a carbon dioxide pipeline network for wyoming - university of
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To the University of Wyoming:The members of the Committee approve the thesis of Mark A. Newcomb presented on
May 13, 2011.
Owen Phillips, Chairperson
Dale Menkhaus, External Department Member
Klaas van ’t Veld
APPROVED:
Owen Phillips, Head, Department of Economics and Finance
Brent Hathaway, Dean, College of Business
Newcomb, Mark A., A Carbon Dioxide Pipeline Network for Wyoming, M.S., Department
of Economics and Finance, August, 2011.
We design a 1,645 km long pipeline network linking several anthropogenic sources of
carbon dioxide (CO2) with regions where enhanced hydrocarbon recovery and geosequestra-
tion are possible. We estimate the pipeline’s capital cost to be 880.5 million USD and that
a network-wide tariff of 0.25 USD/mcf ($4.87/tonne) would be sufficient to earn a 12% rate
of return given a 30 year life-span. The pipeline would ship CO2 from underground and
anthropogenic sources to oil and coal-bed methane fields for use in enhanced hydrocarbon
recovery. In the process, these same fields could permanently sequester at least thirty years
of Wyoming’s total annual emissions of CO2. Diameter, cost and tariff calculations are car-
ried out using an interactive MATLAB program developed specifically for this project. The
program is intended to aid future research.
1
A CARBON DIOXIDE PIPELINE NETWORK FOR
WYOMING
by
Mark A. Newcomb, B.S.E.E.
A thesis submitted to theDepartment of Economics and Finance
and theUniversity of Wyoming
in partial fulfillment of the requirementsfor the degree of
MASTER OF SCIENCEin
ECONOMICS AND FINANCE
Laramie, WyomingAugust 2011
Copyright c© 2011
by
Mark A. Newcomb
ii
I dedicate this thesis to my loving wife, Allison, who has provided endless support and
encouragement. I also dedicate it to my son Charlie. You typed your first letters in the
middle of this thesis.
iii
Contents
List of Figures vi
List of Tables viii
Acknowledgments x
Chapter 1 Introduction 1
1.1 The Need for This Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Overview and Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Chapter 2 CO2 Supply and Demand 8
2.1 Sources of CO2 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.1 Enhanced Oil Recovery (EOR) . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Enhanced Coalbed Methane Recovery (ECBM) . . . . . . . . . . . . 20
2.1.3 Deep Saline Aquifer Sequestration Potential . . . . . . . . . . . . . . 37
2.2 Sources of CO2 Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3 Matching Supply and Demand . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Chapter 3 Diameter Calculations and Network Design 45
3.1 Diameter Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Network Routing and Design Considerations . . . . . . . . . . . . . . . . . . 50
3.3 Pipeline Route Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
iv
Chapter 4 Pipeline Cost Estimation Model 66
4.1 Pump Station Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Chapter 5 Tariff Calculation 72
5.1 Comparisons with Other Estimates . . . . . . . . . . . . . . . . . . . . . . . 75
Chapter 6 Conclusion 82
Appendix A Simulation Input Parameters for ECBM Models 85
Appendix B MATLAB Code for Pipeline Diameter, Cost and Tariff Calcu-
lations 87
References 122
v
List of Figures
2.1 Stylized example of enhanced oil recovery using CO2. . . . . . . . . . . . . . 10
2.2 The Lost Soldier field with its three producing horizons—the Tensleep, Madi-
son and Cambrian—would be listed as three separate FRC’s in (Cook, 2009a). 12
2.3 Example of a five-spot well pattern for ECBM production. Five-spot well
patterns for EOR would have the same arrangment. . . . . . . . . . . . . . . 15
2.4 Sample injection and recovery history for the Lance Creek/Leo FRC. . . . . 16
2.5 CO2 daily and cumulative purchases for the Lost Solder/Tensleep FRC. . . . 18
2.6 CO2 demand by basin for oil prices of $70, $100 and $120 per barrel. . . . . 20
2.7 In-seam process by which enhanced coalbed methane recovery sequesters CO2
and produces incrementally more methane (CH4). . . . . . . . . . . . . . . . 21
2.8 Example of how CBM recovery occurs. For a coal seam with initial conditions
P = 1200 psia and gas content = 308 scf/ton, water must be pumped until
the pressure is reduced to the critical desorption pressure at around 275 psia
at which point desorption occurs and gas is produced. . . . . . . . . . . . . . 27
3.1 NPS versus MMcfpd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Statewide overview of the pipeline network, sources of CO2, targeted EOR
fields and targeted coal fields. . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3 Southwestern pipeline sections and their relation to fields screened for EOR. 55
3.4 Green River Basin sections as designed and their relationship to sources and
fields screened for EOR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
vi
3.5 Wind River Basin sections as designed and their relationship to fields screened
for EOR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.6 Bighorn Basin sections as designed and their relationship to screened fields. . 61
3.7 Southeast sections as designed and their relationship to sources and screened
fields. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.8 Southern Powder River Basin sections as designed and their relationship to
sources, screened fields and coalbed methane fields. . . . . . . . . . . . . . . 63
3.9 Northern Powder River Basin sections as designed and their relationship to
sources, screened fields and coalbed methane fields. . . . . . . . . . . . . . . 64
4.1 LCC versus MMcfpd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 LCC versus MMcfpd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.1 Tariff versus MMcfpd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2 Tariff versus MMcfpd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.3 Tariff versus MMcfpd and segment length (km). . . . . . . . . . . . . . . . . 77
A.1 Some parameter values used for Robertson’s (2008) coal-seam model. . . . . 85
A.2 Some parameter values used for Robertson’s (2009) coal-seam model. . . . . 85
A.3 Some parameter values used for Ross’s (2009) coal-seam model. . . . . . . . 86
vii
List of Tables
2.1 EOR CO2 demand: By basin given prices of $70/bbl for oil and $2.25/mcf
for CO2 — cumulative and in terms of mass flow rates (Cook, 2009a). . . . . 19
2.2 ECBM CO2 demand by basin, cumulative and in terms of mass
flow rates. Basins in which ECBM production is negligible or for which no
estimates have been made are not listed. . . . . . . . . . . . . . . . . . . . . 36
2.3 CO2 potential supply and demand by basin in terms of mass flow
rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1 Pipeline layout for the Power County Energy Center-Jim Bridger-Wind River
Basin line based on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR. 57
3.2 Pipeline layout for the Wind River Basin-Bighorn Basin line based on mass
flow rates given $70/bo and $2.25/mcf CO2 for EOR. . . . . . . . . . . . . . 60
3.3 Pipeline layout for the Jim Bridger-Dave Johnston line based on mass flow
rates given $70/bo and $2.25/mcf CO2 for EOR. . . . . . . . . . . . . . . . . 60
3.4 Pipeline layout for the Dave Johnston-Wyodak line based on mass flow rates
given $70/bo and $2.25/mcf CO2 for EOR. . . . . . . . . . . . . . . . . . . . 65
4.1 Estimation results for equation (4.3) . . . . . . . . . . . . . . . . . . . . . . 68
5.1 Tariff per segment for the Green River and Wind River Basins portion of CO2
pipeline network, based on mass flow rates given $70/bo and $2.25/mcf CO2
for EOR applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
viii
5.2 Tariff per segment for the Bighorn Basin portion of CO2 pipeline network,
based on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR applications. 79
5.3 Tariff per segment for the Southeastern portion of CO2 pipeline network based,
on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR applications. . 79
5.4 Tariff per segment for the Powder River Basins portion of CO2 pipeline net-
work, based on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR
applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
ix
Acknowledgments
I would like to thank Professor Phillips for encouraging me to take this project on and
guiding me through its many twists and turns. Thank you, Professor Menkhaus, for making
time to read my thesis and come up with excellent thoughts and questions for my defense.
And thank you very much, Professor van ’t Veld, for encouraging me to dive into the field
of economics and for encouraging me to learn MATLAB.
Thank you Allison and Charlie for moving with me down to Laramie and for providing
encouragement and support. And thank you for granting me the time to “vacation” in
Midland, Texas to attend a conference on enhanced oil recovery!
Thank you Mom and Dad and sisters Lisa and Maria for your regular motivational talks.
Mark A. Newcomb
University of Wyoming
August 2011
x
Chapter 1
Introduction
1.1 The Need for This Research
Widely considered a waste gas and leading cause of climate change, carbon dioxide is also
a commodity that could generate significant economic activity in Wyoming through en-
hanced oil recovery (aka tertiary recovery or, more specifically, EOR) and enhanced coalbed
methane recovery (ECBM).1 EOR operators use CO2 to enhance the recovery of stranded
oil in depleted oil reservoirs. ECBM operators use CO2 to enhance the recovery of coalbed
methane from coal seams. Meanwhile large amounts of the CO2 that is used in the enhanced
hydrocarbon recovery process can be permanently sequestered in the reservoirs from which
the hydrocarbon is extracted.2 Both depleted oil reservoirs and coalbed methane fields are
plentiful in Wyoming, and the revenues from EOR and ECBM can generate positive profits
for pipeline operators and/or be used to offset the costs of CO2 capture from anthropogenic
sources (Herzog et al., 2005).
Wyoming annually produces around 57 million metric tonnes (Mt) of CO2 (EIA, 2009b).
1Often the term ‘enhanced hydrocarbon recovery’ covers both enhanced oil and enhanced coalbed methaneproduction. And though enhanced oil recovery actually encompasses a wide variety of techniques, our useof the term refers specifically to CO2-based recovery techniques.
2Strictly speaking, permanent sequestration involves several aspects. Geologic and hydrologic criteriamust be met (e.g. the reservoir must have a competent cap rock seal, something most oil and gas reservoirshave by nature). And a legal and regulatory framework to govern a host of monitoring, verification andaccountability (MVA) issues must also be in place. IPCC (2005) is a good source of general informationabout all aspects of carbon capture and sequestration, including economic and regulatory considerations.
1
Of this amount, about 8 Mt comes from underground sources and is already available for
EOR and/or ECBM. This CO2 is produced as a by-product of gas streams containing other
valuable gasses such as methane and helium. Most of it is shipped via pipeline to four oil
fields for EOR while a small amount is re-injected into the reservoir from whence it came.
The remainder of Wyoming’s CO2 emissions enter the atmosphere via flue gasses from the
combustion of fossil fuels. However there are over one-hundred more EOR field-reservoir
combinations (FRC’s) within Wyoming’s borders, representing over 500 Mbo of potential oil
production, that could enter EOR production if some of this CO2 could be made available.
Furthermore, vast coal fields in the Powder River and Green River Basins could theoretically
utilize CO2 to enhance the recovery of methane.
Capturing CO2 from coal-fired power plants, even when sold for EOR, is currently uneco-
nomical. However, nationwide research partnerships are currently undertaking substantial
research targeting all aspects of carbon capture and sequestration (CCS), including reducing
the cost of capture technology (DOE, 2011b). It is within the realm of possibility that im-
provements in technology brought on by this research, combined with subsidies or taxes, will
make CCS in conjunction with EOR and ECBM economically viable.3 The combination of
looming regulation and intensive research reinforces the need to examine CCS infrastructure
requirements in Wyoming under an assumption that the majority of the state’s fixed-point
carbon emissions are not only captured but also have a place to go. In short, any vision of a
world using captured anthropogenic CO2 as an input into the production of fossil fuels must
include the infrastructure to deliver the CO2 to those producers.
According to INGAA (2009), CO2 can be transported over land in trucks and by rail,
but pipelines are the most efficient means of shipping large quantities with minimal risk. In
2009 over 5,800 km (∼3,600 miles) of pipeline shipping over 40 Mt of CO2 per year existed in
the U.S. (INGAA, 2009). Wyoming’s relatively sparse population, long distances and open
3See Cook (2010) and Cook (2009b) for a thorough treatment of recently proposed legislation and thepotential implications for CCS. Cook (2009b)’s analysis finds that some of the recently proposed tax/subsidypolicy schemes would favor the sequestration provider which in turn would favor the construction of CO2
pipelines. Also see ACES (2011) as an example of recent regulatory efforts. See DOE (2010) for an updateof the ongoing efforts funded in part by the Department of Energy (DOE). Moniz (2011) makes a strongcase that cost-lowering capture technologies are imminent.
2
landscape make the state especially conducive to pipeline transport of CO2.4 This paper
calculates total demand and daily average demand for CO2 in Wyoming from potential EOR
and ECBM operations, summarizes how the demands are calculated, matches the demand
to existing and planned sources of supply, designs a CO2 pipeline of sufficient diameter to
transport our estimated daily quantities from sources to regions of demand, estimates the
pipeline’s capital cost and proposes a tariff structure sufficient to earn an industry standard
internal rate of return.
Cook (2010) calculates that existing oil fields with EOR potential could ultimately
consume as much as 289.2 million metric tons (Mt) (∼5.5 Tcf) of CO2 while producing
another 768 million barrels of oil (MMbo). Using the sequestration rates of existing EOR
operations to forecast future sequestration rates, at least 1.35 Tcf (∼28-69 Mt) of the CO2
could be permanently sequestered underground by EOR operations. If studies done by
Robertson (2008, 2009); Ross et al. (2009) and Nelson et al. (2005) on ECBM are accurate,
the process could enhance coalbed methane production (CBM) by between 2.5 and 12.6 Tcf
while permanently sequestering 1.0 to 6.5 billion tonnes of CO2 (between 20 to 110 years
worth of Wyoming’s current annual CO2 emissions).
We obtain average daily flow rates representative of the levels of consumption mentioned
above by estimating the pace of EOR and ECBM development based on past rates of de-
velopment. We then match estimated rates of CO2 consumption with average annual rates
of CO2 production from several of Wyoming’s largest emitters as well as three more sources
currently under construction or in the planning process. Given the resulting estimates of
average daily flows of CO2, we design a 1,645 km (∼1,045 mile)-long pipeline that links
sources of supply to regions of demand. The line ranges in diameter from 6 to 34 inches and
requires 13 booster stations. We estimate the total cost to build the pipeline and booster
stations to be around 880.5 million USD (2009 dollars). Based on this estimate, and on
our calculation that the network as a whole would ship about 2,428 MMcfpd (∼46.6 million
tonnes per year), a levelized tariff of $0.25/mcf ($4.87/tonne) is sufficient to earn a 12% rate
4In CCS jargon, pipelines connect sources with sinks. EOR and ECBM sites are considered sinks, as arethe two most prominent sites for pure geo-sequestration in Wyoming, the Moxa Arch and the Rock SpringsUplift.
3
of return given a 30-year operational life expectancy.5
We note that EOR and ECBM, as well as geosequestration, are evolving technologies
undergoing much current research. Most notably, the American Recovery and Reinvestment
Act designated $3.4 billion, to be administered by the Department of Energy, to power
plants planning to implement CCS technology (NETL, 2009).6 In EOR, research is being
done on how to simultaneously optimize CO2 storage and oil recovery, which may increase
the amount of CO2 stored as well as the amount of oil recovered (Kovscek and Cakici, 2005;
Kovscek and Wang, 2005). Two other areas of research indicate that depleted oil reservoirs
and their surrounding geology could contain even more recoverable oil than once thought,
potentially adding sequestration potential as well. One is the discovery of residual oil zones
(ROZ’s) linked to existing depleted oil reservoirs that could use CO2 floods to produce oil
above and beyond the oil available in the main play (Melzer, 2010). The other is a recent line
of research by Han and McPherson (2009) indicating that saline aquifers in the saline-only
section below the oil-water contact underlying reservoirs may make good sequestration sites.
Our pipeline model rests on estimates that are less certain than what one might normally
see in an academic study. We have done our best to answer three broad questions based on
existing studies and existing development: What is the total potential demand for CO2 from
state-wide enhanced hydro-carbon recovery? At what rate will the demand come on line
(all at once or over a period of time) given a surge in supply driven by capture mandates?
And what will the geographic distribution of this demand be as it comes on line? And we
have done our best to follow the existing literature in estimating CO2 injection rates (which
impact pipeline flow rates) and total reservoir (sink) capacity. But we don’t know how
accurate our predicted injection rates will prove to be. Nor do we know the extent to which
injection rates will be volatile or relatively stable. Nor do we know what the true capacity
of reservoirs is, both individually and in aggregate. Finally, we don’t know whether CCS
will ever be economically viable, a condition largely determined by government policy and
partly by the pace of technological innovation.
5This calculation excludes a ramp-up period during which time the pipeline would operate at less thanfull capacity.
6Also see MIT’s Carbon Capture & Sequestration Technologies home page, http://sequestration.mit.edu/,for access to links and other sources of information about international CCS projects and studies.
4
Uncertainties of such magnitude render our pipeline network somewhat of a thought
experiment. As such, limits must be set. Our interpretation of EOR and ECBM production
potential and storage capacity is taken strictly from published research or from industry
presentations delivered at EOR and CCS conferences. Where estimates among different
strands of research vary, notably in the simulations of ECBM in Powder River Basin coals,
we strive to present and summarize the available research to allow one to use their own
judgement when considering the wide range of estimates for CO2 demand and methane
recovery. Hence we believe our thought experiment to be a relatively informed view—and
perhaps even a moderately sound prediction—of one possible carbon management scenario
out of the many currently facing Wyoming’s and the nation’s policy makers.
1.2 Previous Research
Other CO2 pipeline networks for Wyoming have been proposed. Reyes (2009) and Jeffries
(2009) have calculated capital costs and associated tariffs for a pipeline network they designed
that incorporates existing anthropogenic sources but targets only EOR fields. Surdam (2010)
presents an in depth evaluation of the Rock Springs uplift for geosequestration. He also
estimates that Powder River Basin EOR operations would require a total of 1.2 Tcf of
CO2 and could produce 120 million barrels of oil more than without a supply of CO2.
He posits that a likely source for CO2 would be coal-to-liquids plants situated relatively
close to the oil fields in need of the CO2. This type of arrangement would lessen the need
for a large trunk line into the basin as we have designed it. Smart and Helmke (2009)
created a test CCS model that captures CO2 from the flue gas of power plants in Wyoming
and Montana for sequestration in the Northern House Creek Field in the Powder River
Basin and in the Moxa Arch anticline near La Barge, Wyoming. As did we, they used
ARC-GIS to design the pipeline route and calculate distance. Their GIS program includes
an interactive component and access to extensive source, sink, geologic and infrastructure
data.7 They used a spreadsheet model to calculate diameters and costs. And they calculated
7See Helmke (2008) as well as the website http://bsi1.msu.montana.edu/CarbonAtlas22/index.html toview the results of her efforts.
5
capture costs and injection costs as well as transportation costs in order to estimate a supply
curve for CO2. However they considered only one field in the Powder River Basin as a
target for CO2, whereas we develop a network of trunk lines connecting several sources with
multiple potential sinks. Finally, MIT’s Energy Lab, a department of their Carbon Capture
& Sequestration Technologies program, has created a comprehensive suite of tools for ARC-
GIS software that allow a user to create a CCS system with a pipeline transportation network
and evaluate its economics.8 However we were unable to get the program to work, perhaps
due to user error.
Meanwhile private efforts to secure new sources of CO2 and link them to EOR fields
are well under way. Denbury is leading the way with plans for the Greencore line (Denbury,
2010). They have completed much (if not all) of the permitting and right of way acquisition
for a 232 mile 20” line that would initially connect the Lost Cabin gas processing facility in
central Wyoming with the Bell Creek Field just over the border in southeastern Montana
(but within the Powder River Basin). According to the report, the line would eventually
incorporate CO2 from a coal to fuels plant near Medicine Bow and an underground coal
gasification facility in the Powder River Basin as well as tap CO2 from the Anadarko line
that currently supplies Salt Creek. One proposed extension would link the Lost Cabin facility
to the Elk Basin field in the Bighorn Basin. Another would extend the Bell Creek line to
the Cedar Creek Anticline in eastern Montana. The line would have an ultimate capacity of
725 MMcfpd.
IPCC (2005) and INGAA (2009) are excellent resources both for a broad overview of
CCS and for estimates of CCS infrastructure costs. Haszeldine (2009) is a condensed source
of information on CCS and presents estimated timelines for the implementation of various
technologies. McCollum and Ogden (2006), McCoy and Rubin (2008), McCoy (2008), Parker
(2000), Vandeginste and Piessens (2008), and Robertson (2009) present more detailed studies
of CO2 transport by pipeline and the costs it entails.
8See the website http://e40-hjh-server1.mit.edu/energylab/wikka.php?wakka=MIT for the program anddocumentation. The home page for MIT’s CC&S Technologies program, http://sequestration.mit.edu/,provides access to comprehensive information about CCS projects and studies from around the world.
6
1.3 Overview and Organization
Chapter 2 details the calculations of CO2 supply and demand across Wyoming. Chapter
3 lays out the pipeline network and details the calculations used to estimate diameters.
Chapter 4 presents the cost estimation model by which we generate the capital cost for each
segment of line, the sum of which is the capital cost (or land construction cost) for the
entire pipeline. Chapter 5 describes our calculation of the tariff required to earn an industry
standard return based on a 30-year operational life expectancy. Chapter 6 concludes the
paper.
7
Chapter 2
CO2 Supply and Demand
2.1 Sources of CO2 Demand
As previously mentioned, CO2 is required for EOR and ECBM. In EOR CO2 is a primary
component of miscible floods that mobilize oil stranded in place, allowing it to be flushed out
of extractor wells. Much of the CO2 resurfaces with the oil and is captured, separated and
recycled in subsequent floods. But eventually as much as 50% of the total amount injected
may remain sequestered (Evans, 2009; Melzer, 2009). CO2 EOR has been practiced since the
1980’s in Wyoming and Texas with the most widespread use occurring in the Permian Basin
of Texas. It is commercially viable even at relatively low oil prices. Moreover, simulation and
screening models have become ever more sophisticated and ever more accurate at determining
which reservoirs will respond favorably to CO2 floods and at predicting how much more oil
a field will produce using EOR. The basic process is shown in figure 2.1.
ECBM involves injecting CO2 into unminable coal seams where the coal undergoes
a chemical process adsorbing (bonding with) CO2 and desorbing (letting go of) methane.
CO2 remains sequestered within the coal seam while helping produce incrementally more
methane compared to traditional pressure depletion coalbed methane recovery (CBM). There
are currently no commercial ECBM operations in existence, but several pilot projects have
demonstrated the potential for enhancing methane production through the injection of CO2
while attaining sequestration rates of nearly 100% (Gunter et al., 2002; Pekot and Reeves,
8
2002; van Bergen et al., 2001; Wong et al., 2007). The interaction between CO2, coal and
methane (CH4) is shown in figure 2.7.
2.1.1 Enhanced Oil Recovery (EOR)
The most certain and immediate demand for CO2 in Wyoming is for use in enhanced oil
recovery (EOR).1 EOR has been practiced for over four decades, and the Lost Soldier and
Tensleep fields in Wyoming have used CO2 for EOR production since the late 1980’s. As
a result the process is relatively well understood, substantial infrastructure is already in
place, and a regulatory framework is in existence, all of which create a stable environment
for continued EOR development.
In the production of oil, primary and secondary recovery efforts typically recover about
45% of the original oil in place (OOIP). One of the best known and most successful methods
for recovering some of the remaining 60-65% is to use CO2 in miscible floods whereby the
injection of 95% pure CO2 mobilizes in situ oil. Slugs of water injected after each slug of
CO2 force the mix of water, CO2 and oil to producer wells where it is brought to the surface
and separated. CO2 floods enable operators to retrieve upwards of 15% more OOIP.2
Currently about 3.3% of the world’s oil is produced from EOR, and some in the industry
forecast this will increase to around 45% by 2020 (Brown, 2009). The U.S. is the world’s
leader in EOR with production rates of around 250,000 bbl per day and total CO2 consump-
tion rates of around 3 Bcfpd. The U.S. Department of Energy estimates nation-wide EOR
reserves of 89 billion bbls (DOE, 2011a). The Permian Basin in west Texas accounts for 1.1
to 1.3 Bcfpd of EOR-based CO2 consumption, while Wyoming accounts for ∼340 MMcfpd
1CO2 EOR is one method for recovering a portion of a reservoir’s original oil in place (OOIP) duringthe third phase of a field’s operating life, commonly known as tertiary production. The first phase, primaryproduction, entails pumping oil to the surface that has been driven to the well bore by the reservoir’s naturalpressure. The second phase, secondary production, entails injecting water or gas to displace oil and drive itto the producing well. The third phase, tertiary production, entails mobilizing as much of the remaining oil(what’s known as the stranded oil) as possible through the use of heat (steam injection), chemicals (chemicalinjection) or gasses that dissolve in the oil while helping push it to the well bore (nitrogen and/or CO2
injection). In this paper, when we refer to EOR, we are referring strictly to the method that uses CO2 tomobilize stranded oil.
2See van ’t Veld and Phillips (2010), Mason and van ’t Veld (2011), Cook (2010) and Alleman (2011)for helpful overviews of how CO2 is used in miscible floods and figure 2.1 for a simplified illustration of theprocess.
9
Figure 2.1: Stylized example of enhanced oil recovery using CO2.Source: Alleman (2011)
(Doll et al., 2009). However, if screening models created by van ’t Veld and Phillips (2010)
are accurate, Wyoming could see its share of CO2 consumption increase to over 720 MMcfpd
based on average injection rates for over the first four years of statewide EOR production
given an oil price of $70/bbl and CO2 price of $2.25.
The following analysis relies heavily on extensive research carried out by Professors
Klaas van’t Veld, Chuck Mason and Owen R. Phillips, as well as PhD student Ben Cook at
the University of Wyoming Department of Finance and Economics, results of which can be
found in van ’t Veld and Phillips (2010), Cook (2009a) and Cook (2010). In brief, the original
screening model developed by van ’t Veld and Phillips (2010) and Cook (2010) required that
field-reservoir combinations (FRC’s) be large enough in scale to take on substantial up front
investment in EOR infrastructure (cumulative production of at least 5 MMbo through the
end of 2005) and have a complete set of production and reservoir data.3 Of these FRC’s,
3Typically one field accesses more than one reservoir. Each reservoir is usually named after the rockformation in which it is contained. Reservoirs with the same name, such as the Madison (a limestone that
10
those with geophysical properties unsuitable for EOR (for example minimum miscibility
pressures greater than the reservoir’s fracture pressure) were eliminated. The remaining
FRC’s were evaluated according to the available data in order to ascertain their production
potential, rate of production, total demand for CO2 and rate of demand for CO2. Based
on the assumptions made by van ’t Veld and Phillips (2010) and Cook (2009a), within
the state’s four primary oil-producing basins—the Powder River, Wind River, Big Horn,
and Green River Basins (PRB, WRB, BHB and GRB respectively)—there are around 100
FRC’s that meet basic screening criteria for EOR.4
Five fields (the Lost Soldier and Tensleep fields—aka Lost Soldier/Wertz—operated by
Merit Energy Company, the Salt Creek and Monell fields operated by Anadarko Petroleum
Company, and the Beaver Creek Field operated by Devon Energy) are already in production.
A sixth field, the Grieve, will begin EOR production in the near future (Fugleberg, 2011a).
These fields rely solely on CO2 produced from a gas stream near La Barge, Wyoming that is
exploited by ExxonMobil for helium and methane as well as CO2. The stream is separated
at the Shute Creek processing facility before being shipped via pipeline to the aforemen-
tioned FRC’s (except for 50 MMcfpd of which that is shipped south to Rangely, Colorado)
(Nevarez, 2009; Page, 2009; Peterson, 2009). Using this CO2, Salt Creek currently produces
∼8,000 bopd while the Lost Soldier/Wertz and Beaver Creek together produce another
∼1,700 bopd (Doll et al., 2009; Page, 2009). Having been in production and undergone
extensive geophysical mapping, operators of these fields have constructed detailed models of
the reservoirs undergoing CO2 floods, and their predictions of future response curves to CO2
flooding are relatively reliable. However the oil, water and gas production and CO2 injection
curves for all other FRC’s, upon which we base our estimates of CO2 demand, can only be
can be oil-bearing given the right geologic structure), may appear in several different fields, each field havinga distinct name. Hence van ’t Veld and Phillips (2010) screened each field-reservoir combination (FRC)as opposed to screening just the field. For example, the Wind River Basin (WRB) field, labeled as SaltCreek East in Cook (2009a), accesses four reservoirs: the Tensleep, Wall Creek, Wall Creek 2 and Muddy.Each reservoir represents a separate stratigraphic layer (aka horizon) of oil-bearing rock. The Salt CreekEast-Tensleep is the FRC representing the Tensleep reservoir (rock layer/horizon) in the Salt Creek Eastfield. In the WRB the Tensleep layer is also an oil-bearing horizon in the Steamboat Butte, Big Sand Draw,Teapot Naval Reserve and Sheldon fields. See figure 2.2 for a graphical example of the Lost-Soldier fieldwith its associated reservoirs.
4See Cook (2010); van ’t Veld and Phillips (2010) and Kinder Morgan (2009) for a more in-depth reviewof the criteria used to screen FRC’s for EOR viability.
11
Figure 2.2: The Lost Soldier field with its three producing horizons—the Tensleep, Madisonand Cambrian—would be listed as three separate FRC’s in (Cook, 2009a).
Source: Eves and Nevarez (2009)
estimated from existing literature.
Three broad sources of uncertainty impact estimations of final overall EOR CO2 demand
as well as the rate of demand (which ultimately determines pipeline diameter): CO2 and
oil price fluctuations, how closely the screening model matches reality (which is related to
geologic factors), and the rate at which EOR projects would come on line given access to
CO2.
