energy content (LHV) of 42.8 MJ per kg and emission factor of 69.4 kg CO2 per
GJ (i.e., 2.97 kg CO2/kg diesel) is applied in the model. Although dependent on the borehole
dimensions and well design, most of the CO2 emitted during the drilling stage arise from energy
used for circulating drilling mud. This stems from pressure losses as mud is pumped into the
bottom through the drillstring to drill bit, and up again to the surface via the annulus. In this
operation, the mud also clears drill cuttings to the surface. For a 5 inch lateral casing in a 6.125
inch open hole, this accounts for about 91% of the total preproduction energy requirements.
Figure 4-7: Breakdown of preproduction energy and direct emissions by activity.
49.95
628.92
13.45
0.04
4811.97
0 1,000 2,000 3,000 4,000 5,000
Well Drilling
Mud Circulation
Hydraulic Fracturing
Mud Gas
Flowback
Emission (Mg CO2e)
96
Well drilling energy requirement captures the rotational energy needed by a top-drive system to
develop the borehole considering just rotational motion as the drilling assembly makes its way into
the shale gas reservoir. Additionally, mud gas is released when the drilling operation encounters a
gas-bearing zone. For our model, we have estimated the amount of mud gas from drill cuttings
through the lateral section of the wellbore. Since our method assumes an over-balanced drilling
operation, it should be expected that this approach determines the lower bound of the potential
mud gas emission. The mud gas emission is estimated as 0.04 Mg CO2e per well. Total CO2
emissions for all activities during the drilling stage is estimated as 678.87 Mg per well. For the
same lateral casing design, hydraulic fracturing energy use represents about 2% of the total;
amounting energy-derived CO2 emission of 13.45 Mg per well. This includes frictional losses as
fracturing fluid is pumped for each stage of fracturing job and the energy needed breakdown
reservoir rock and propagate fractures into the rock. As expressed in equation (14), energy input
for hydraulic stimulation derives from the pump work (which is based on ๐๐ ๐ข๐๐๐๐๐); therefore, the
hydrostatic head contribution to the fracturing pressure is not assigned to the pump.
97
Figure 4-8: Energy requirements for shale gas well development with lateral casing sizes
corresponding to 6 1/8, 7 1/2, and 8 3/4 inches lateral borehole diameters, respectively.
Figure 4-8 shows the effect of different well dimensions and lateral casing designs on both the
overall preproduction energy requirement and that of each development activity. It is observed that
for smallest lateral diameters investigated, energy use for mud circulation dominates the total
inputs. For the other lateral casing design sizes, total energy input can be significantly lower but
dominated more by rotational energy for drilling with the top-driver. Consequently, variabilities
in well trajectory, well casing design, formation type and resource deposition attributes are
important when considering individual development project performance in terms of energy use
and GHG emissions. This awareness is also essential for optimizing well development activities
by tailoring decision parameters to specific formations/plays to minimize energy intensity and
GHG emission impacts. For the Montney Formation wells considered, the average overall
preproduction potential GHG emission is estimated as 5300 Mg CO2e per well, corresponding to
an average total energy use of 4,083 GJ per well. On the basis of preproduction requirements,
energy return on invested energy (EROI) for Montney shale gas is estimated as 3,400.
98
Furthermore, if the entire shale gas development projects in the Montney Formation during 2017
of 505 wells is sampled [20, 32], this amounts to an aggregate potential GHG emission impact of
about 2.68 Mt CO2e from unconventional gas Montney Formation operations alone.
Figure 4-9: Sensitivity of preproduction emission estimates to well design and estimation
parameters.
Figure 4-9 illustrates the sensitivity of preproduction emission estimates to modelling parameters
and other resource deposition attributes. To calculate these sensitivities, baseline GWP of 28 is
applied to methane from all sources, so that sensitivity of results to GWP is computed over a range
of 21 to 36. It can be observed that the completion flowback gas is a potential source major
variability in well-level preproduction GHG emission. Nevertheless, individual well-level
emission estimates might vary according to differences in parameter values and development
3500
2830
4430
4430
730
5550
5200
4440
4450
26330
0 5,000 10,000 15,000 20,000 25,000 30,000
GWP
Methane Content
Frac Stages
Lateral Casing
Flowback
Total Energy and Methane Emissions (Mg CO2e)
High
Low
99
practices as shale gas projects are initiated across many parts of the world. For instance, Vafi and
Brandt [24] estimated GHG emissions from drilling and hydraulic fracturing in two U.S. shale
basins (Bakken and Eagle Ford) and obtained values of 417 and 510 Mg of CO2e per well,
respectively. For the same activities, our model estimated 692 Mg of CO2e per well for the
Montney Formation. Taken together, at the global scale, understanding impacts of preproduction
emissions on collective capacity to achieve climate targets deserves more attention than is
currently accorded, and predictive modelling can serve as an essential tool to extend current
knowledge of future impacts of impending developments in the natural gas supply chain.
Consequently, as more gas is increasingly tapped from various shale plays worldwide, regulatory
controls can be designed to accelerate implementation of mitigative development strategies that
help to curtail environmental impacts of more gas in the global energy pool. Already, technologies
like green completion have been proposed to control flowback gas emissions from unconventional
oil and gas projects.
Conclusions
Shale gas is a type of unconventional gas found in pockets within a petroleum reservoir rock.
Energy use and emissions during well development is the main differentiator of conventional and
unconventional gas. We propose predictive modelling as an approach to quantify preproduction
energy requirements and the attendant energy and direct GHG emissions. Detailed modelling
workflow is presented indicating the main activities and events contributing the overall impacts of
new shale gas development. Proposed model is applied to 1,403 wells in the Montney Formation
in Western Canada. Our results suggest that the distribution of energy and emission impacts among
100
the development operations might differ from how it is normally perceived. Depending on well
trajectory and dimensions, energy use for mud circulation can predominate those of the other
activities including the rotational energy requirement for a top-drive drilling system and the pump
work utilized for hydraulic stimulation. Average preproduction energy need is estimated at 4083
GJ per well. Nevertheless, as more gas reservoirs are developed, occasioned by increasing gas
demand, proper appreciation of the implications on climate change mitigation efforts can be better
grasped on the basis of overall annual preproduction emissions, in accordance with the design of
climate policy directives and targets. From this viewpoint, annual potential preproduction GHG
emission from unconventional gas wells in the Montney Formation in 2017 is estimated to be 2.68
Mt CO2e.
4.1 References
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of shale gas life cycle greenhouse gas emissions for electric power generation. Proceedings
of the National Academy of Sciences, 111(31), E3167-E3176.
[2] Lamb BK et al. (2016) Direct and indirect measurements and modeling of methane emissions
in Indianapolis, Indiana. Environmental science & technology, 50(16), 8910-8917.
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101
[5] Stephenson T, Valle JE, Riera-Palou X (2011) Modeling the relative GHG emissions of
conventional and shale gas production. Environ Sci Technol 45(24):10757โ10764.
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and carbon dioxide emissions from the natural gas supply chain. Journal of Cleaner
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[7] Mac Kinnon MA, Brouwer J, & Samuelsen S (2017) The role of natural gas and its
infrastructure in mitigating greenhouse gas emissions, improving regional air quality, and
renewable resource integration. Progress in Energy and Combustion Science.
[8] Scanlon BR, Reedy RC, & Nicot JP (2014) Comparison of water use for hydraulic fracturing
for unconventional oil and gas versus conventional oil. Environmental science & technology,
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[9] Atherton E et al. (2017) Mobile measurement of methane emissions from natural gas
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Eagle Ford shale. Energy & Fuels, 31(2), 1440-1449.
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102
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freshwater consumption associated with Bakken tight oil. Proceedings of the National
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Golden, CO), NREL/TP-6A50-55538.
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consumption of Marcellus shale gas. Environ Sci Technol 47(9):4896โ4903.
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Sedimentary Basin.
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emissions. Proceedings of the National Academy of Sciences 112(51), 15597-15602.
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natural gas from shale formations. Clim Change 106(4):679โ690.
103
[24] Vafi K, Brandt A (2016) GHGfrack: An open-source model for estimating greenhouse gas
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104
Designing for Innovation: Process and Technology Configurations for Oil
Sands Production
Abstract
Bitumen from the oil sands, an unconventional oil resource, is the major source of Canadian oil
production, representing about 70% of total daily volumes. However, the high viscosity of the oil
and the nature of its deposition necessitate the use of special recovery techniques that are currently
energy, economic, and emissions intensive. Recent lows in the price of crude oil and evolving
environmental conservation goals call for the development of processes and technologies to
improve operational performance. This work presents a multi-criteria assessment approach based
on mixed-integer linear programming model to simultaneously assess impacts of innovative
process and technology configurations using three operational performance indicators representing
economic, emission, and energy intensities of design options deployable in either Brownfield or
Greenfield facilities. Facility operation is constrained to account for field conditions via governing
physical and operational equations. The proposed model identifies Pareto-optimal facility
configurations for in-situ heavy oil recovery from the oil sands. We identify opportunities for
design options using solvent-based recovery or non-condensable gas additives to improve current
standards of industry performance. However, to move significantly beyond current in-situ
economic and environmental performance requires new configurations beyond the incremental
ones emerging from current technology.
105
Nomenclature
๐ช๐ ,๐,๐ Capitalized cost of unit ๐ in segment ๐ of design ๐
๐ช๐ ,๐,๐๐ Original cost
๐ช๐ ,๐,๐๐น Replacement cost
๐ช๐ ,๐,๐๐ด Maintenance cost
๐ช๐ ,๐,๐๐ฌ Energy cost
๐ช๐ ,๐,๐๐ณ Labor cost
๐ป๐ Annual taxes on design ๐
๐ซ๐ Decontamination cost for design ๐
๐ Continuous annual interest rate (fraction)
๐ Interval of replacement/lifetime (years)
5.1 Introduction
The evolution of commercially feasible oil resources has undergone a major shift over the past
decade from conventional oil reserves to unconventional oil resources, and this trend is expected
to continue into the future [1]. Unconventional oil resources include heavy oil and extra heavy oil
as well as tight rock oil, summarized in Table 5-1. These resources are generally mobility
challenged โ immobile either due to high viscosity or entrapment in small pores within a reservoir
rock which translates to very low reservoir permeability. Put together, the shale oil in the United
States, oil sands in Canada, and the extra-heavy oil in Venezuela are estimated to constitute over
80% of world unconventional oil [2]. The data in Table 5-1 lists the ranges of oil viscosity and
reservoir permeability that are found for these resources as well as the ranges of oil mobility:
expressed as the ratio of permeability to viscosity. The data reveals that the mobilities of heavy oil
(HO) and extra heavy oil (EHO) and tight rock oil (TRO) are typically much lower than that found
in conventional oil reservoir.
