Supplemental Information: The system-wide economics of a ...iopscience.iop.org/1748-9326/8/3/034030/media/erl468459...-EOR production in the Texas Gulf Coast This section describes

S-1

Supplemental Information:

The system-wide economics of a carbon dioxide capture, utilization,

and storage network: Texas Gulf Coast w/ pure CO2–EOR flood

Carey W King1, Gürcan Gülen

2, Stuart M Cohen

3,*, Vanessa Nuñez-Lopez

4

1 Center for International Energy and Environmental Policy, The University of Texas at Austin

2275 Speedway, Stop C9000, Austin, TX 78712, USA

E-mail: [email protected]

2 Center for Energy Economics, Bureau of Economic Geology, The University of Texas at Austin

1801 Allen Parkway, Houston, TX 77019, USA


3 Mechanical Engineering Department, The University of Texas at Austin, 204 E. Dean Keeton St., Stop

C2200, Austin, TX 78712, USA E-mail: [email protected]

4 Bureau of Economic Geology, The University of Texas at Austin, 10100 Burnet Rd., Bldg 130, Austin,

TX 78758, USA


* Formerly associated with The University of Texas at Austin during the production of this research. Currently

affiliated with National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA.

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Table of Contents

Additional Intermediate Tables and Figures of Results and Inputs .............................................................. 3

Methods for estimating costs for CO2-EOR production in the Texas Gulf Coast ........................................ 7

Capital costs .............................................................................................................................................. 7

Costs for drilling and equipping costs for new wells ............................................................................ 7

Lease Equipment Costs for new production wells .............................................................................. 12

Lease Equipment Costs for CO2 injection wells ................................................................................. 16

H2O injection wells – lease costs ........................................................................................................ 19

CO2 recycling plant ............................................................................................................................. 20

CO2 trunk likes for field level distribution .......................................................................................... 20

Operating and Maintenance costs for CO2-EOR .................................................................................... 20

Annual Operating and Maintenance Costs .......................................................................................... 20

Recycle compression, lifting, and ‘other’ electricity needs ................................................................ 24

General and Administrative (G&A) Costs .......................................................................................... 25

Percentage of annual O&M costs for personnel ................................................................................. 25

Methods for estimating costs for injection of CO2 into saline reservoirs in the Texas Gulf Coast............. 26

Saline CO2 injection costs ....................................................................................................................... 26

CO2 injection well pressure, flow rate and radius ............................................................................... 26

Capital costs for drilling saline injection wells ................................................................................... 27

Operating and Maintenance costs for saline injection wells ............................................................... 27

Methods for estimating CO2 pipeline capital costs ..................................................................................... 28

Carbon Capture Utilization and Storage (CCUS) pipeline network segment description ...................... 28

References (Supplemental Information) ..................................................................................................... 30

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Additional Intermediate Tables and Figures of Results and Inputs

Table S1. The number of 5-spot patterns is equal to the number of injection wells, and the number of production

wells is approximately 15%–50% higher depending upon the total number of patterns.

Field

Number 5-spot

patterns =

number of CO2

injection wells

Number

oil

production

wells

Number water

injection wells

(40% of CO2

injection wells)a

Number of saline CO2

injection wells

(Scenarios: 1/2/3/4)

Conroe 98 113 39 0/0/609/1731

Hastings 79 93 32 48/47/590/1062

Webster 70 83 28 0/0/145/434

Tom O’Connor 55 66 22 34/34/588/107

Seeligson 24 31 9 0/0/247/38

Oyster Bayou 14 20 5 0/0/196/48

East White Point 14 20 5 0/0/168/57

Tomball 43 53 17 0/0/637/361

Fig Ridge 16 22 6 0/0/145/68

Gillock 9 14 4 0/0/62/47

Total 324 515 167 82/81/3387/3953

a: Water injection wells are not modeled as having separate capital costs. They are assumed drilled and then

converted to CO2 injection wells during the course of operation of the field. Thus, our model considers only the

operational costs of the water injection wells.

Table S2. The modeled oil recovery and amount of net CO2 delivered to each oil field (over 20 years of the

analysis). The oil recovery is approximately 12–15% of the OOIP of each injection pattern. M = mega (106), t =

metric ton, BBL = barrel.

Total Field

(Scenarios 1 and 2)

Total Field

(Scenarios 3 and 4) Per Injection Pattern

Oil

recovery

(1,000

BBL)

Net CO2

delivery

(MtCO2)

Oil

recovery

(1,000

BBL)

Net CO2

delivery

(MtCO2)

Total

delivered

CO2:oil

ratio

(tCO2/BBL)

CO2

injection

(HCPV)

Oil

recovery

(%

OOIP)

Years of

operation

(20 if not

@ max

HCPV)

Conroe 62,247 50.2 108,122 75.9 0.70 4.5 15.2 20 (21)

East White

Point 12,436 5.3 12,479 5.3 0.43 5.0 13.8 13 (12.5)

Fig Ridge 5,654 2.7 6,696 3.0 0.45 5.0 13.3 7 (6.1)

Gillock 10,696 3.6 13,664 4.3 0.31 5.0 12.1 16 (15.9)

Hastings 67,570 57.4 89,172 67.9 0.76 4.1 14.1 20 (21)

Oyster

Bayou 17,543 6.0 25,301 7.6 0.30 5.0 12.6 19 (19)

Tom

O’Connor 68,593 28.1 83,863 32.1 0.38 5.0 13.4 19 (19)

Seeligson 29,873 12.6 42,030 16.8 0.40 4.0 12.0 20 (20)

Tomball 18,515 12.8 18,736 12.9 0.69 5.0 15.9 9 (8.2)

Webster 52,418 44.2 81,740 58.7 0.71 4.1 14.4 20 (21)

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Table S3. The parameters assumed for each power plant that is modeled to capture CO2 in the slow or fast scenarios

include the fraction of captured CO2, the heat rate, fixed operating and maintenance costs (FOM), and variable

operating and maintenance costs (VOM).