Van ’t Veld and Phillips (2010) address price uncertainty by analyzing 300 CO2 purchase
contracts from Permian Basin operations and finds that CO2 prices are linked to the price
of oil according to the approximate relationship
PCO2 = $0.50 + 0.025 ∗ Pbbl (2.1)
where PCO2 is the price per mcf for CO2 and Pbbl is the price of oil per barrel. As the
price of oil rises, contract prices for CO2 are revised upwards, typically on a quarterly basis.
12
Rents collected from higher oil prices are therefore shared by CO2 and EOR producers while
downside risk is hedged for EOR operations (van ’t Veld and Phillips, 2010). Incidentally, as
oil prices rise, the demand for CO2 continues to increase despite the associated increase in
the price of CO2 (van ’t Veld and Phillips, 2010). Under our assumption of a CCS regime,
it’s worth pondering whether this relationship would still hold. Indeed the laboratory work
of Cook (2010) indicates it may not.
Cook (2010) uses Monte Carlo analysis to extend van ’t Veld and Phillips (2010)’s work.
He assigns probability distributions to OOIP, injectivity, maximum well-pattern spacing and
incremental EOR oil response, then traces out CO2 ‘demand’ and incremental oil supply
curves along with 95% confidence bands based on the results from 500 simulations when
the four variables are allowed to vary independently.5 Only those results that meet a 20%
internal rate of return (IRR) are included in calculating the totals. The results of the Monte
Carlo simulation show that aggregate demand over all four basins given $3.00/mcf for CO2
amounts to 6.4 Tcf with a 95% confidence interval from 5.4-7.2 Tcf. While 1.8 Tcf variation
in cumulative demand is substantial, our use of the average demand over the first four years
of operation dilutes the overall impact of changes in oil and/or CO2 prices. Furthermore
Cook (2010)’s Table 42 indicates that if prices for CO2 were to fall, as they may under a
scenario in which emitters are forced by regulations to seek sequestration providers, CO2
demand would increase by no more than 11%. Finally, the incremental oil supply curves
generated by Cook (2010) show that the bulk of EOR activity will be undertaken at oil
prices below $100/bbl.6
The upshot for EOR CO2 demand in Wyoming is that most screened EOR operations
are profitable at prices well below $100 per bbo and that higher prices will not substantially
impact the rates of demand that we use to calculate pipeline diameters (see figure 2.6). What
5The quotation marks around demand are there because the curves are not true demand curves that showhow much CO2 would be demanded at a given price at a point in time. Rather they show the cumulativedemand over a variable period of time (determined by the operating life-span of the projects) when the priceof oil is known and follows a fixed path over that period of time.
6When Cook (2010) takes into consideration variability in oil prices as well as uncertainty surroundinginjectivity and oil response curves, he finds that Wyoming’s overall EOR potential is 768.03 MMbo with a95% confidence interval of 554-931 MMbo. But he adds that achieving that potential requires between 3,788and 6,608 Bcf of injectable CO2, an amount well above currently available supply.
13
increases in demand that do result from oil prices above $100/bbl could likely be met by
relatively low-cost increases in pump capacity at key points in the line. Hence it’s plausible
to assume that even if oil prices remain well above $70/barrel our pipeline design would
change little if at all.
The second source of uncertainty is hard to avoid. Geologic factors (structure, rock type,
reservoir thickness, reservoir depth, fault patterns, etc.) vary among FRC’s, and a field’s
production rates can be heavily dependent on these characteristics (e.g. the discovery that
a fault had been mis-mapped enabled Devon to substantially improve the efficiency of their
CO2 flood in the Beaver Creek field by reconfiguring the field to match the fault’s actual
location (Chodur, 2010)). The analog model assumes that, not withstanding such differences,
what ultimately drives a field’s suitability for EOR are injectivity and the hydrocarbon pore
volume (HCPV) accessed by a single well pattern.7
Fundamental differences in geology between fields may cause injection and production
forecasts based solely on differences in HCPV and injectivity to differ substantially from
actual injection and production curves. EOR operators themselves are best equipped to
evaluate the true potential of their FRC’s, and even they undergo some trial and error in
optimizing injection versus field response as field development progresses from phase to phase.
Hence we are comfortable noting this uncertainty but consider the screening model to provide
suitable guidance for the level of generality at which we are working in the construction of
our pipeline model.
A third significant source of uncertainty is related to the timing and pace of EOR de-
velopment. Pipeline capacity (dictated by pipeline diameter) must be able to accommodate
aggregate peak demand, and peak demand is driven by the extent to which the peak de-
mands of individual fields occur at the same or similar times. As can be seen from figure
2.4, demand is high early in the process then drops off quickly as increasing amounts of CO2
are recycled. In other words, predicted CO2 injection remains constant, but CO2 purchases
decrease rapidly after CO2 flooding is initiated.
7A well pattern is typically a square cluster of five wells with one at each corner and one in the middle.The wells at the corners are used for injection while the one at the center is used for production. See Figure2.3.
14
Figure 2.3: Example of a five-spot well pattern for ECBM production. Five-spot well pat-terns for EOR would have the same arrangment.
Source: Oldenburg and Benson (2001)
If federal energy policy implemented CCS requirements and/or financial incentives for
CCS, then within a relatively short amount of time a large supply of CO2 could become
available, possibly creating an environment in which all viable EOR fields would try to enter
production at nearly the same time. This could result in a jump in CO2 demand.8 The peak
flows demanded under such a jump scenario would require pipeline diameters well in excess
of those needed to supply CO2 under a more paced scenario.
Fortunately there is reason to believe CO2 demand would be spread out over several
years rather than spike over a much shorter period of time. Switching to CO2 floods requires
extensive screening, modeling and planning as well as time and capital (i.e. construction
of spur pipelines, drilling of new wells and reconfiguring existing wells), all of which takes
time. Partly for this reason fields tend to enter EOR production in phases. Salt Creek, for
example, is being developed in 16 phases. Six have been completed over a six year time
span, and a seventh is now underway.9 Given the time to switch an FRC to CO2-EOR as
well as the phasing of field development, it’s unlikely that all screened FRC’s would attain
peak levels of CO2 demand within a relatively short span of time. Cook (2009a) breaks
CO2 injection rates into an initial average over the first four years of operation and a second
8Cook (2010)’s research supports this line of thinking—see Chapter 1 of his paper.9As of June, 2010 Anadarko was injecting 420 MMcfpd of CO2 through 338 injection wells to produce
more than 1,000 BOPD. CO2 injection began at Salt Creek in 2004 (Roux and Anderson, 2010).
15
Figure 2.4: Sample injection and recovery history for the Lance Creek/Leo FRC.Source: Phillips et al. (2009)
average over the remaining operating life of a field up to thirty years. Given these choices,
calculating EOR demand based on the average injectivity over the first four years seems to
strike the best balance between accommodating potential high peak flows related to a jump
condition and the time-intensive requirements of switching to CO2-EOR production.
Evidence from the field also indicates that estimating demand based on the injection
rates over the first four years of operation may lead to demand calculations that are on the
low side. According to Cook (2009a), the Salt Creek field would have an average demand
over 4 years of ∼65 MMcfpd while in fact they are purchasing 110 MMcfpd, and the Beaver
Creek field would demand 7.1 MMcfpd over the first four years of operation while in fact
they have contracted for 40 MMcfpd (Reyes, 2009).
Beaver Creek poses an interesting example of the difficulty in estimating pipeline ca-
pacity based on CO2 purchases. Prior to initiation of EOR, reservoir engineers may have
forecasted that they would need to inject as much as 40 MMcfpd at some point during EOR
16
production—perhaps for only a few days, perhaps longer—in order to assure a relatively
high probability of success. Indeed Watson (2010) shows that cumulative CO2 purchases
for the Lost Soldier/Wertz FRC (aka Bairoil) from January, 1986 through December, 2009
amounted to around 310 Bcf, which averages to a flow rate of around 35 MMcfpd—see figure
2.5. Actual daily purchases, however, vary widely. In the first four years of EOR, peak daily
purchases sometimes amounted to seven or eight times the lowest daily purchases. In the
ten years leading up to 2010, purchases were substantially smoother, but on more than one
occasion they dropped by half.10
It seems unlikely that the operators of the Beaver Creek EOR project would purchase
40 MMcfpd if indeed average injection rates were as low as 7.1 MMcfpd. The additional cost
incurred by purchasing 40 MMcfpd when injectivity averages only 7.1 MMcfpd is substantial
(e.g. after four years Devon would have purchased around 48,000 MMcf more CO2 than if
purchases averaged 7.1 MMcfpd, and the additional cost would be around $108 million given
a price of $2.25 per mcf). One could assume, then, that actual injectivity is probably higher
than 7.1 MMcfpd. One upshot is that calculating EOR demand based on average forecast
injectivity rates over thirty years would almost surely underestimate demand, and perhaps
by an egregious amount.
All things considered, the above analysis leads us to believe that the average injection
rate over the first four years as forecast by the screening model is the best choice for es-
timating mass flows through the pipeline that are sufficient to meet EOR demand. The
daily average demand over the first four years balances the front-loading of CO2 purchases
with the phasing in of EOR production that is likely to occur if supplies of CO2 are made
available. The column in Cook (2009a) that contains these estimates is the sixth column
from the left.
Finally, one more uncertainty in our calculations is related to the potential difference
between EOR as it is practiced now and as it would be practiced under a CCS scenario.
Currently EOR operators incur a cost for the CO2 they purchase, leading them to optimize
by minimizing the ratio of CO2 injected to oil produced. However if CCS were required,
10Using the four-year average injection rate in Cook (2009a), the combined CO2 demand from all producinghorizons in the Lost Soldier/Wertz would amount to ∼61 MMcfpd.
17
Figure 2.5: CO2 daily and cumulative purchases for the Lost Solder/Tensleep FRC.Source: Watson (2010)
there may be an incentive to inject and sequester higher volumes of CO2 while maximizing
oil recovery. This so-called ‘cooptimization’ process is detailed by Kovscek and Wang (2005),
Kovscek and Cakici (2005) and Han and McPherson (2009). If indeed this is the case, our
estimates of both CO2 demand and EOR oil production could be on the low side.
The screening model used by van ’t Veld and Phillips (2010) and developed by Cook
(2009a) identifies profitable FRC’s and estimates production curves given a range of oil and
CO2 prices. For rough calculations, we use the estimated oil production and CO2 demand
curves given an oil price of $70/barrel and a CO2 price of $2.25 per mcf (∼$40 per tonne),
and we assume that oil and CO2 prices remain constant over the life of the project.11 The
assumed internal rate of return used to define profitable FRC’s is 20%. The demand resulting
from their screening model is listed in table (2.2). By basin, EOR operations would demand
on average 115.3, 146.2, 368.0 and 89.7 MMcfpd for the Powder River, Wind River, Big
11According to industry sources, a rough rule of thumb is that one barrel of oil requires between 5 and 10mcf CO2 and that over the life of an EOR operation 30-40% of the CO2 will ultimately remain sequestered.
18
Horn, and Green River Basins respectively (Cook, 2009a). Excluding the demand from Salt
Creek, Lost Soldier/Wertz and Beaver Creek that is already being met by CO2 from Shute
Creek, the estimated demand for the four basins would be 115.3, 74.3, 368, and 28.6 MMcfpd
respectively. Finally, the screening model indicates that CO2 demand for EOR would not
increase significantly even for oil prices around $120 per bbo (figure 2.6).12
Table 2.1: EOR CO2 demand: By basin given prices of $70/bbl for oil and $2.25/mcf forCO2 — cumulative and in terms of mass flow rates (Cook, 2009a).
Basin: CumulativeCO2 De-mand (Bcf)
Avg. CO2 De-mand years 1-4(MMcfpd)
PV of Prof-its ($MM)
IncrementalOil Prod.(MMbo)
Big Horn 1,476.3 368.0 1,372.7 191.7Green River 335.4 89.7 1,015.0 92.8GreenRiver*
127.7 28.6 204.5 36.6
PowderRiver
392.5 115.3 368.5 61.1
Wind River 466.3 146.2 844.4 71.3WindRiver*
290 74.3 410.9 42.7
Statewide 2,760.5 719.2 3,513.8 416.9Statewide* 2,286.5 586.2 2,356.6 332.1*Estimates excluding demand from FRC’s for which pipelines are already in service.
Using GIS data from WOGCC (2010b), we include all the FRC’s listed in Cook (2009a)
on the pipeline maps shown in figures 3.2 through 3.9. Field locations are represented by
an oil drop and labeled with their respective field name except on the statewide map (figure
3.2) where the labels have been removed to avoid clutter.
12Mason and van ’t Veld (2011) extend the research done by van ’t Veld and Phillips (2010) and createEOR demand curves for CO2 in the Powder River and Green River Basins given low, reference and high oilprices as listed in forecasts made by the Energy Information Agency in 2009. They find that at our assumedprice of $2.25 per mcf (∼$40 per tonne), EOR demand for the Powder River Basin and Green River Basinwould be about 2.1 Mt per year (∼104 MMcfpd) and 0.35 Mt per year (∼18 MMcfpd) respectively. As canbe seen in Table 2.1, these are close to our estimates and therefore do not materially impact our pipelinediameter calculations.
19
70 100 1200
50
100
150
200
250
300
350
400
450
Oil Price ($/bo)
CO
2 de
man
d (M
Mcf
pd)
BHB
GRB
PRB
WRB
Figure 2.6: CO2 demand by basin for oil prices of $70, $100 and $120 per barrel.Source: Cook (2009a)
2.1.2 Enhanced Coalbed Methane Recovery (ECBM)
The second type of enhanced hydro-carbon recovery that requires CO2 is ECBM. Much of
the world’s supply of coal resides in seams that are too deep or too thin to mine economi-
cally. In ECBM CO2 is injected into such ‘unminable’ coal seams where it chemically adsorbs
to the coal matrix, displacing residual methane molecules. The CO2 remains sequestered
while the displaced methane increases cumulative recovery rates relative to those attainable
through standard coalbed methane recovery (aka primary pressure depletion). ECBM is also
a highly effective means of sequestering CO2. Nationwide, DOE Regional Carbon Sequestra-
tion Partnerships (RCSP’s) have identified a potential storage capacity of 60 to 117 billion
tonnes in unminable coal seams (NETL, 2010). Figure 2.7 shows the basic process by which
20
coal adsorbs CO2 and desorbs methane.
Figure 2.7: In-seam process by which enhanced coalbed methane recovery sequesters CO2
and produces incrementally more methane (CH4).Source: Chai and Shimada (2010)
While ECBM is not currently practiced on a commercial scale, several pilot studies have
demonstrated its feasibility, and more are underway.13 One of the larger pilots completed
to date, and one of the richest sources of data, comes from a study done on the Allison
unit in northern New Mexico (Taillefert and Reeves, 2003). Smaller pilots in the U.S. have
been undertaken in Illinois (Frailey, 2011), Alabama (Pashin, 2011), Virginia (Ripepi and
Carpenter, 2011), New Mexico (Grigg and Oudinot, 2011) and North Dakota (Hamling,
2011). Pilots outside the U.S. include the Dayton project in Alberta (Faltinson, 2007; Gunter
et al., 2002), the Qinshui project in Shanxi Province, China (Wong et al., 2007), the Ariake
project in Japan (Fujioka et al., 2010), the Sulcis project in Italy (Amorinao et al., 2005)
13An easy way to scan recently updated news about ECBM and other types of sequestration pilots is togo to the website http://www.coal-seq.com/Tech Transfer.asp and scroll down to forums where there is ahyperlink to Presentations from the Seventh International Forum on Geologic Sequestration of CO2 in CoalSeams and Gas Shale Reservoirs.
21
and the RECOPOL field project in Poland (Bergen et al., 2007).
In Wyoming, the Powder River Basin, Greater Green River Basin and Williston Basin all
have unminable coal seams with theoretical potential for ECBM. The best potential exists in
the Powder River Basin where there are already over 28,000 CBM wells and where extensive
research by Ross et al. (2009), Robertson (2008), Robertson (2009) and Nelson et al. (2005)
have helped reduce the uncertainties surrounding ECBM production. The Green River Basin
is less studied but known to have extensive coal deposits meeting the requirements for ECBM
production. The Williston Basin is less likely to see extensive ECBM activity because of the
type of its coal, and because much of its coal resides at depths that are less than ideal. A
simulation carried out by Robertson (2008) indicated that Williston Basin coals have CO2
injection rates and methane production rates so low as to be relatively unfeasible under
almost any scenario, leading us to eliminate the Williston Basin from our analysis. Perhaps
further analysis will find otherwise.
Sources for estimates of sequestration potential in PRB coals include Ross et al. (2009),
Robertson (2008), Robertson (2009), Nelson et al. (2005), Reeves (2003) and NETL (2010).
Nelson et al. (2005) estimates the CO2 storage capacity in the unminable portion of the
Wyodak Anderson coal zone to be 6.2 billion tonnes. Robertson (2008) estimates the se-
questration potential of unminable coals in the PRB to be ∼5.9 billion tonnes. Robertson
(2009) finds the sequestration potential of unminable PRB coals to be ∼152 billion tonnes
in total (see discussion below regarding this anomalous figure). Reeves (2003) estimates
total storage within the PRB to be 14 billion tonnes. Ross (2007) estimates that unminable
coalbeds in the PRB can sequester 1.3 to 1.8 billion tonnes while Ross et al. (2009) estimate
storage amounts between 1.15 and 1.59 billion tonnes. Finally, estimates compiled by the Big
Sky Carbon Sequestration Partnership and published in NETL (2010) list the total storage
potential in the PRB as 11 billion tonnes and the storage potential of the Green River Basin
as 44 million tonnes.
Due to the wide range of estimates and the subsequent impact on mass flow rates
in a pipeline network (which impact pipeline diameters), we provide a somewhat in-depth
overview of ECBM and the complexities involved in modeling coal and simulating fluid flows
22
in coal seams. In doing so we hope to provide readers with resources to evaluate our estimates
and follow up with their own research.
A good overview of the CBM process is given by Aminian (2005). A more in depth
description is given by White et al. (2005), a summary of which we provide here. Coal is
formed from marsh-like environments rich in organic material. With time and pressure the
organic matter lithifies (a process known as coalification) into a rock containing macerals
(organic matter) and minerals (inorganic matter). It occurs in seams that vary in thick-
ness, spatial distribution, fracture spacing, porosity and permeability (i.e. coal seams are
anisotropic). Coal is dual porosity in that it has micro-pores (tiny pores within the matrix)
and macropores (fractures). Fractures (aka cleats) form during coalification and are of two
types, face cleats that are planar and continuous, and butt cleats that are discontinuous and
perpendicular to face cleats. The spacing, aperture and connectivity of cleats predominantly
dictate a seam’s permeability, which can range from 0.1 to 100 mD (usually 0.1 to 10 mD),
while the orientation of the cleats dictates how well a seam responds to hydraulic fracturing
(discussed below).
Coalbed methane forms when plant matter that has been deposited at rates fast enough
to prevent decay becomes exposed to increasing temperature and pressure during burial. Bio-
genic methane forms as bacteria break down the organic matter, and thermogenic methane
forms later when the coal is buried at greater depths (i.e. greater pressures) and tempera-
tures. Powder River coalbed methane is mostly biogenic in origin.
At equilibrium conditions, the size of the micropores causes methane to exist almost
entirely (∼95-98% by volume) in a chemically bonded (adsorbed) state, typically modeled
as one-layer thick. It is held in place by hydrostatic pressure. Methane desorbs from the
matrix once enough water is pumped from the coal seam to lower the hydrostatic pressure
to the desorption point (the critical desorption pressure). Desorbed methane molecules flow
through coal first by diffusion through the micropores of the matrix according to Fick’s Law of
Diffusion and then through the fracture system (cleats) via laminar flow according to Darcy’s
Law. The economic viability of CBM and/or ECBM depends on the total gas-in-place and
the ability of the gas to travel through the seam to a producing well (gas deliverability).
23
The first is dictated by coal chemistry and porosity while the second is dictated largely by
permeability.
The surface area of the micro pores and the confining hydrostatic pressure are the
primary factors controlling carbon dioxide and methane storage volumes (adsorption capac-
ities). Adsorption capacities are modeled using Langmuir monolayer desorption and adsorp-
tion isotherms. The shape of the isotherm is governed by two parameters, the Langmuir
pressure and Langmuir volume, both of which are determined through laboratory analysis
of a sample of coal. The Langmuir volume is the maximum amount of gas storage capacity
at infinite pressure, and the Langmuir pressure is the pressure at which gas storage capacity
is one half the Langmuir volume (see figure (A.3) for an example). Gas storage capacity
increases with Langmuir volume and decreases with Langmuir pressure according to the
Langmuir equation.14
Finally, coal is not a rigid solid, it shrinks with the desorption of water and hydrocarbon
molecules and swells with the adsorption of gas molecules (White et al., 2005). This so-called
matrix shrinking and swelling can impact ECBM injectivity and recovery rates. As methane
is depleted in a reservoir, matrix shrinkage causes a dramatic increase in permeability (Palmer
et al., 2006). On the other hand, injection of CO2 causes matrix swelling and a decrease in
permeability (Pekot and Reeves, 2002).
In ECBM, injected CO2 molecules preferentially displace CH4 molecules. The displace-
ment ratio depends on the rank of the coal. In high rank anthracite coal the ratio of CO2
to CH4 molecules is around 2:1, while in the low rank sub-bituminous coals of the PRB the
ratio is between 7:1 and 10:1. Incremental methane production increases as the injected
CO2 plume comes into contact with an ever wider expanse of coal. Eventually the CO2
reaches the producing well, an event known as ‘breakthrough‘. At breakthrough, most of
the incremental methane has been produced, and CO2 is following pathways along which
most molecular displacement has already occurred. The percentage of the coal seam that
actually comes into contact with the CO2, what’s known as sweep efficiency, matters greatly.
Methods such as hydraulic fracturing, horizontal drilling and alternating slugs of nitrogen
14See Aminian (2005) for a general Langmuir Equation and Ross (2007) for a more detailed extendedLangmuir equation that is more commonly used in reservoir simulation software.
24
with slugs of CO2 can be used to increase sweep efficiency. Differences in simulation results
can reflect differences in how the models handle sweep efficiency, so it is important to pay
attention to how the CO2 is delivered to the target coal seam.
Results from the pilot projects mentioned above indicate that CO2 storage in coal is
feasible in the short term, although MVA efforts are still underway at most pilot projects
to verify that CO2 stored in coal seams is also stable in the long-term. Results furthermore
indicate that injecting CO2 enhances the recovery of methane in every instance, though in
varying amounts. Fujioka et al. (2010) reports that injecting CO2 enhanced the recovery
of methane to rates of about five times pre-injection rates. But Grigg and Oudinot (2011)
found that methane recovery rates increased barely at all (26 MMcf increase out of total
production of 18,390 MMcf).
The pilots also reveal that losses in injectivity rates due to matrix swelling are indeed
a universal phenomenon but that the effect varies. In terms of PRB coals, Ross (2007);
Ross et al. (2009) find that the net matrix shrinkage in their simulation leads to a 10%
reduction in injectivity, but they add that matrix shrinkage can be mitigated by hydraulic
fracturing, (aka fracking).15 In the Allison unit pilot project, Reeves et al. (2003) found
‘clear evidence of significant coal permeability reduction with CO2 injection’ but did not try
to mitigate the permeability reduction with hydraulic fracturing. Data from other pilots
indicates that the loss of permeability can be mitigated by strategic placement of producers
and injectors relative to the orientation of face and butt cleats in combination with fracking.16
Researchers have also partially offset the injectivity losses associated with matrix swelling
through refinements in fracking techniques, adjustments to CO2 injection temperatures and
pressures, and alternating slugs of nitrogen with slugs of CO2. Evidence from the pilot
studies also indicates that multi-seam injection, advanced well completion techniques and
horizontal drilling promise substantially higher injection rates, fewer wells and smaller surface
footprints for large-scale ECBM projects (Hamling, 2011). Finally, research is also underway
on developing and improving mathematical modeling techniques to account for changes in
15Fracking is a widely used procedure that involves injecting water, chemicals and solid particles (propents)under high pressure to enhance CBM recovery by mitigating the permeability reduction that occurs due tosuch matrix swelling and by cleaning wellbores of drilling fines (Colmenares and Zoback, 2007).
16See for example Frailey (2011) and Bergen et al. (2007).
25
coal properties during injection (Koperna, 2011). In general, scientists are optimistic about
the technology and have suggested that larger pilot projects would assure the public and
investors that the technology works (Ripepi and Carpenter, 2011).
The economics of ECBM are driven by the price operators must pay for CO2, the
price they receive for methane, costs surrounding the capture, transportation and injection
process, and the ratio of CO2 injected to the amount of incremental methane extracted.
The amount of incremental methane (CH4) recovered using ECBM versus CBM depends on
adsorption isotherms for CO2 and CH4 and varies according to coal rank. The ratio of CO2
molecules adsorbed to CH4 molecules desorbed within the pore space is dependent on coal
rank. Burrus (2003) found ratios in North Dakota lignites (low ranking) to be greater than
13:1, in PRB subbituminous coals to be between 7:1 and 10:1, and in anthracites (high-
ranking) to be around 2.5:1. Among the subituminous type coals of the PRB, isotherms
vary according to depth, but they are spatially relatively alike (Stricker et al., 2006). Hence
adsorption isotherms found through laboratory analysis of coals in one part of the basin
can be used to estimate adsorption/desorption ratios basin-wide. Note, however, that this
ratio has economic implications for ECBM when an operator must pay for CO2. Since
operators would seek to produce as much methane as possible using as little purchased CO2
as possible, they would seek out higher ranking coals, making PRB and GRB coals less
attractive as targets for ECBM under a non-sequestration policy scheme.
Another factor with significant economic implications is the amount of incremental
recovery an ECBM operator can expect versus traditional CBM pressure depletion. The
more incremental recovery, the better the odds an operator will be able to cover the capital
and operating costs of ECBM. Simulations on PRB coals by Robertson (2009) indicate that at
average unminable depths methane recovery rates using ECBM are around 1.35 times those
of recovery rates using traditional pressure depletion. Simulations by Ross et al. (2009) on
PRB coals find the ratio to be 1.5 to 5. They find that hydraulic fracturing plays the largest
role in determining the final ratio, but that bottom-hole pressures and the orientation of
least principal stress also have an affect.
Of the pilot projects completed to date, none found that ECBM was economical given
26
Figure 2.8: Example of how CBM recovery occurs. For a coal seam with initial conditionsP = 1200 psia and gas content = 308 scf/ton, water must be pumped until the pressureis reduced to the critical desorption pressure at around 275 psia at which point desorptionoccurs and gas is produced.
Source: Aminian (2005)
current prices for CO2 and methane. And when the costs of capture are included (in an
ECBM CCS scheme) the economics became even less attractive. Reeves and Oudinot (2005)
found that ECBM in the Allison unit was uneconomical at gas prices around $2.20/mcf but
became economical at gas prices higher than $2.57 (based on a price for CO2 of $0.30/mcf
(∼$5.88/tonne)).17 On the other hand, simulations based on models built using data from
the Qinshui pilot found that a 90-well ECBM project with CCS, using CO2 from a fertilizer
plant, was economical based on a price for CO2 of $11.98 per tonne (∼$0.67/mcf) (Deng
et al., 2008). Yet another economic evaluation of CCS carried out by Robertson (2009), using
CO2 captured from the flue gas stream of a typical coal-fired power plant and transported
80 km via pipeline for use in a hypothetical PRB ECBM project, shows the CCS process
17These prices are in 2003 dollars.
27
(not just the ECBM component) from source to sink to be uneconomical based on a natural
gas price of about $8 per mcf and a transportation tariff of about $0.45 per mcf (∼$8.90 per
tonne). He points out, however, that under the right regulatory environment, CO2 could
attain a price such that CCS with ECBM becomes economically viable. Recent simulations
and calculations carried out by Mason and van ’t Veld (2011) indicate that ECBM is not
profitable if CO2 costs operators more than $2 per tonne. However all projects are profitable
if operators were to receive $3 per tonne. At that price and above (or below, depending on
how you look at it), demand would peak at about 200 Mt CO2 per year or more than 10,000
MMcfpd, a level with dramatic implications for any pipeline network that needs to supply
CO2 to the PRB.18
Powder River Basin
We calculate that if CO2 was attainable at economically viable prices, ECBM in the PRB
could generate demand for another 1.0 to 7.0 billion tonnes of CO2 (31.4 Tcf) depending
on whose coal seam model and simulation results we use, Ross et al. (2009)’s or Robertson
(2008)’s. Even the low estimate amounts to hundreds of millions of cubic feet of CO2, or
thirty-plus years’ worth of the entire state’s annual production of CO2.