106
Table 5-1: Mobility of unconventional oil resources [3, 4, 5].
Parameter Viscosity, cP Permeability, mD Mobility, mD/cP
Category Low High Low High Low High
Conventional oil in Sandstone Reservoir
0.3 1 100 1,000 100 3,333
Heavy oil in Sandstone Reservoir
1,000 50,000 500 4,000 0.01 4
Extra heavy oil in Sandstone Reservoir
100,000 2,000,000 500 7,000 0.00025 0.07
Tight rock oil 0.3 1 0.001 0.1 0.001 0.33
Bitumen from oil sands is a type of extra heavy oil found naturally mixed with water and fine-
grained sand [6]. To produce bitumen from oil sands, the mobility of the oil phase must be raised
by reducing its viscosity to below a few tens of centipoise. Typically, the viscosity is reduced by
heating the bitumen within the reservoir by using steam. This requirement, in turn, implies that
energy must be invested in the recovery process before oil production occurs. This, for all existing
commercial thermal stimulation recovery processes where fuel combustion occurs to generate
steam, further implies that there are greenhouse gas (GHG) emissions associated with these
recovery operations; for most of these recovery processes, water consumption is also a major
concern.
Commercial scale bitumen production from surface-based oil sands started with the hot-water
process, developed by Karl Clark at the Alberta Research Council, in the 1950s [3]. In this process,
raw oil sands is mixed with wet steam to produce a dense but mobile pulp which, with more
107
agitation and dilution with hot water, separates the bitumen from the sand by gravity.
Unfortunately, this process was limited to surface-minable oil sands, whereas more than 90% of
the oil sands reserves are found at depths that are not suitable for surface mining (about 65m and
below) [3]. This necessitated work on in-situ extraction techniques. Breakthrough in-situ
production technology was realized with the advent of Cyclic Steam Stimulation (CSS) and Steam-
Assisted Gravity Drainage (SAGD) in the 1980s and 1990s, respectively, which was supported by
improvements in directional drilling and horizontal wells.
CSS in the oil sands involves the sequential injection of steam into a reservoir and production of
bitumen from the reservoir through the same vertical well [6]. Due to the high pressure involved
in the process, it requires that the overburden be more than 300 m thick and uses a three-stage
process in which steam is injected through a borehole and allowed to stay in the reservoir for some
time to allow the heat to disperse, after which production commences [2]. After about 20 years of
studying and improving the process, commercial CSS production was achieved in 1985 [7].
However, the use of CSS technology was to be limited by the fact that majority of the in situ
recoverable deposits are relatively shallow, and thus, require lower-pressure techniques to produce
bitumen [7]. For this reason, the SAGD recovery process was developed.
SAGD uses a pair of horizontal wells where one is placed below the other at a separation of about
5 m. Steam is then injected via the top well into the formation to mobilize bitumen and under the
action of gravity, it drains to the bottom well from which it is pumped out to the surface [6]. There
is also multilateral technology โ with multiple horizontal wells drilled in the same formation โ
predominantly used to produce extra heavy oil in the Orinoco Belt, in Venezuela. Despite huge oil
108
sands resources in Canada, there remains a significant portion found in thin reservoirs (<10 m pay
zone) or reservoirs with inadequate cap-rock having a top water-bearing zone, where the current
commercial production technologies cannot be economically and efficiently deployed [8, 9]. To
access such resource, new processes have to be developed and demonstrated.
A number of factors account for the developments and growth of unconventional resources
witnessed in the past years. These include: technological advances/breakthroughs, fluctuations in
market price (and price expectations) of crude oil, dwindling conventional reserves, and the desire
for energy security and self-sufficiency [10]. However, environmental concerns and regulatory
requirements have become a major challenge confronting the industry causing a focus on new
developments to reduce the environmental impact of recovery technologies [11]. The combination
of resource attributes and peculiarities of the depositional environments hosting bitumen make
extraction processes more emissions intensive relative to most conventional resources [12]. The
emission intensity often arises from the energy needs of the operations being met via fuel
combustion. The energy, emissions, and technological requirements of production activities
determine the supply cost of each bitumen barrel [13, 14]. Therefore, identifying innovative
production pathways can usher in the best combinations of technologies and processes to reduce
energy, CO2 emissions and economic costs of oil sands production. Emerging technologies and
processes for future deployment in the oil sands aim to address these challenges.
Innovation in the in-situ oil sands industry is evolving through an earlier stage dominated by new
technologies to first mobilize and extract the oils economically [15]. At the current stage, there is
more focus on moving to cleaner technologies with lower GHG emissions and water consumption;
109
a shift to technologies where the environmental impact is reduced or minimized. This is due to the
greater awareness of the emissions from these processes relative to other oil resources, as well as
the pricing of carbon dioxide which impacts the economics of these processes [16]. To understand
environmental impacts of oil sands development, past studies have generally applied a Life Cycle
Analysis (LCA) approach which typically describes impacts without crafting a pathway for
improvement [17, 18, 19]. Other studies which investigated the effect of incremental innovations
on current recovery process performance often focus only on the subsurface design requirements
of a single process design option, without considering the implications of subsurface parameters
on the surface facility requirements [16, 20, 21, 22]. This presents a pitfall when comparing results
obtained from disparate design and operating conditions, in order to evaluate potential
performance improvements through process and technology innovations. Thus, we emphasize the
need for a holistic and consistent framework enabling multi-criteria evaluation of emerging oil
sands bitumen recovery systems.
This work presents a multi-criteria approach for assessing the opportunity for innovative recovery
processes to improve current operating performance in oil sands bitumen production. The SAGD
process is taken as the existing benchmark method for bitumen recovery from oil sands since the
majority of the undeveloped reserves are not suitable to the other existing production approaches,
such as CSS and surface-mining. Emerging processes and technologies aim to improve the
observed limitations of SAGD. The systems considered include solvent-based, steam-solvent, and
non-condensable gas processes. We adopt a modular design philosophy based on the various unit
operations needed to produce the bitumen from oil sands. Modularization is important for the oil
sands industry due to challenging weather conditions and nature of the terrain where oil sands are
110
produced in Canada. It is also considered economical and expedient as facility components can be
factory fabricated, brought to site and assembled faster. The determination of optimal production
configurations of processes and technologies is captured as a mixed-integer linear programing
(MILP) problem with multiple objectives of minimizing the energy, CO2 emissions, and overall
capitalized cost of a 30,000 barrels per day oil sands facility. We identify various processes and
technologies that are deployable at the surface and subsurface segments of the production facility,
and impose various conditions on the model to effect compatibility and exclusivity requirements
of modular components in the facility design.
5.2 Oil Sands Production
As shown in Figure 5-1, current oil sands recovery operations can be broadly categorized into 3
segments, including; reservoir operations, separation (water and oil recovery and treatment), and
steam generation. Through identification of challenges with the current processes and
technologies, the oil sands recovery operations can be innovated by developing new recovery
techniques to address the identified limitations of SAGD. Emerging technologies aiming to reduce
energy use, such as solvent-based process, are transformational because there is no requirement
for water use and steam generation โ the main source of energy intensity. They also have fewer
components which also results in smaller overall facility footprints. SAGD-additive processes
introduce incremental changes to the benchmark SAGD process design by adding solvents, non-
condensable gases or chemicals to steam for injection into the oil sands reservoir. Figure 5-2 shows
a superstructure of the emerging design options considered in this study.
111
Figure 5-1: Benchmark in-situ oil sands recovery process design with SAGD.
Figure 5-2: Superstructure of in-situ oil sands recovery via steam, solvent and NCG
methods.
112
This evolution of oil sands processes and technologies can be illustrated in a simplified innovation
framework as presented in Figure 5-3; where limitations of a pre-existing recovery technique are
tackled via process or technology changes or both. Under the framework, identified operational
challenges are cast as key performance indicators which should drive the modification or
replacement of existing operational process and technology configurations. To achieve a holistic
view of the impact of design improvements entails a multi-criteria evaluation based on the
identified performance indicators. Existing tools for process design and technology assessment are
often focused on singular objectives, which presents a pitfall in the current industrial era where
environmental conservation goals have risen in priority in various decision-making and policy
processes. For instance, life cycle assessment is commonly used to quantify environmental
performance; pinch and exergy analyses address energetic performance; while economic viability
is often assessed using present value and rate of return metrics. However, as sustainability issues
take center stage in regulatory and operational decision making, it is now crucial to simultaneously
assess multiple criteria in making design choices.
113
Figure 5-3: Oil sands process and technology innovation framework for surface and
subsurface operations.
We apply a multi-objective approach to assess performances of oil sands recovery operations via
steam-solvent, pure solvent, and steam-gas system designs. Instead of mobilizing bitumen from
the reservoir using steam alone, steam-additive processes inject mixtures of steam and either a
solvent, surfactant or non-condensable gas into the reservoir. Pure solvent process designs injected
heated solvents which mobilize the bitumen by both dilution and heating โ with the combined
effect of lowering the viscosity. For this work, a conventional SAGD design producing 30,000
barrels per day at a steam-to-oil ratio of 3 m3 (cold water equivalent)/m3(produced bitumen) is
taken as the benchmark. Since the emerging system designs under consideration aim to address
limitations of SAGD centered on economic, emissions, and energy intensities; we take these as the
performance indicators to be improved by the new design configurations. Although water
requirement is also an issue for SAGD, it is handled indirectly because the impacts of water use
can be captured under the chosen three performance criteria.
114
5.3 Study Approach
Four different subsurface oil sands production technologies are investigated using reservoir
simulation data to determine recovery operation requirements at the surface level. The subsurface
process options include: use of steam as sole injectant (SAGD), co-injection of steam and solvent,
co-injection of steam and non-condensable gas, and pure solvent injection processes. Normal
butane is used as solvent while carbon dioxide is the non-condensable gas.
Depending on the type of subsurface operations, surface facility choices have to be made in
accordance. Industry standard SAGD process flowsheet from Canadian Oil Sands Innovation
Alliance (COSIA) is used as the benchmark SAGD configuration [23]. Facility costs for the
benchmark SAGD is obtained from the Petroleum Technology Alliance Canada (PTAC) industry
report [24]. Each system unit in a particular process design is scaled, where applicable, relative to
the benchmark SAGD process design using the sixth-tenths rule [25].
Mass and energy balances are performed on both the surface and subsurface segments of each
operating configuration. Only CO2 emissions from electricity, process fuel and natural reservoir
gas are accounted for as GHGs in the mass balance. Overall design superstructure optimization
problem is formulated as a mixed-integer linear programming optimization problem which is
solved using CPLEX solver in the AMPL software package [26]. Simulation data for the model is
listed in Table 5-2.
5.3.1 Mathematical programming model
115
Here, the choice of best performing oil sands production process is posed as a multi-objective
mixed integer linear programming problem. In accord with emerging industry practice, we adopt
a modular design approach which considers each unit operation within every sub-segment of an
entire design configuration to be subjected to our design choices to preserve, modify, replace or
eliminate it.
5.3.1.1 Objective Function
The objective function minimizes a sum of the performance criteria, given as:
๐ง = โ ๐ค๐๐ธ๐๐ ๐ = {๐๐๐๐๐๐ฆ, ๐๐๐๐ ๐ ๐๐๐, ๐๐๐๐๐๐๐๐} (1)
where ๐ is a set of the three intensities to be minimized in the optimization and ๐ค is the weighting
factors. The design modularity concept furnishes information on attributes of individual system
units, including their throughput capacities, energy utilization, and cost components.
System energy requirements of a given design comprises energy needed at the reservoir for
pumping, energy used at the separator to recover solution gas, energy associated with raw bitumen
and produced water; and energy required to vaporize steam and/or solvent for injection into the
reservoir:
๐ธ๐๐๐๐๐๐ฆ = โ ๐ฅ๐,๐,๐๐,๐,๐ ๐ป๐,๐,๐ (2)
where ๐ represents a particular design configuration, ๐ is the facility segment, ๐ is a system unit,
๐ฅ is a unit selection binary variable and ๐ป is the unit-level energy requirement which consists of
heat and power needs. Energy input can be expressed in terms of enthalpies of material streams in
each system unit, based on general thermodynamic relations [18]:
116
๐ป = ๐ป0 +(๐โ๐๐ ๐)
๐+ โซ [
๐
๐โ
๐
๐2(๐๐
๐๐)๐] ๐๐ + โซ ๐ถ๐
0๐๐๐
๐0๐
0 (3)
where ๐ป0 is the reference energy state at ๐0 (298.15 K) and ๐0 (1 atm). Values of ๐ป at various
conditions can be obtained from enthalpy tables and correlations. Younglove and Ely [27] reported
results for light hydrocarbons. Enthalpy tables for water and carbon dioxide are available in [28]
and [29]. Likewise, process emissions can be obtained from material balances on each facility
configuration, whereas the economic variables capture the cost to build and operate each
technology option. These can be represented as follows;
๐ธ๐๐๐๐ ๐ ๐๐๐ = โ ๐ฅ๐,๐,๐๐,๐,๐ ๐บ๐,๐,๐ (4)
๐ธ๐๐๐๐๐๐๐๐ = โ ๐ฅ๐,๐,๐๐,๐,๐ ๐ถ๐,๐,๐ (5)
where ๐บ represents CO2 intensity covering electricity, process fuel and reservoir emissions, and ๐ถ
is the economic cost term. We note that each pair of component systems (and their unit operations)
are either complementary or mutually exclusive. For complementarity, the unit operations can be
part of the same design configuration, whereas for mutual exclusivity they cannot. For
complementary units, we impose the following constraint:
โ ๐ฅ๐,๐,๐(๐,๐)โ๐๐๐ = ๐(๐๐
๐) โ ๐,๐๐๐ โ ๐๐
๐ (6)
117
where ๐๐๐ is a subset of complementary processes or technologies in a design structure, ๐ โ which
can be captured under a single superstructure, as illustrated in Figure 5-2, and ๐ is the cardinality
operator. The following constraint applies for subsets of mutually exclusive components:
โ ๐ฅ๐,๐,๐(๐,๐)โ๐๐ ๐ = 1 โ ๐,๐๐
๐ โ ๐๐ ๐ (7)
The groups of processing units which constitute the subsets of ๐๐๐ are selected by identifying the
units which must operate together in each design configuration being assessed, whereas the groups
in ๐๐ ๐ are the collections of units which cannot be operated together in the same design
superstructure. Considering the design options in Figure 2, the heater and steam generator form a
subset in ๐๐ ๐ under the boiler segment. In the separator segment, gas recovery, solvent recovery
and bitumen recovery units constitute one group in ๐๐๐ while gas recovery, bitumen recovery and
water recovery units are another subset in ๐๐๐.
The economics of a given design is determined by the capitalized cost, which is a metric for
encapsulating the various cost parameters associated with the design in a way that enables
comparisons on the same basis with alternative design choices [30]. Capitalized cost metric is
computed by:
๐ถ๐,๐,๐ = ๐ถ๐,๐,๐0 +
๐ถ๐,๐,๐๐
(๐๐๐กโ1)+
๐ถ๐,๐,๐๐
(๐๐โ1)+๐ถ๐,๐,๐๐ธ
๐+๐ถ๐,๐,๐๐ฟ
๐+
๐๐
(๐๐โ1)+
๐ท๐
(๐๐๐กโ1) (8)
118
All cost variables are defined in the nomenclature section.
Table 5-2: Simulation data based on field and reservoir modelling observations [31 โ 33].
Reservoir pressure 3,000 kPag
GOR 8 m3/m3
Produced gas CO2 30 mol%
Bitumen rate 30,000 bbl/day
Boiler efficiency 80%
Facility utilization 90%
Electricity need 16.3 MW
Steam generation pressure 12,000 kPag
SORSAGD (Pure Steam) 3 m3/m3
SOR (Steam+Solvent) (2/3)SORSAGD
SOR (Steam+NCG) (19/21)SORSAGD
SOR (Pure Solvent) 3 m3/m3
Solvent volume (Steam+Solvent) 20%
NCG volume (Steam+NCG) 5%
Solvent make-up 30%
5.4 Results
Each subsurface process design option requires a matching surface facility configuration. Various
reservoir simulation data for each subsurface design option is used to evaluate surface facility
requirements; including technology alternatives, energy and material flows. Solvent and NCG
injection are done at either the reservoir pressure or steam injection pressure in the benchmark
SAGD process. Figure 5-4 compares overall process energy use for alternative design
configurations, under the two additive injection conditions. While energy input does not vary
significantly between injection conditions, as a result of the relatively smaller energy use for
additives injection compared to other energy needs of the processes, energy input for the pure
solvent systems is lowest compared to the other design options.
119
Figure 5-4: Process energy requirements at two different additive conditions.
Figure 5-5 shows overall process CO2 emissions for each design option; with electricity needs
sourced from either the Alberta grid system or in-house gas power plants, as is typical for the oil
sands industry. Emission intensities of Alberta grid and combined cycle gas power plants are
reported as 760 kgCO2/MWh and 390 kgCO2/MWh, respectively [34]. For individual process
design options, additives injection at reservoir or reference SAGD conditions did not translate to
major differences in emissions since total process energy inputs are similar for each case, as
observed in Figure 5-4. However, the pure solvent configuration has the lowest emission impact โ
which is even lower when electricity needs are met from a NGCC plant. Overall, solvent-based
processes have lower emissions compared to pure SAGD or with NCG additive; consistent with
their energy intensities.
0 10,000 20,000 30,000 40,000 50,000
Pure SAGD
Steam+Solvent
Steam+NCG
Pure Solvent
Energy Input (GJ/day)
Op
erat
ing
Co
nfi
gura
tio
n
Energy Input (reservoir condition) Energy Input (reference condition)
120
Figure 5-5: Process CO2 emission when electricity is supplied by either a natural gas
combined cycle plant or from the Alberta grid systems.
The use of capitalized cost metric allows for simultaneous assessment of the economics of
building, operating and decommissioning particular process and facility designs. Figure 5-6
compares capitalized cost of the operating configurations under reservoir and reference additive
injection conditions. Emerging process configurations (pure solvent, steam+NCG, and
steam+solvent) show lower cost numbers relative to conventional SAGD. The pure solvent design
has the lowest capitalized cost of $3.6 billion (in 2018 dollars). With respect to additive injection,
capitalized costs are higher in the reference condition case due to the higher operating costs as a
result of elevated operating pressures. Overall, solvent based designs show better economics
following their lower energy inputs and fewer surface facility components. However, this result
comes with the assumption on solvent costs of $5/bbl. To understand the sensitivity of capitalized
cost estimates to solvent costs, we explore other cost scenarios such as $25/bbl and $50/bbl in
0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000
Pure SAGD
Steam+Solvent
Steam+NCG
Pure Solvent
Emission (kgCO2/day)
Op
erat
ing
Co
nfi
gura
tio
n
Electricity Emissions @ngcc intensity Electricity Emissions @grid intensity
121
Figure 5-7, to represent the situations where more expensive solvents might be more suitable and
effective for improving production rates.
Figure 5-6: Capitalized costs of each design at the two fluid injection conditions (reference
and reservoir conditions).
0 1E+09 2E+09 3E+09 4E+09 5E+09
Pure SAGD
Steam+Solvent
Steam+NCG
Pure Solvent
Capitalized Cost (C$)
Op
erat
ing
Co
nfi
gura
tio
n
Capitalized cost (reservoir condition) Capitalised cost (reference condition)
122
Figure 5-7: Effect of solvent cost on the capitalized cost of each design.
From Figure 5-7, it is seen that the economics of solvent-based recovery systems depends on
solvent cost. Higher cost solvents can escalate operating costs given that a portion of volumes
injected is lost in the reservoir formation. Capitalized costs for Steam+NCG and Pure SAGD
designs are unaffected since solvent is not used. Table 5-3 lists the optimal ranges of values of the
three intensities (energy, emission and economic) investigated for oil sands production. On the
basis of oil sands bitumen energy content of 6.71 GJ/bbl [35], the Pure Solvent design has the
highest energy return of 134 relative to Pure SAGD value of about 7.5. However, if solvent losses
are included in the energy balance, butane solvent has an energy content of 4.40 GJ/bbl so that if
25% of injected solvent is lost in the reservoir, then the energy return for the Pure Solvent process
becomes 5.8 โ even lower than the benchmark SAGD. The overall superstructure optimization
0 5E+09 1E+10 1.5E+10
Pure SAGD
Steam+Solvent
Steam+NCG
Pure Solvent
Capitalized Cost (C$)
Op
erat
ing
Co
nfi
gura
tio
n
Solvent Costs at $50/bbl Solvent Costs at $25/bbl Solvent Costs at $5/bbl
123
yields pareto-optimal solutions, dependent on parameters like solvent costs and additive injection
conditions. Table 5-4 shows values of the objective function for the cheap ($5/bbl) and expensive
($25/bbl) solvent cases. The combination of lower energy requirements and reduced surface
processing facility components improves overall objective function value for solvent-based
operations. However, cost of solvent (and loss of solvent) remains a major challenge limiting the
economic performance.