In the PRB the Wyodak-Anderson and Big George coal zones, constituents of the
Tongue River Member of the Tertiary Fort Union Formation, produce the vast majority
of CBM. The coals are largely sub-bituminous in nature with a methane content of around
16-76 scf/t (vs. methane content of 150 to 500 scf/t for higher-ranking bituminous and
anthracitic coals in the San Juan Basin) (Ayers, 2002). In 2009 the Wyoming Oil and Gas
Conservation Commission listed 364 CBM-producing reservoirs tapped by 28,531 CBM wells
that produced 550,576,470 mcf of methane (Barclay et al., 2009). The average lifetime of
a CBM well in the PRB is 7-15 years (Ross, 2007). In 2008 methane production from the
Big George was ∼320,893,437 mcf, while production from the Wyodak-Anderson totaled
18In comparison, we design our pipeline around a flow rate into the basin of about 2,410 MMcfpd (requiringa pipeline NPS of 34”). Based on their CO2 demand curve for ECBM, shown in Figure 3, our estimatedCO2 flow rate into the basin (most of which is slated for ECBM) would only be attained when the price forCO2 dropped to around $1.40 per tonne. At the price we assume, $2.25 per tonne, no ECBM activity wouldbe undertaken.
28
∼56,062,922 mcf. Production from all other formations totaled 111,252,435 mcf (Barclay
et al., 2009). Due to the proliferation of wells within the Big George and Wyodak-Anderson
coal zones, ECBM studies on PRB coals have focused on those zones .19
Estimates of the volume of unminable coal range from 197 by Nelson et al. (2005) to
278 billion tonnes (217-307 billion short tons) by Flores and Bader (1999b). Flores and
Bader (1999b) estimate that the total volume of coal in the Wyodak-Anderson formation
ranges from 416 to 582 billion tonnes (459 - 642 billion short tons) with a 90 percent level of
confidence. DeBruin (2001) estimates that gas volumes in PRB coals total 25.2 Tcf, while
the Potential Gas Committee (2006) estimates that volumes are closer to 18.5 Tcf, 4,637
billion cubic feet of which are considered ‘probable.’ According to Shp files constructed by
Ellis et al. (1999b) and evaluated by Driess (2008) using ARC-GIS software, the surface
area of Wyodak-Anderson coals at depths greater than 1,000 ft. amounts to approximately
1,937,487 acres (∼7.841 billion square meters) with an average seam thickness of around 100
ft. (Ellis et al., 1999b; Robertson, 2009).
Total CO2 sequestration and methane production depend on the surface area of un-
minable coals, which dictates how many well-patterns can access the coal, as well as the
total volume of coal and the average seam thickness. The GIS data provided by Ellis et al.
(1999b) amounts to 6,055 320-acre five-spot patterns with 160-acre well spacing, or twice
that number of 160-acre five-spot patterns with 80-acre well spacing.20 However not all of
this acreage is available for new (so-called ‘greenfield’) development. Mason and van ’t Veld
(2011) find that of the unminable coal in the PRB at average depths of 1,750 ft. and 1,250
ft., 97% and 51% respectively is already under CBM production. Thus incremental produc-
tion from wells that switch from CBM to ECBM (so-called ‘brown-field development’), as
19Recent revisions in nomenclature for PRB coals label the Big George as the Smith/Big George coalbed located within the Wyodak Rider coal zone within the Ft. Union Formation. The Anderson Rider,Anderson and Lower Anderson coal beds are located within the Upper Wyodak, right beneath the WyodakRider in the stratigraphic column (Copeland and Ewald, 2008). However in most of the recent literaturethe Smith/Big George and Anderson coal beds have been grouped together as the Wyodak-Anderson, andthe ‘Big George’ coal is where five beds from the Anderson and Canyon coals merged into one, an area ofunminable coal in the central PRB about 950 square-miles in extent (Flores and Bader, 1999b).
20A five-spot pattern is made up of four producing wells, one in each corner, and one injector well inthe center. A 320-acre five-spot pattern of purely producing wells can be converted to a 160 acre five-spotpattern for ECBM production by drilling one injector well in the center of each 320-acre square, resulting inone well per 40 acres.
29
well as incremental production from greenfield ECBM development, must both be considered
when calculating total incremental production from ECBM in the PRB. Although this study
does not include an adjustment for brownfield development, we note that our final estimated
volumes are well within their theoretical upper limit of total potential demand (see Figure
3 of Mason and van ’t Veld (2011)).
We also note that in order for unminable coals in the PRB to be suitable sites for the
permanent sequestration of CO2, they must be completely isolated from aquifers suitable for
human consumption. While much of the CO2 would be adsorbed within the coal matrix, a
substantial amount would remain outside the matrix and form a plume dominated by Darcy
flow. Preliminary simulation results from the Ariake pilot project in Japan calculate that
only about 11 percent of the CO2 would be adsorbed in the short-term (Chai and Shimada,
2010). And simulations done by Ross et al. (2009) on PRB coals also predict that much of
the injected CO2 would form a plume and find its way to the top of the seam within the
operating life of the project. If the seam is not overlain by a competent seal, then indeed
there is a possibility that the CO2 could contaminate nearby aquifers (Zoback et al., 2004).
Evidence from the Powder River Basin indicates that the vast majority of coal seams
that would be targeted for ECBM are overlain by competent seals (Zoback et al., 2004).
As for the water quality in PRB coal seams, Rice et al. (2000) find that while many of the
coal-seam aquifers have levels of salinity and total dissolved solids (TDS) above what are
considered safe levels for drinking, there are some that would be considered safe by state and
federal drinking water standards. However they note that the flow regimes of the aquifers
tend to be towards the center, deeper portions of the basin and away from agricultural
and drinking-water wells. Although in our eyes this issue appears to be unresolved, it has
not prevented researchers from going forward with estimates of CO2 storage potential in the
PRB. We go by the estimates generated by these researches, but we do not deny that ultimate
storage potential may be less than research indicates due to the potential for groundwater
contamination.
Since there have not been ECBM pilot studies on PRB coals, researchers rely on simula-
tions to estimate CO2 sequestration and CH4 production curves. These simulations require
30
the input of several parameters, some of which can only be determined through labora-
tory studies on coal samples extracted as cores from well-bores. Such cores are not readily
available for PRB coals, and certain parameters, such as those for relative permeability and
matrix shrinking and swelling, must be inferred based on information from other coals and
on mathematical models (Ross et al., 2009). The physical characteristics of PRB coals also
vary from area to area, resulting in a fairly wide spread of cleat permeability and porosity
estimates. Predictions of CO2 storage potential vary according to how researchers choose to
address these issues when designing their coal seam models and ECBM simulations.
Reeves (2003) calculates CO2 storage volumes based on estimates of original CBM in
place, applying CO2/CH4 replacement ratios typical of the sub-bituminous type coals that
comprise the Wyodak-Anderson and Big George coal zones to estimate the final storage
capacity. Robertson (2008, 2009) and Ross et al. (2009) use reservoir flow simulation software
to analyze theoretical models of Wyodak-Anderson and Big George coals. They each use
a different flow-simulation software package, and they each use different parameter values
for their coal-seam model. Ross et al. (2009) use the Generalized Equation-of-State Model
Compositional Reservoir Simulator (GEM) while Robertson (2008) and Robertson (2009)
use the Comet3 package developed by Advanced Resources International. According to a
study carried out by Law et al. (2002), the two simulators handle the specific challenges
posed by gas and fluid flow simulation in coal in much the same way, so differences in results
probably arise from model construction and differences in the estimation of parameter values.
Tables showing the parameter values used to construct the models simulated in Robertson
(2008) and Ross et al. (2009) are given in Appendix A.
Robertson (2008) uses a homogenous, single-layer model with parameter values as listed
in A.1, Appendix A. He analyzes a base-case pressure depletion (CBM) scenario to which
he compares an ECBM scenario with CO2 injection started at the initiation of recovery
efforts (time 0). He defines ‘unminable’ as any coal deeper than 1,000 ft (304.8 m.), uses
an average depth of 1500 ft (457.2 m) for the unminable portion of the Wyodak-Anderson,
and an average coal-seam thickness of 100 ft. (30.5 m). He does not model the effects of
fracking. He calculates that one quadrant of a 320-acre five-spot pattern serviced by one
31
injection well would sequester ∼245,000 tonnes (∼270,000 tons) of CO2 with breakthrough
occurring in 6.3 years.21 This amounts to a basin-wide total of ∼6.5 Gt (5.9 billion tonnes)
(Robertson, 2008).
Robertson (2009) uses a one-layer model very similar to Robertson (2008) and analyzes
a base-case conventional pressure depletion CBM operation to which he compares two ECBM
scenarios: one using pure CO2 and one using ‘flue gas’ (a mix of nitrogen and CO2), both
starting injection upon the initiation of recovery efforts. He defines the depth and thickness
of the coal seam and the injection scenario as above and uses a one-layer, homogenous
coal seam with no fracking (Robertson, 2009). Discarding the flue gas scenario due to
insufficient volumes of sequestered CO2, he focuses on pure CO2 injection and finds that
∼6.3 million tonnes (∼6.9 million tons) of CO2 per quadrant is sequestered over 19 years
(when breakthrough occurs and injection ceases) amounting to 27.6 million tons per 320-acre
pattern (1.45 million tons/year/well) (Robertson, 2009). Using the GIS data from Flores and
Bader (1999b), this would amount to a sequestration potential for the entire basin of over
∼151.5 billion tonnes (167 billion tons), which is orders of magnitude greater than estimates
made by Robertson (2008), Ross (2007), Ross et al. (2009), Reeves (2003) and Nelson et al.
(2005).
Ross et al. (2009) conducted a detailed reservoir characterization study and fluid flow
analysis of the Big George coal to investigate the possibility of CO2 migration into overlying
strata. They simulate CO2 injection and CH4 recovery from a multi-layer model developed
using geostatistical techniques to capture the intrinsic heterogeneity of a 16 m (53 ft.) thick
coal seam accessed from a 160 acre 5-spot pattern with 80-acre spacing. Their simulations
include scenarios with and without matrix shrinkage and swelling and with and without a
100 m. horizontal fracture to mimic fracking.
In their base-case simulation, five years of CBM are followed by 13 years of ECBM.
They find that upon breakthrough one 160-acre five-spot pattern would sequester 92,000
- 103,000 tonnes of CO2 per well and produce between 9.5 and 11 million m3 of methane
21Wyoming statutes allow 40-acre spacing for gas wells unless specified otherwise. In the southwesterncorner of the state, township 12 - 28 and Range 89 - 121 are specified as 160-acre spacing (Barclay et al.,2009).
32
(∼310 to 330 MMscf) over a 13-year injection period. Basin-wide storage based on the USGS
GIS data would amount to between 1.15 and 1.28 billion tonnes (Ellis et al., 1999b; Ross
et al., 2009). However, since no data regarding BHP was available for the wells used in their
model, they used a production well BHP of 1700 kPa based on history matching. Lowering
the production well BHP to values typical of BHP’s in the San Juan Basin (∼350 kPa)
would increase CO2 storage to ∼131,000 tonnes (1.59 billion tonnes basinwide) and increase
methane production to ∼17.6 million m3 (622 MMscf).22 For gas in place, Ross et al. (2009)
used a mixture of 90% CH4 and 10% Nitrogen. They note that in reality, the typical gas
content of PRB coals is 72% CH4, 22% N2 and 5% CO2, meaning that their estimates of CO2
storage are upper bounds (i.e. injected CO2 would have 5% fewer pore spaces to occupy,
so breakthrough and the cessation of CO2 injection both occur earlier in the process) (Ross
et al., 2009).
These figures are lower than Robertson’s due to a buoyancy effect picked up by their
multi-layer model. At the depths and pressures considered by Ross, CO2 is lighter than
the surrounding material and flows upwards through the cleats, overriding the bulk of the
methane-bearing coal and dramatically reducing sweep efficiency (Ross et al., 2009).23 Single-
layer models do not capture buoyancy, and as a result they tend to overestimate breakthrough
times and storage volumes (Ross, 2007; Ross et al., 2009).24 Ross et al. (2009) simulated
flows within a single-layer model constructed from the same parameter values as their multi-
22Both the injection and production wells have BHP’s, and each is given a constraint in fluid flow simula-tions. Because the gas is stored by sorption, a low producer well BHP is required to recover a large amount ofthe original gas-in-place. The injector BHP constraint determines the maximum injection rate. Ross (2007)does a sensitivity analysis on the injector BHP constraint and finds that raising it from 4000 to 5000 kParesults in an increase in CO2 sequestration of 105% and an increase in CH4 production of 140%. It alsoreduces breakthrough to 5900 days versus 6720 in the base case. Ross et al. (2009) focus on the productionwell BHP constraint, decreasing it from 1700 to 345 kPa, which results in an increase in CO2 sequestrationof around 125% and an increase in CH4 production of around 172% .
23Ross (2007) estimates that only 25% of the coal volume will come into contact with the CO2. Incidentally,this buoyancy effect also demonstrates why sequestration of CO2 in unminable coal seams requires that theformation be overlain by a competent impermeable layer such as shale or slate. If it weren’t, then CO2 couldseep into overlying formations and possibly contaminate sources of potable water. The majority of coal inthe PRB is in fact overlain by impermeable rock formations (Ross et al., 2009; Zoback et al., 2004).
24Ross et al. (2009) and Ross (2007) are confusing on this point. They claim that the single-layer modelwill ‘underestimate’ breakthrough times. Yet they find that breakthrough for a single-layer model occursin 5420 days versus 2460 days for a six-layer model. Nonetheless, their comparative simulations reflect themuch higher predicted values for CO2 storage in the single-layer case versus the six-layer case.
33
layer model and found that the one-layer model predicted levels of CO2 adsorption 325% -
340% above those predicted by their multi-layer model. They point out that pre-injection
simulations in the RECOPOL pilot project proved erroneous compared to actual results
likely because of the use of a one-layer model (Ross et al., 2009). And Ross (2007) suggests
that ‘it is imperative that....models with fine gridding (i.e. multi-layer models) in the vertical
direction be used,’ to avoid over-estimating quantities of sequestered CO2.
Of the three simulation-based estimates Robertson (2009)’s estimate of 152 billion
tonnes basin-wide storage seems anomalously high. Of the remaining two, Ross et al. (2009)
is the lowest. Their simulation predicts that 5 years of CBM followed by 13 years of ECBM
would sequester around 91,000 tonnes of CO2 per 160-acre 5-spot pattern. This amounts to
a basin-wide total of 1.22 billion tonnes. Robertson (2008)’s is the higher of the two and
predicts that six to seven years of strictly ECBM production would result in the storage of
490,000 tonnes of CO2 per 320-acre 5-spot pattern, amounting to a basin-wide total of ∼5.9
billion tonnes. For the purposes of this paper, we conservatively choose the average of Ross
et al. (2009)’s lower (1.15 billion tonnes) and upper (1.59 billion tonnes) estimates, which
amounts to 1.32 billion tonnes of total storage.
Assuming this amount of cumulative demand for CO2 in the PRB, is it possible that
demand could theoretically outstrip supply? Given the almost right-angled shape of the
demand curve for CO2 in the PRB as shown in Mason and van ’t Veld (2011), this seems to
be a possibility. If Wyoming maintains current levels of fossil fuel-based emissions, and if all
currently known proposed coal to fuels and fertilizer projects come on line, then the state’s
total emissions by the year 2015 could amount to just over 72.6 Mt per year. Subtracting
the four-year average demand for CO2 from EOR would leave 70.7 Mt for ECBM (∼3,600
MMcfpd). Given Ross et al. (2009)’s per-well estimate that one well would sequester 6,629
tonnes of CO2 in one year, 1.32 billion tonnes of total demand would require 9,935 injection
wells coming on line all at once and operating for just under 17.5 years on average. This
would require a mass flow rate of just over ∼65 Mt per year (3,400 MMcfpd), theoretically
a rate that could be met by statewide supply.
Is it reasonable to assume that almost 10,000 wells could feasibly enter production within
34
a relatively short span of time? One would think that constraints, such as permitting time
and infrastructure construction, would constrain the pace of ECBM development, no matter
how low the price for CO2. Data from the Wyoming Oil and Gas Conservation Commission
(WOGCC) provides a rough measure of the potential pace of new well development. In 2008
4,626 CBM wells were permitted in Wyoming, and another 1,395 were issued through July,
2009 (WOGCC, 2009). Given that rate of development, it is reasonable to assume that 5,000
new wells could theoretically come on line every year for 12 years, amounting to 60,000 new
wells, enough to account for the amount of supply generated under a capture mandate.
Finally, how should we calculate the daily average flow-rates that determine pipeline
diameters? Here we undertake a little bit of hand-waving. Our calculation that the total
acreage of unminable coal in the PRB could support as many as 12,110 160-acre 5-spot
patterns implies that the theoretical upper limit on ECBM-related wells is 60,550, including
producers and injectors (roughly half would be injectors). If ECBM production came on
line over a 12-year span at the rate of 5,000 wells per year, and if the wells that came on
line in the last year operated for 17 to 18 years, demand for CO2 would be spread over 30
years. At peak development there would be around 60,000 wells in operation.25 All things
considered, spreading our total estimated demand of 1.32 billion tonnes over 30 years seems
the appropriate choice in terms of what is realistic in the way of well-development rates.
The resulting demand for CO2 in the PRB from theoretical ECBM development amounts to
a mass flow rate of 2,362 MMcfpd.
Green River Basin ECBM
The predominant coal-bearing formations in the Green River Basin are the Paleocene (55.8 -
65.5 million year-old) Fort Union and Eocene (33.9 - 55.8 million year-old) Wasatch Forma-
tions (Flores and Bader, 1999a). The Fort Union Formation contains at least three prominent
coal zones, the Deadman coal zone, found in the lower 200 feet of the formation, and two
unnamed zones. The Deadman coal zone, with beds as thick as 32 feet (∼9.8 m), was de-
posited in swamps associated with meandering rivers (Flores and Bader, 1999a). Due to the
25This is in the ballpark of a prediction made by Stricker et al. (2006) that the PRB would see totaldevelopment of at least 60,000 wells.
35
depositional environment, coal deposits in the Green River Basin are less planar in nature
relative to those of the Powder River Basin, leading to lower overall estimates of total coal
volumes and total CO2 sequestration and CH4 production capacity.
Green River Basin coals have been studied less for ECBM and more for their potential to
supply coal-fired electric power plants, especially the 2,110 Mw Jim Bridger Plant near Rock
Springs. Hence we base estimates of ECBM CO2 sequestration and CH4 recovery potentials
on estimates of CBM potential and use typical CO2/CH4 sequestration/recovery ratios to
calculate daily mass flow rates (Nelson et al., 2005). According to the NETL (2010), total
storage potential of the GRB is 836 Bcf (44 Mt). The Potential Gas Committee (2006)
estimates likely recoverable reserves of CBM within the Greater Green River Basin to be
2.5 Tcf. Given recovery rates of 5% (a reasonable estimate according to Ross (2007)), then
approximately 125 Bcf of methane are available for recovery. Using a conservative ratio of
5:1 for CO2 sequestered to methane recovered, total CO2 storage would be 625 Bcf (31.8
Mt).26 For this level of generality, the two estimates (44 Mt versus 31.8 Mt) are not far off.
Taking the higher of the two, and averaging demand over 30 years, would amount to mass
flow rates of 78.7 MMcfpd.
Table 2.2: ECBM CO2 demand by basin, cumulative and in terms of mass flowrates. Basins in which ECBM production is negligible or for which no estimates have beenmade are not listed.
Basin: Cumulative CO2 Demand (Mt) 30 Year Avg. CO2 Demand (MMcfpd)Green River 44 78.7
Powder River 1,320 2,362Statewide 1,364 2,440.7
26Typical ratios of CO2 stored to CH4 recovered in PRB sub-bituminous coals vary from 7:1 to 10:1(Burrus, 2003). However GRB coals are deeper and of higher rank (bituminous). Hence the ratio would belower (Burrus, 2003; Ellis et al., 1999a).
36
2.1.3 Deep Saline Aquifer Sequestration Potential
Wyoming possesses two of the nation’s best sites for geosequestration, the Rock Springs
uplift and the Moxa Arch. Both are forms of deep saline acquifers. The former was stud-
ied extensively by the Wyoming Geological Survey as a potential site for the FutureGen
CCS pilot project, and the latter, located west of LaBarge, is being evaluated as a site for
geosequestration in Phase III of the DOE-funded Big Sky Carbon Sequestration Partnership.
The two together have the capacity to store hundreds of years of Wyoming’s total annual
emissions of CO2.
Currently there is no economic benefit from pure sequestration, especially since there is
no prospect of recovering any of the cost via incremental gains in hydrocarbon production.
Whether or not CO2 is ever shipped to these storage sites depends entirely on the implemen-
tation of carbon capture mandates, a policy not currently under consideration at either the
state or federal level. Thus we leave the potential demand for CO2 from the Rock Springs
Uplift and Moxa Arch out of our final calculations, but we mention them here due to the
massive quantity of CO2 that these structures are estimated to be capable of storing.
The Rock Springs uplift is a 50 mile by 35 mile, doubly-plunging anticline (dome)
containing two potential sequestration reservoirs, the Pennsylvanian Weber Sandstone and
the Mississippian Madison Limestone, that meet all the criteria for the permanent storage of
CO2 (Surdam and Jiao, 2007). According to a detailed analysis carried out by the Wyoming
State Geological Survey as part of the application process for the FutureGen IGCC CCS
project, the two reservoirs’ combined CO2 storage capacity is 23.6 billion tonnes (equal to
about 485 years’ worth of total state-wide emissions) (Surdam and Jiao, 2007). The 2,110-
megawatt Jim Bridger power plant, producing approximately 12 million metric tonnes (Mt)
of CO2 per year (812 MMcfpd), lies within the eastern portion of the uplift, affording it
the possibility of sequestering its emissions on site if its CO2 does not get used for EOR or
ECBM. In fact, 30 years’ worth of the entire state’s coal-fired power plant emissions (∼50
million tonnes or 2,685 MMcfpd) would theoretically occupy less than 10% of the uplift’s
total combined capacity.
The Moxa Arch is another doubly-plunging anticline located in the southern Green River
37
Basin and is currently under study as a large-scale sequestration site by the DOE-funded Big
Sky Carbon Sequestration Partnership. Phase III of the study will inject up to 3 million tons
of CO2 into the 11,000 ft.-deep Nugget Sandstone over the course of three years. The test
CO2 will be provided by a nearby gas processing plant at Riley Ridge operated by Cimarex
Energy. The plant will produce about 1.36 million tonnes of CO2 per year (∼73 MMcfpd)
as a byproduct of producing helium and methane (BSCSP, 2009). Current estimates of
sequestration potential in the Moxa Arch are around 8.53 billion tonnes, or nearly 200 years’
of Wyoming’s total CO2 emissions from fossil fuel electric power generation (BSCSP, 2009).
2.2 Sources of CO2 Supply
It is well known that the number one constraint on increases in EOR production is a lack
of available CO2 supply (Kuuskraa, 2010). In Wyoming there are three categories of CO2
supply: natural underground sources, anthropogenic supply from coal-to-fuels plants, and
existing anthropogenic point sources, mostly in the form of coal-fired electric power gener-
ating stations. The first category is comprised of CO2 stripped from gas streams produced
either for the CO2 or for other salable gasses within the stream. The second includes CO2
from plants that capture the molecule as part of the fuel production process. The quantity
of CO2 produced by these first two categories is currently below levels that would support
EOR in Wyoming at its maximum potential. So Reyes (2009) and Jeffries (2009) have pro-
posed utilizing the third category of potential supply: CO2 emitted as a product during the
combustion of fossil fuels. If CO2 from existing anthropogenic sources were made available,
it could potentially provide quantities sufficient to meet the state’s total potential demand
for CO2 from EOR and to sustain a large ECBM industry.
CO2 from existing anthropogenic sources has to be captured from flue gas, a process that
requires retrofitting existing plants with capture technology.27 Not only is this a high upfront
27There are three known technologies for capturing CO2 from fuel: post-combustion using amine-basedscrubbing technology, pre-combustion in which CO2 is captured in the process of transforming coal intoliquid or gas fuels, and oxy-fuel combustion, a process by which flue gas is reincorporated into the combustionprocess to create a CO2-rich flue gas (K. Bliss et al., 2010). We know of no plans to build an oxy-fuel plantin Wyoming at this time.
38
capital investment, but the capture process itself, along with the required compression, is
costly. Due to the high overall cost of capture, existing coal-fired power plants are not
currently a viable source of CO2. Nonetheless, we include them as a source in our calculations
as a theoretical solution to the existing shortfall in CO2 supply and to inform debates about
future energy policies.
The first category, underground sources, has proven to be economically viable and
currently supplies numerous profitable EOR operations throughout Texas, Mississippi and
Wyoming with CO2. The second category includes two coal to fuels plants (Medicine Bow
Fuels and LINC Energy) that are either permitted or under construction. Both plants show
substantial promise of providing reliable supplies of CO2, and contracts have already been
signed for most of the CO2 the plants will produce (OGJ, 2011c). The third category, exist-
ing anthropogenic sources, is by far the largest potential source of supply. However procuring
the CO2 is costly given existing technology, and without subsidies that lower or eliminate
capture costs, regulations on emissions, developments in technology, or some combination of
all three, these plants would not likely comprise a reliable source of supply.
Underground sources currently produce as much as 500 MMscfpd (∼9.3 million tonnes
per year) (Moritis, 2009; Towler et al., 2008). Of that amount 450 MMscfpd (∼7.5 million
tonnes per year) is produced at a gas-processing plant owned and operated by Exxon-Mobil
near La Barge, Wyoming.28 Currently ExxonMobil is shipping 320 - 340 MMcfpd (∼6.5
Mt per year) CO2 through 48 miles of 24” pipeline and 112 miles of 20” pipeline (Parker,
2009). However, Towler et al. (2008) points out that with additional compression the existing
pipeline has the capacity to handle up to 605 MMcfpd (∼11.2 Mt per year). A second gas
plant at Lost Creek produces 50 MMcfpd (∼1 Mt per year), of which 70% will be shipped via
a 232 mile long pipeline operated by Denbury (who purchased Encore, the original developer
of the project) to the Bell Creek field in southeast Montana. The remaining 30% will be
reserved for use in possible acquisitions in the PRB (Moritis, 2009). If Cimarex Energy
28Towler et al. (2008) writes that at the time of their research the plant was producing 450 MMcfpdand venting 200 Mmcfpd. DeBruin (2001) lists the CO2 production at the La Barge facility at around 435MMcfpd. According to Parker (2009) and Thomas (2009) the plant re-injects, rather than vents, the CO2.The composition of the gas stream from which the CO2 is produced is 66% CO2, 21% methane, 7% nitrogen,5% hydrogen sulfide, and 0.6% helium (Towler et al., 2008).
39
chooses to sell CO2 from the Riley Ridge plant, some 70 to 80 MMcfpd could be added to
the above quantities in the near term.29 Current plans call for sequestering the CO2 on-site
back into the producing formation (Gearino, 2010).
Anthropogenic emitters are by far the largest potential source of CO2 (Doll et al., 2009).
In 2007, coal-fired power generation in Wyoming emitted 45.1 million tonnes of carbon
dioxide (EIA, 2009b). Five power plants are responsible for the bulk of the state’s carbon
emissions: the Jim Bridger (15.7 Mt, ∼816 MMcfpd, located in the north-central GRB), the
Laramie River Station (12.3 Mt, ∼639 MMcfpd, located between Cheyenne and Casper),
the Dave Johnston (5.7 Mt, ∼295 MMcfpd, located in the southern PRB), the Naughton
(5.3 Mt, ∼276 MMcfpd, located in the western portion of the GRB) and the Wyodak plants
(1.7 Mt, ∼81 MMcfpd, PRB) (EORI, February, 2011b).
The cost of capturing and compressing CO2 from existing anthropogenic sources is
higher than from the other two sources. Mohan (2009) and INGAA (2009) calculate that
capture costs vary from around $34 per tonne for gasification combined cycle plants to as
much as $70 per tonne for existing fossil fuel plants. And the DOE notes that, using existing
technology, retrofitting existing plants with post-combustion technology adds around 80%
to the cost of electricity (DOE, 2010).30 A price for CO2 of $2.25 per mcf amounts to $40
per tonne. Hence ECBM and EOR are uneconomical at capture costs that could range as
high as $80 per tonne.
The economics of utilizing existing anthropogenic CO2 sources has been evaluated by
several researchers. An analysis done on CCS combined with ECBM by Robertson (2009)
estimates that capturing CO2 from the Wyodak pulverized coal (PC) power plant would cost
∼$42/ton ($46.2 per tonne), and that transporting the CO2 via a 50-mile (80-km) pipeline
would cost ∼$0.46 per mcf ($9 per tonne), resulting in a total cost for the CO2 of $3.03 per
mcf ($59.38 per tonne). Since he estimates that ECBM increases methane recovery rates by
only 17%, an $8.00/mcf price for methane means that spending $3.03 to inject 1 mcf CO2
29According to Bleizeffer (September 19, 2010), Denbury holds a 42.5% interest in Riley Ridge, indicatingCO2 sales are a possibility.
30The report further notes that extensive efforts are underway to bring the impact on electricity costsdown to 35%. If that were to happen, it seems feasible that a tax/subsidy structure could be implementedthat makes post-combustion capture from existing plants a realistic option.
40
would return $1.36 worth of additional methane—clearly uneconomical.31
Other estimates of the size of subsidy required to make ECBM economical have been
made by Massarotto (2007). He models CCS using ECBM in Australian coals and finds
that capture costs are 75% of total costs and that the unit cost of CO2 avoided is $52/ton
($57/tonne), a value within range of Robertson (2009)’s ∼$60/tonne cost for delivered CO2
(2007 Dollars). These numbers are based on ECBM production rates that are 50% higher
than CBM production rates. He finds that the incremental revenues from ECBM, even with
a ‘low fieldgate’ price of $2.25/GJ (∼$2.38/mcf), would be sufficient to cover 46% of the
total CCS costs, reducing the net CCS costs to ∼$27.3/ton ($30/tonne) (Massarotto, 2007).