Table 5-3: Energy, CO2 emission, and economic intensities of the bitumen recovery process
design options.
Intensity
Operating
Configuration
Energy
(GJ/bbl.)
Emission
(kgCO2/bbl.)
Economic
($/kbbl.)
Pure SAGD 1.32 85.92-90.74 151.56
Steam+Solvent 0.90 59.21-64.03 137.25-191.63
Steam+NCG 1.20 78.29-83.11 145.90
Pure Solvent 0.05 5.83-10.65 120.47-446.77
For bitumen reservoirs, new technologies that have been proposed, whether Steam+Solvent or
Steam+NCG or steam plus chemicals (e.g. surfactants) or pure solvent, all live within the context
of the pure SAGD well configuration with an upper horizontal injection well and a lower horizontal
production well. Thus, the main drive mechanism in the processes, despite the improvement to
124
SAGD, is gravity drainage which in and of itself presents a productivity-limiting constraint on the
system. Considering that gravity drainage is proportional to the product of the difference of the
densities (draining liquid density - depletion chamber gas phase density) and the acceleration due
to gravity, that is ฮ๐๐, there is little that can be done to increase gravity-induced drainage since
there is little that can be done to raise ฮ๐๐. Therefore, the only way to raise the production
performance is to lower the viscosity further which can be done by solvents but this can impact
the economics of the process. This implies that to significantly raise the overall process
performance and improve efficiencies (energy, emissions, and economic) of the family of
incremental, SAGD-like processes and other alternative recovery process designs that use similar
well configurations will not likely be possible beyond marginal gains. Although our results
evidence that steam plus additives designs have merit for performance enhancements, the results
with respect to economics are mixed. This signifies a strong need to step beyond the pure SAGD
well configuration to provide other drive forces that could enhance oil flow rates within the
reservoir, beyond that of gravity drainage.
Table 5-4: Pareto optimal operating configurations for oil sands production.
Constraint Optimal Operating Configuration
Cheap solvent Pure Solvent (โ ๐ธ๐๐ = 126.4)
Expensive solvent Steam+NCG (โ ๐ธ๐๐ = 145.9)
125
5.5 Conclusions
Process and technology evolution for oil sands production show a pattern of innovation in the
recovery operations where challenges with state-of-the-art recovery techniques at any time become
the focus of process improvement efforts. When this pre-existing innovation strategy is combined
with the growing adoption of modular designs and replication of proven technologies at both green
and brownfield oil sands projects, the impacts of technology and process innovations can be better
evaluated through an integrated approach. We presented an integrated linear programming model
to assess the energy, environmental and economic impacts of emerging design configurations for
oil sands production. The process and technology assessment problem is formulated as a
superstructure optimization. Our solutions observe performance improvements with additive-
based oil sands recovery methods, including solvent-based and non-condensable gas type process
designs. Energy returns of Pure Solvent process is estimated to be up to 18 times higher than the
benchmark SAGD design. However, adoption of solvent-based recovery systems may be hindered
by the effect of solvent costs and losses on overall process economic performance. The results
suggest that to achieve significant economic and environmental performance beyond that of
current technologies, oil sands operators will have to look to new configurations that are not
incremental changes to the current technologies. For example, processes where additional drive
mechanisms beyond that of gravity should be pursued.
126
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Systems Analysis Research Initiative, 1(4), 15-22.
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emissions of the Alberta oil sands (1970โ2010). Environmental Research Letters, 8(4), 044036.
131
On Designing Carbon Dioxide Utilization Pathways for Sustainability
Abstract
Carbon dioxide utilization (CDU) processes require energy inputs which determine their
environmental impacts and sustainability attributes. However, this can be easily neglected if a full
processing cycle analysis approach is not undertaken to assess the component systems needed to
achieve conversions of CO2 into useful products. This study evaluates several CDU pathways for
producing fuels, chemicals, and polymer materials by considering the effects of input energy
options and the processing systems configurations on sustainability merits of CDU as a climate
mitigation strategy. In addition to the energy and emission intensities of CDU processes, two other
sustainability metrics are proposed to assess performances of alternative CO2 conversion
pathways. The results suggest that deployment of CDU to produce methanol, synfuel, and
polyurethane polymer are promising options for climate policies targeting CDU geared towards
environmental conservation.
Nomenclature
CDU Carbon Dioxide Utilization SMR Steam Methane Reforming
DAC Direct-Air-Capture CUF CO2 Utilization Factor
DAC-OA Open-Air DAC PUR Polyurethane Polymer
DAC-PP Power-Plant DAC SFuel Synthetic Fuel
FA Formic Acid EtOH Ethanol
MeOH Methanol elect Electrolysis
132
Carbon Dioxide Utilization
As generation and release of global warming gases appear unstoppable, it appears more likely that
our societies will use more mitigative than eradicative actions to counter carbon dioxide emissions
[4]. Current annual anthropogenic greenhouse gas (GHG) emissions is estimated at about 35 Gt
[3] and the cumulative atmospheric CO2 content is about 3,000 Gt [4]. Among other mitigative
measures, Carbon Dioxide Utilization (CDU) is receiving more interest as a strategy to combat
global climate change.
CDU is the use of CO2 in its raw form, e.g. for enhanced oil recovery, or the transformation of
CO2 to generate products, e.g. fuels, chemicals or materials with economic value [1,4]. The
emission reduction benefits of CDU may accrue directly when CO2 is converted into products that
delay carbon release to the atmosphere or indirectly if the product substitutes an equivalent amount
of fossil fuel that would have been consumed in its stead [1,4]. Additional benefits occur when a
carbon-intensive hydrocarbon is displaced by the CDU product [1]. Some studies have envisaged
closed-loop CO2 cycling energy systems where CDU processes generate fuels which are
subsequently consumed and recycled back after combustion [18, 25]. Such CDU processes would
result in a steady level of anthropogenic CO2 in the atmosphere. As global energy demand rises,
more atmospheric CO2 could be drawn into the cycle for energy production.
However, CDU has been consigned to a supportive position with respect to emission avoidance
strategies based on renewable energy or methods which offer permanent storage of CO2 [3,4].
Unfortunately, renewable energy deployment at scale is handicapped by variability and non-
133
dispatchability attributes. Recent studies question the ability of renewable energy systems to
satisfy energy needs at the global scale, considering, for example, the land area that would be
required if solar or wind energy were to be harnessed [19,31]. Prominent among actions proposed
for climate mitigation is carbon capture and storage (CCS) where emissions are fixed in permanent
storage. The interest in CCS stems from focus on fossil fuels to supply a large fraction of global
energy demand in future energy mix scenarios [23,24,27]. Unfortunately, CCS has not gained
serious traction despite being fully mature because it cannot be driven by free market forces
without regulatory support or government financing or both [23,24,26,30]. Another limitation of
CCS โ including CO2-EOR โ is geographical constraint; their most economic deployment would
be in places with existing oil reservoirs. However, CDU deployment can be tailored to locations
with CO2 feedstock and product demand. To overcome geographical limitations of CCS and CO2-
EOR (e.g. by building and maintaining CO2 transport networks including pipelines, rail, and ship)
require substantial investment that would be difficult to finance without an economically viable
use of CO2 โ unless carbon taxation and/or government financing are implemented. Matching
available CO2-EOR potential regionally with their CO2 storage capacity only amounts to a global
cumulative capacity to remove ~35 Gt CO2 via EOR โequivalent to current annual GHG emissions
[3]. However, at some point, after breakthrough from injection to production wells, EOR
operations would produce the majority of CO2 injected. The ability to flexibly place non-EOR
CDU systems makes them compatible with ongoing transformation of existing centralized energy
generators into distributed systems. Consequently, utilization of CO2 has been proposed because
of its potential to activate market forces to drive useful applications of CO2 in CDU pathways that
are self-sustaining with respect to energy, environment, and economics [18,20,21,23,25,26,30].
134
The low energy state and consequent stability of CO2 implies that CDU processes are faced with
major energy hurdles [23,25,29]. A closer look also reveals significant energy requirements (and
thus, costs) to implement CDU at commercial scale to achieve CO2 emission reduction goals [3,4].
Therefore, in most cases, one finds that the challenge is with overcoming this energy barrier.
Whether capturing CO2 emissions from sources, building, operating, and maintaining CO2 storage
networks, injecting CO2 into depleted reservoirs for storage and to recover more oil, converting
CO2 into products with storage objectives, or monitoring and controlling storage media and
infrastructure, underneath all of this is the energetic barrier for CO2 conversion, which then
translates to net environmental impacts and costs.
Nevertheless, due to declining overall energy returns โ as sweet-spot conventional fossil resources
dwindle with more focus on unconventional resources โ attention must be on the potential of CO2-
based fuels as a sustainable substitute to fossil fuels. If global fuel requirements were met with
CO2-derived fuels, the energy barrier (hence cost and associated emissions) to pursue such CDU
processes must be low enough to be economic and less environmentally harmful than existing
fossil fuels. Combining a low-emission energy source with a high-efficiency CO2 conversion
process could yield a CDU route that could be used to supply a significant portion of global fuel
demand [4].
Assen et al. [4] applied an LCA-based environmental evaluation approach and showed that time-
corrected global warming potentials and time-resolved emission profiles provide reduced global
135
warming impacts of delayed CO2 emissions via CDU-based products. This was due to lower risk
of irreversible environmental impacts from release of large amounts of GHGs within a short time.
Thus, CDU can mitigate climate change by reducing GHG emissions and by storing CO2 in
products [4]. According to the United States Department of Energy (DOE), CDU challenges
include finding the best way to provide energy needed for conversion and developing the right
technologies for product realization [2]. The DOE proposed that CDU is an alternative to storage
in areas with geological limitations for CO2 storage [2]. Zimmermann and Schomacker [1]
investigated methods to assess CDU technologies including performance measures such as
economics, technical feasibility, environmental impact, and social factors.