In other words, any tax on carbon in excess of $27.3 per tonne (or a capture subsidy in
tandem with caps on CO2 production) may motivate operators of coal-fired electric power
plants in Australia to capture carbon and sell it to ECBM producers. Still, the bottom line
is that existing large-scale emitters are unlikely to provide a reliable supply of CO2 without
capture subsidies, caps on emissions, or both.
We note here that in the design of our pipeline we have taken into consideration the
relationship between annual average mass flows, or nominal flows, maximum design flows and
the constraints imposed by post-combustion capture efficiency. CO2 production levels for
power plants are typically listed in terms of nominal flows (tonnes per year). For our pipeline
calculations, we convert nominal flows to daily flow rates (MMcfpd). But if base-load plants
operate on average at 80% capacity, peak CO2 output any given day could be 25% higher
than its average daily flow rate (nominal flow divided by 365 days per year), and pipelines
must be designed to ship these peak volumes. However existing capture technologies are
not 100% efficient. Thambimuthu et al. (2005) reports CO2 capture efficiencies in the 63 to
94% range. At 80% capture efficiency, peak pipeline capacity equals the daily average as
calculated without capture (e.g. if a plant produces CO2 at a rate of X MMcf per year, then
we use X/365 = .8 x (X/365) x 1.25 as the flow rate that our pipeline must accommodate.).
31However Ross et al. (2009) concludes that ECBM can produce five times the amount of CH4 comparedto primary production and up to eight times the amount when used in conjunction with fracking. In thiscase, spending $3.03 to inject 1 mcf CO2 would return $15 to $24 worth of additional methane (disregardingadditional costs related to fracking). This suggests that the ratio of ECBM recovery levels to CBM recoverylevels can make a large difference to the level of subsidy needed to make CCS with ECBM economical.
41
As for the third category of potential CO2 supply, there are currently three projects
likely to come on line by 2015: a coal-to-fuels plant near Medicine Bows, an underground
gasification plant not far from Casper and a fertilizer plant near American Falls, Idaho.32
Most certain is the coal-to-fuels plant owned by DKRW Advanced Fuels LLC and operating
as Medicine Bow Fuel & Power LLC. DKRW expects the plant to be in service by late 2014,
to produce 20,000 bbl of diesel per day, and to capture 83% of its carbon emissions (DKRW,
2011; Kelly, 2009). DKRW is optimistic enough about coal-to-fuels technology to predict
that by 2020 they will be producing 44,000 bbo per day.33 According to OGJ (2011c),
Denbury has contracted for the plant’s entire CO2 production of about 200 MMcfpd and
plans to utilize it for EOR in the Rocky Mountain region. And according to (Reyes, 2009),
this plant will come on line with ∼210 MMcfpd in 2013 (∼3.9 million tonnes).34
The second potential anthropogenic pre-combustion source is a proposed underground
coal gasification plant to be built by Linc Energy Limited, an Australian firm, about 70
miles north of Casper (Bleizeffer, 2009; Conner, 2009; Linc Energy, 2009). Reyes (2009) lists
the plant as producing 115 MMcfpd CO2, although Covell (2009) suggests that a typical
UCG plant that generates 200 MW of electricity would produce ∼172 MMcfpd (∼3.2 mil-
lion tonnes) CO2 per year. Linc recently purchased three nearby fields, Big Muddy, South
Cole Creek and South Glenrock B, with estimated EOR production of 70 million barrels
(Fugleberg, 2011b).
The third source is a planned fertilizer and fuel plant located near American Falls,
Idaho, known as the Power County Advanced Energy Center and operated by Refined Energy
Holdings (Reyes, 2009). This plant would produce ∼90 MMcfpd by 2011 and 175 MMcfpd
by 2015 (Reyes, 2009; Vanderau, 2008). Latest reports, however, indicate that the project
is on hold pending funding (O’Connell, 2011).
32Because there is considerable interest in China and China’s pace of alternative fuel development, wenote that according to OGJ (2011b) China is building four coal-to-fuels plants in Xinjiang Province.
33They claim that with this technology the supplies of coal in the U.S. represent the equivalent of 437billion bbo or 165% of Saudi Arabia’s total oil reserves (DKRW, 2011).
34In estimating the flow rates for our pipeline, we eliminate from our calculations the CO2 that is currentlysupplying EOR operations. While Denbury has contracted for ownership of Medicine Bow Fuel’s CO2, it isnot currently supplying existing EOR or ECBM operations. So we include it in our pipeline calculations inthe off chance that our line ships the CO2 towards one of their target fields.
42
If completed as proposed, these three sources would contribute ∼500 MMcfpd to state-
wide supplies by the year 2015. Adhering to our assumption that all known potential sources
can and will be made available, we model our pipeline based on production of 500 MMcfpd
from these three sources.
2.3 Matching Supply and Demand
Under our assumption that CO2 capture technology is employed on existing pulverized coal-
fired power plants, the Green River and Powder River Basins would supply the bulk of
the state’s CO2. The bulk of CO2 production and demand is in or near the Powder River
Basin where three large power plants, the Laramie River Plant, the Dave Johnston Plant
and the Wyodak Anderson Plant, and two future sources, Medicine Bow Fuels and Linc
Technology, could meet roughly half of the CO2 demand from EOR and ECBM. State-wide,
total annual demand falls just shy (by ∼104 MMcfpd) of total annual supply (see table
2.3). That means that the additional demand from ECBM on the eastern half of the state
can be accommodated by CO2 emissions in the western half of the state. Furthermore a
pipeline linking Western and Eastern Wyoming would potentially be able to ship CO2 in
either direction in the event there were a need to move supplies from the PRB region to the
Rock Springs Uplift for geosequestration.
43
Table 2.3: CO2 potential supply and demand by basin in terms of mass flow rates.
Basin: NaturalSup-ply**
ExistingAnthropo-genic**
FutureAnthropo-genic**
TotalSup-ply**
EORDe-mand**
ECBMDe-mand**
TotalDe-mand**
Big Horn 0 0 0 0 368.0 0 368.0Green R. 450.0 1,092.0 175.0 1,658.0 89.7 78.7 168.4GreenR.*
0 1,092.0 175.0 1,267.0 28.6 78.7 107.3
PowderR.
0 1,015.0 325.0 1,350.0 115.3 2,362.0 2,556.4
Wind R. 50.0 0 0 50.0 146.2 0 146.2Wind R.* 0 0 0 0 74.3 0 74.3Statewide 500.0 2,107.0 500.0 3,107.0 719.2 2,440.7 3,159.9Statewide* 0 2,107.0 500.0 2,607.0 586.2 2,440.7 3,026.9*Estimates excluding supply and demand for which pipelines are already in service.
**All values are in MMcfpd.
44
Chapter 3
Diameter Calculations and Network
Design
CO2 has certain physical properties that distinguish it from other substances commonly
shipped by pipeline. These properties and how they influence CO2 pipeline design, as well
as schematics describing the capture, compression, drying and shipping of CO2, are laid out
in further detail in McCoy (2008), IPCC (2005), Towler et al. (2008), Zhang et al. (2006)
and INGAA (2009). In short, CO2 must be relatively pure (at least 90% pure), dry (most
water removed prior to shipping), and in a sub-cooled liquid or super-critical state (at typical
pipeline operating temperatures, this means it must be compressed to pressures well in excess
of its critical pressure (∼1072 psi or 7.39 MPa)). The fact that liquid and supercritical CO2
compress (shrink) under increasing pressure must also be taken into consideration. Zhang
et al. (2006) finds that pipeline transport of CO2 as a subcooled liquid is the most efficient
means so long as pipeline temperatures do not exceed 31.1 C (∼88 F.), which is not a difficult
criterion for underground pipelines to meet.
3.1 Diameter Calculations
We use four equations out of Menon (2005) to calculate diameter: one derived from the
general energy balance equation for a flowing liquid in a pipeline, one known as the Panhandle
45
A, one known as the Panhandle B, and one known as the Weymouth equation. We take
the larger of the four calculated diameters to use for the final nominal pipeline size (NPS).1
Our calculations are based on an estimated pressure drop of 35 kPa/km that we fix for
all calculations. Our procedure for calculating diameter using the energy balance equation
for a liquid flowing in a pipe closely follows McCoy (2008). However he adjusts his final
pressure drop by using his calculated diameter to back-calculate actual pressure drop.2 For
our purposes, the change in pressure drop obtained by the back-calculation changes the
diameter by only a little and the capital cost by almost nothing at all. In our model, the
slightly larger pipeline diameter that results from back-calculating pressure-drop is accounted
for through the addition of pumps to maintain pressure. And since pump capital costs, which
are based on pump size and power requirements, are a relatively insignificant part of the
overall capital cost, we leave out the final calculation of actual pressure drop.3
Our derived diameter is the optimal inner diameter for the given mass flow rate over
the given distance and associated pressure drop. If our calculated diameter does not exactly
match the inner diameter for an existing NPS (it rarely does), we adjust our calculated
diameter upwards to match the next highest NPS. A graph of nominal pipeline sizes versus
the range of mass flow rates encountered in our network is shown in figure 3.1. We calculate
pipeline thickness based on McCoy (2008), but find that calculations using his method result
in thicknesses about 10% less than those listed in INGAA (2009). We adjust ours accordingly
(upwards by 10%) and use the results to calculate the total tons of steel required in each
segment of line, which we eventually sum to find the total amount of steel required in the
network as a whole. This may be of interest if one wants to research whether an increase
in demand for steel generated by the construction of a network of this scale would influence
the price of steel.
1Pipe is manufactured in regular intervals known as nominal pipeline sizes (NPS). For NPS’s of 12” andbelow, the NPS is the actual inner diameter. For NPS’s of 14” and above, the NPS is the outer diameter,and the final inner diameter depends on the pipe wall thickness.
2The actual pressure drop will always differ from the estimated pressure drop because the final innerdiameter associated with the NPS is always larger than the diameter calculated via the equations.
3Note that EOR and ECBM producers who need to make sure that the final pressure of the CO2 comingout of the pipeline is greater than minimum miscibility pressures and below reservoir fracture pressureswould require an exact calculation of pressure drop along any spur line delivering CO2 from the trunk line.Typical injection pressures are greater than 7.0 MPa (Zhang et al., 2006).
46
0 500 1000 1500 2000 25005
10
15
20
25
30
35
Mass Flow Rate (Mmscfpd)
NPS
(inc
hes)
Nominal Pipeline Size vs. Mass Flow (100km segment)
Figure 3.1: Nominal Pipeline Size versus mass flow rate as generated by our model.
The entire algorithm is coded using MATLAB to create an interactive design environ-
ment that allows someone with relatively limited knowledge of CO2 and pipelines to calculate
pipeline diameters based on segment length, the given mass flow rate and whether or not a
pump station is required. The routine will calculate a network either segment by segment
or by evaluating a pre-configured system using an Excel spreadsheet. MATLAB can easily
handle sensitivity analysis and Monte Carlo simulations, providing subsequent researchers
opportunities to enhance our analysis.
In our model, whether a pump station is needed or not depends on two factors: distance
traveled since the last pump station and whether or not new CO2 is coming into the line at
some point within the segment.4 Efficient pipeline transport requires that CO2 be shipped
in a supercritical or sub-cooled liquid phase over the entire length of the line.5 Fluids
4Elevation rise can also decrease pressure in the line, but we ignore this effect for this version of ourmodel. Also, the need for pump stations can be obviated by using larger diameter pipelines, providingpipeline operators a choice between a larger diameter line or more pump stations (Heddle et al., 2003;McCoy, 2008).
5Technically speaking compressors pressurize CO2 in its gaseous phase (at pressures below 7.38 MPa)while pumps pressurize CO2 in its supercritical phase (INGAA, 2009; McCollum and Ogden, 2006). Howeverthe term compression is often used interchangeably. The distinction is important in that compression requiresvastly greater amounts of energy per unit CO2 (hence much higher costs) than does pumping (McCollum
47
undergoing pipeline transport experience head (energy) loss due primarily to frictional forces.
As a result, the farther the fluid travels, the lower the pressure within the line. If pressure
drops enough the flow can become two-phase, and efficiencies gained while in liquid phase
are lost. We assume all CO2 comes on-line having been pressurized at the source to 15.3 MPa
(∼2220 psi). Since the critical point for CO2 is 31C (87.98F) and 7.38 MPa (1,073.1 psi),
engineers generally abide by the rule that the pressure of CO2 within the pipeline should
not drop below 10.3 MPa (1,494 psi) (Heddle et al., 2003; Towler et al., 2008). According to
McCollum and Ogden (2006), pressure drop with a CO2 line is around 35 kPa/km. Thus,
given an initial pressure of 15.3 MPa, a rule of thumb is that a pump station is required
roughly every 150 km (∼90 miles).6
Also, since we assume that the CO2 entering the line from a new source is pressurized
to 15.3 MPa (∼2220 psi), CO2 already within the line must be re-pressurized to match this
pressure at the point where the new CO2 is entering the line. Hence a pump station is also
required wherever new sources are coming on-line. Altogether our network requires 13 pump
stations.
Compression requires large upfront capital cost as well as substantial operations and
maintenance (O&M) costs due to high rates of power consumption. We assume that the
cost of compressing CO2 to 15.3 MPa at the source will be borne by the supplier (whether
through subsidies, output price increases or both) and therefore include only the cost of
in-line pumping in our O&M costs.
The MATLAB routine also calculates capital costs (segment by segment and network-
wide), O&M costs, per mcf and per tonne tariffs, pipe wall thickness, and tons of steel. As a
final step, the routine calculates the overall length of pipe in the network, the network-wide
tariff per-mcf and per-tonne as well as the total amount of steel in the network. The code is
included in Appendix B.7
Our calculated diameters either match or are within one NPS of those produced by
and Ogden, 2006).6Zhang et al. (2006) finds that the maximum distance between pump stations for pipeline on level ground
is around 196 km, so we feel placing a pump station every 150 km is sufficiently conservative.7Portions of the code, specifically those regarding the solution to the implicit Colebrook function for the
friction factor, were developed using problems and solutions from Recktenwald (2000).
48
Smart and Helmke (2009)’s model (though their model only goes up to an NPS of 24”). Our
calculated NPS’s are generally one NPS lower than those calculated by McCoy and Rubin
(2008). For example, McCoy and Rubin (2008) calculates an NPS of 16” for a 100-km
segment shipping 5 Mt per year (∼260 MMcfpd), while we calculate an NPS of 14”.
The difference lies partly in how they handle head (pressure) loss within the line. Under
their assumptions, inlet and outlet pressures are fixed no matter what the length of the
segment in question (i.e. they vary head loss per km as segment length changes). Our inlet
pressure is fixed, but our outlet pressure varies according to length (i.e. we leave head loss
per km fixed no matter what the length of the segment). As a result, the longer a length
of pipe evaluated in their model, the wider the diameter must be in order to meet the fixed
head loss. In their model, shipping 5 Mt per year would require an NPS of 14” in segments
between 30 and 60 km in length, an NPS of 16” in segments between 60 and 110 km in
length, and an NPS of 18” in any segment longer than that. Our model calculates an NPS
of 14” regardless of segment length. However, segments longer than 150 km in length would
require a pump station to maintain appropriate pressure within the line. For 10 Mt per year
transported over a distance of 75 km, McCoy and Rubin (2008) calculates an NPS of 20”
while we calculate an NPS of 18”. They do not show results for mass flow rates above 10
Mt per year.
When compared to existing pipelines shipping similar quantities, our calculated diam-
eters are generally on the low side. Kinder Morgan (2010) lists the 30” Cortez Pipeline as
having a capacity of 1,300 MMcfpd (we calculate an NPS of 26”). Kinder Morgan (2010)
also lists the 386 MMcfpd Bravo Pipeline as having an NPS of 20” (we calculate 16”); the
330 MMcfpd Sheep Mountain Pipeline as having an NPS of 20” (we calculate 16”) as well
as a stretch of 480 MMcfpd 24” line (for which we calculate 18”); the 600 MMcfpd Central
Basin Pipeline as having an NPS ranging from 26” down to 16”, the capacity of which could
be increased to 1200 MMcfpd if pump capacity were increased (we calculate 20”); the Este
Pipeline as having a capacity of 250 MMcfpd for a 14” stretch of line (we calculate 12”)
and a capacity of 150 for a 12” stretch of line (we calculate 12”); the 160 MMcfpd Slaughter
Pipeline as having an NPS of 12” (we calculate 12”); the West Texas and Llano Lateral
49
Pipelines as having an NPS of 8 to 10” to carry ‘approximately’ 100 MMcfpd (we calculate
10”); and the 270 MMcfpd Canyon Reef Carriers Pipeline as having an NPS of 16” (we
calculate 14”). Denbury’s Green Pipeline is 24” in diameter and expected to ship up to 800
MMcfpd (we calculate 22”). Finally, the 205 mile Weyburn Pipeline from a coal gasifica-
tion plant in Beulah, North Dakota to the Weyburn EOR field in Saskatchewan ships 250
MMcfpd and is 14” in diameter to the border where it drops to 12” diameter (we calculate
14”).
Pipeline segments, distances, diameters and land construction costs (LCC) are listed in
tables 3.1 through 3.4.
3.2 Network Routing and Design Considerations
Of the two broad categories of pipeline infrastructure design, point-to-point or integrated
backbone, we choose the latter. Chrysistomidis et al. (2009) show that an integrated system,
though more costly up front, ultimately provides the lowest average CO2 shipping cost while
supporting a better overall market for CO2 with lower barriers to entry. Chrysistomidis et al.
(2009) point out, however, that the relative benefits of the integrated design depend on high
early utilization rates and low policy uncertainty regarding carbon prices. In choosing the
integrated design, we assume that there is sufficient certainty in carbon policy and pricing
such that CCS becomes not only viable but required, resulting in full capacity utilization.
Designing for potential capacity is common in the natural gas industry, where according to
EIA (2010a), pipelines are designed so that additional capacity can be met by adding com-
pression capacity. Moreover natural gas regulations require that pipelines passing through
highly populated areas lower their operating pressure (EIA, 2010a). In Wyoming this is a
minor consideration that we ignore for our calculations.
The ultimate routing and design of the pipeline depends on the timing and type of
new sources of supply as well as the viability of ECBM. Currently the sole economically
viable source of CO2 in Wyoming is from natural sources as a byproduct of gas streams
that contain other valuable gasses. For the near term, the five EOR projects already on line
50
have contracted for all available supply, and with its acquisition of Encore, Denbury now
has possession of the soon-to-be-developed Lost Cabin source (currently slated for use in the
Bell Creek Field just over the border in Montana). Other than the possible source from the
Cimarex Energy plant on the Moxa Arch, no other sources of natural supply currently exist
(Doll et al., 2009).
Anthropogenic sources are left to shoulder the load for the remaining demand. There
are two general categories of anthropogenic sources: existing coal-fired power plants that
require retrofitting with scrubbing technology, and planned coal-to-fuels and coal gasification
plants that can utilize less costly pre-combustion processes to capture CO2. Of the in-place
anthropogenic emitters, coal-fired electric power generating plants retrofitted for carbon
capture (likely amine scrubbing of flue gas) easily comprise the largest potential source.
However according to Rochelle (2009), amine scrubbing technologies would likely not be
fully deployed until 2018 under even relatively optimistic scenarios. Boosting supply in any
meaningful amount beyond what is available from natural sources may then be left up to
coal-to-fuels projects. Currently there is no clear winner in the competition for most viable
of the ‘clean-coal’ options, and many experts feel that both scrubbing and coal-to-fuels will
eventually play a role (Doll et al., 2009; Evans, 2009; Kelly, 2009).8 The fact remains that
utilizing all three sources of supply is currently the only way to meet the existing demand
from EOR fields. And if ECBM were to prove viable, whatever CO2 captured in excess of
EOR needs could be utilized by ECBM operators and sequestered in coal seams. We account
for both opportunities in the design of our pipeline.
If indeed coal-fired power plants come on-line with the full amount of their current
output, the general flow of CO2 would be from the Southwest and Southern parts of the
state towards the CBM and EOR fields in the Northern, Northeastern and Eastern parts
of the state. As can be seen in table 2.3, demand in the PRB outweighs supplies, even
given the full development of all potential sources. This necessitates an east-west connector
which logically originates from the Jim Bridger Power Plant and passes by Medicine Bow
8As of April, 2011, the Medicine Bow coal-to-fuels plant is under construction, and contracts for 200MMcfpd have been signed with Denbury, who plans to use the CO2 for EOR in the Rocky Mountain Region(OGJ, 2011c).
51
Fuels before linking with the line from the Laramie River Plant that leads towards the PRB.
However if PRB EOR and/or ECBM operators fail to come on-line or were to go off-line for
a period of time, an east-west line could also reverse-ship CO2 from the large emitters in the
east towards the Rock Springs uplift, or to pipelines that could ship it northward to EOR
operators in the Big Horn and Wind River Basins, two basins that have substantial EOR
demand and little or no local supply.
There exists yet one more solution to the possibility that at times CO2 supply will
outpace CO2 demand from EOR and ECBM. Trading could be allowed so that producers
who have access to active EOR, ECBM and/or sequestration sites could effectively sequester
CO2 produced elsewhere. For example ECBM is still in its trial phase and may not come
on-line for several years, even after the implementation of a cap and trade or carbon tax,
leaving CO2 producers such as the Laramie River Plant without a ‘buyer’ (or perhaps the
term ‘sequesterer’ would be more appropriate) for their CO2. In that case the Jim Bridger
Plant, for example, might sequester (in the Rock Springs Arch) and/or ship more than its
mandated quota of CO2 to EOR producers in the Big Horn and Wind River Basins, then
sell or trade the difference to the Laramie River Plant.
We reiterate that the listed EOR sites are those FRC’s that meet technical screening
criteria and that are profitable given a price for oil of $70/bo, a price for CO2 of $2.25/mcf,
and a 20% rate of return (Cook, 2009a; van ’t Veld and Phillips, 2010). Furthermore ECBM
demand is largely based on the work of Ross et al. (2009), from which we extrapolated
using volume estimates for coal with ECBM potential from Ellis et al. (1999a) and Ellis
et al. (1999b). The pipeline is designed to be a trunk line and as such is routed directly
from major sources to regions of potential high CO2 demand. We assume regional EOR and
ECBM producers will work in conjunction with pipeline development companies to design
and build the grid system necessary to deliver CO2 from the trunk line to its final destination.
We break the line into segments. Each segment initiates from either a potential source
of CO2 or from the end of another segment and terminates at either a source of demand
for CO2, at another source for CO2, or at a pump station. We route the pipeline along
existing right-of-ways (ROW’s) based on the assumption that existing pipelines by and large
52
are logically placed relative to geographic and terrain features. In doing so we assume that
additional pipelines could be laid through existing ROW’s, either by the existing owners or
through profit-sharing agreements, at lower costs compared to obtaining permits for new
ROW’s. Nonetheless our cost estimation model includes ROW costs, so if a pipeline were in
actuality constructed along an existing ROW, it is possible that overall capital costs would
decrease slightly from our model’s prediction.
We constructed maps using a student version of ARCGIS 9.2 based on GIS data sup-
plied on the Map Server link at WOGCC (2010a), from WYGISC (2009) and from van ’t
Veld (2009). A map of the entire network is shown in figure 3.2. Although individual seg-
ments are not shown on the printed maps, they have been drawn into ARC-GIS in such a
way that the network can be reconfigured by manipulating, deleting or creating additional
segments. The software readily produces distances which, combined with mass flow rates,
can be entered into the MATLAB routine to calculate land construction costs for any chosen
pipeline configuration.
3.3 Pipeline Route Description
The first two segments connect the proposed Power County Energy Center, a fertilizer and
liquid fuels plant to be located southwest of American Falls, Idaho, to the Naughton Power
Plant near Kemmerer, Wyoming 235 kilometers away. If completed as proposed, this line
would ship about 175 MMcfpd by the year 2015 (Vanderau, 2008). One pump station is
required at the 150-km (93-mile) mark and one at the Naughton Plant to pressurize the line
back up to 15.3 MPa.9 The Naughton Plant generates on average 269 MMcfpd, increasing
the available supply of CO2 to 444 MMcfpd.
Figures 3.3 and 3.4 show the pipeline route as envisioned in the southwest and Green
River Basin. From the Naughton plant, the line (segment no. 3) heads east for 21 km (∼13
mile) along existing gas and oil pipelines to a major natural gas pipeline junction. At this
point, a southerly spur could supply 10 MMcfpd to the Painter Reservoir near Evanston,
9Lacking suitable GIS data for Idaho, we sketched the proposed line and calculated its length using GoogleEarth.
53
Figure 3.2: Statewide overview of the pipeline network, sources of CO2, targeted EOR fieldsand targeted coal fields.
while a northerly trending spur could supply 6 MMcfpd to the Dry Piney, Hogsback, Tip
Top, McDonald Draw and Green River Bend fields. Segment no. 4 continues in an east-
erly direction to a junction with the 24” ExxonMobil CO2 pipeline (EM1). At this point
the ExxonMobil line splits into a southeast trending line towards Rock Springs and a 20”
northeast trending line towards the Bairoil field in the southeast corner of Fremont County.
Towler et al. (2008) shows that the existing line from Shute Creek to Salt Creek, with spurs
to Rock Springs, Rangely, Monell, Baroil, Beaver Creek and Hartzog Draw, could support a
flow of 605 MMcfpd given 2,000 hp of additional compression at 85% efficiency located at the
point where the Baroil, Beaver Creek and Salt Creek lines diverge. ExxonMobil currently
plans to increase CO2 shipped from the LaBarge facility to around 320 MMcfpd, meaning
it is feasible that 280 of the 434 MMcfpd coming from the Naughton plant could be added
54
to the ExxonMobil line to supplement shipments to Rangely, Baroil, Patrick Draw and Salt
Creek (or to ship to other fields if the line is ever extended). Since ExxonMobil may dis-
agree with this finding or not agree to add more to their line, we find it prudent to design a
separate line (segment no. 5) along existing natural gas lines near Rock Springs that lead to
the Jim Bridger power plant. Since the combined span of the two segments is greater than
150 km, a logical point for a pump station would be at the EM1 junction. A pump station
is also required at the Jim Bridger Plant to re-compress the 429 MMcfpd already in the line
to 15.3 MPa, the pressure of the CO2 captured and compressed by the plant.
Figure 3.3: Southwestern pipeline sections and their relation to fields screened for EOR.
According to EORI (February, 2011b), the Jim Bridger Plant could contribute around
816 MMcfpd, bringing the total available CO2 supply at that point to 1,244 MMcfpd. By
our calculations (see table 2.1) at least 437 MMcfpd is needed to fulfill the demand from
55
FRC’s in the Green River, Big Horn and Wind River Basins that currently have no supply
and that could be accessed by a northerly-trending trunk line from the Jim Bridger Plant.
We allot 537 MMcfpd to this line, leaving 100 MMcfpd of spare capacity in the event that
using the average CO2 injection rate over the first four years undershoots the actual flow
rates required by the targeted fields. The remaining 707 MMcfpd is reserved for an easterly-
trending trunk line to target EOR and ECBM fields in the easter half of the state and to
supply ECBM in the Green River Basin.
Figure 3.4: Green River Basin sections as designed and their relationship to sources andfields screened for EOR.
From the Jim Bridger Plant, the northern branch stretches for a distance of 480 km to
the end of the line near Elk Basin. There are currently no significant sources of CO2 along
that stretch. The distance requires three pump stations to maintain proper pressure within
the line. We break the 480-km stretch into eleven segments, each segment ending at a point
56
from which spur lines could be built to supply EOR operations and/or provide opportunities
for sequestration (table 3.1).
Table 3.1: Pipeline layout for the Power County Energy Center-Jim Bridger-Wind RiverBasin line based on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR.
Green River BasinSeg.no.
From: To: Length(km/miles)
Massflow(Mm-scfpd)
NPS(in)
LCC(MillUSD)
1. PCEC Pump Station I 150/93 175 12 40.5*2. Pump Station I Naughton I 85/53 175 12 24.9*3. Naughton Painter Jct. 21/13 444 18 12.2*4. Painter Jct. ExxonMobil line
(EM1)64/40 428 18 32.8*
5. EM1 Jim Bridger 90/56 428 18 44.0*6. Jim Bridger EM2 23/14 537 18 30.2*7. EM2 Crooks Gap 102/63 537 18 47.08. Crooks Gap Big Sand Draw 53/33 531 18 30.2*9. Big Sand Draw Beaver Creek 15/9 516 18 9.210. Beaver Creek Butte Jct. 36/22 516 18 19.311. Butte Jct. Lake Cr. /Mur-
phy Dome91/57 467 18 45.6*
*Includes pump station capital cost
The first of these segments, no. 6, runs north-northwest to the ExxonMobil line. Seg-
ment no. 7 parallels that line to a junction in the ExxonMobil line from which the Lost
Soldier/Wertz and Beaver Creek fields are supplied. At this point 6 MMcfpd could be de-
livered to the Crooks Gap/Happy Springs area, 3 of which is needed at Mahoney Dome.10
Segment no. 8 stops at Big Sand Draw with a demand of 15 MMcfpd (see figure 3.5).11
Number 9 leads to the junction with the Beaver Creek field which currently has its own
10Crooks gap is listed twice in Cook (2009a), once in the Green River Basin associated with the LakotaReservoir and once in the Wind River Basin associated with the Muddy Reservoir. However WOGCC(2010b) lists only one Crooks Gap. Most likely both the Muddy and the Lakota are two different reservoirsaccessed by the same field, so we combine the injection rates and include them under the Green River BasinCrooks Gap.
11The first of the three required pump stations is located 28 km from the end of this segment.
57
supply. Since there is no question that Beaver Creek’s 8” line is too small to handle the 516
MMcfpd needing to be shipped under our assumptions, we design a separate line. Segment
no. 10 stops at a point along the right-of-way for southwest-northeast trending gas and oil
pipelines (labeled as Butte Junction in table 3.1). From there a spur could supply 49 MMcfpd
to Steamboat Butte, Pilot Butte and Sheldon. Segment no. 11 enters the Bighorn Basin,
stopping near the Lake Creek and Murphy Dome fields (CO2 demand ∼11 MMcfpd).12
Segment no. 12 (table 3.2) leads to a junction near the town of Kirby, from which a
northwest trending spur could be built to supply the numerous fields on the basin’s western
margin (see figure 3.6). These fields in total demand ∼182 MMcfpd, reducing the flow rate
in the line at that point to 273 MMcfpd. Segment no. 13 returns the pipeline to roughly the
center of the basin where an easterly trending spur could supply ∼14 MMcfpd to fields on the
basin’s eastern margin (see figure 3.6).13 Segment no. 14 follows a gas line north-northwest
to a point we label Cody Spur where a westerly spur could access the Shoshone field near
Cody. Segment no. 15 supplies the Byron (58 MMcfpd), Garland (22 MMcfpd) and Whistle
Creek (1 MMcfpd) fields, and no. 16 supplies Elk Basin South (12 MMcfpd) and Elk Basin
(63 MMcfpd).14
The eastern branch leaving the Jim Bridger Plant leads to the proposed Medicine Bow
coal to liquids plant (table 3.3). Setting aside 50 MMcfpd for ECBM in the Green River
Basin leaves 657 MMcfpd to ship through the line headed eastward towards Medicine Bow
Fuels. The first segment (no. 17) stops at the CO2 line into Monell (aka Patrick Draw) where
9 MMcfpd could be offloaded for the Brady field either into the existing line or into a spur.
Shipments could be increased in the event that Monell requires more CO2 than what they
are currently getting from ExxonMobil. Segment 18 stops near Sinclair in case a spur needs
to be added from there towards Mahoney Dome. A pump station is required at Sinclair due
to distance and another at Medicine Bow Fuels to equilibrate pressure in the line.
Medicine Bow Fuels may contribute another 210 MMcfpd, creating a total volume of
12The second of the three required pump stations is located 20 km from the end of this segment.13Cook (2009a) lists another field in this part of the Basin, named Enigma, that would demand∼3 MMcfpd.
However according to Christofferson (2008), the Enigma underwent an ASP chemical flood between 2001and 2008 and may not need CO2. Thus we leave it out of our demand calculations.
14The third of the three pump stations is located 15 km from the end of segment no. 15.
58
Figure 3.5: Wind River Basin sections as designed and their relationship to fields screenedfor EOR.
858 MMcfpd to ship northwards towards the PRB. A pump station is needed at Medicine
Bow Fuels to re-pressurize the 648 MMcfpd already in the line. To keep all options open,
we end segment no. 20 at a point where the line meets with a natural gas line operated
by Colorado Interstate Gas, approximately 25 miles east of Medicine Bow. Segment no. 21
brings the western half of the states CO2 to a junction where it combines with the bulk of
the eastern half of the state’s CO2 (labeled as Dave Johnston Plant Junction).
Two major coal-fired power plants represent significant potential sources, namely the
Dave Johnston plant (295 MMcfpd) and the Laramie River plant (639 MMcfpd). We divert
10 MMcfpd of Dave Johnston’s production westward (segment no. 24) to supply the Teapot
Naval Reserve and Salt Creek, in the event that more were needed there (or for sequestration
59
Table 3.2: Pipeline layout for the Wind River Basin-Bighorn Basin line based on mass flowrates given $70/bo and $2.25/mcf CO2 for EOR.
Bighorn BasinSeg.no.
From: To: Length(km/miles)
Massflow(Mm-scfpd)
NPS(in)
LCC(MillUSD)
12. Lake Cr. /Mur-phy Dome
Kirby 26/16 455 18 14.6
13. Kirby Basin Center 19/12 273 14 8.214. Basin Center Cody Spur 70/44 259 14 24.915. Cody Spur Byron 30/19 257 14 13.7*16. Byron Elk Basin 15/9 175 12 5.5
BHB excess CO2 given $70/bo and $2.25/mcf CO2: 100 MMcfpd
*Includes pump station capital cost
Table 3.3: Pipeline layout for the Jim Bridger-Dave Johnston line based on mass flow ratesgiven $70/bo and $2.25/mcf CO2 for EOR.
East Wind River/South Powder River BasinsSeg.no.
From: To: Length(km/miles)
Massflow(Mm-scfpd)
NPS(in)
LCC(McCoy/New-comb)Mill US $
17. Jim Bridger Monell Jct. 24/15 657 20 15.618. Monell Jct. Sinclair 120/75 648 20 65.6*19. Sinclair Med Bow Fuels 77/48 648 20 44.3*20. Med. Bow Fuels Col. Int. Gas 40/25 858 22 27.221. Col. Int. Gas Dave J. Plant
Jct.106/66 858 22 66.2*
22. Laramie RiverPlant
Dave J. PlantJct.
109/68 639 20 59.7*
*Includes pump station capital cost
purposes).15 We also assume that the 11 MMcfpd required by Glenrock South could be
15According to Mullen (2010), the Grieve is ideal for CO2 but unable to obtain a source. Instead the
60
Figure 3.6: Bighorn Basin sections as designed and their relationship to screened fields.
supplied via a spur directly from the Dave Jonhston Plant. The remaining 274 MMcfpd is
shipped eastward to the Dave Johnston Plant Junction (Dave J. Jct.) to merge with the
Medicine Bow Fuels supply and the Laramie River Plant supply (table 3.4). The three flows
combined amount to 2,410 MMcfpd. From the Dave J. Jct. CO2 would flow north for 120
km into the Powder River Basin Proper (segment no. 25).
As noted under the calculation of CO2 demand in Chapter 1, ECBM in the Powder
River Basin may have the potential to utilize as much as a billion tonnes of CO2, seques-
tering almost 100% in the process.16 We estimate that if that were the case, ECBM in
owners have chosen to use a chemical flood. We note their need and design the pipeline to accommodate it.16Recall that the one billion tonnes figure came from applying the results from Ross et al. (2009)’s simu-
lation basin-wide, and that it is the lowest estimate for total CO2 ECBM storage capacity for the PRB outof all the research we examined for this paper. Note that NETL (2010) lists CO2 storage potential in thePRB to be as high as 11 billion tonnes.
61
Figure 3.7: Southeast sections as designed and their relationship to sources and screenedfields.
the PRB could utilize around 2,362 MMcfp. Furthermore, as seen in figures 3.8 and 3.9,
the preponderance of ECBM CO2 demand would be from fields located in the central and
west-northwest portions of the basin, while the preponderance of EOR demand is located in
the eastern portions of the basin. How a trunk line should be routed into the basin hinges
on which source of demand one believes will predominate. If EOR ultimately provers to be
the sole source of CO2 demand, then the routing as presented in Jeffries (2009) and Reyes
(2009) would be the logical choice, as it brings CO2 more directly to fields such as the Skull
Creek field near Newcastle and the dense cluster of fields between Gillette and Sundance.17
Our trunk line skims the eastern edge of the largest concentration of CBM fields before
17Surdam (2010) presents a third option, which is to utilize CO2 emissions from PRB coal-to-fuels plantssituated so that they have quick access to coal as well as to sequestration sites and EOR fields.
62
Figure 3.8: Southern Powder River Basin sections as designed and their relationship tosources, screened fields and coalbed methane fields.
bending to the west to access fields in the west-northwest quadrant of the PRB, a route
that splits the difference between the EOR fields and the bulk of the ECBM fields. Thus
CO2 from Dave J. Jct. would be shipped northward to a point where it intersects a pipeline
operated by Western Gas Resources about 45 km (28 miles) south of Gillette. From there 11
MMcfpd could be shipped eastward towards Skull Creek and possibly Lance Creek for EOR.
However costs for the spur may be prohibitive. We calculate a land construction cost of
$5.2 million, resulting in a tariff of $0.30/mcf or $5.84/tonne. The bulk of the 650 MMcfpd
offloaded at this point would be slated for ECBM producers to the west of the line.
The next distribution point would be near the Maysdorf FRC where just under 50
MMcfpd CO2 could be distributed to Maysdorf and several field-reservoir combinations 30
to 35 km east-northeast of Maysdorf. We also off-load another 650 MMcfpd for ECBM
producers that would mostly be located west of the line. From Maysdorf the line continues
northwards for about 26 km to the Wyodak Plant (near Gillette) which produces about 81
63
MMcfpd (segment no. 27).
We estimate that EOR fields east-northeast of Wyodak would demand just under 35
MMcpd (figure 3.9). We offload 604 MMcfpd from the pipeline and combine it with the
remaining 46 MMcfpd to supply 650 MMcfpd to the ECBM fields located west and north
of the Wyodak Plant. That leaves 445 MMcfpd to ship to the end of the line near the Big
Horn Gas Gathering facility. This somewhat arbitrary point was chosen due to its central
location within the USGS GIS maps of known coalbed methane reservoirs in the northern
PRB (Ellis et al., 1999b). All the remaining CO2 would be off-loaded at this point to be
utilized for ECBM in fields located in the upper central and eastern portions of the PRB.
Figure 3.9: Northern Powder River Basin sections as designed and their relationship tosources, screened fields and coalbed methane fields.
Finally, we note that the Sussex West, Cellars Ranch, and Sandbar East fields, all of
which were successfully screened by Cook (2009a) for EOR, would be more conveniently
64
supplied by CO2 from the Lost Cabin plant via Denbury’s Greencore line (Evans, 2010;
Moritis, 2009). However we are not aware of any plans to supply these fields via the Greencore
line at this time.
Table 3.4: Pipeline layout for the Dave Johnston-Wyodak line based on mass flow ratesgiven $70/bo and $2.25/mcf CO2 for EOR.
Powder River BasinSeg.no.
From: To: Length(km/miles)
Massflow(MM-cfpd)
NPS(in)
LCC mil-lion US $
23. Dave J. Plant Dave J. PlantJct.
12/7 274 14 5.7*
24. Dave J. Plant Grieve Jct. 39/24 10 4 3.225. Dave J. Plant
Jct.Phillips etc. 120/75 2,410 34 131.3*
26. Phillips etc Maysdorf 24/15 1,760 30 25.927. Maysdorf Wyodak 26/16 1,060 24 21.028. Wyodak Big Horn Gas
Gathering58/36 445 18 29.1
*Includes pump station capital cost
The total length of pipeline required by our network as designed is 1,682 km (1,045
miles) and includes 13 pump stations. To grasp the scale of the network, we note that the
construction of the 204 mile Weyburn CO2 pipeline from Beulah, North Dakota to Weyburn
in Saskatchewan, Canada averaged 2-3 miles per day (Brown, 2009). Thus our network could
theoretically be constructed within a two-year time span.
65
Chapter 4
Pipeline Cost Estimation Model
As with similar techno-economic models, we use data for natural gas pipelines to estimate
capital costs (aka land construction costs or LCC). Such costs are filed with the Federal
Energy Regulatory Commission (FERC) and compiled by the Oil and Gas Journal.1 Promi-
nent among past studies are those done by Parker (2000), Heddle et al. (2003) and McCoy
and Rubin (2008). They reason that dry CO2 (CO2 having a relative humidity well below
its dew point) transported at pressures similar to those for natural gas (greater than 10
MPa) would require materials similar to those used in natural gas pipelines (Heddle et al.,
2003; McCoy and Rubin, 2008; Parker, 2000; Seiersten, 2002).2 However in INGAA (2009),
the authors note that the differences in pipeline materials and construction details between
natural gas and CO2 pipelines are significant enough, especially for diameters greater than
36”, to require adjustments in capital cost calculations based on natural gas pipeline data.
For this reason Essandoh-Yeddu and Gulen (2008) incorporate a cost-escalation factor to
bring costs in line with industry estimates for CO2 pipelines in Texas. We do not include
an escalation factor for our final capital cost calculations, but our graphs include one line
representing our estimated costs and one line representing our estimated cost plus a 10%
escalation factor.
1In an article about pipeline construction cost equations based on information given by companies toFERC, the authors note that it’s impossible to know if there have been inconsistencies in the reporting ofthe data. Nevertheless, they feel it is the best data that is publicly available (Brown et al., 2011).
2CO2 needs to be relatively dry for EOR and sequestration purposes as well (Seiersten, 2002; van ’t Veldand Phillips, 2010).
66
Cost data in the Oil and Gas Journal is broken into four categories: materials, labor,
ROW and miscellaneous. McCoy and Rubin (2008) estimate a Cobb-Douglas function of
the form
Kpl = a0 ∗ La1 ∗Da2nps, (4.1)
which in log-log form becomes
logKpl = b1 + a1logL+ a2logDnps. (4.2)
Kpl is the pipeline capital cost (land construction cost) in 2009 dollars, L is pipeline length
in kilometers, Dnps is the nominal pipeline size in inches and b1 = log(a0). In Cobb-Douglas
form the sum of the parameters a1 and a2 indicate whether or not the capital cost exhibits
increasing (> 1), decreasing (< 1) or constant (= 1) returns to scale. In log-log form,
parameters represent cost elasticities for pipeline length and diameter.
Using ten years’ (1995 - 2005) worth of cost data for natural gas lines, McCoy and Rubin
(2008) estimate a log-linearized Cobb-Douglas production function for each of the four cost
categories, and they evaluate returns to scale within each category. Readers may find their
results interesting, though not surprising—for example material, labor and miscellaneous
costs exhibit increasing returns to scale (lower costs per unit for longer and larger lines) but
ROW costs increase one-for-one with length.
We adopt a similar model but focus solely on total construction costs. Our data rep-
resents fifteen years worth of land construction costs for lines constructed in the Central
geographic region (which includes Wyoming) as defined by the EIA (EIA, 2010a).3 Costs
are taken from annual articles in the Oil and Gas Journal and are adjusted to 2009 dollars
using the Marshall and Swift equipment cost index (Smith, 2006, 2007, 2008, 2009; True,
1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003; True and Stell, 2004) and (Mar-
shall and Swift Index, 2009). We eliminate lateral lines (extensions to existing lines) and
loops (segments that parallel existing lines) that may skew the results downward, resulting
in 34 data points. Pipelines that cross roads and rivers are also excluded from the data as
3McCoy and Rubin (2008) found significant cost differences between regions, with costs in the Centralregion significantly lower than costs in the Northeastern region.
67
listed in the Oil and Gas Journal. By regressing LCC on length and diameter we estimate
the parameters b1, a1, and a2. Results are listed in table 4.1. The R2 of almost 0.96 indicates
that most of the variation in cost is explained by variation in length and diameter, while the
estimated values for parameters are statistically significant at the 1% level.
Table 4.1: Estimation results for equation (4.3)
Variable Coefficient (Std. Err.)log nps 1.249∗∗ (0.222)log length 0.854∗∗ (0.041)Intercept 4.389∗∗ (0.290)
N 34R2 0.958F (2,31) 354.853.Significance levels : † : 10% ∗ : 5% ∗∗ : 1%
By our model, approximate land construction costs can be estimated according to the
following equation,
Kpl = 24, 491 ∗D1.249 ∗ L0.854. (4.3)
Figure 4.1 shows the construction cost and construction cost plus ten percent versus the
mass flow rate predicted by our model for a 100 km segment. The stepped appearance is due
to the fact that a one-step increase in NPS (representing a 2” increase in inner diameter)
accommodates a range of mass flow rates. Our model predicts a capital cost of 880.5 million
USD for the entire network of 28 segments and 13 pump stations. In per-inch-mile terms,
our model predicts a cost of $62,539 for a 125-km long, 16”-diameter line (substantially lower
than the estimate of $78,116 per-inch-mile listed in INGAA (2009) for a segment of identical
length and diameter), and a bit less than Phillips (2009)’s estimate of $70,000 per inch-mile.
However CO2 pipelines require thicker pipe (more welding and higher transportation
costs), CO2-resistant elastomers around valves and other fittings, and fracture arrestors
every 1,000 feet due to higher fracture propagation related to the slower decompression
characteristics of CO2 (INGAA, 2009). According to Essandoh-Yeddu and Gulen (2008),
industry estimates that the additional cost for these reinforcements is about 10%, which
would increase our per inch-mile costs to about $69,000.
68
0 500 1000 1500 2000 25000
2
4
6
8
10
12 x 107
Mass Flow Rate (Mmcfpd)
LCC
(200
9 U
S $)
Cost vs. Flow (100km segment)
LCC110% LCC
Figure 4.1: Capital cost and capital cost plus 10% versus mass flow rate given a 100 km longsegment.
Our model agrees well with another cost model for natural gas pipelines created by
the Pacific Northwest National Laboratory (PNNL) (Brown et al., 2011). PNNL generated
equations for average cost/mile for three of the four cost categories reported in the Oil &
Gas Journal (materials, labor and ROW). The equations are based on 30 years’ worth of
data representing 2,000 pipeline segments from all regions. Like McCoy and Rubin (2008),
they too developed different equations for different regions. Rather than follow the EIA’s
delineation of regions by state, they chose their own, each representing an area of similar
geography and population distribution. The region that includes Wyoming also includes
Nevada, Idaho, New Mexico, Arizona, Montana, Utah and Colorado. Cost equations for
that region are listed as
Cmat = 53, 904 ∗ exp(0.0678) ∗D,
Clab = 2.065 ∗ 7127.9 ∗D1.1641,
Crow = 2.302 ∗ (1112.9 ∗D + 19180) .
69
0 500 1000 1500 2000 25000
2
4
6
8
10
12 x 107
Mass Flow Rate (Mmcfpd)
LCC
(200
9 U
S $)
Cost vs. Flow Newcomb and PNNL
NewcombPNNL
Figure 4.2: Capital cost as calculated by Newcomb’s model vs. as calculated by the modelgenerated by Pacific Northwest National Laboratories (segment length of 100 km).
Cmat, Clab and Crow are materials, labor and right of way costs, respectively while D
is diameter. Total cost per mile is simply the sum of the three category costs. Multiplying
this total by the length of any segment gives the total LCC for that segment. Figure 4.2
compares costs generated by our model to those generated by PNNL’s over a range of mass
flow rates given a segment length of 100 km.
Finally, we note that Denbury Resources is currently completing a 320-mile, 24” pipeline
that will ship up to 800 MMcfpd at a projected total cost, including right of way, materials,
and engineering and installation costs, of approximately $730 million or around $95,000 per
inch per mile (Denbury, 2009; Evans, 2009). Using our model, we can replicate this pipeline
in two ways. The first is to calculate the cost of the entire line, 515 km in length, all at once
without pump stations. In doing so we calculate an NPS of 24”, a total capital cost of $268.7
million and a per-inch-mile cost of around $56,300. The second, which is the method used in
the cost calculation for our network, is to calculate the cost of four segments: three of length
150 km, each with a pump station at the 150-km mark, and one of length 65 km that has
no pump station. In this scenario NPS is calculated to be 22”, total capital cost is $307.9
70
million, and per inch-mile cost is about $70,400. Considering that McCoy and Rubin (2008)
find construction costs in the Central region to be almost 34% lower than in the Midwest
region, we believe our cost estimates are accurate within a margin of error appropriate for
the scale at which we are working.
4.1 Pump Station Costs
Recall from Chapter 2 that we do not include the cost of compression at the source but
include pump stations in the line to maintain super-critical pressures. Our design requires
13 pump stations. We calculate pumping costs based on equations found in McCollum and
Ogden (2006). We use an electricity cost of 4.92 cents/kWh, the average price of industrial
electricity in Wyoming over ten years as of June, 2010 (EIA, 2010b). Note, however, that
under most CCS scenarios, the cost of electricity could be substantially higher. Also note that
EOR requires that CO2 be pressurized above minimum miscibility pressure upon delivery,
which may necessitate booster stations at the terminus of the line (van ’t Veld and Phillips,
2010). And if the CO2 is over-pressurized relative to the needs of the EOR or ECBM
producer, a system of orifices must be used to lower the pressure (Towler, 2009).
71
Chapter 5
Tariff Calculation
We calculate a tariff using a simple static net present value (NPV) model. In the model
there is a one-time up-front investment required to cover the pipeline and pump station
capital costs.1 Subsequent future cash flows are based on the tariff per unit of CO2 times
the quantity of CO2 net of taxes and operating costs. Cash flows are discounted at a rate
consistent with the natural-gas-shipping industry average return on equity of 12% (Tonery
and Perez, 2010). The operational life-span of the pipeline is taken to be 30 years.2 We do
not, however, include a ramp-up period which would delay the maximum level of cash-flows
and result in higher tariffs. By setting the NPV equal to 0, we are essentially establishing the
minimum tariff at which the pipeline (or any segment thereof) could be built and operated
and still earn the desired return on equity. The model as formulated is
NPV =30∑t=1
QtTCO2(1− τtax)− xp − xom(1 + r)t
−Ktot, (5.1)
where Qt is the quantity of CO2 shipped (MMcf per year), TCO2 is the tariff (per mcf), τtax
is the tax rate (royalties, severance and property taxes), xp is the cost of pumping (electric
1It is realistic to expect that the network will be built in phases rather than all at once. Long-termcontracts among the beneficiaries of a particular phase (EOR and/or ECBM concerns and a pipeline concern),would likely support a combination of corporate debt and federal subsidies or guarantees sufficient to payfor that phase (INGAA, 2009).
2Some EOR fields are estimated by Cook (2009a) to operate for 40 years while others may operate foronly a year. As a trunk line, the pipeline may operate for more than 30 years. But 30 years is a typicalplanning horizon for pipeline operators.
72
rate times kW’s required), xom is the pipeline operating and maintenance cost, r is the cost
of capital and Ktot is the total cost of constructing the pipelines and pump stations.3
Qt is input by the user, τtax is set at 35% and xom is set at 2.5% of capital costs
(McCollum and Ogden, 2006).4 As for xp, according to EIA (2010b) the average price
of electricity for the industrial sector in Wyoming between 1990 and June, 2010 was 4.92
cents per KWh, a price obviously exclusive of any costs related to carbon capture. Since
our model is contingent on a CCS scenario that involves costly post-combustion capture
and compression, actual electricity prices could be 30% to 50% higher in the absence of
subsidies (INGAA, 2009). Predicting electricity prices in an environment of mandatory CCS
is beyond the scope of this paper, so we proceed for now based on past costs for industrial
electricity and set xp = $0.0492. Ktot is the land construction cost as estimated by our
cost-estimation model (based on segment length and NPS) plus the capital cost for pump
stations (based on the size of the pump as measured by horsepower). Our choice of r is
based on Tonery and Perez (2010) who lists 12% as the industry average return on equity for
operating natural gas pipelines. The article points out that some companies have reported
returns as high as 25% but that these companies are being investigated by the Federal Energy
Regulatory Commission (FERC) under suspicion that such rates of return are unreasonably
high.5 Results are in 2009 dollars.
The tariffs we calculate are dependent on a discount factor that only applies given several
important assumptions. First, we assume that some form of regulation will impose a cost
on the emission of carbon sufficient to incentivize the capture of carbon, whether from flue
gas or through coal-to-fuels technologies.6 Second, we assume a level of stability on the the
3There would also likely be a charge associated with defraying the costs of the regulatory agency thatoversees CO2 pipeline operations. But the fee is small and not worth including. According to FERC (2011b),payments made by operators of natural gas companies for this purpose amount to less than 0.2% of revenues.
4Heddle et al. (2003) uses a value of $5,000/mile (2002 dollars, $6,650/mile in 2009 dollars) independentof pipeline diameter.
5FERC is given statutory authority to regulate rates for natural gas under sections 4 and 5 of the NaturalGas Act (NGA), thereby capping profit potential for pipeline operators. CO2 pipelines are currently regulatednot by FERC but by the U.S. Department of Transportation’s Surface Transportation Board (STB), whowas given authority to regulate interstate pipelines transporting commodities other than water, oil or naturalgas. The STB only acts to regulate rates when a complaint is filed (Kelliher, 2008). Under current regulation,operators of CO2 pipeline networks may have more leeway in charging rates that could earn a rate of returnabove the average for the pipeline industry in general.
6According to Coddington (2010), the only regulatory impetus for CCS associated with power generation
73
regulatory front such that CO2 pipeline operators will be comfortable in assuming stability
in earnings over the course of the operating lifetime of the line.7 Taken together, these two
assumptions should lead to the fulfillment of the third and most important assumption: there
is a reliable supply of CO2 available such that EOR and ECBM producers are comfortable
committing to contracts for a level of supply sufficient to meet their requirements. Without
substantial certainty on each of these major fronts, the risk of an investment in a CO2
pipeline would be higher. Given higher risk, investors would demand a higher rate of return,
driving up the hurdle rate and increasing the discount factor.
To find TCO2 , first calculate an annuity factor, A, for a 12% cost of capital:
A =1
0.12− 1
0.12∗(
1
(1 + 0.12)30
). (5.2)
Annual payments, P , sufficient to pay back the initial investment of the pipeline’s capital
cost equal the total capital cost of the pipeline or segment, Ktot, divided by the annuity factor:
P =Ktot
A. (5.3)
These payments are also known as the equivalent annual cash flow. The tariff is then
calculated as
TCO2 =xc + xom + P
Q(1− xt). (5.4)
The tariff can also be found using a financial calculator or in MATLAB using the pvvar
function. Since the internal rate of return (IRR) is the discount rate that makes NPV
equal to 0, a suitable cross-check on the tariff is to make sure that the stream of cash flows
generated by charging our calculated tariff results in an NPV of 0 given an IRR of 12%. In
MATLAB the rate of return of a stream of cash flows can be found using the irr function. In
our code we do the calculations according to the formulas above, since pvvar and irr are part
of the Financial Toolbox, which is inexpensive for student use but less so for professional use.
thus far stems from recent EPA decisions regarding CO2 as a pollutant. Regulations regarding the emissionof CO2 due to this ruling only impact new sources of emissions or major plant upgrades, in which caseCCS must be considered as a possible best available control technology (BACT). Operators will not have toimplement CCS if they can prove that it is too costly or technologically unattainable.
7See Wolfe (2010) for a good overview of the current regulatory structure surrounding shipping CO2 bypipeline.
74
0 500 1000 1500 2000 25000
1
2
3
4
5
Mass Flow Rate (Mmscfpd)
Tarif
f (U
S $/
tonn
e)
Tariff per tonne vs. Mass Flow (100km segment)
$/tonne$/tonne for 110% LCC
Figure 5.1: Tariff ($ per tonne) versus mass flow rate given a 100-km long segment.
Per-segment tariffs are listed in tables 5.1 through 5.4. For a 100-km segment of pipeline,
the calculated tariff per mcf in US cents is given in figure 5.1. Tariff versus segment length,
given a mass flow rate of 350 MMcfpd, is shown in figure 5.2. And tariff versus both length
and mass flow is shown in figure 5.3.
5.1 Comparisons with Other Estimates
We considered three different approaches to calculating tariffs within our network. One
would be segment by segment, as listed in tables 5.1 through 5.4. The second would be to
calculate the tariff over the distance traveled by the CO2 from its source to where it would
exit the pipeline, a method similar to how natural gas transmission tariffs are listed according
to areas and segments.8 By this method, operators of the Elk Basin field would pay the sum
of the per-segment tariff over the ten segments required to ship CO2 from the Jim Bridger
plant to the off-loading point at the terminus of the trunk line, an amount totaling $7.46
8See FERC (2011a) for an example of a tariff schedule filed with FERC by a natural gas transmissioncompany.
75
0 50 100 1500
0.5
1
1.5
2
2.5
Segment Length (km)
Tarif
f (U
S $/
tonn
e)
Tariff per tonne vs. Length (350 MMcfpd)
$/tonne$/tonne for 110% LCC
Figure 5.2: Tariff ($ per tonne) versus segment length given a mass flow rate of 350 MMcfpd.
per tonne ($0.38 per mcf). Operators off-loading CO2 at the Kirby junction, that is several
segments closer, would pay only $4.69 per tonne ($0.24 per mcf). The third approach is to
estimate a levelized, system-wide tariff. At full capacity, 2,428 MMcf of CO2 would move
through our network in a day. If this flow rate were maintained over a full year, the levelized
tariff under our set of assumptions, and given a capital cost of $880.5 million, would be $4.87
per tonne ($.25 per mcf). In making comparisons with other models, we generally use the
segment-by-segment calculations.