In general, the choice of both energy source and conversion process determine technical feasibility
and economic viability of a CDU technology. Moreover, CDU requires processes that do not
generate more CO2 than is removed from the atmosphere [2]. Since CO2 generation from the
process is tied to energy source, choosing the right energy source is a crucial step to achieve
potential environmental benefits [2]. The energy requirement for CDU may be supplied from
highly reactive chemical species (e.g. hydrogen), electricity, heat, or even light โ as in
photosynthesis [4]. Given the likely differences of environmental impacts and unit costs of energy
from different sources, in addition to the investments needed to develop CDU options, this
necessitates joint consideration of energy, emissions and economics of CDU pathways to evaluate
anticipated benefits of CO2 recycling.
136
Dowell et al. [3] used energy return on energy invested (EROEI) to compare methanol fuel
produced from CDU and gasoline. However, EROEI does not recognize the differences of
emissions arising from different energy sources. Also, the authors did not acknowledge that CDU
has a net CO2 recycling effect unlike gasoline which introduces new CO2 emissions. Van-Dal and
Bouallou [13] simulated a CO2-derived methanol production process by separating energy
requirements into heat and electricity. Only one CO2 capture technology was considered and the
energy source for their electrolytic-based hydrogen was unspecified. Demirci and Miele [14]
studied hydrogen production processes and observed that both type of process and energy source
determine hydrogen cost. Schultz [15] showed that the use of modular nuclear reactors can greatly
improve process efficiency and economics of hydrogen production. A number of other researchers
have examined wind and solar power for CDU [16,17,18], but none has used a comparative
approach to analyse CO2 capture technology, conversion pathway, and energy options.
Here, we consider energy options for CDU and promising options to capture and convert CO2 to
chemicals, fuels, and materials. We evaluate overall processing chain CO2 emission and energy
penalty for each CDU pathway. Energy sources assessed include chemical, heat, and electricity
from hydrogen, natural gas, solar thermal, nuclear heat, nuclear electricity, renewables (wind and
solar), hydropower, gas and coal. CO2 feedstock is provided via amine or direct air capture from
either open air or a power plant facility. CDU processes considered include production of formic
acid, methanol, ethanol, synthetic fuel, and polyurethane polymer. Hydrogen production from both
electrochemical and thermochemical routes is explored. The full process chain energy requirement
and CO2 emissions are evaluated for each CDU pathway. Comparisons of CO2 utilization
137
potentials of the CDU processes are drawn to highlight the most promising pathways for CDU
deployment.
Energy Options, Process Emissions and CDU Systems
The energy requirements for CDU processes include chemical, electrical, and heat energies. Figure
6-1 depicts a generic CDU system consisting of an energy source, CO2 feedstock source, hydrogen
source, and CO2 conversion unit. Hydrogen is the most common chemical energy source to
activate CO2 to reaction and is often produced from steam methane reforming (SMR) or
electrolysis [25,28,29]. However, hydrogen has to be generated by using heat or electricity.
Additionally, capturing CO2 feedstock also requires energy either as heat or electricity. Prior to
the compression stage of CO2 capture systems, the energy required to operate amine-based capture
is predominantly heat, whereas the energy required for direct capture from either open-air or from
surroundings of a fossil-based power plant is principally power [16,17]. Heating requirements may
be satisfied directly by fuel combustion, heat from nuclear reactions, or solar thermal heating
[29,30]. Heating may also be supplied from electricity.
138
Figure 6-1: Superstructure of the CDU system indicating the component units and the
flows of materials and energy.
Table 6-1 shows the technology and processing alternatives for each unit of the CDU system and
their energy options. Figure 6-2 shows the energy inputs and process emissions for the production
of hydrogen from either electrolysis or steam-methane reforming (SMR). For hydrogen
production, the intensities for electrolytic and SMR sources are evaluated for all energy options
(listed in Table 6-1). Since electrolysis uses electricity, process emission and energy inputs are
calculated for all electricity generation options considered here. SMR primarily uses heat energy
139
to produce hydrogen, so heat energy sources or electricity can be used to drive the reaction. When
electricity is used to provide heating, 2% of the energy is assumed to be lost as heat [13, 22, 28].
Hydrogen production is one segment of CDU processes and no CO2 is consumed in that segment,
therefore energy input is expressed per unit of hydrogen produced. For electrolysis, energy input
is highest for solar PV which has low efficiency but lower emissions than coal or natural gas
combined cycle power plants without CO2 capture. SMR hydrogen has lower energy and emission
intensities due to higher process heating efficiency. Energy input for hydrogen from electrolysis
ranges from 200 to 900 GJ per tonne, whereas CO2 emissions for nuclear power ranges from almost
zero to about 47 tonnes per tonne of hydrogen. SMR is generally less energy intensive than
electrolytic hydrogen production.
For generation of CDU products, the process energy requirement is the sum of those of the
component units, including hydrogen production, CO2 capture, and CO2 conversion units. Figures
6-3 and 6-4 show the ranges of overall process energy inputs and emissions for the various CDU
energy, process, and product options considered, including synfuel, methanol, ethanol, formic
acid, and polyurethane polymer. The energy input is expressed in GJ per metric tonne of utilized
CO2. Generally, energy resource inputs are higher for production pathways using an electrolytic
hydrogen source. CO2 capture from air, whether open air or near a power plant (flue gas), is less
energy intensive than amine-based capture. However, the energy requirement of direct air capture
is primarily electricity whereas for amine it is heat. Nevertheless, this may not inform the
economics of both systems since heat might be more cheaply available than electricity for
particular processing configurations. Details of the energy inputs and process emissions for each
of the CDU products assessed under various processing configurations and energy options is
140
provided in the supplementary information (Appendix C, Sections SC.2 and SC.3). For all the
other products except PUR, the best CDU system configuration consists of hydrogen production
from SMR driven by nuclear heat, direct air CO2 capture from power plant (flue gas) and process
energy needs supplied from hydro power. In the case of PUR, the best configuration comprises of
hydrogen from electrolysis powered by hydroelectricity, direct air CO2 capture from power plant
(flue gas), and process energy needs also supplied from hydro power.
Table 6-1: CDU technology configurations and energy options.
CDU Process Unit Process Options Energy Options
Hydrogen production
Electrolysis
Steam methane reforming
Power
{
๐๐จ๐๐ฅ ๐๐ฒ๐๐ซ๐จ ๐๐ข๐ง๐ ๐๐๐ฌ ๐๐ฎ๐๐ฅ๐๐๐ซ ๐๐จ๐ฅ๐๐ซ ๐๐ก๐๐ซ๐ฆ๐๐ฅ๐๐จ๐ฅ๐๐ซ ๐๐
Heat{
๐๐จ๐ฐ๐๐ซ ๐๐จ๐ฅ๐๐ซ ๐๐ก๐๐ซ๐ฆ๐๐ฅ๐๐ฎ๐๐ฅ๐๐๐ซ ๐๐๐ฌ
Chemical{๐๐ฒ๐๐ซ๐จ๐ ๐๐ง
CO2 Capture
Direct Air Capture
Monoethanolamine
CO2 Conversion
Polyurethane polymer
Formic Acid
Synthetic Fuel
Ethanol
Methanol
141
Figure 6-2: Energy input and process CO2 emissions for hydrogen production from SMR
and Electrolysis.
142
Figure 6-3: Overall process energy inputs for the various CDU processes and product
options.
143
Figure 6-4: Overall process CO2 emissions for the various CDU processes and product
options.
CO2 Utilization Process Performances
Table 6-2 lists CO2 utilization performances of the product pathways considered here. Based on
CO2 utilization factor (CUF), synfuel, methanol, ethanol, and polyurethane polymer all have
appreciable carbon utilization potentials. A comparison of emission factors, as in Figure 6-5,
indicates that ethanol is the most intensive with respect to the CO2 emission factor (CEF). CDU
processes operating on energy from coal, natural gas combined cycle plants without carbon
capture, natural gas heating, or solar PV are unlikely to provide CO2 mitigation benefits. The most
optimal CDU configurations consist of nuclear energy powered electrolytic hydrogen or wind
144
energy powered hydrogen. In all cases, the pathways to convert CO2 to polymer material have the
lowest CEF values, indicating the potential to sequester more carbon than other production options.
Table 6-2: Carbon dioxide utilization performance of product pathways. We define CO2
Utilization Factor (CUF) as a sustainability criterion which indicates the potential for a given
CDU pathway to sequester CO2 emissions in the product, either on temporary or permanent
basis. The CUF is given by the ratio of the amount of CO2 utilized in a specific processing
chain to the amount of product produced.
CDU Product
Quantity
(metric tonne)
CO2 Used
(metric tonne)
CO2 Utilization
Factor
Polyurethane Polymer 1.00 1.74 1.7
Formic Acid 22.90 21.90 1.0
Synthetic Fuel 2.33 7.33 3.1
Ethanol 2.55 4.87 1.9
Methanol 5.31 7.30 1.4
145
Figure 6-5: Comparison of the CO2 emission factors for the CDU system configurations of
energy options and product pathways. The process CO2 Emission Factor is the ratio of the
process CO2 generated to the process CO2 utilized. SMR = steam methane reforming,
NGCC = natural gas combined cycle, and PV = photovoltaic.
Proper accounting of energy and emission intensities of CDU must incorporate total balances for
the utilization process and consider differences in timescales between alternative conversion
pathways. Our results suggest opportunities for the use of clean and renewable energy sources,
direct air capture, and promising CO2 conversion pathways to produce methanol, synfuel, and
polymer materials. The next key consideration is economics.
146
Methods
The total energy requirements of each CDU pathway is calculated by using Aspen Custom
Modeller. The energy need of CO2 conversion processes include sensible and latent heats supplied
to bring reactants to the temperature at which the desired reaction occurs, reaction enthalpy for
endothermic pathways, and hydrogen feed for reactions that are activated by hydrogen as a
reactant. For exothermic reactions, only the net energy need is supplied by a heat source. Carbon
dioxide and hydrogen feedstocks for each processing cycle are calculated by using reaction
stoichiometry, as shown in Equation (1), where ๐ represents the moles of each reaction species for
given conversion pathway. Unconverted feedstock can be recycled.