Direct comparisons of our estimated tariffs to others in the literature also must take
into account whether or not compression costs are included, as well as whether the mass
flow rates under consideration are low enough to be in the steep (red) portion of the the
tariff surface depicted in Figure 5.3. Compression has substantial capital and operating costs
that increase tariffs. Furthermore tariffs at low volumes vary widely due to rapid changes
in pipeline capital costs before efficiencies of scale are achieved at flow rates of around 350
MMcfpd (again see Figure 5.3), and some estimates in the literature are based on relatively
low-flow scenarios. Robertson (2009), for example, calculates the total capital cost, including
compression, for shipping 2,383,000 m3 per day (84 MMcfpd) via an 80 km (∼50 mile)
76
0 50 100 150 200 250 0500
10001500
20002500
0
1
2
3
4
5
6
7
8
9
Mass flow rate (MMcfpd)
Tariff vs. length and flow rate
Segment Length (km)
Tarif
f ($/
tonn
e)
Figure 5.3: Surface plot of tariff ($ per tonne) versus segment length (km) and mass flowrate (MMcfpd).
77
Table 5.1: Tariff per segment for the Green River and Wind River Basins portion of CO2
pipeline network, based on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR ap-plications.
Green River and Wind River BasinsSeg.no.
From: To: Length(km)
Massflow(Mm-scfpd)
NPS(in)
Tariff($ permcf/$ pertonne)
1. PCEC Pump Station I 150 175 10 0.15/3.022. Pump Station I Naughton 85 175 10 0.09/1.853. Naughton Painter Jct. 21 444 18 0.02/0.344. Painter Jct. ExxonMobil line
(EM1)64 428 18 0.05/1.01
5. EM1 Jim Bridger 90 428 18 0.07/1.366. Jim Bridger EM2 23 537 18 0.02/0.307. EM2 Crooks Gap 102 537 18 0.06/1.088. Crooks Gap Big Sand Draw 53 531 18 0.04/0.869. Big Sand Draw Beaver Creek 15 516 18 0.01/0.2210. Beaver Creek Butte Jct. 36 516 18 0.02/0.4611. Butte Jct. Lake
Cr./MurphyDome
91 467 18 0.07/1.37
pipeline to be $522,000/km (which equals a total cost of $41,760,000). His cost estimates for
compression are based on the equipment used at the ExxonMobil Shute Creek facility and
come to around $39,500,000. He calculates a tariff of $0.000201/m3/km ($0.00000916/cf/mile
or $0.46/mcf). Mohan (2009) estimates transportation costs to be $8 - $9 per ton (∼$7.25 -
$8.15 per tonne or ∼$0.37 - 0.42/mcf). INGAA (2009) lists transportation costs for a flow
rate of 19,139 tonnes per day (361 MMcfpd), through a 75-mile (120-km) long pipeline that
is 16” in diameter, as $3.25 per tonne (∼$0.17/mcf). By our calculations the tariff would be
$1.59 per tonne.
Assuming a 30-year operating period and a 15% capital recovery factor, McCoy and
Rubin (2008) calculate that the cost of transporting 5 Mt of CO2 (∼260 MMcfpd, or ap-
proximately equal to the annual production of an 800 MW power plant) through a 100-km
78
Table 5.2: Tariff per segment for the Bighorn Basin portion of CO2 pipeline network, basedon mass flow rates given $70/bo and $2.25/mcf CO2 for EOR applications.
Bighorn BasinSeg.no.
From: To: Length(km/miles)
Massflow(Mm-scfpd)
NPS(in)
Tariff($ permcf/$ pertonne)
12. Lake Cr. /Mur-phy Dome
Kirby 26 455 18 0.02/0.40
13. Kirby Basin Center 19 273 14 0.02/0.3714. Basin Center Cody Spur 70 259 14 0.06/1.1915. Cody Spur Byron 30 257 14 0.04/0.8216. Byron Elk Basin 15 175 12 0.02/0.39
Table 5.3: Tariff per segment for the Southeastern portion of CO2 pipeline network based,on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR applications.
East Wind River/South Powder River BasinsSeg.no.
From: To: Length(km/miles)
Massflow(Mm-scfpd)
NPS(in)
Tariff($ permcf/$ pertonne)
17. Jim Bridger Patrick Draw 24 657 20 0.01/0.2918. Patrick Draw Sinclair 120 648 20 0.07/1.4119. Sinclair Med. Bow Fuels 77 648 20 0.05/0.9320. Med. Bow Fuels Col. Int. Gas 40 858 22 0.02/0.3921. Col. Int. Gas Dave J. Plant
Jct.106 858 22 0.05/1.07
22. Laramie RiverPlant
Dave J. PlantJct.
109 639 20 0.06/1.27
segment of pipeline in the Central region to be around $0.77 per tonne (2004 USD), while
we calculate a tariff of $1.60 per tonne (2009 USD) for shipping a similar amount over a
similar distance.9
9They calculate a transportation cost of $1.16 per tonne for the Midwest region using a similar set ofassumptions.
79
Table 5.4: Tariff per segment for the Powder River Basins portion of CO2 pipeline network,based on mass flow rates given $70/bo and $2.25/mcf CO2 for EOR applications.
Powder River BasinSeg.no.
From: To: Length(km/miles)
Massflow(MM-cfpd)
NPS(in)
Tariff($ permcf/$ pertonne)
23. Dave J. Plant Dave J. PlantJct.
9/6 300 16 0.01/0.29
24. Dave J. Plant Grieve Jct. 39 10 4 0.20/3.9025. Dave J. Plant
Jct.Phillips etc. 120 2,410 34 0.04/0.80
26. Phillips etc Maysdorf 24 1,760 30 0.01/0.1827. Maysdorf Wyodak 26 1,060 24 0.01/0.2428. Wyodak Big Horn Gas
Gathering58 445 18 0.04/0.80
Smart and Helmke (2009) calculate transportation costs for ten pipelines designed to
transport CO2 from various power plants in Wyoming and Southern Montana to the Northern
House Creek Field sequestration site in the PRB and to the Moxa Arch sequestration site
near La Barge. They find that, given a 12% interest rate and 30-year facility time span, the
average annual transportation cost per ton ranges from $0.89 to $7.32.
The most direct comparison would be to the rates generated by the example network
proposed by Reyes (2009) and Jeffries (2009). Their network costs roughly a billion US
Dollars. Under one scenario, tariffs are roughly $15 per tonne ($0.90 per mcf), while under a
second scenario tariffs are roughly $8 - $9.50 per tonne ($0.45 - $0.55 per mcf). However they
assume a ten-year return on capital and a ten-percent blended cost of capital. Assuming a
ten-year, rather than 30-year, return on capital substantially increases the tariff. Assuming
a ten-percent cost of capital brings it back down, but not to a level matching our results.
For example, under the scenario described above in INGAA (2009), decreasing the return
on capital to ten years raises the tariff to $3.61 per tonne. Lowering the discount factor to
ten-percent brings it down to $2.00/tonne, a rate still well above our calculated $1.59 per
80
tonne.
Our tariffs are lower than those generated by Jeffries (2009) for three possible reasons.
One is the economies of scale achieved in our pipeline by shipping large quantities. The vast
majority of segments ship quantities in excess of 350 MMcfpd, thereby incurring tariff rates
well into the blue range in figure 5.3. The second may be due to our chosen cost of capital.
In actuality companies may seek a higher rate of return, which in turn would drive up tariff
rates. Finally, our calculated tariffs are only for shipment via the trunk line. Shipment from
the trunk line to the point of demand would incur additional costs and increase the overall
tariff.
81
Chapter 6
Conclusion
This study seeks to build on the substantial (and growing) body of research at the University
of Wyoming that examines the energy industry in Wyoming from the perspective of both
productivity and carbon management. The research is being carried out by professors and
students across many different departments that are associated with the School of Energy
Resources, the School of Environment and Natural Resources and the Carbon Management
Institute. Research carried out through the Enhanced Oil Recovery Institute, affiliated with
the University of Wyoming, has also made significant contributions to the field of CO2
management and enhanced hydrocarbon recovery. Their research plays a significant role in
the development of our vision of a statewide pipeline network for shipping CO2. We seek to
fill an important niche, not only by designing the pipeline network, but also by developing
tools that could be used by others who are considering doing research on energy and CCS
infrastructure projects.
In determining the final design capacity of the pipeline, we had to choose whether
to design the pipeline to handle much, if not most, of the state’s production of CO2, or
whether to design the pipeline to handle the current demand for CO2 based on current CO2
and fossil fuel prices. At today’s prices, all the available supply of CO2 is spoken for, and
no further sources are known to exist. In order for more CO2 to become available, either
the cost of fossil fuels must rise to a point such that the owners of enhanced hydrocarbon
recovery operations are able and willing to purchase CO2 captured at substantial cost from
82
anthropogenic sources, or the cost to the emitters of capturing CO2 must be reduced through
subsidies. Or, of course, some combination of these two options must occur. Since private
efforts have already built one network and laid the groundwork for a second—networks
considered economical given current prices—we felt it would be more beneficial to examine
the other extreme: a scenario of mandated or subsidized capture such that the price for CO2
faced by owners of enhanced hydrocarbon operations is in line with prices that exist today.1
Under such a scenario, the supply of CO2 is dramatically increased.
In deciding to assume a mandatory capture scenario, we faced considerable uncertainty
in estimating the total sequestration potential of the state’s enhanced hydrocarbon recov-
ery industry. As a result, there is ample opportunity for future research. Our forecasts of
injection rates, sequestration rates and mass flow rates should be revised as energy policy
and technology evolve. Given the growing focus on efforts to utilize our nation’s vast coal
resources in a manner that avoids releasing the bulk of their carbon content into the atmo-
sphere, it is safe to assume that current and future research will provide much data that
should be used to revise the estimates and assumptions made in our study. Indeed coal
gasification and coal liquefaction are two technologies that appear to be both feasible and
on the verge of economic viability (OGJ, 2011a,c).
Our research required seven phases. The first was to compile the research done by
van ’t Veld and Phillips (2010) and Cook (2010) to estimate EOR demand. The second
was to analyze research on ECBM in Wyoming coals and determine theoretical mass flow
rates that could be generated from ECBM activity. The third was to calculate total CO2
supply based on the assumption that costs are imposed on the emission of CO2, thereby
incentivizing emitters to capture and compress their CO2 emissions. The fourth was to
develop an algorithm for calculating pipeline diameters in order to determine final capital
costs. The fifth was to develop a cost estimation model specific to Wyoming. The sixth was
to lay out the network and calculate its capital cost. The seventh and final phase was to
estimate the tariff (or tariff structure) that a pipeline operator would need to charge in order
to earn back their capital expenditure based on a 12% return on equity (any tariff higher
1The ACES legislation (aka Waxman-Markey Bill) is the best example of a regulatory effort that wouldhave subsidized capture and possibly brought this scenario to fruition (Cook, 2010).
83
than our calculation would earn a positive return on capital).
Each of these phases presents opportunities for more research across many fields, from
geology to reservoir engineering to economics to environmental and energy-policy analysis.
While the pipeline network contemplated by this research may never get built, the process
may be useful none the less. Our pipeline-diameter algorithm and cost-estimation models
will hopefully be valuable tools for students and professors interested in evaluating the costs
associated with various CCS and energy transportation schemes. And though this study in
general is somewhat of a thought experiment, we feel it is helpful in framing future debates
about energy and climate policy.
84
Appendix A
Simulation Input Parameters for
ECBM Models
Figure A.1: Some parameter values used for Robertson’s (2008) coal-seam model.Source: Robertson (2008)
Figure A.2: Some parameter values used for Robertson’s (2009) coal-seam model.Source: Robertson (2009)
85
Figure A.3: Some parameter values used for Ross’s (2009) coal-seam model.Source: Ross et al. (2009)
86
Appendix B
MATLAB Code for Pipeline
Diameter, Cost and Tariff
Calculations
Users may calculate a diameter, capital cost and tariff either one segment at a time or
several segments at once by using a pre-configured network laid out in an Excel xls file. If
calculating segment by segment, follow the instructions. If calculating the diameter and cost
of a segment longer than 150 km, include a pump station at the 150-km mark. If calculating
the diameter and cost for a segment terminating at a junction with a new source coming on
line at 15.3 MPa, include a pump station at the end point of the segment. After entering all
segments, the routine asks for the total amount of CO2 being shipped. This is the sum of all
CO2 coming on-line. Entering this value allows the program to calculate the network-wide
tariff.
If calculating a pre-configured network, create a spreadsheet using the included template.
Save the template in the directory from which you are working, otherwise MATLAB won’t
find it.
The algorithm uses an iterative process to find a diameter associated with a Reynolds
number and Fanning friction factor that are appropriate for that particular diameter. We
assume that all CO2 entering the pipeline network has been compressed to 15.3 MPa (2,219
87
psi) and cooled to 15 C (59 F.). Pressures within the pipeline should remain above 10.3 MPa
in order to comfortably avoid dropping below critical pressure (Heddle et al., 2003). So we
include pump stations where needed to account for pressure drop or to equalize pressures
wherever new CO2 enters the supply line (i.e the CO2 stream already in the line must be
repressurized to match the pressure of the freshly compressed CO2 entering the line (15.3
MPa)). To account for pressure drop and to maintain critical pressure, we place a pump
station wherever CO2 has traveled about 160 km without re-pressurization.1
Contents
• BEGIN
• MANUAL ENTER
• NPS
• PUMP CAPITAL COST
• PIPE CAPITAL COST
• TARIFF
• DISPLAY OUTPUT
• CALC ARRAY
• BUILD ARRAY
• CALC TOTALS
• GRAPH ARRAY
• FIND ROW & COL:
• DIAM VS FLOW (length set at 100 km)
• LCC VS FLOW (length set at 100 km)
• LCC VS FLOW (KM = 100, NEWC VS PNNL)
• TARIFF VS FLOW (length set at 100 km)
• TARIFF VS LENGTH (mass flow rate set at 350 Mmscfpd)
1Based on McCollum and Ogden (2006), we assume a pressure drop of 35 kPa per kilometer. Others,including Heddle et al. (2003), assume a maximum pressure drop of 49 kPa per kilometer. INGAA (2009)provides a good overview of CO2 transportation requirements as well as pipeline design considerations anddesign parameters for different flow rates.
88
• TARRIFF SURFACE CALCULATIONS
• TARRIFF SURFACE
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code for calculating pipeline diameter, NPS, pipe wall wall, pump %
% capital costs, total pipe and pump capital cost and tariff. The code %
% allows calculations to be made either one seg at a time or for more %
% than one seg at a time by passing in a pre-configured spreadsheet. %
% Graphs can also be generated to evaluate capital cost versus mass flow %
% rate, tariff versus mass flow rate, NPS versus mass flow rate and %
% tariff versus length %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
BEGIN
clear all;
close all;
global r;
global mps;
graph = 0;
blank = (’ ’);
disp(blank)
disp(blank)
MANUAL ENTER
query = input(...
89
’Are you calculating one segment at a time? [y/n]: ’,’s’);
if query == ’y’
enter = 1;
seg = 0;
while enter
clear D D_final pa_1 pa_2 f fan Re NPS iter diff;
seg = seg + 1; % Increase the seg counter by one.
disp(blank) % Create space between last output.
disp(blank)
fprintf(’SEGMENT NUMBER %d.\n’,seg) % print seg no.
disp(blank)
% Input parameter values
km(seg) = input...
(’Enter length of the pipeline seg in km: ’);
mile(seg) = km(seg)*0.621371192;
disp(blank)
query = input(’Is a pump station required? [y/n] ’,’s’);
disp(blank)
pump_dist(seg) = 0;
pump(seg) = 0;
if query == ’y’
pump(seg) = 1;
pump_dist(seg) = input(...
’Length of span over which pressure drop occurs (km): ’);
end
disp(blank)
mmcf(seg) = input(’Enter mass flow rate per day (MMcfpd): ’);
90
NPS
% Call a function that calculates NPS, wall thickness and tons of
% steel:
[tonnespd(seg) pa_1 pa_2 mps(seg) NPS(seg) wall(seg) tons(seg)]...
= pl_nps_calc(km(seg),mmcf(seg));
PUMP CAPITAL COST
% Call a function that calculates pump cap costs and power
% requirements:
[K_pump(seg), Wp(seg), Wp_hp(seg)] = ...
pl_pump_calc(pump(seg),pump_dist(seg),tonnespd(seg),pa_1,pa_2);
PIPE CAPITAL COST
% Estimate capital cost using the statistical model developed by
% Newcomb:
K_pl(seg) = 24491*(NPS(seg)^1.249193)*(km(seg)^0.8540587);
K_tot(seg) = K_pl(seg) + K_pump(seg);
% Oil Gas Journal equations (developed by Pacific Northwest National
% Laboratory) see OGJ, Jan. 3, 2011:
mat_cost = 53904*exp(0.0678*NPS(seg));
lab_cost = 2.065*7127.9*NPS(seg)^1.1641;
row_cost = 2.302*(1112.9*NPS(seg)+19180);
K_tot_pnnl(seg) = mile(seg)*(mat_cost+lab_cost+row_cost)...
+ K_pump(seg);
% Estimate cost per inch-mile:
per_in_mile(seg) = (K_tot(seg)*1.609)/(NPS(seg)*(km(seg)/1.609));
91
% Estimate cost per tonne
tonnespy(seg) = tonnespd(seg)*365;
costptonne(seg) = K_tot(seg)/(30*tonnespy(seg));
TARIFF
% Call a function that calculates the tariff:
[tariff_mcf(seg) tariff_tonne(seg)] = pl_tar_calc(K_tot(seg),...
Wp(seg),mmcf(seg));
DISPLAY OUTPUT
% Build a structure array with 50 pl segs, each containing fields
% for seg length, mass flow rate, NPS, pl wall, tons of steel,
% capital costs for each seg and pumps using Newcomb’s cost model,
% total capital cost, tariff per mcf and tariff per tonne.
% Populate the structure with values as calculated per segment:
pl(1,50) = struct(’seg_length’,[],’mmcfpd’,[],’NPS’,[],...
’pl_wall’,[],’tons’,[],’K_pump’,[],...
’K_pl’,[],’K_tot’,[],’tariff_mcf’,[],’tariff_tonne’,[]);
pl(seg).seg_length = km(seg);
pl(seg).mmcfpd = mmcf(seg);
pl(seg).NPS = NPS(seg);
pl(seg).pl_wall = wall(seg);
pl(seg).tons = tons(seg);
pl(seg).K_pump = K_pump(seg);
pl(seg).K_pl = K_pl(seg);
pl(seg).K_tot = K_tot(seg);
pl(seg).tariff_mcf = tariff_mcf(seg);
pl(seg).tariff_tonne = tariff_tonne(seg);
92
disp(blank)
disp(blank)
disp(blank)
fprintf(...
’NPS for a %d km seg with flow rate %d MMcfpd is %d inches.\n’,...
km(seg), mmcf(seg), NPS(seg))
disp(blank)
fprintf(...
’Land construction cost: %4.1f million USD.\n’,...
K_tot(seg)/10e+5)
if pump(seg)
fprintf(...
’Pump station capital cost: %4.1f million USD.\n’,...
K_pump(seg)/10e+5)
disp(blank)
end
disp(blank)
global r;
fprintf(...
’For a %d%% cost of capital, the tariff is %3.2f USD/mcf.\n’,...
r*100,tariff_mcf(seg))
fprintf(’(%3.2f USD/tonne).\n’,tariff_tonne(seg))
disp(blank)
query = input(...
’Is there another seg to calculate? [y/n] ’,’s’);
if query == ’y’, enter = 1; else enter = 0; end;
end
93
else
%clear D D_final pa_1 pa_2 f fan Re NPS iter diff pl;
clear all;
close all;
global r;
global mps;
graph = 0;
blank = (’ ’);
disp(blank)
enter = 0;
disp(blank)
fprintf(’Your data file must be placed in the folder from which\n’)
fprintf(’you are currently working.\n’)
disp(blank)
file_name = input(’Enter the name of your pipeline data file: ’,’s’)
new_array = xlsread(file_name);
no_segs = size(new_array,2);
seg = 0;
CALC ARRAY
while seg < no_segs
seg = seg + 1;
km(seg) = new_array(2,seg);
mmcf(seg) = new_array(3,seg);
94
pump(seg) = new_array(4,seg);
pump_dist(seg) = new_array(5,seg);
mile(seg) = new_array(2,seg).*0.621371192;
% Call a function that calculates NPS, wall thickness and tons of
% steel:
[tonnespd(seg) pa_1 pa_2 mps(seg) NPS(seg) wall(seg) tons(seg)]...
= pl_nps_calc(km(seg),mmcf(seg));
% Call a function that calculates pump cap costs and power
% requirements:
[K_pump(seg), Wp(seg), Wp_hp(seg)] = ...
pl_pump_calc(pump(seg),pump_dist(seg),tonnespd(seg),pa_1,pa_2);
% Estimate capital cost using the statistical model developed by
% Newcomb:
K_pl(seg) = 24491*(NPS(seg)^1.249193)*(km(seg)^0.8540587);
% Total capital cost for this segment:
K_tot(seg) = K_pl(seg) + K_pump(seg);
% Oil Gas Journal equations (developed by Pacific Northwest National
% Laboratory) see OGJ, Jan. 3, 2011:
mat_cost = 53904*exp(0.0678*NPS(seg));
lab_cost = 2.065*7127.9*NPS(seg)^1.1641;
row_cost = 2.302*(1112.9*NPS(seg)+19180);
K_tot_pnnl(seg) = mile(seg)*(mat_cost+lab_cost+row_cost)...
+ K_pump(seg);
95
% Call a function that calculates the tariff:
[tariff_mcf(seg) tariff_tonne(seg)] = pl_tar_calc(K_tot(seg),...
Wp(seg),mmcf(seg));
% Build a structure array with 50 pl segs, each containing fields
% for seg length, mass flow rate, NPS, pl wall, tons of steel,
% capital costs for each seg using Newcomb’s cost model,
% total capital cost, tariff per mcf and tariff per tonne.
% Populate the structure with values as calculated per segment:
pl(1,50) = struct(’seg_length’,[],’mmcfpd’,[],’NPS’,[],...
’pl_wall’,[],’tons’,[],’K_pump’,[],...
’K_pl’,[],’K_tot’,[],’tariff_mcf’,[],’tariff_tonne’,[]);
pl(seg).seg_length = km(seg);
pl(seg).mmcfpd = mmcf(seg);
pl(seg).NPS = NPS(seg);
pl(seg).pl_wall = wall(seg);
pl(seg).tons = tons(seg);
pl(seg).K_pump = K_pump(seg);
pl(seg).K_pl = K_pl(seg);
pl(seg).K_tot = K_tot(seg);
pl(seg).tariff_mcf = tariff_mcf(seg);
pl(seg).tariff_tonne = tariff_tonne(seg);
end
end
BUILD ARRAY
disp(blank)
disp(blank)
96
% Build cell array, pl_array, for export to Excel csv spreadsheet:
pl_array = cell(11,seg+1);
pl_array{1,1} = ’seg No.’;
for i = 1:seg, pl_array(1,i+1) = {i}; end;
pl_array{2,1} = ’seg Length (km)’;
pl_array{3,1} = ’Flow Rate (MMcfpd)’;
pl_array{4,1} = ’NPS’;
pl_array{5,1} = ’Wall Thickness (in)’;
pl_array{6,1} = ’Steel (tons)’;
pl_array{7,1} = ’Pump Cost (2009 $$)’;
pl_array{8,1} = ’Pipeline Cost (2009 $$)’;
pl_array{9,1} = ’Total Cost(2009 $$)’;
pl_array{10,1} = ’Tariff/mcf (2009 $$)’;
pl_array{11,1} = ’Tariff/tonne (2009 $$)’;
% Concatenate the values stored in the struct array labeled pl first by
% column then by row:
d(1,1:seg) = cat(2,pl.seg_length);
d(2,1:seg) = cat(2,pl.mmcfpd);
d(3,1:seg) = cat(2,pl.NPS);
d(4,1:seg) = cat(2,pl.pl_wall);
d(5,1:seg) = cat(2,pl.tons);
d(6,1:seg) = cat(2,pl.K_pump);
d(7,1:seg) = cat(2,pl.K_pl);
d(8,1:seg) = cat(2,pl.K_tot);
d(9,1:seg) = cat(2,pl.tariff_mcf);
d(10,1:seg) = cat(2,pl.tariff_tonne);
% Add the numerical data to the cell array pl_array:
97
[rows, cols] = size(pl_array);
for row = 2:rows
for col = 2:cols
pl_array{row,col} = [d(row-1,col-1)];
end
end
CALC TOTALS
Find the total distance, mass flow, tons of steel, pump cap ex, pipeline cap ex, pipeline plus
pump capex:
Totals = sum(d,2);
length_total = Totals(1);
mmcfpd_total = Totals(2); %Note: this does NOT represent total supply
tons_total = Totals(5);
K_pump_total = Totals(6);
K_pl_total = Totals(7);
K_tot_total = Totals(8);
Wp_tot = sum(Wp);
% Calc an overall tariff for the entire network:
T = 30; % Use a 30-year life-time for calculating NPV
%r = .12; % Use a 12% cost of capital for discounting cash streams
A = 1/r - 1/(r*(1+r)^T); %global r carries over from pl_tar_calc.m
payment_total = K_tot_total/A;
% Here Q_ann_tot needs to be the total overall supply coming on line:
Q_tot = input(...
’Enter the total network-wide supply to be shipped (MMcfpd): ’);
98
Q_ann_tot = Q_tot*365;
X_OM = .025*K_tot_total;
X_PUMP = .0492*Wp_tot*365*24;
X_TAX = .35; %35% tax rate
tariff_total = (payment_total + X_PUMP + X_OM)/(Q_ann_tot*(1-X_TAX));
tariff_mcf_total = (round((tariff_total/1000)*100))/100;
tariff_tonne_total = (round(((19.59701321/1000)*tariff_total)*100))/100;
disp(blank)
disp(blank)
fprintf(’NETWORK TOTALS:\n’)
disp(blank)
fprintf(...
’The capital cost of the entire network is %6.1f million USD.\n’,...
K_tot_total/10e+5)
disp(blank)
fprintf(...
’The network-wide tariff averages out to %3.2f USD/mcf or\n’,...
tariff_mcf_total)
fprintf(’%3.2f USD/tonne.\n’,tariff_tonne_total)
disp(blank)
fprintf(...
’The network is %d km long and will require %3.0f tons of steel.\n’,...
length_total,tons_total)
%}
% Export data as a comma separated file to an Excel spreadsheet named
% pl_output.csv:
pl_data = pl_array(2:rows,2:cols);
disp(blank)
99
disp(blank)
fprintf(’Writing file to Excel file pl_ouput.csv. Columns correspond to\n’)
fprintf(...
’segs and rows correspond to labels in pl_array. Ignore Warning.\n’)
disp(blank)
disp(blank)
% Download the file as file name pl_output.csv in folder pl_data:
%xlswrite(’pl_output.csv’,pl_data)
% Use this when testing the program
xlswrite(’pl_output.csv’,pl_data)
if graph
GRAPH ARRAY
% Here we generate a structure array populated with data for graphs.
% Flow rates range from 50 to 2400 Mmscfpd in increments of 50 and pipeline
% lengths range from 20 to 160 km in increments of 10. We then generate a
% vector of construction costs and tariffs for a segment length of 100 km.
% Next we generate a vector of tariffs for a mass flow rate of 350 mmcfpd.
% Finally we generate two vectors, diam and flow, to be used on the x axes
% of the graphs. Variables with ’ten’ are for costs 10% greater than
% calculated.
clear all;
close all;
km_low = 10;
km_high = 160;
100
array_height = (km_high - km_low)/10 + 1;
mmcf_low = 50;
mmcf_high = 2400;
array_width = (mmcf_high - mmcf_low)/50 + 1;
graph_array(array_height,array_width) = struct(’km’,[],’mmcf’,[],...
’tonnespd’,[],’NPS’,[],’K_tot’,[],’K_tot_ten’,[],’K_tot_pnnl’,[],...
’tar_mcf’,[],’tar_tonne’,[],’tar_nl_mcf’,[],’tar_mcf_ten’,[],...
’tar_tonne_ten’,[],’in_mile’,[]);
row = 1;
for km = (km_low:10:km_high) %Loop through lengths (rows)
col = 1;
for mmcf = (mmcf_low:50:mmcf_high) %Loop through flow rates (cols)
% Populate cells of structure with data:
graph_array(row,col).mmcf = mmcf;
graph_array(row,col).km = km;
% NPS FOR GRAPHS
% Call a function that calculates NPS, wall thickness and tons
% of steel:
[tonnespd pa_1 pa_2 NPS wall tons] = pl_nps_calc(km,mmcf);
graph_array(row,col).NPS = NPS;
% PIPE CAPITAL COSTS FOR GRAPHS
% Estimate capital cost using the statistical model developed
% by Newcomb (assume no pump station costs):
graph_array(row,col).K_tot = 24491*(NPS^1.249193)*(km^0.8540587);
graph_array(row,col).K_tot_ten = graph_array(row,col).K_tot*1.1;
101
% Estimate capital cost using Oil Gas Journal equations (developed
% by Pacific Northwest National Laboratory) see OGJ, Jan. 3, 2011:
miles = 0.6214*km;
mat = 53904*exp(0.0678*NPS);
lab = 2.065*7127.9*NPS^1.1641;
r_way = 2.302*(1112.9*NPS+19180);
graph_array(row,col).K_tot_pnnl = miles*(mat+lab+r_way);
graph_array(row,col).in_mile = ((graph_array(row,col).K_tot/km)...