๐1๐ถ๐2 + ๐2๐ป2 โ ๐3[๐ถ๐ท๐ ๐๐๐๐๐ข๐๐ก] + ๐4[๐๐๐๐ ๐๐๐๐๐ข๐๐ก] (1)
Due to the nature of polymeric reactions, data on the chemistry of polyurethane production is
obtained from [5], instead of using stoichiometric balancing. Hydrogen feedstock is supplied by
either electrolysis or SMR, and the feedstock CO2 is provided by a CO2 capture plant which can
operate the amine process capturing CO2 from flue gas or direct CO2 capture from open air or in
the vicinity of a large-scale CO2 emitting source like combustion power plants. The
thermodynamic energy requirements for CO2 separation using amine or direct air capture (DAC)
are obtained from literature [17,19]. This energy need is predominantly heat for the amine process
[17] whereas for DAC it is mostly power [19].
147
Both heat and power requirements of each CDU process can be met by using one of the electricity
sources listed in Table 6-2. However, pure heat sources such as solar thermal, nuclear heat and
natural gas combustion can only provide heating needs of the process. Lifecycle electricity
emission intensities for various generation options are obtained by using the National Energy
Technology Labโs (NETL) LCAT PowerSim software [33]. For renewable energy technologies
such as solar photovoltaic (PV), solar thermal, and wind power, emissions are calculated based on
the CDU system energy requirement whereas for the other energy technologies the calculation is
based on the process energy input. Natural gas combustion emission intensity of 50.3 kg per GJ is
used in the model. Overall energy requirement of each CDU process is computed as the sum of
the energy needs at each component unit of the process. Therefore, the energy resource input to
each pathway (๐ธ๐ผ) is calculated using efficiencies of the energy sources as listed in the
supplementary information (Appendix C, Table SC.1):
๐ธ๐,๐๐ผ =
๐ธ๐,๐๐
๐๐ (2)
where ๐ธ๐,๐๐ is the energy requirement per unit of CO2 used in the CDU process unit ๐, supplied by
energy option ๐. We define CO2 Utilization Factor (CUF) as an additional sustainability criterion
which indicates the potential for a given CDU pathway to sequester CO2 emissions in the product,
either on temporary or permanent basis. The CUF is given by the ratio of the amount of CO2
utilized in a specific processing chain to the amount of product produced. We also define process
CO2 Emission Factor (CEF) as the ratio of the process CO2 generation to the process CO2
148
utilization. These parameters enable us to compare various CDU pathways on their CO2 retainment
and release bases.
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152
Conclusions and Recommendations
7.1 Conclusions
Unconventional resources are the growing source of reserves additions in many countries, driven
by a number of factors including, conventional fossil fuels depletion, technical innovation,
growing global energy demand, energy security, energy independence and a host of economic
reasons. Since reserve additions are often from lower quality, less reachable, premature (e.g. oil
shale) or postmature (e.g. oil sands) deposits; development and production of unconventional
resources faces three major challenges in the form of higher energy, environmental and economic
intensities. Since these challenges actually feedback into one another, impacts assessment studies
have to adopt a more circumspect approach to quantify efficiencies, GHG emissions and costs of
unconventional resources development along with the implications for current policy objectives
and societal sustainability. To that end, the research documented in this thesis presented methods
combining analytical modelling and physical parameters to evaluate impacts of shale gas and oil
sands developments using the stated three intensities as the assessment metrics.
Chapter three addressed issues with existing literature attempts to quantify emissions and
economic impacts of shale gas development. A new method to quantify well completion methane
releases during the flowback regime using gas-practical initial production data was presented and,
for the first time, validated with actual field measurement of flowback emissions. This settles the
controversy on accuracy of flowback emission estimates. On this basis, economic implications of
mitigation were assessed under various scenarios to ascertain potential value of reduced emission
153
development under alternative gas handling policies. Mitigation costs were estimated considering
cost of reduced emission completion and the economic value of the captured natural gas. Mean
estimate of potential methane emissions from U.S. and Canadian wells in 2015 are 2347 Mg CO2e
per completion and 1859 Mg CO2e per completion, respectively. Accordingly, these amount to
average potential profits from captured gas of US$17,200 and US$11,200 in the U.S. and Canada.
In the urgent desire to reduce GHG emissions globally, gas penetration in the energy system is
growing but shale gas reservoirs also deplete faster than conventional gas, implying that higher
frequencies of new development wells are needed to augment declining production as energy
demand continues to grow. In chapter four, the research builds on the foregoing by presenting a
more complete model of preproduction stages during shale gas development; considering energy
use, energy returns and emissions at each development step. Complete analytical models are
presented to cover various preproduction activities and events including well drilling, mud
circulation, mud gas generation, hydraulic fracturing, and well completion flowback. This ushered
new understanding on distribution of energy inputs among the activities and contributions of
individual operations to overall preproduction GHG emissions. To gain concrete grasp of climate
impacts of increasing shale gas development requires a new perspective on the evaluation of annual
global warming impacts, in line with climate mitigation goals. Considering shale gas development
at the Canadian Montney formation in 2017 alone, total potential GHG emissions is 3090 Mg CO2e
per well. However, on a preproduction basis and dependent on EUR estimates, energy returns from
the same formation is estimated as 3400.
154
In chapter five, the role of process and technology innovations is investigated with respect to the
energetic, environmental and economic indicators of oil sands production process performance.
Three emerging oil sands production process designs are assessed relative to the benchmark SAGD
design. The emerging design configurations include pure solvent, steam-solvent and steam with
non-condensable gas systems. A new assessment method based on mathematical programming is
developed to simultaneously evaluate and compare intensities of the emerging process designs,
taking into account both surface and subsurface facility requirements. The results indicate that
steam additive processes are just incremental to SAGD and can only offer marginal performance
improvements. Pure solvents systems are promising for significant performance improvement but
are limited by high operating costs due to need to make-up for solvent losses. On energy returns
basis, pure solvent process could perform better than benchmark SAGD by up to 18 times. A
proposal for greater performance enhancements through new well configurations and exploration
of other driving forces other than gravity is also provided.
To understand sustainable climate strategies to mitigate environmental impacts of fossil-fuel
derived GHG emissions, chapter six investigates the role of carbon dioxide utilization as a climate
mitigation strategy to reduce global warming impact of emissions. Various pathways for managing
CO2 emissions through transformation into synthetic fuels, chemicals and polymer materials are
studied looking at their energy requirements and net environmental impacts. Full processing cycle
assessments are performed to determine overall energy and emission intensity of each product
option. Best CDU pathways are identified as net fixers of CO2 and they exhibit potential to achieve
significant reductions of the emissions. Promising CDU options include production of methanol,
synfuel and polyurethane materials.
155
7.2 Recommendations
One question that has often arisen with regards to natural gas emissions is on the possibility of
extending predictive modelling to entire gas supply chains. Here, a discussion on how one might
go about answering such research question is presented along with the challenges confronted by
such efforts. One may consider an entire gas supply network extending from upstream segment to
final consumption by end users, as shown in Figure 7-1. The system control area (SCA) can be
defined as the geographical area covered by this supply chain in which flows of methane or GHG
emissions is to be quantified.
Figure 7-1: Segments of a typical natural supply chain.
Implementation of predictive modelling across supply chains would need to be complemented with
measurement data from specific points in the network, subject to the area within which the analyses
is to be performed. In the presence of adequate data from selected points in the network, one could
perform a total material balance over the SCA, accounting for molar flows of the substance of
interest to the study. Since climate mitigation goals and emission reduction targets are often set for
156
whole countries or geographical regions, the material balance can be resolved to the annual basis
so that yearly environmental conservation performance can be benchmarked against policy targets.
7.2.1 Methane Accounting
Perform material balance for methane across the SCA in terms of total molar flows
Overall material balance to consider imports, exports, storage (including injections and
withdrawals), production, and utilization
Breakdown each element of the total balance into their various components (e.g. utilization
can be divided into combusted and converted methane)
Determine optimal points in the supply chain to minimize data requirement of the
modelling
Apply discrete calculus to quantify GHG emissions over specified periods
157
Appendices
Appendix A
Supplementary Information: On Methane Emissions from Shale Gas Development
A.1 Initial Production Data
There can be confusion with regards to the right type of initial production data to use for estimating
the amount of flowback gas. We have argued that initial production testing (IPT) is not the
adequate initial production metric to signal the transition from multiphase fluid flow to gas-only
production during shale gas well completions. We also mentioned that using peak gas (PG)
production data overestimates the emissions. Figures SA.1 and SA.2 compare flowback emission
estimates using PG data and our recommended metric, the gas-practical initial production (GPIP),
alongside actual field measurement of flowback emissions under the Natural Gas STAR program
by EPA. The two figures represent alternative gas handling scenarios where either 70% or 95% of
the total potential methane emissions are captured through reduced emission completion. It is
observed that while our approach using GPIP correlates better with actual measurements, the use
of historical peak gas production data overestimates the emissions.
158
Figure SA.1: Average emission rates using initial production and peak gas methods,
compared to Natural Gas STAR measurements. Flowback periods of 3 and 9 days are used
in each case at 70% capture of the potential emissions
159
Figure SA.2: Average emission rates using initial production and peak gas methods,
compared to Natural Gas STAR measurements. Flowback periods of 3 and 9 days are used
in each case at 95% capture of the potential emissions
Additionally, we have observed that the average production in the first month could also be used
to estimate flowback emission with a better performance than IPT and historical peak gas.
However, it is not better than GPIP in predicting the emissions when compared to actual
measurements because it does not capture the transition in flow regimes properly; as wells change
from producing mostly liquid at flowback initiation to mostly gas production at flowback
completion. Figure SA.2 and SA.3 are parity plots of estimated and measured potential emissions
for the same wells assuming 3- and 9-day flowback periods โ based on average first month
production data and actual flowback measurement data provided in Allen et al. [5]. The figures
show that if the estimate is based on the average first month production, the accuracy of the
estimated potential emissions can be very uncertain โ being either overestimated or
160
underestimated. It overestimates the emissions at the earlier periods of the flowback when mostly
fracturing liquid is produced and underestimates the emissions when mostly gas is produced due
to the suppressing effect of the larger quantity of liquid produced at the earlier parts of the flowback
period. This is because reservoir permeability is shared between the gas and the liquid flowback,
which results in a lower average flowback gas amount for the first month (when liquid saturation
in the reservoir was highest). The mean of the 9-day flowback estimate is 5020 MgCO2e per
completion, which is outside the 95% CI of the field measurements. However, there is a general
trend that points to the capacity of initial production data-based models to considerably
approximate the actual flowback gas quantities. We have validated the use of gas practical IP in
the body of this paper by comparison with actual field measurements, but cannot present a parity
plot of the estimate based on gas practical IP because the corresponding potential emission
measurement data is unavailable.
161
Figure SA.3: Parity plot of estimated emissions (using average first month production) and
measurement data for the same wells
Figure SA.4: Parity plot of estimated emissions (using average first month production) and
measurement data for the same wells
A.2 Statistical analysis of potential emission estimates at the individual shale play level
162
Here, statistical analyses of the results are presented at the shale play level by considering
histograms of the data (Figures SA.5-SA.11). All the figures show the signature long-tailed
distribution of methane emissions. Descriptive statistics are presented in Table SA.1 for each play
(means, medians, standard deviations and interquartile ranges) as follows:
Table SA.1: Basin-level statistical attributes of the potential emission estimates (all estimates
are presented in Mg CO2e/completion). CI=Confidence Interval; SD=Standard Deviation;
IQR=Interquartile Range (i.e. Q3-Q1)
Shale play Mean CI (95% level) Median SD IQR
Barnette 988 851-1125 808 822 1296
Fayetteville 1078 1019-1138 1004 497 691
Haynesville 3608 3237-3979 3327 2281 2237
Marcellus 2860 2732-2989 2484 1918 2229
Woodford 1896 1689-2102 1618 1533 1922
Duvernay 1426 1161-1691 1437 772 512
Montney 1861 1735-1987 1726 1317 1457
163
Figure SA.5: Barnett shale potential emissions distribution
Figure SA.6: Fayetteville shale potential emissions distribution
164
Figure SA.7: Haynesville shale potential emissions distribution
Figure SA.8: Marcellus shale potential emissions distribution
165
Figure SA.9: Woodford shale potential emissions distribution
Figure SA.10: Duvernay shale potential emissions distribution
166
Figure SA.11: Montney shale potential emissions distribution
A.3 Influence of natural gas prices on net revenue from REC
Overall effect of natural gas price and green completion cost uncertainties are evaluated by
considering all the wells together, irrespective of the shale play. The results are also tested
nationally for the Canadian and United States shale plays in Figures SA.14 and SA.15. We observe
that the average green completed Canadian well has about the same net revenue as the average
green completed U.S. well.
167
Figure SA.12: Cumulative density of net revenue at various gas prices and high REC cost
scenario (all shale plays inclusive)
Figure SA.13: Cumulative density of net revenue at various REC costs and medium gas price
scenario (all shale plays inclusive)
168
Figure SA.14: Cumulative density of net revenue at various gas prices and average REC cost
(US plays only)
Figure SA.15: Cumulative density of net revenue at various gas prices and average REC cost
(Canadian plays only)
169
A.4 Table of quartile values at the various REC costs and gas prices
Table SA.2: Quartiles of the net revenue ($ per well) for each REC cost and natural gas price
scenario (all plays inclusive)
Quartil
e
Low REC Cost Aveg REC Cost High REC Cost
LowP MedP HigP LowP MedP HigP LowP MedP HigP
P25 1299 7598 13897 -5701 598 6897 -
58701 -52402 -46103
P50 6336 17672 29007 -664 10672 22007 -
53664 -42329 -30993
P75 14002 33004 52006 7002 26004 45006 -
45998 -26996 -7994
P100
122545
250090
377635
115545
243090
370635 62545
190090
317635
A.5 List of sources of measurement data1
(1) Norwood P, Campbell L (2013) Flowback emissions and regulations. Environmental
Resources Management, Oil & Gas Environmental Conference 3-4 December, Dallas Texas, USA.
(2) Omara M, et al. (2016) Methane Emissions from Conventional and Unconventional Natural
Gas Production Sites in the Marcellus Shale Basin. Environmental Science & Technology 50(4),
2099-2107.
1 Note that for a measurement study, potential emission equals the sum of measured and captured emissions. But in
the absence of emission controls, measured emission and potential emission are equal [3].
170
(3) Allen DT, et al. (2013) Measurements of methane emissions at natural gas production sites in
the United States. Proceedings of the National Academy of Sciences 110(44), 17768-17773.
(4) Goetz, JD, et al. (2015) Atmospheric Emission Characterization of Marcellus Shale Natural
Gas Development Sites. Environmental Science & Technology 49(11), 7012-7020.
(5) Armendariz A, et al. (2009) Emissions from natural gas production in the Barnett shale area
and opportunities for cost-effective improvements. Environmental Defence Fund. Weblink:
http://www.edf.org/sites/default/files/9235_Barnett_Shale_Report.pdf
(6) Fernandez R, et al. (2005) Cost-effective methane emissions reductions for small and midsize
natural gas producers. SPE, Journal of Petroleum Technology, 57(6), 34-42.
(7) Environmental Protection Agency (2011) Reduced emissions completions for hydraulically
fractured natural gas wells. Weblink: https://www.epa.gov/natural-gas-star-program/reduced-
emission-completions-hydraulically-fractured-natural-gas-wells
For sources that report only the range of their emission measurements, both the lower and upper
values are included in the MS dataset. Allen et al. [5] provided 27 data points of field
measurements of flowback gas from US shale wells. There are a few instances where the average
of snapshot measurements of flowback emissions are used (such as 4 data points from Omara et
al. [6]), in such cases we estimate the cumulative flowback using the average of the two flowback
periods considered (i.e. 6 days). Moreover, Omara et al. [6] reported controlled emissions where
the data for one well is for the flared emission while those of the other three wells are for captured
potential emissions. For the flared emission we assume this represents 50% of the potential
emissions, and for the captured methane we assume it accounts for only 10% of the potential
171
emissions. EPAโs emission reduction strategy is to consider 50% of potential emissions as
controlled by capturing or flaring [7], so in all cases the EPA strategy serves as a lower bound to
how we account for the controlled emissions.
Table SA.3: Field measurement data of flowback methane from various sources
Mg CO2e per completion Source
317.22516 (3)
22.94649 (3)
157.0023 (3)
21738.78 (3)
19323.36 (3)
2012.85 (3)
1710.9225 (3)
8655.255 (3)
5233.41 (3)
6.964461 (3)
4911.354 (3)
1739.1024 (3)
80.514 (3)
10.86939 (3)
8252.685 (3)
7044.975 (3)
7528.059 (3)
9.66168 (3)
5.23341 (3)
4.186728 (3)
12.0771 (3)
15.70023 (3)
13.68738 (3)
8.85654 (3)
177.1308 (3)
102.25278 (3)
144.12006 (3)
1878.079683 (1)
3130.132805 (1)
219.1092963 (1)
7637.524044 (1)
172
1533.765074 (1)
2817.119524 (1)
1158.149138 (1)
4131.775302 (1)
939.0398414 (1)
1565.066402 (5)
2191.092963 (6)
313.0132805 (5)
1565.066402 (5)
939.0398414 (5)
1565.066402 (5)
146.9732408 (1)
2551.5 (7)
970.2 (4)
280.6272 (2)
459.648 (2)
341.712 (2)
168.4368 (2)
A.6 References
[1] Howarth RW, Santoro R, Ingraffea A (2011) Methane and the greenhouse-gas footprint of
natural gas from shale formations. Climatic Change 106(4): 679-690.
[2] United States Department of Energy (2011) Life-cycle analysis of shale gas and natural gas.
Energy Systems Division, Argonne National Laboratory. Weblink:
https://greet.es.anl.gov/publication-shale_gas
[3] Gates I (2013) Basic reservoir engineering, First Ed., Kendall Hunt Inc.
[4] OโSullivan F, Paltsev S (2012) Shale gas production: potential versus actual greenhouse gas
emissions. Environmental Research Letters, 7(4), 044030.
[5] Allen DT, et al. (2013) Measurements of methane emissions at natural gas production sites in
the United States. Proceedings of the National Academy of Sciences 110(44), 17768-17773.
173
[6] Omara M, et al. (2016) Methane Emissions from Conventional and Unconventional Natural
Gas Production Sites in the Marcellus Shale Basin. Environmental Science & Technology 50(4),
2099-2107.
[7] Environmental Protection Agency (2011) Reduced emissions completions for hydraulically
fractured natural gas wells. Weblink: https://www.epa.gov/natural-gas-star-program/reduced-
emission-completions-hydraulically-fractured-natural-gas-wells
174
Appendix B
Supplementary Information: Predictive Modelling of Energy and Emissions from Shale Gas
Development
B.1 Modelling parameters and variables data ranges
Modelling data are obtained from actual field measurements reported in various sources, including
HPDI database [1], GeoScout database [2], CALFRAC Montney Case Study [3] and Fazaelizadeh
[4]. Reported information covers values obtained from Canadaโs Montney Formation and other
general parameters on drilling of directional wells [4, 6]. See Figure SB.1 for details of drilling
and well configuration.