*1.6329)/NPS;
% TARIFFs FOR GRAPHS
% Call a function that calculates the tariff (assume Wp = 0):
Wp = 0;
[graph_array(row,col).tar_mcf, graph_array(row,col).tar_tonne]...
= pl_tar_calc(graph_array(row,col).K_tot,Wp,mmcf);
[graph_array(row,col).tar_mcf_ten,...
graph_array(row,col).tar_tonne_ten]...
= pl_tar_calc(graph_array(row,col).K_tot_ten,Wp,mmcf);
[graph_array(row,col).tar_nl_mcf...
graph_array(row,col).tar_nl_tonne]...
= pl_tar_calc(graph_array(row,col).K_tot_pnnl,Wp,mmcf);
col = col+1; % Move to next column (to next mass flow rate).
end
row = row+1; % Move to next row (to next length).
end
FIND ROW & COL:
Here we find the row that corresponds to 100 km and the column that corresponds to mmcf
102
= 350. These will be used to make the graphs.
for i = 1:(row-1)
if graph_array(i,1).km == 100, this_row = i; break; end;
i=i+1;
end
for j = 1:(col-1)
if graph_array(1,j).mmcf == 350, this_col = j; break; end;
j=j+1;
end
flow_vec = [graph_array(this_row,:).mmcf];
nps_vec = [graph_array(this_row,:).NPS];
K_tot_vec = [graph_array(this_row,:).K_tot];
K_ten_vec = [graph_array(this_row,:).K_tot_ten];
K_tot_pnnl_vec = [graph_array(this_row,:).K_tot_pnnl];
tar_t_vec = [graph_array(this_row,:).tar_tonne];
tar_t_ten_vec = [graph_array(this_row,:).tar_tonne_ten];
km_vec = [graph_array(:,this_col).km];
tar_t_vec2 = [graph_array(:,this_col).tar_tonne];
tar_t_ten_vec2 = [graph_array(:,this_col).tar_tonne_ten];
% Here we produce graphs of diameter vs mass flow, cost vs mass
% flow, tariff vs. mass flow and tariff vs length for cost as calculated
% and for costs 10% higher than calculated.
DIAM VS FLOW (length set at 100 km)
pl_fig_diam_flow(flow_vec,nps_vec)
103
LCC VS FLOW (length set at 100 km)
pl_fig_lcc_flow(flow_vec,K_tot_vec,K_ten_vec)
LCC VS FLOW (KM = 100, NEWC VS PNNL)
pl_fig_lcc_flow2(flow_vec,K_tot_vec,K_tot_pnnl_vec)
TARIFF VS FLOW (length set at 100 km)
pl_fig_tar_flow(flow_vec,tar_t_vec,tar_t_ten_vec)
TARIFF VS LENGTH (mass flow rate set at 350 Mmscfpd)
pl_fig_tar_length(km_vec,tar_t_vec2,tar_t_ten_vec2)
TARRIFF SURFACE CALCULATIONS
This cell generates a 3-D surface of tariff versus segment length and mass flow rate.
clear all;
close all;
km_low = 5;
km_high = 240;
mmcf_low = 50;
mmcf_high = 2400;
kmVec = km_low:5:km_high;
mmcfVec = mmcf_low:50:mmcf_high;
dim = size(kmVec,2);
tar_mcfMat = zeros(dim,dim);
104
tar_tonneMat = zeros(dim,dim);
tar_nl_mcfMat = zeros(dim,dim);
tar_nl_tonneMat = zeros(dim,dim);
kmMat = zeros(dim,dim);
mmcfMat = zeros(dim,dim);
row = 1;
for km = kmVec %Loop through lengths (rows)
col = 1;
for mmcf = mmcfVec %Loop through mass flow rates (cols)
% NPS FOR SURFACE
% Call a function that calculates NPS, wall thickness and tons of
% steel:
[tonnespd pa_1 pa_2 NPS wall tons] = pl_nps_calc(km,mmcf);
% PIPE CAPITAL COSTS FOR SURFACE
% Estimate capital cost using the statistical model developed by
% Newcomb (assume no pump station costs):
K_tot = 24491*(NPS^1.249193)*(km^0.8540587);
% Estimate capital cost using Oil Gas Journal equations (developed
% by Pacific Northwest National Laboratory) see OGJ, Jan. 3, 2011:
miles = 0.6214*km;
mat = 53904*exp(0.0678*NPS);
lab = 2.065*7127.9*NPS^1.1641;
r_way = 2.302*(1112.9*NPS+19180);
K_tot_pnnl = miles*(mat+lab+r_way);
105
%Calculate per inch-mile cost:
in_mile = ((K_tot/km)*1.6329)/NPS;
% TARIFFs FOR SURFACE
% Call a function that calculates the tariff (assume Wp = 0):
Wp = 0;
[tar_mcfMat(row,col), tar_tonneMat(row,col)] =...
pl_tar_calc(K_tot,Wp,mmcf);
[tar_nl_mcfMat(row,col), tar_nl_tonneMat(row,col)] =...
pl_tar_calc(K_tot_pnnl,Wp,mmcf);
col = col+1; %Move to next column (to next mass flow rate).
end
row = row+1; %Move to next row (to next length).
end
TARRIFF SURFACE
grid on;
[kmMat,mmcfMat] = meshgrid(kmVec,mmcfVec);
surf(kmMat,mmcfMat,tar_tonneMat);
xlabel(’Segment Length (km)’,’Fontsize’,10);
ylabel(’Mass flow rate (MMcfpd)’,’Fontsize’,10);
zlabel(’Tariff ($/tonne)’,’Fontsize’,10);
title(’Tariff vs. length and flow rate’,’Fontsize’,14);
view(20,20);
end
function [tonnespd,pa_1,pa_2,mps,NPS,thickness,tons]...
= pl_nps_calc(km,mmcf)
106
% Carry out conversions to SI units
mcf = mmcf*1000;
meters = km*1000;
tonnespd = (1/19.01752794)*mcf; % MMcf to tonnes/day.
kgps = tonnespd*1000/(24*60*60); % Tonnes/day to kg/s.
kgpd = tonnespd*1000; % kilograms per day
m3pd = 556.0125245*tonnespd; % meters^3 per day
mpa_1 = 15.3; % upstream pressure is 15.3 MPa
psia_1 = 145.037738*mpa_1; % Convert to psi for velocity calc below.
% McCoy06 lists a press drop of 35 kPa/km. Herzog06 lists 49Pa/m.
pa_1 = mpa_1*1000000; % Convert MPa to Pa for McCoy-Rubin model.
pa_2 = pa_1 - 35*meters; % pressure drop of 35 kPa/km (=35Pa/meter).
% Pressure varies non-linearly. McCoy and Rubin (2009) equation (3):
P_ave = (2/3)*(pa_2+pa_1-pa_2*pa_1/(pa_2+pa_1)); %Pascals
kpa_1 = mpa_1*1000; % Convert initial pressure to kPa
kpa_2 = pa_2/1000; % Coinvert ending pressure to kPa
% Beginning and ending elevations (6,000 ft. ~ 1830 m).
h_1 = 1830; % m above sea level
h_2 = 1830;
% Compressibility and viscosity both change as pressure and temp change. We
% assume a constant temp (12C), but a variable pressure due to pressure drop.
% Calculate compressibility (dimensionless), dynamic viscosity (Pa-s; note
% that 1 Pa-s = 10 Poise) and density (kg/m^3) using data from
% \citep{co2_calc}. We pick the compressibility, dynamic viscosity and
% density for the rounded integer value of pressures between 10 and 15 MPa.
%{
rho = 938; %McCoy personal correspondence
dv = 103;
107
Z_ave = .26;
%}
% My algorithm for generating the parameters above:
P_int = round(P_ave*10^-6);
if P_int == 10
Z_ave = 0.204;
dv = 93.82;
rho = 908.26;
elseif P_int == 11
Z_ave = 0.223;
dv = 95.99;
rho = 916.49;
elseif P_int == 12
Z_ave = 0.241;
dv = 98.05;
rho = 924.12;
elseif P_int == 13
Z_ave = 0.259;
dv = 100.03;
rho = 931.23;
elseif P_int == 14
Z_ave = 0.277;
dv = 101.94;
rho = 937.90;
else Z_ave = 0.295;
dv = 103.78;
rho = 944.18;
end
%}
108
% Soil Temps range from -2 to 8C in Wyoming’s relatively cool climate. But
% Towler06 uses 15 C. So we do too. 15 C = 288 K.
T_ave = 288.15; % Avg ground temperature, K.
T_f = 293.15; % Flowing gas temperature, K.
R = 8.314472; % Ideal gas constant Pa m^3/mol K
M = 44.0096; % CO2 kg/kgmol
epsilon = 0.0000457; % roughness in m
gr = 9.8; % acceleration of gravity m/s^2
G = 44.0096/28.9265; % Specific gravity of 100% pure CO2
P_b = 81.22; % Base pressure at 6000 ft. above sea level kPa
poise = dv/10; % Convert Pa-s to Poise
efficiency = 0.95; % Pipeline efficiency (could be as low as .85)
if h_1 ~= h_2
s = 0.0684*G*(h_1-h_2)/(T_f*Z_ave); %Elevation adjustment term
km_e = km*(exp(s)-1)/s; % Equivalent length adjusted for elev.
else
km_e = km;
s=0;
end
% Iterative approach to calculating diameter using the fanning friction
% factor. Start with an initial internal diameter based on a flow velocity
% of 1.36 m/s:
%McCoy in m:
%D(1,1) = sqrt( (4*(kgps/10))/(pi*rho*1.36) ); %m
%Menon in mm:
D(1,1) = 1000*sqrt( (4*(kgps/10))/(pi*rho*1.36) ); %mm
% Calculate the Reynold’s number using the initial diameter guess.
109
% McCoy:
%Re(1,1) = 4*kgps/(dv*pi*D(1,1));
% Menon:
Re(1,1) = 0.5134*(P_b/T_ave)*(G*m3pd/poise*(D(1,1)*1000));
% Use the Reynold’s number to calculate the Darcy/Moody Friction Factor
% using the function ’moody’ that solves for f. McCoy’s model uses the
% Fanning friction factor which equals Darcy/Moody divided by 4:
f(1,1) = moody(epsilon/D(1,1),Re(1,1)); % Calculates Darcy fric fac
fan(1,1) = f(1,1)/4; %Converts to fanning for McCoy
% Use f to estimate dimater ala Menon, p. 419:
D(1,2) = (m3pd/((11.4946e-4/sqrt(f(1,1)))*(T_ave/P_b)*...
((kpa_1^2-exp(s)*kpa_2^2)/(G*T_f*km_e*Z_ave))^0.5))^0.4;
%{
% Use fanning to estimate diameter ala McCoy-Rubin:
D(1,2) = ((-64*Z_ave^2*R^2*T_ave^2*fan(1,1)*kgps^2*meters)/...
(pi^2*(M*Z_ave*R*T_ave*(pa_2^2-pa_1^2)+2*gr*P_ave^2*M^2*...
(h_2-h_1))))^(1/5);
%}
% Use iterative process to find final diameter:
diff = abs(D(1,2)-D(1,1));
if diff > 10^-6
iter = 2; %Counter
maxiter = 5000;
while diff > 10^-6
% Re-estimate Reynolds no.:
110
% McCoy-Rubin:
%Re(1,iter) = 4*kgps/(dv*pi*D(1,iter));
% Menon:
Re(1,iter) =...
0.5134*(P_b/T_ave)*(G*m3pd/poise*(D(1,iter)*1000));
% Re-estimate the fanning friction factor
f(1,iter) = moody(epsilon/D(1,iter),Re(1,iter));
fan(1,iter) = f(1,iter)/4;
iter = iter+1;
% Calculate a new diameter:
D(1,iter) = (m3pd/((11.4946*10^-4/sqrt( f(1,iter-1) ))*...
(T_ave/P_b)*((kpa_1^2-exp(s)*kpa_2^2)/...
(G*T_f*km_e*Z_ave))^0.5))^0.4;
%{
D(1,iter) = ((-64*Z_ave^2*R^2*T_ave^2*fan(1,iter-1)*...
kgps^2*meters)/(pi^2*(M*Z_ave*R*T_ave*(pa_2^2-pa_1^2)...
+2*gr*P_ave^2*M^2*(h_2-h_1))))^(1/5);
%}
diff = abs(D(1,iter) - D(1,iter-1));
if iter == maxiter
disp(blank);
fprintf(’No convergence after %d iterations.\n’,iter);
break;
end
end
end
111
clear diff;
% McCoy:
%D = (3*39.37008).*D; %Convert meters to inches.
% Menon:
D = 39.37008.*(D./1000);
% Calculate velocity at the end of the segment.
global mps;
mps = (4*(kgps/10))/(pi*rho*(D(1,iter)*.0254)^2); %m/s
%}
% Calculate diameter according to Menon’s Panhandle A:
phA_a = 4.5965e-03*efficiency;
phA_b = (T_ave/P_b)^1.0788;
phA_c = ((kpa_1^2-kpa_2^2)/(G^0.8539*T_f*km*Z_ave))^0.5394;
D_phA = (m3pd/(phA_a*phA_b*phA_c))^(1/2.6182);
D_phA = D_phA/25.4; % Converted to inches
% Calculate diameter according to Menon’s Panhandle B:
phB_a = 1.002e-02*efficiency;
phB_b = (T_ave/P_b)^1.02;
phB_c = ((kpa_1^2-kpa_2^2)/(G^0.961*T_f*km*Z_ave))^0.51;
D_phB = (m3pd/(phB_a*phB_b*phB_c))^(1/2.53);
D_phB = D_phB/25.4; % Converted to inches
% Calculate diameter according to Menon’s Weymouth:
wey_a = 3.7435e-03*efficiency;
wey_b = T_ave/P_b;
wey_c = ((kpa_1^2-kpa_2^2)/(G*T_f*km*Z_ave))^0.5;
D_wey = (m3pd/(phB_a*phB_b*phB_c))^(1/2.667);
112
D_wey = D_wey/25.4; % Converted to inches
% Choose the largest of the four diameters:
x1 = [D(1,iter) D(1,iter) D(1,iter)];
y1 = [D_phA D_phB D_wey];
x2 = [D_phA D_phA D_phA];
y2 = [D(1,iter) D_phB D_wey];
x3 = [D_phB D_phB D_phB];
y3 = [D(1,iter) D_phA D_wey];
x4 = [D_wey D_wey D_wey];
y4 = [D(1,iter) D_phA D_phB];
if x1 >= y1
D_final = D(1,iter);
elseif x2 >= y2
D_final = D_phA;
elseif x3 >= y3
D_final = D_phB;
else
D_final = D_wey;
end
%Find NPS: An optimal inner diameter (inches) has been calculated that
%accomodates the exact given mass flow rate. To find the nominal
%pipeline size (NPS) that handles that flow rate, we must adjust the
%diameter to the next highest even integer. For final inner
%diameters 12" or less, NPS is the inner diameter. For final inner
%diameters greater than 12" NPS is the outer diameter. Hence we need
%to calculate pipe wall thickness and add that to the calculated inner
113
%diameter, then increase the outer diameter to the next highest even
%integer, then recalculate the thickness according to that diameter.
%Use formula for pipe wall thickness from McCoy-Rubin08. We adjust the
%wall thickness by a factor of 15% to address the additional design
%requirements for CO2 pipelines (Yeddu and ICF on CCS).
p_mop = 15.3; %p_mop = max operating press = 15.3 MPa
stress = 483; %Min yield stress (S) = 483 MPa
des_fac = 0.72; %Design factor inrod’d to add margin of safety
jnt_fac = 1.0; %Joint factor = 1
% Create a vector of nominal pipeline sizes:
NPS_vec = [4:2:60]; % NPS in inches
% Create a vector of thicknesses for each NPS:
NPS_vecm = NPS_vec./39.37008; % Convert to meters for thick calc
thick_vec = 1.15*((p_mop.*NPS_vecm)./(2*stress*jnt_fac*des_fac)); %m
thick_vec = 39.37008*thick_vec; % Convert back to inches.
% Create a vector of inner diameters corresponding to each NPS:
D_innervec = NPS_vec-2*thick_vec;
if D_final < 12 % If calculated diameter is less than 12", then...
D_final = ceil(D_final);
if mod(D_final,2) == 0
NPS = D_final;
else
NPS = D_final + 1;
end
thickness = zeros(1,100);
D_outer = zeros(1,100);
thickness(1,1) = 1.15*((p_mop*NPS)/(2*stress*jnt_fac*des_fac));
D_outer(1,1) = NPS + thickness(1,1);
thickness(1,2) = 1.15*((p_mop*D_outer(1,1))/...
114
(2*stress*jnt_fac*des_fac));
diff = thickness(1,2) - thickness(1,1);
count = 2;
maxcount = 100;
while diff > 10^-6
D_outer(1,count) = NPS + thickness(1,count);
count = count+1;
thickness(1,count) = 1.15*((p_mop*D_outer(1,count-1))/...
(2*stress*jnt_fac*des_fac));
D_outer(1,count) = NPS + thickness(count);
diff = D_outer(1,count) - D_outer(1,count-1);
if count == maxcount
disp(blank);
fprintf(’No thickness convergence.\n’);
break
end
end
thickness = thickness(1,count);
D_outer = D_outer(1,count);
%}
% For inner diameters greater than 12 inches, the NPS is the outer
% diameter. We use a loop to calculate the wall thickness for NPS (i.e.
% outer diameters) between 14 and 50 inches, subtract two times the wall
% thickness to find the actual inner diameter corresponding to each NPS.
% Then we compare the inner diameter based on flow to the inner diameter
% corresponding to each NPS. When we find the region containing our
% calculated inner diameter, we assign NPS to be the next highest even
% integer above that value.
else
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% Search D_innervec to find the argument that is barely larger than
% calculated diameter, D_final
counter = 1;
while D_final > D_innervec(counter)
counter = counter + 1;
end
NPS = NPS_vec(counter); %Choose the NPS that represents the even
% integer which matches the final inner diameter found by comparing
% D_final to D_innervec.
thickness = thick_vec(counter); %Choose the thickness for the
%NPS that’s one size greater than where the counter stopped.
D_inner = NPS - 2*thickness;
D_outer = NPS;
area_inner = pi*(D_inner/2)^2;
end
% Calculate the amount of steel needed for the segment in tons:
% For diameter use NPS - .5*t, convert L km to miles using .62137;
% in*in*miles*63,360in/mile/(1648in^3/ft^3) = cubic feet of steel) and
% (ft^3*489.54 lbs/ft^3)/2000 lbs/ton) = tons
tons = ((((NPS-.5*thickness)*pi)*thickness*(km*0.62137)*63360)/...
(12*12*12))*(489.54/2000);
end
function f = moody(rel_rough,Re,verbose)
% Finds friction factor by solving the Colebrook equation (Moody Chart).
% The friction factor depends only on the Reynolds number which depends on
% pipe diameter and other gas properties.
116
%
% Synopsis: f = moody(ed,Re)
%
% Input: rel_rough = pipe roughness (epsilon)/inner_diameter
%
% Output: f = friction factor
%
% Note: Accounts for laminar and turbulent flow
if Re<0
error(sprintf(’Reynolds number = %f cannot be negative.’,Re));
elseif Re<2000
f = 64/Re; return %laminar flow
end
if rel_rough>0.05
warning(sprintf(’epsilon/diameter ratio = %f is not on Moody chart.’,...
rel_rough));
end
if Re<4000, warning(’Re = %f in transition range’,Re); end
% Use fzero to find f from Colebrook equation. coleFun is an inline
% function object to evaluate F(f,e/d,Re). fzero returns the value of f
% such that F(f,e/d,Re) = 0 (approximately). fi = initial guess from
% Haaland equation. Iterations of fzero are terminated when f is known to
% whithin +/- dfTol
coleFun =...
inline(’1.0/sqrt(f) + 2.0*log10( rel_rough/3.7 + 2.51/(Re*sqrt(f)) )’,...
117
’f’,’rel_rough’,’Re’);
fi = 1/(1.8*log10(6.9/Re + (rel_rough/3.7)^1.11))^2; %initial guess of f
dfTol = 5e-6;
f = fzero(coleFun,fi,optimset(’TolX’,dfTol,’Display’,’off’),rel_rough,Re);
% check f
if f<0
error(sprintf(’Friction factor = %f, but cannot be negative’,f))’;
end
end
function [K_pump,Wp,Wp_hp] = pl_pump_calc(pump,pump_dist,tonnespd,pa_1,pa_2)
% Turn off ‘pump‘ while generating graphs:
% If a pump station is required calculate the amount of power
% required to pump the CO2 from p_2 MPa up to p1 (15.3 MPa):
if pump
pa_1 = pa_1/10^6;
pa_2 = pa_2/10^6; %Convert Pa back to MPa
pa_2 = pa_1 - 0.001*35*pump_dist;
if pa_2 == 10
rho = 886.543;
elseif pa_2 == 11
rho = 895.968;
elseif pa_2 == 12
rho = 904.587;
elseif pa_2 == 13
rho = 912.548;
elseif pa_2 == 14
rho = 919.956;
118
else rho = 926.896;
end
etap = 0.75; %Efficiency of the pump
Pfinal = 15.3;
Pcutoff = pa_2;
%1000 = # of kg/tonne, 10 = bar/MPa, 24 = # hours/day, 36 = # m^3
%The pumping power required in kW and in horse power is
Wp = ((1000*10)/(24*36))*((tonnespd*(Pfinal - Pcutoff))/(rho*etap));
Wp_hp = Wp*1.34132; %Convert kW to hp.
%Calculate the capital cost of the pump and scale to 2009 dollars.
K_pump = 1.18*(((1.11*10^6)*(Wp/1000)) + 0.07*10^6);
else
K_pump = 0;
Wp = 0;
Wp_hp = 0;
end
end
function [tariff_mcf,tariff_tonne] = pl_tar_calc(K_tot,Wp,mmcf)
T = 30; %Assume 30-year lifespan for pipeline.
%T = input(’Enter estimated life of the pipeline in years: ’);
%r = input(’Enter the desired cost of capital in percent: ’);
global r;
r = 12; % According to Petroleum Economist return on equity for
% pipeline operators is 12%.
r = r/100;
%x_tax = input(’Enter the tax rate on CO2 in %: ’);
x_tax = 35; %percent (corporate, royalty, severance, property)
119
x_tax = x_tax/100; %Tax in decimal.
% According to McCollum, O&M costs are 2.5% of capital costs
x_om = .025*K_tot;
% According to EIA Wyoming electricity data, the price for
% industrial use electricity is 4.92 cents/kWh. So the annual
% pump operating costs are
x_pump = .0492*Wp*365*24;
% Total annual mass flow is
Q_ann = mmcf*365;
% For T = 30 years*365 days, the present value of a cash flow at time t is
% pv = (Q_ann*tariff*(1 - x_tax) - x_pump - x_om)*(1/(1+r)^t). The sum of
% these values is the present value of the discounted cash flows. Factoring
% out (Q_ann*tariff*(1 - x_tax) - x_pump - x_om) from the sum results in
% (Q_ann*tariff*(1 - x_tax) - x_pump - x_om)*sum(1/(1+r)^t). The net present
% value must be greater than zero for a profitable project. So we must find
% a tariff such that
% sum[(Q_ann*tariff*(1 - x_tax) - x_pump - x_om)] - K_tot > 0.
% Let A = the sum of a geometric series: A = sum(1/(1+r)^t) for t = 1:T,
% then A = 1/r - 1/(r*(1+r)^T).
% Let payment = (Q_ann*tariff*(1 - x_tax) - x_pump - x_om);
% Then payment*A = K_tot when NPV = 0. Solving for tariff in payment
% results in the minimum tariff for obtaining a ROR such that NPV = 0.
A = 1/r - 1/(r*(1+r)^T);
% Since payment*A = K_tot:
payment = K_tot/A;
% payment = (Q_ann*tariff*(1 - x_tax) - x_pump - x_om), solving for tariff:
tariff = (payment + x_pump + x_om)/(Q_ann*(1-x_tax));
120
% Calc per mcf and per tonne and round tariffs to nearest cent:
tariff_mcf = (round((tariff/1000)*100))/100;
tariff_tonne = (round(((19.59701321/1000)*tariff)*100))/100;
% Test tariff calculation to make sure NPV is 0 for the ROR that results
% from tariff as calculated above:
%Stream = ones(1,T+1);
%Stream = payment*Stream;
%Stream(1,1) = -K_tot;
%ROR = irr(Stream); % Uses functions irr and pvvar from
%NPV = pvvar(Stream,ROR); % finance toolbox
%NPV2 = pvvar(Stream,.12);
end
121
References
ACES (2011). H.R. 2454 ‘American Clean Energy and Security Act of 2009’.www.govtrack.us/congress/billtext.xpd?bill=h111-2454.
Alleman, D. (2011). National Energy Technology Lab: Exploration and Production Tech-nologies. www.netl.doe.gov/technologies/oil-gas/.
Allinson, G. and Nguyen, V. (2003). CO2 Geological Storage Economics, Technical report,School of Petroleum Engineering, University of New South Wales.
Aminian, K. (2005). Coalbed Methane-Fundamental Concepts, Technical report, Petroleumand Natural Gas Engineering Department, West Virgina University.
Amorinao, C., Bencini, R., Cara, R., Cinti, D. and Deriu, G. (2005). CO2 geological storageby ECBM techniques in the Sulcis area (SW Sardinia Region, Italy. Presented at theSecond International Conference on Clean Coal Technologies for our Future.
Ayers, W. B. (2002). Coalbed gas systems, resources, and production and a review of con-trasting cases from the San Juan and Powder River basins, AAPG Bulletin 86(11): 1853–1890.
Barclay, N., Hutton, D., Meyer, R., Ondler, S. and Pierro, P. (2009). 2008 Wyoming Oiland Gas Statistics, Technical report, Wyoming Oil and Gas Conservation Commission.
Bergen, F. V., Pagnier, H. and der Meer, L. V. (2007). Field experiement of ECBM in theSilesian Coal Basin of Poland (RECOPOL), International Coalbed Methane Symposium,Curran Associates, Inc., Tuscaloosa, Alabama (USA).
Bleizeffer, D. (2009). Coal gasification plan advances, Casper Star Tribune .
Bleizeffer, D. (September 19, 2010). Denbury buys into Riley Ridge gas project, Casper StarTribune .
Bock, B., Rhudy, R., Herzog, H., Klett, M., Davidson, J., Ugarte, D. D. L. T. and Simbeck,D. (2003). Economic Evaluation of CO2 Storage and Sink Enhancement Options, Finaltechnical report, TVA, PO Box 1010, Muscle Shoals, AL 35662.
Braitsch, J. (2008). 2008 Carbon Sequestration Atlas of the United States and Canada,Report/publication, Big Sky Carbon Sequestration Partnership.
122
Brown, D., Cabe, J. and Stout, T. (2011). National Lab uses OGJ data to develop costequations, Oil & Gas Journal 109(1).
Brown, K. (2009). Practical Business Aspects of the CO2 EOR Process. Presented at theThird Annual Wyoming CO2 Conference.
BSCSP (2009). Big Sky Carbon Sequestration Partnership. www.bigskyco2.org/research/.
Burrus, R. C. (2003). CO2 adsorption in coals as a function of rank and composition: Atask in USGS research on geologic sequestration of CO2, Technical report, United StatesGeological Survey.
CARMA (2009). Carbon Monitoring for Action (CARMA) Plant Details. carma.org/.
Chai, Z. and Shimada, S. (2010). Preliminary Study of CO2 Storage in Coal-Bearing For-mation in the Ariake Area, Kyushu, Japan. Presented at the MAGEEP Symposium.
Chodur, V. (2010). Beaver Creek Field Update; Madison CO2 Flood. Presented at theFourth Annual Wyoming CO2 Conference.
Christofferson, B. (2008). Enigma: A Case Study in ASP Implementation. Presented at theEnhanced Oil Recovery Conference, Jackson, Wyoming.
Chrysistomidis, I., Zakkour, P., Bhom, M., Beynon, E., de Filippo, R. and Lee, A. (2009).Assessing issues of financing a CO2 transportation pipeline infrastructure, Energy ProcediaI: 1625 – 1632.
CO2 Properties (2009). CO2 Tables. www.carbon-dioxide-properties.com/.
Coddington, K. (2010). CCS and Impacts of the EPA Regulations. Presented at the CarbonManagement Workshop, Houston, Texas.
Colmenares, L. B. and Zoback, M. D. (2007). Hydraulic fracturing and wellbore completionof coalbed methane wells in the Powder River Basin, Wyoming: Implications for waterand gas production, AAPG Bulletin 91(1): 51–67.
Conner, E. (2009). Gas Tech on cutting edge. Wyoming Association of Rural Water Systems:www.warws.com/documents/GasTechoncuttingedgeofcoalenergy.pdf.
Cook, B. R. (2009a). FRC Reports. Tables of results from simulations on FRC injectivityand production using the van’t Veld/Phillips screening model. Contact Benjamin R. Cook:[email protected] or Owen R. Phillips: [email protected].
Cook, B. R. (2009b). Impact of Subsidies and Taxes on Private Bargaining: ExperimentalInsights on Carbon Sequestration Markets. www.uwyo.edu/bencook/jobmarket.asp.
Cook, B. R. (2010). Essays on Carbon Policy and Enhanced Oil Recovery, PhD thesis,University of Wyoming, Department of Economics and Finance.
123
Copeland, D. A. and Ewald, M. L. (eds) (2008). Water associated with coal beds in Wyoming’sPowder River Basin, Wyoming Geological Survey.
Covell, J. R. (2009). Powder River Basin Underground Coal Gasification. Presented at ThirdAnnual Wyoming CO2 Conference.
de Figueiredo, M., Herzog, H., Joskow, P., Oye, K. and Reiner, D. (2007). Regulating CarbonDioxide Capture and Storage, Technical report, Center for Energy and EnvironmentalPolicy Research (CEEPR).
DeBruin, R. H. (2001). Carbon Dioxide in Wyoming; Information Pamphlet 8, Technicalreport, Wyoming State Geological Survey, Larami, Wyoming.
Denbury (2009). The Green Pipeline Project. www.denbury.com/.
Denbury (2010). Denbury: Branching Out; 2010 Presentation to Wyoming Pipeline Author-ity. www.wyopipeline.com/presentations.asp.
Deng, X., Mavor, M., Macdonald, D., Gunter, B., Wong, S., Faltinson, J. and Li, H. (2008).ECBM Technology Development at Alberta Research Council. Presented at Coal-Seq VIForum, Houston.
DKRW (2011). DKRW Advanced Fuels LLC Opportunity Overview.www.dkrwadvancedfuels.com/Careers/Current-Job-Openings-96.html.
DOE (2010). Retrofitting the Existing Coal Fleet with Carbon Capture Technology.www.fossil.energy.gov/programs/powersystems/pollutioncontrols/.
DOE (2011a). U.S. Department of Energy Enhanced Oil Recovery/CO2 Injection. fos-sil.energy.gov/.
DOE (2011b). U.S. Department of Energy Fossil Energy News. www.fossil.energy.gov/.
Doll, T., Evans, T. and Melzer, L. S. (2009). North American CO2 Status. Presented at theThird Annual Wyoming CO2 Conference.
Driess, K. (2008). GIS analysis of PRB coals as evaluated in USGS Professional Paper1625-A, Chpt PS. Personal communication.
EIA (2008). U.S. Electric Power Industry Estimated Emissions by State (EIA-767 and EIA-906). http://www.eia.doe.gov/.
EIA (2009a). EIA Weekly Natural Gas Update. tonto.eia.doe.gov/oog/info/ngw/ngupdate.asp.
EIA (2009b). 1990 - 2007 U.S. Electric Power Industry Estimated Emissions by State (EIA-767 and EIA-906). www.eia.doe.gov/cneaf/electricity/.
EIA (2010a). EIA: About Natural Gas Pipelines. www.eia.doe.gov/.
124
EIA (2010b). EIA Retail Electricity Prices. www.eia.doe.gov/cneaf/electricity/.
Ellis, M., Gunther, G., Ochs, A., Schuenemer, J., Power, H., Stricker, G. and Blake, D.(1999a). Coal Resources, Greater Green River Basin, U.S. Geological Survey ProfessionalPaper 1625-A, United States Geological Survey.
Ellis, M., Gunther, G., Ochs, A., Schuenemer, J., Power, H., Stricker, G. and Blake, D.(1999b). Coal Resources, Powder River Basin, U.S. Geological Survey Professional Paper1625-A, United States Geological Survey.
EORI (2011a). Report: Enhanced Oil Recovery Could Mean Billions $.www.uwyo.edu/eorcommission/oil.htm.
EORI (February, 2011b). EORI: Energy and the Future in Wyoming.www.uwyo.edu/eori/resources/index.html.
EPAct (2009). Inventory of assessed federal coal resources and restrictions to their develop-ment. In compliance with the energy policy act of 2005, section 437, U.S. Department ofEnergy publication, EPAct 2005. www.fossil.energy.gov/epact/.
Essandoh-Yeddu, J. and Gulen, G. (2008). Economic modeling of carbon dioxide integratedpipeline network for enhanced oil recovery and geologic sequestration in the Texas GulfCoast region: presented at the 9th International Conference on Greenhouse Gas ControlTechnologies (GHGT-9), GCCC Digital Publication Series #08-03k, Gulf Coast CarbonCenter and Center for Energy Economics.
Evans, T. (2009). Anthropogenic CO2 Sources - June 2009. Presented at the Third AnnualWyoming CO2 Conference.
Evans, T. (2010). Defining Denbury. Presented at the Fourth Annual Wyoming CO2 Con-ference.
Eves, K. E. and Nevarez, J. J. (2009). Update of Lost Soldier/Wertz Floods; Living in aConstrained CO2 Environment. Presented at the 15th Annual CO2 Flooding Conference:co2conference.net/presentations.html.
Faltinson, J. (2007). CO2 Storage and Enhanced Methane Production, Presented at 4th IEAMonitoring Network Meeting, Edmonton, Alberta, Alberta Research Council.
FERC (2011a). FERC Gas Tariffs of ANR Company. anrebb.transcanada.com/.
FERC (2011b). Gas Assessment Table for FERC Administrative Charges - FY 2010.www.ferc.gov/industries/gas/annual-charges.asp.
Flores, R. and Bader, L. (1999a). Fort Union Coal in the Greater Green River Basin, EastFlank of the Rock Springs Uplift, Wyoming: A Synthesis, Technical report, United StatesGeological Survey.
125
Flores, R. and Bader, L. (1999b). Fort Union Coal in the Powder River Basin, Wyoming andMontana: A Synthesis, U.S. Geological Survey Professional Paper 1625-A, U.S. GeologicalSurvey, chapter PS.
Frailey, S. M. (2011). Illinois Basin: Tanquary CO2 (Coal) Injection Pilot. Presented atCoal-Seq VII Forum, Houston, Texas.
Fugleberg, J. (2011a). Elk Petroleum cuts key deal for revived Grieve oilfield, Casper StarTribune .
Fugleberg, J. (2011b). Linc Energy buys three Wyoming oil fields, Casper Star Tribune .
Fujioka, M., Yamaguchi, S. and Nako, M. (2010). CO2-ECBM field tests in the Ishikari CoalBasin of Japan, International Journal of Coal Geology 82(287-298).
Gearino, J. (2010). Sequestration plant study goes public, Casper Star Tribune .
Goudar, C. T. (2008). Comparison of the iterative approximations of the Colebrook-Whiteequation, Hydrocarbon Processing .
Grigg, R. and Oudinot, A. (2011). Southwest Regional Partnership on Carbon Sequestration(SWP) Pump Canyon CO2 ECBM/CO2 Sequestration Demonstration Test Site. Presentedat Coal-Seq VII Forum, Houston, Texas.
Gunter, W. D. (2009). Coalbed Methane, A Fossil Fuel Resource with the Potential for ZeroGreenhouse Gas Emissions - the Alberta, Canada Program 1996 - 2009: A Summary,Technical report, Alberta Research Council.
Gunter, W. D., Mavor, M. J. and Robinson, J. R. (2002). CO2 storage and enhanced methaneproduction: field testing at Fenn-Big Valley, Alberta, Canada, with application, Technicalreport, Alberta Research Council Inc.
Gunter, W., Wong, S., Law, D., Sanli, F., Jianping, Y. and Zhiqiang, F. (2005). En-hanced Coalbed Methane (ECBM) Field Test at South Zinshui Basin, Shanxi Province,China, Presented at GCEP workshop, Beijing, Alberta Research Council and China UnitedCoalbed Methane Corporation.
Hamling, J. A. (2011). CO2 Storage Pilot Study for Lignite Coal. Presented at Coal-SeqVII Forum, Houston, Texas.
Han, W. S. and McPherson, B. J. (2009). Optimizing geologic CO2 sequestration by injec-tion in deep saline formations below oil reservoirs, Energy Conversion and Management50: 2570–2582.
Haszeldine, R. S. (2009). Carbon Capture and Storage: How Green Can Black Be?, Science325(1647).
Heddle, G., Herzog, H. and Klett, M. (2003). The Economics of CO2 Storage, Technicalreport, MIT Laboratory for Energy and the Environment (LFEE).
126
Helmke, E. (2008). Carbon Atlas and GIS Efforts Review. Big Sky Carbon SequestrationPartnership: www.bigskyco2.org/info/presentations?order=title&sort=asc.
Herzog, H. (2006). A GIS-Based Model for CO2 Pipeline Transport and Source-Sink Match-ing Optimization. Presented at WESTCARB Annual Business Meeting.
Herzog, H., Li, W., Zhang, H., Diao, M., Singleton, G. and Bohm, M. (2007). West CoastRegional Carbon Sequestration Partnership: Source-Sink Characterization and GeograhicInformation System-based Matching, Technical report, California Energy Commission.
Herzog, H., Smekens, K., Dadhich, P., Dooley, J., Fujii, Y., Hohmeyer, O. and Riahi, K.(2005). Cost and Economic Potential, Cambridge University Press, chapter 8. Intergov-ernmental Panel on Climate Change Special Report: Carbon Capture and Sequestration.
IEA (n.d.). IEA GHG Gas R&D Programme. www.ieaghg.org.
INGAA (2009). Developing a Pipeline Infrastructure for CO2 Capture and Storage: Issuesand Challenges. INGAA Foundation: www.ingaa.org/cms/7626.aspx.
IPCC (2005). Intergovernmental Panel on Climate Change: Special Report on Carbon Cap-ture and Sequestration, Cambridge University Press.
Jeffries, B. (2009). One Version of a Fully Built Out CO2 Pipeline Grid. Presented at theThird Annual Wyoming CO2 Conference.
K. Bliss et al. (2010). A policy, Legal, and Regulatory Evaluation of the Feasibility of aNational Pipeline Infrastructure for the Transport and Storage of Carbon Dioxide, Topicalreport, Interstate Oil and Gas Compact Commission.
Kelliher, J. T. (2008). Testimony of the Honorable Joseph T. Kelliher Chairman FederalEnergy Regulatory Commission Before the Committee on Energy and Natural ResourcesUnited States Senate. www.ferc.gov/.
Kelly, R. (2009). DKRW Advanced Fuels LLC Opportunity Overview. Presented at theThird Annual Wyoming CO2 Conference.
Kinder Morgan (2009). Reservoir Properties for EOR.http://www.kindermorgan.com/business/co2/flood.cfm#thumb.
Kinder Morgan (2010). Kinder Morgan CO2 Pipeline Info. www.kne.com/business/CO2.
King, B. and Long, G. (2010). U.S. Coalbed Methane; Past, Present and Future.eia.doe.gov/pub.
Koperna, G. J. (2011). The Coal-Seq Consortium: Advancing the Science of CO2 Sequestra-tion in Coal Bed and Gas Shale Reservoirs. Presented at Coal-Seq VII Forum, Houston,Texas.
127
Kovscek, A. and Cakici, M. (2005). Geologic storage of carbon dioxide and enhanced oilrecovery II. Cooptimization of storage and recovery, Energy Conversion and Management46: 1941–1956.
Kovscek, A. and Wang, Y. (2005). Geologic storage of carbon dioxide and enhanced oilrecovery. I. Uncertainty quantification employing a streamline based proxy for reservoirflow simulation., Energy Conversion and Management 46: 1920–1940.
Kuuskraa, V. A. (2010). White Paper #2; Challenges of Implementing Large-scale CO2
Enhanced Oil Recovery with CO2 Capture and Storage. Prepared for the Symposium onthe Role of Enhanced Oil Recovery in Accelerating the Deployment of Carbon Captureand Storage: web.mit.edu/mitei/docs/reports/eor-css/kuuskraa.pdf.
Kuuskraa, V. and Ferguson, R. (2008). Storing CO2 with Enhanced Oil Recovery, Technicalreport, DOE/NETL.
Law, D. H.-S., van der Meer, B. and Gunter, W. B. (2002). Numerical Simulator ComparisonStudy for Enhanced Coalbed Methane Recovery Process, Part I: Pure Carbon DioxideInjection, Technical Report SPE 75669, Society of Petroleum Engineers.
Linc Energy (2009). Linc Energy Contracts With GasTech Inc. www.lincenergy.com.
Marshall and Swift Index (2009). Marshall and Swift Equipment Cost Index, Journal ofChemical Engineering 116: 60.
Mason, C. F. and van ’t Veld, K. (2011). Economic Analysis of Geologic Carbon Seques-tration Potential for the BSCSP Region using Enhanced Coalbed Methane Recovery andEnhanced Oil Recovery. Deliverable ED3 for the Big Sky Carbon Sequestration Partner-ship Phase II; received via personal communication.
Massarotto, P. (2007). Greening Queensland’s black coal, Report, University of Queensland.
McCollum, D. L. and Ogden, J. M. (2006). Techno-Economic Models for Carbon Diox-ide Compression, Transport, and Storage, Technical report, Institute of TransportationStudies, UC Davis.
McCoy, S. T. (2008). The Economics of CO2 Transport by Pipeline and Storage in SalineAquifers and Oil Reservoirs, PhD thesis, Carnegie Mellon University, Pittsburgh, PA.
McCoy, S. T. and Rubin, E. S. (2005). Models of CO2 Transport and Storage Costs andTheir Importance in CCS Cost Estimates. Fourth Annual Conference on Carbon Captureand Sequestration conference proceedings.
McCoy, S. T. and Rubin, E. S. (2008). An engineering-economic model of pipeline transportof CO2 with application to carbon capture and storage, Greenhouse Gas Control 2: 219–229.
128
Melzer, L. S. (2009). Concurrent CO2 EOR and Carbon Capture and Storage (CCS). Pre-sented at the Third Annual Wyoming CO2 Conference.
Melzer, S. (2010). Residual Oil Zones. Presented at the Enhanced Oil Recovery Conference,Jackson, Wyoming.
Melzer, S. L. (2007). The Nuts and Bolts of CO2 Enhanced Oil Recovery. melzerconsult-ing.com/; PUBLICATIONS link.
Menon, E. S. (2005). Piping Calculations Manual, McGraw-Hill.
MIT CCS Model (2011). MIT ARC-GIS CCS model. e40-hjh-server1.mit.edu/.
Mohan, H. (2009). Study places CO2 capture cost between $34 and $61/ton, Oil & GasJournal 107(38).
Mohitpour, M., Golshan, H. and Murray, A. (2007). Pipeline Design & Construction: APractical Approach, third edn, ASME Press, New York, NY.
Mohrbacher, D., Swaddle, P., Yin, P. and Jones, V. (2011). Enhanced Oil Recovery InstituteMarch, 2011 Newsletter.
Moniz, E. J. (2011). Game Changers. Presented at 2011 EIA Energy Conference.
Moritis, G. (2009). Special Report: More CO2 projects likely as more CO2 sources becomeavailable, Oil & Gas Journal 107(45).
Morse, D., Rupp, J., Mastalerz, M., Harpalani, S. and Frailey, S. (2011). Illinois Basin:Geology and Coal Characterization at the Tanquery Site (MGSC). Presented at Coal-SeqVII Forum, Houston, Texas.
MOVECBM (2008). Monitoring and Verification of Enhanced Coalbed Methane. globalen-ergyobservatory.org.
Mullen, C. (2010). Grieve Field Chemical Flood. Presented at the Enhanced Oil RecoveryConference, Jackson, Wyoming.
Murrell, G. (2009). EORI Screening & Scoping tools for Wyoming Oil Fields.www.uwyo.edu/eori/resources/.
Nelson, C. R., Steadman, E. N. and Harju, J. (2005). Geologic CO2 sequestration potentialof the Wyodak-Anderson coal zone in the Powder River Basin, Topical Report for U.S.Department of Energy, Plains CO2 Reduction (PCOR) Partnership, Energy and Environ-mental Research Center: University of North Dakota.
NETL (2009). Carbon Sequestration: Regional Carbon Sequestration Partnerships.www.netl.doe.gov/technologies/.
NETL (2010). 2010 Carbon Sequestration Atlas of the United States and Canada.
129
Nevarez, J. J. (2009). Implementing Surveillance Tools and Processes to Improve Perfor-mance and Identify Possible Opportunities in Mature CO2 Floods. Presented at the ThirdAnnual Wyoming CO2 Conference.
O’Connell, J. (2011). Plans for fertilizer plant stalled due to funding woes, Idaho StateJournal .
OGJ (2011a). Carbon Energy starts flaring UCG syngas, Oil & Gas Journal 109(19).
OGJ (2011b). China Report Shows Expanding Gas Supply to 2015, Oil & Gas Journal109(16).
OGJ (2011c). Denbury to buy anthropogenic CO2 for EOR use, Oil & Gas Journal 109(11).
Oldenburg, C. M. and Benson, S. M. (2001). CO2 Injection for Enhanced Gas Productionand Carbon Sequestration, Technical Report SPE 74367, SPE, Lawrence Berkeley NationalLaboratory.
Page, J. (2009). Salt Creek Field CO2 Flood Performance. Presented at the Third AnnualWyoming CO2 Conference.
Palmer, I., Mavor, M. and Guntor, B. (2006). Permeability Changes in Coal Seams dur-ing Production and Injection. Presented at the fifth International Forum on GeologicSequestration of CO2 in Unminable Coalseams (Coal-Seq V), Houston, Texas, November8-9.
Parker, M. (2009). Utilizing the LaBarge experience to support the global development ofCCS. Presented at the Third Annual Wyoming CO2 Conference.
Parker, N. (2000). Using Natural Gas Transmission Pipeline Costs to Estimate HydrogenPipeline Costs, Master’s thesis, University of California at Davis, One Shields Avenue,Davis, CA 95616.
Pashin, J. C. (2011). CO2 Sequestration Field Test in Mature Coalbed Methane Reservoirsof the Black Warrior Basin. Presented at Coal-Seq VII Forum, Houston, Texas.
Pekot, L. J. and Reeves, S. R. (2002). Modeling the effects of matrix shrinkage and differ-ential swelling on coalbed methane recovery and carbon sequestration, Technical report,Advanced Resources International.
Peterson, C. (2009). The Beaver Creek Field Madison CO2 Project - Spring 2009 Update.Presented at the Third Annual Wyoming CO2 Conference.
Phillips, O. (2009). Scoping Cost Assumptions. Presented at the Third Annual WyomingCO2 Conference.
Phillips, O. R., van’t Veld, K. T. and Cook, B. R. (2009). Scoping Profitable CO2 Projectsin Wyoming. E-mailed from primary author: [email protected].
130
Plasynski, S. I. and Damiani, D. (2008). Program facts; carbon sequestration through en-hanced oil recovery, Technical report, U.S. Department of Energy National Energy Tech-nology Laboratory.
Pump Fundamentals (2010). Fluid Design. www.pumpfundamentals.com/PIPE%20ROUGHNESS%20VALUES.pdf.
Rao, A. B. and Rubin, E. S. (2006). Identifying Cost-Effective CO2 Control Levels forAmine-Based CO2 Capture Systems, Ind. Eng. Chem. Res. 45: 2421–2429.
Recktenwald, G. (2000). Numerical Methods with MATLAB, Prentice Hall.
Reeves, S. and Oudinot, A. (2005). The Allison Unit CO2 – ECBM Pilot: A Reservoir andEconomic Analysis, Technical report no. 0522, Advanced Resources International.
Reeves, S. R. (2003). Assessment of CO2 Sequestration and ECBM Potential of U.S.Coalbeds, Topical Report DEFC26-00NT40924, U.S. Department of Energy.
Reeves, S. R. (2004). The Coal-Seq Project: Key Results from the Field, Laboratory, andModeling Studies, Proceedings of the 7th International Conference on Greenhouse GasControl Technologies, Vol. GHGT-7, Vancouver, BC, Canada.
Reeves, S., Taillefert, A. and Pekot, L. (2003). The Allison Unit CO2 – ECBM Pilot: A Reser-voir Modeling Study, U.S. Department of Energy Award Number DE-FC26-0NT40924,Advanced Resources International.
Reyes, B. (2009). Utilizing a Geographical Information System (GIS) for the Developmentof a Wyoming CO2 Infrastructure, Presented at Third Annual Wyoming CO2 Conference.
Rice, C. A., Ellis, M. and Bullock, J. H. (2000). Water co-produced with coalbed methanein the Powder River Basin, Wyoming: preliminary compositional data, Open-File Report00-372, United States Geological Survey.
Ripepi, N. and Carpenter, S. (2011). Advancing the Science of CO2 Sequestration in CoalSeam & Gas Shale Reservoirs. www.coal-seq.com/; link to Forums.
Robertson, E. P. (2008). COMET3 Simulation of ECBM in Powder River Basin Coals.Simulation carried out for the DOE funded Big Sky Partnership for an economic analysisof PRB coals being done by University of Wyoming Department of Economics researchers.
Robertson, E. P. (2009). Economic analysis of carbon dioxide sequestration in Powder Riverbasin coal, International Journal of Coal Geology 77: 234–241.
Rochelle, G. T. (2009). Amine Scrubbing for CO2 Capture, Science 325(1652).
Ross, H. E. (2007). Carbon Dioxide Sequestration and Enhanced Coalbed Methane Recovery inUnminable Coalbeds of the Powder River Basin, Wyoming, PhD thesis, Stanford UniversityDepartment of Geophysics.
131
Ross, H., Hagin, P. and Zoback, M. (2009). CO2 storage and enhanced coalbed methanerecovery: Reservoir characterization and fluid flow simulations of the Big George coal,Powder River Basin, J. Greenhouse Gas Control (doi: 10.1016/j.ijggc.2009.06.002).
Roux, B. and Anderson, J. (2010). Salt Creek and Monell CO2 Project; Status Update and4D Seismic Applications. Presented at the Fourth Annual Wyoming CO2 Conference.
SECARB (2010). Southeast Regional Carbon Sequestration Partnership (SECARB).www.co2captureandstorage.info/project specific.php?project id199.
Seiersten, M. (2002). Corrosion in high pressure CO2 pipelines, Technical report, Institutefor Energy Technology, N-2027 Kjeller, Norway.
Skovholt, O. (1994). CO2 Transportation System, Energy Conversion and Management34(9-11): 1095–1103.
Smart, F. and Helmke, E. (2009). Documentation - Big Sky Geological Supply Curve v1.00.Received via e-mail from Klaas van ’t Veld.
Smith, C. E. (2006). U.S. gas carriers’ 2005 net incomes climb; construction costs plummet,Oil & Gas Journal 104(30): 55.
Smith, C. E. (2007). US oil carriers’ 2006 net incomes rebound; labor increases push upconstruction costs, Oil & Gas Journal 105(31): 62.
Smith, C. E. (2008). Natural gas pipeline profits surge; oil flat, Oil & Gas Journal106(35): 53.
Smith, C. E. (2009). Pipeline profits, capacity expansion plans grow despite increased costs,Oil & Gas Journal 107(34).
Smith, C. E. (2010). Pipelines continue growth despite lower earnings; oil profits grow, Oil& Gas Journal 108(41).
Smith, C. E., True, W. R. and Stell, J. (2005). U.S. gas carriers see 2004 net jump; con-struction plans rebound, Oil & Gas Journal 103(33): 50.
SPE (2005). Laboratory and Simulation Investigation of Enhanced Coalbed Methane RecoveryGas Injection, number SPE 95947.
Stricker, G. D., Flores, R. M., McGarry, D. E., Stillwell, D. P., Hoppe, D. J., Stillwell, C. R.,Ochs, A. M., Ellis, M. S., Osvald, K. S., Taylor, S. L., Thorvaldson, M. C., Trippi, M.,Grose, S. D., Crockett, F. J. and Shariff, A. J. (2006). Gas Desorption and AdsorptionIsotherm Studies of Coals in the Powder River Basin, Wyoming and Adjacent Basins inWyoming and North Dakota, Open File Report 2006 - 1174, United States GeologicalSurvey.
Surdam, R. (2010). Site Characterization of the Highest Priority Geologic Formations forCO2 Storage in Wyoming. www.netl.doe.gov/publications/.
132
Surdam, R. C. and Jiao, Z. (2007). The Rock Springs Uplift: An outstanding geological CO2
sequestration site in southwest Wyoming, Technical report, Wyoming State GeologicalSurvey.
Taillefert, A. and Reeves, S. (2003). Screening Model for ECBM Recovery and CO2 Se-questration in Coal, Topical Report for U.S. Department of Energy COl-Seq V1.0, U.S.Department of Energy and Advanced Resources International.
Thambimuthu, K., Soltanieh, M. and Abanades, J. C. (2005). Capture of CO2, CambridgeUniversity Press, chapter 3. Intergovernmental Panel on Climate Change Special Report:Carbon Capture and Sequestration.
Thomas, S. (2009). LaBarge Field And Shute Creek Facility. Presented at the Third AnnualWyoming CO2 Conference.
Tonery, L. M. and Perez, T. S. (2010). US gas-pipeline earnings examined, PetroleumEconomist (2025883911).
Towler, B. F. (2009). Personal communication.
Towler, B. F., Agarwal, D. and Mokhatab, S. (2008). Modeling Wyoming’s Carbon DioxidePipeline Network, Energy Sources, Part A 30: 259–270.
True, W. R. (1994). U.S. pipelines report mixed results for 1993, Oil & Gas Journal92(48): 38.
True, W. R. (1995). U.S. interstate pipelines ran more efficiently in 1994, Oil & Gas Journal93(48): 39.
True, W. R. (1996). U.S. pipelines continue gains into 1996, Oil & Gas Journal 94(38): 39.
True, W. R. (1997). Construction plans jump; operations skid in 1996, Oil & Gas Journal95(31): 37.
True, W. R. (1998). Weather, construction inflation could squeeze North American pipelines,Oil & Gas Journal 96(33): 15.
True, W. R. (1999). U.S. pipelines experience another tight year, reflect merger frenzy, Oil& Gas Journal 97(34): 13.
True, W. R. (2000). More construction, higher costs in store for U.S. pipelines, Oil & GasJournal 93(36): 68–86.
True, W. R. (2001). Profitable 2000, higher demand; push U.S. natural gas constructionplans, Oil & Gas Journal 99(36): 66–80.
True, W. R. (2002). Fed data show solid 2001 for US pipeline companies, more gas capacityplanned, Oil & Gas Journal 100(38): 52.
133
True, W. R. (2003). U.S. pipeline companies solidly profitable in 2002, scale back construc-tion plans, Oil & Gas Journal 101(34): 60–90.
True, W. R. and Stell, J. (2004). U.S. construction plans slide; pipeline companies experienceflat 2003, Oil & Gas Journal 102(32): 52.
van Bergen, F., Pagnier, H., van der Meer, L., van den Belt, F., Winthaegen, P. and Krzysto-lik, P. (2001). Development of a field experiment of ECBM in the upper Silesian coal basinof Poland (RECOPOL), Technical report, Netherlands Institute of Applied Geoscience andCentral Mining Institute (Poland).
van ’t Veld, K. T. (2009). van ’t veld, personal communication.
van ’t Veld, K. T. and Phillips, O. R. (2010). The economics of enhanced oil recovery: CO2
demand and incremental oil production in the Powder River Basin, The Energy Journal31(3): 31–55.
van Wageningen, W. and Maas, J. (2007). Reservoir Simulation and Interpretation of theRECOPOL ECBM Pilot in Poland, Technical report, Shell International Exploration &Production. Presented at the 2007 International Coalbed Methane Symposium: coal-seq.com/, Tech Transfer link.
Vandeginste, V. and Piessens, K. (2008). Pipeline design for a least-cost router applicationfor CO2 transport in the CO2 sequestration cycle, Greenhouse Gas Control 2: 571–581.
Vanderau, S. (2008). Power County Advanced Energy Center: Project Summary and CO2Discussion. Presented at 2011 EIA Energy Conference.
Watson, M. (2010). Development and Performance Update of the Cambrian Reservoir inBairoil. Presented at the Fourth Annual Wyoming CO2 Conference.
White, C. M., Smith, D. H., Jones, K. L., Goodman, A. L., Jikich, S. A. and LaCount,R. B. (2005). Sequestration of Carbon Dioxide in Coal with Enhanced Coalbed MethaneRecovery-A Review, Energy & Fuels; An American Chemical Society Journal 19(3).
WOGCC (2009). Wyoming Oil and Gas Conservation Commission: Coalbed APD’s Ap-proved. wogcc.state.wy.us/.
WOGCC (2010a). Wyoming Oil and Gas Convservation Commission (WOGCC).wogcc.state.wy.us/.
WOGCC (2010b). Wyoming Oil and Gas Conservation Commission Map Server.http://wogccms.state.wy.us/.
Wolfe, L. J. (2010). CO2 Pipelines in the West and Regulatory Issues. www.bigskyco2.org/.
Wong, S., Law, D., Deng, X., Robinson, J., Kadatz, B., Gunter, W. D., Jianping, Y., Sanli,F. and Zhiqiang, F. (2007). Enhanced Coalbed Methane - Micro-Pilot Test at SouthZinshui, Shanxi, China, International Journal of Greenhouse Gas Control I: 215 – 222.
134
WYGISC (2009). Wyoming Geographical Information Systems Center.http://www.uwyo.edu/wygisc/.
Zhang, Z., Wang, G., Massarotto, P. and Rudolph, V. (2006). Optimization of pipelinetransport for CO2 sequestration, Energy Conversion and Management 47: 702–715.
Zoback, M., Lucier, A. and Colmenares, L. (2004). Assessing Seal Capacity of Exploited Oiland Gas Reservoirs, Aquifers, and Coal Beds for Potential Use in CO2 Sequestration, Tech-nical report, Stanford University Global Climate and Energy Project. gcep.stanford.edu.
135