Table SB.1: Modelling parameters and data ranges for preproduction activities/events
during shale gas development
Data Value
DRILLING
๐ผ๐ 90o (0 โ 135o)
๐ผ๐โ1 30o (0 โ 45o)
๐ผ 0o (0 โ 45o)
๐ 60o (0 โ 90o)
f๐๐๐ 0.2
f๐๐๐ป 0.3
f๐ถ๐ 0.3
175
ฮฒ 0.833 (0.5 - 0.868)
๐๐ต๐ผ๐,๐๐๐ป 145 N
๐๐ต๐ผ๐,๐ถ๐ 145 N
๐๐ต๐ผ๐,๐๐๐,2 300 N
๐๐ต๐ผ๐,๐๐๐,1 419 N
๐๐ท๐ถ 2.13 kN/m
๐๐ท๐ 0.285 kN/m
๐๐ท๐ถ 0.1m (Do = 0.15519m, Di = 0.12136m)
๐๐ท๐ 0.09m (0.09421m, 0.0889m)
โ๐๐ท๐ถ 670 m (10m each)
โ๐๐ต๐ผ๐ 0.33m (0.3 โ 0.33m)
โ๐๐ท๐ 4380 m (20m each)
โ๐๐๐๐ 2500 m (2000 โ 3300m)
โ๐๐๐๐ป 2250 m (1200 โ 2500m)
โ๐๐ถ๐ 300 m (100 โ 400m)
๐๐๐๐ท,๐๐๐ 1300 kg/m3
๐๐๐๐ท,๐๐๐ป 1600 kg/m3
๐๐๐๐ท,๐ถ๐ 1600 kg/m3
๐๐๐ผ๐๐ธ 7800 kg/m3
๐ท๐๐๐,1 8 ยพ inch
๐ท๐๐๐,2 7 ยฝ inch
176
๐ท๐๐๐ป 6 1/8 inch
๐ท๐ถ๐ 6 1/8 inch
๐ ๐๐๐๐๐ 0.27 m/sec
๐ ๐๐๐๐๐ป 0.15 m/sec
๐ ๐๐๐ถ๐ 0.15 m/sec
๐ ๐๐๐๐๐ 100 rpm
๐ ๐๐๐๐๐ป 55 rpm
๐ ๐๐๐ถ๐ 55 rpm
โต 69.4 kg CO2/GJ Diesel
๐ 0.4 (0.35 โ 0.45)
FLUID CIRCULATION
๐ถ๐ 0.95
๐ด๐ก 5 nozzles, each 0.5 in diameter
๐๐ 150 โ 450 gpm (depends on section being drilled)
๐ฃ๐ ๐๐/๐ด๐๐,๐
ฮผ๐๐๐ท,๐ 25 cp (0.025 Pa.s)
๐๐ 15 lbf/100 ft2 (7.182 N/m2)
๐ท๐๐,๐ Based on DP and DC inner diameters
๐ท๐๐,๐ Based on DP and DC outer diameters
๐ทโ๐๐๐,๐ Based on BIT size
๐๐๐ข๐๐ 0.8
177
๐๐๐๐ท Depends on section being drilled (use above values)
MUD GAS
๐ 7% (1 โ 15%)
๐๐ 0.021 (0 โ 0.211)
โ๐๐ถ๐+๐๐๐ป 2550 m
๐ต๐ 1.63 (0.12 โ 4)
๐ท๐ถ๐/๐๐๐ป 6 1/8 inch
๐๐๐บ 0.668 kg/m3
๐ 21 (new range: 28 โ 36)
๐ 78.8% (45 -95%)
HYDRAULIC FRACTURING
๐๐๐๐ Hydrostatic pressure (โ๐๐๐๐๐๐, where h=TVD)
๐ 4.5 m3/min
๐ก โ ๐ก๐ 225 min (220 โ 235)
๐ N=0.25 (0.13 - 0.3)
๐ 18.43 (where P is in MPa and t is in mins)
๐๐๐ข๐๐ 0.8
๐๐๐๐๐๐โ๐๐๐ฃ๐๐ 0.4
โต 69.4 kg CO2/GJ Diesel
๐ 15 (7 โ 30) number of stages
๐ท๐๐๐ ๐๐๐ 5 in (0.127 m)
178
๐๐๐๐๐ 1030.51 kg/m3
๐๐๐๐๐ 1 cp (0.001 Pa.s)
๐ 0.00008 pipe roughness (for friction loss)
FLOWBACK
๐๐,๐๐๐๐ 5181400 m3 (56039 โ 35497000 m3)
๐ 0.75 (0.6 โ 1)
๐ 21 (new range: 28 โ 36)
๐๐๐บ 0.668 kg/m3
๐ 78.8% (45 -95%)
PRODUCTION
๐ธ๐๐ 945850000 m3 (204730 โ 20912000000 m3)
B.2 Parameter estimation for flowback model
To determine parameter value for the flowback gas model, a nonlinear least squares problem is
formulated as min๐{โ (๐๐(๐) โ ๐๐)
2๐๐=1 }, expressed in the vector form as:
๐น(๐) = (
๐1(๐) โ ๐1๐2(๐) โ ๐2
โฎ๐๐(๐) โ ๐๐
)
min๐๐น(๐)
179
where ๐ is the parameter to be determined, ๐ is the proposed and ๐๐ is the estimate based on HPDI
reported data on Gas Practical IP as demonstrated by Umeozor et al. [5]. The optimal value of ๐ is
then obtained through an iterative procedure that minimizes the sum of squares of the ๐๐กโ iterate;
๐๐+1 = ๐๐๐๐๐๐๐โ๐ {โ[๐๐(๐๐) + โ๐๐(๐๐)(๐๐ โ ๐๐โ1) โ ๐๐]2
๐
๐=1
}
This is implemented in MATLAB using both the Trust-Region-Reflective and the Levenberg-
Marquardt algorithms [7].
B.3 Analytical modelling of drilling forces
There are a number of forces to come to play as the drilling assembly makes its way into the target
resource formation [4]. Figure SB.1 illustrates the derivation approach. Overall drilling forces on
the assembly can be expressed as sum of the forces on each section (๐) โ within a vertical, slanting,
horizontal or curved segment โ given as:
Straight Sections:
๐๐ = โ{๐ฝ๐คโ๐(cos ๐ผ ยฑ ๐ sin ๐ผ}๐
๐โ1
๐=1
Curved (dogleg) sections:
๐๐ = โ{๐ฝ๐คโ๐ (sin ๐ผ๐ โ sin๐ผ๐โ1๐ผ๐ โ ๐ผ๐โ1
ยฑ ๐๐cos ๐ผ๐โ1 โ cos๐ผ๐
๐ผ๐ โ ๐ผ๐โ1)}๐
๐โ1
๐=1
180
Figure SB.1: Schematic illustration and resolution of forces on drilling assembly
The signs are indicative of direction of movement of the drilling assembly. The resultant forces
are additive for upward movement of the pipe but subtractive for downward lowering. Further
details on the derivation can be found in [4].
B.4 Montney Shale Drilling Activity
Figure
SB.2: Drilling activity in Montney shale basin (total drilling activity in 2017 is 505 wells) [8]
181
B.5 References
[1] Drilling Information Database (2018), Texas, United States.
[2] GeoLogic Systems Canada (2018), GeoScout Montney formation, Western Canadian
Sedimentary Basin.
[3] CALFRAC Canada (2015), Case Study: High rate annular coiled tubing fracturing in Montney
formation, Western Canadian Sedimentary Basin.
[4] Fazaelizadeh, M. (2013). Real Time Torque and Drag Analysis during Directional Drilling
(Doctoral dissertation, University of Calgary).
[5] Umeozor, E. C., Jordaan, S. M., & Gates, I. D. (2018). On methane emissions from shale gas
development. Energy, 152, 594-600.
[6] Vafi, K., & Brandt, A. (2016). GHGfrack: An open-source model for estimating greenhouse
gas emissions from combustion of fuel during drilling and hydraulic fracturing. Environmental
science & technology, 50(14), 7913-7920.
[7] Beck, A. (2014). Introduction to nonlinear optimization: theory, algorithms, and applications
with MATLAB (Vol. 19). Siam.
[8] Shale Experts (2018) Montney Shale Drilling Activity (Q1-2015 to Q2-2018). Web Link
(Accessed September 2018): https://www.shaleexperts.com/plays/montney-shale
182
Appendix C
Supplementary Information: On Designing Carbon Dioxide Utilization Pathways for Sustainability
SC.1 Efficiencies of various electricity and heat energy sources: Table SC.1 below lists efficiencies of
energy options used to model the carbon dioxide utilization (CDU) systems considered in this study.
Table SC.1: Various energy options for the CDU processes and their efficiencies2.
Energy Option Energy Source Efficiency (%)
Electricity Hydro 90
Solar PV 17
Solar Thermal 20
Wind 40
Nuclear 35
NGCC w/o Capture 50
NGCC w/Capture 48
Coal w/o Capture 40
Coal w/Capture 38
Heat Solar thermal 50
Nuclear 70
Natural gas 80
2 NETL LCAT PowerSim Values (Energy Conversion Facility CO2 Emissions Report).
183
SC.2 Process energy inputs and emissions for electrolytic hydrogen production using various energy
sources
Figure SC.1: Process energy inputs and emissions for hydrogen production from electrolysis.
184
SC.2 Process energy inputs and emissions for SMR hydrogen production using various energy sources
Figure SC.2: Process energy inputs and emissions for hydrogen production from SMR.
185
SC.3 Process energy inputs and emissions for production CDU products under various process
configurations and energy options. Figures SC.3 to SC.7 show the results for the five CDU products
considered in this study.
Figure SC.3: Process energy inputs and emissions for PUR production using various energy options.
The CDU configuration highlighted in yellow color consists of hydrogen from electrolysis driven by
hydro power, direct air CO2 capture from flue gas, and process energy needs also supplied from
hydro power.
1.088, 0.023
0
0.5
1
1.5
2
2.5
0 5 10 15 20 25 30
Pro
cess
Em
issi
on
s (t
-CO
2/t-
Pro
du
ct)
Energy Input (GJ/t-CO2 Used)
Polyurethane Polymer Production
186
Figure SC.4: Process energy inputs and emissions for synfuel production using various energy
options. The CDU configuration highlighted in yellow color consists of hydrogen from SMR driven
by nuclear heat, direct air CO2 capture from flue gas and process energy needs supplied from hydro
power.
12.992, 1.165
0
5
10
15
20
25
0 20 40 60 80 100 120 140 160
Pro
cess
Em
issi
on
s (t
-CO
2/t-
Pro
du
ct)
Energy Input (GJ/t-CO2 Used)
Synfuel Production
187
Figure SC.5: Process energy inputs and emissions for ethanol production using various energy
options. The CDU configuration highlighted in yellow color consists of hydrogen from SMR driven
by nuclear heat, direct air CO2 capture from flue gas and process energy needs supplied from hydro
power.
34.247, 1.066
0
5
10
15
20
25
0 50 100 150 200 250 300
Pro
cess
Em
issi
on
s (t
-CO
2/t-
Pro
du
ct)
Energy Input (GJ/t-CO2 Used)
Ethanol Production
188
Figure SC.6: Process energy inputs and emissions for methanol production using various energy
options. The CDU configuration highlighted in yellow color consists of hydrogen from SMR driven
by nuclear heat, direct air CO2 capture from flue gas and process energy needs supplied from hydro
power.
13.053, 0.513
0
2
4
6
8
10
12
0 20 40 60 80 100 120 140 160
Pro
cess
Em
issi
on
s (t
-CO
2/t
-Pro
du
ct)
Energy Input (GJ/t-CO2 Used)
Methanol Production
189
Figure SC.7: Process energy inputs and emissions for formic acid production using various energy
options. The CDU configuration highlighted in yellow color consists of hydrogen from SMR driven
by nuclear heat, direct air CO2 capture from flue gas and process energy needs supplied from hydro
power.
4.636, 0.120
0
1
1
2
2
3
3
4
0 10 20 30 40 50 60
Pro
cess
Em
issi
on
s (t
-CO
2/t
-Pro
du
ct)
Energy Input (GJ/t-CO2 Used)
Formic Acid Production