Plant Name

Rated

Capacity

(MW)

Output

Capacity

Without

CO2

Capture

(MW)

Heat Rate Without

CO2 Capture

(MMBtu/MWh)

CO2

Emissions

Rate Without

Capture

(tCO2/ MWh)

Output

Capacity

With CO2

Capture

(MW)

Heat Rate

With CO2

Capture

(MMBtu/

MWh)

Emissions

Rate With

CO2 Capture

(tCO2/

MWh)

Big Brown 1187 988 10.70 1.06 735 14.37 0.14

Fayette Power Project 1 615 498 10.68 0.99 378 14.06 0.13

Fayette Power Project 2 and 3 1075 893 10.68 0.99 678 14.06 0.13

J K Spruce 566 475 10.82 1.01 359 14.31 0.13

J K Spruce 2 750 600 10.82 1.01 454 14.31 0.13

J T Deely 932 617 14.07 1.31 421 20.60 0.19

Limestone 1850 1570 9.61 0.95 1209 12.48 0.12

Oak Grove 1 855 684 9.31 0.91 533 11.94 0.12

Oak Grove 2 855 684 9.31 0.91 533 11.94 0.12

San Miguel 410 314 12.15 1.20 223 17.12 0.17

Sandow No 4 591 514 11.16 1.10 377 15.22 0.15

W A Parish 1 thru 6 and 8 3354 1833 10.38 0.95 1413 13.47 0.12

WA Parish 7 615 498 10.38 0.95 384 13.47 0.12

Plant Name

Is CO2

Capture

Installed in

“Slow”

scenarios?

Fraction of

CO2

Removed

With

Capture

CO2

Capture

Energy

(MWh/

tCO2)

CO2

Capture

FOM Cost

($/MWh)

Non-

Fuel/CO2

Capture

VOM Cost

($/MWh)

Big Brown No 0.9 0.269 4.7 4.5

Fayette Power Project 1 Yes 0.9 0.269 4.9 4.2

Fayette Power Project 2 and 3 No 0.9 0.269 4.7 4.2

J K Spruce No 0.9 0.269 4.8 4.3

J K Spruce 2 Yes 0.9 0.269 4.9 4.3

J T Deely No 0.9 0.269 6.5 5.4

Limestone No 0.9 0.269 4.5 4.1

Oak Grove 1 No 0.9 0.269 4.8 4.0

Oak Grove 2 No 0.9 0.269 4.8 4.0

San Miguel No 0.9 0.269 5.7 5.0

Sandow No 4 No 0.9 0.269 4.7 4.6

W A Parish 1 thru 6 and 8 No 0.9 0.269 6.9 4.1

WA Parish 7 Yes 0.9 0.269 4.8 4.1

NOTES:

Rated Capacity: The installed nameplate capacity of the power plant or generation unit.

Output Capacity Without Capture: A weighted capacity value used in the dispatch model that enables representative

results for annual generation by fuel (e.g. compensates for annual maintenance of power plants).

Output Capacity With Capture: Same as Output Capacity Without Capture except it accounts for parasitic losses to

run CO2 capture processes within the power plant.

CO2 Capture FOM cost is additional FOM cost for capture systems only and includes additional labor, maintenance,

and administration for capture systems.

Non-fuel/CO2 capture VOM cost is additional VOM cost for capture systems only and includes solvent management

cost (solvent makeup, thermal reclaimer makeup, degradation waste disposal) and additional water use for

CO2 capture (for process water and additional cooling)

Non-fuel VOM costs for non-capture systems included in the ERCOT dispatch model (not listed in Table S3) are

assumed at $5.75/MWh for all coal-fired facilities.

S-5

(S1a)

(S1b)

(S1c)

0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh

ERCOT Generation (Baseline, Scenario 1)

0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh

ERCOT Generation (Scenario 1)

0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


COAL w/ CAPTURE COAL NUCL WIND + OTH HYDRO GASCHP NGCC NGGT NGST MISCPEAK

0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


S-6

(S1d)

Figure S1a-d. The quantity of electricity generated by each type of fuel is slightly different for each scenario. A

“baseline” generation mix is shown on the left for each scenario to compare how the dispatch model forecasts

generation if there were no coal-fired plants with CO2 capture. All scenarios assume the same amount of generation

each year (e.g. 532 TWh in 2031). NUCL = nuclear, GASCHP = natural gas combined heat and power facilities,

NGCC = natural gas combined cycle, NGGT = natural gas (combustion) gas turbines, NGST = natural gas steam

turbines, MISCPEAK = miscellaneous peaking facilities.

0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


COAL w/ CAPTURE COAL NUCL WIND + OTH HYDRO GASCHP NGCC NGGT NGST MISCPEAK

0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


0

100

200

300

400

500

600

2012 2016 2020 2024 2028 2031

TWh


S-7

Methods for estimating costs for CO2-EOR production in the Texas

Gulf Coast

This section describes the process for calculating the costs for enhanced oil recovery (EOR) using carbon

dioxide (CO2) in South Texas. The costs are grouped into (i) capital costs and (ii) annual costs (e.g.

operating and maintenance costs that are a function of the number of wells operating or amount of oil/

CO2 flows occurring each year).

Capital costs are comprised of the following factors:

drilling and well equipping for CO2 injection wells for EOR (worked over or side-tracked using

existing wells)

drilling and well equipping for EOR oil production wells (worked over or side-tracked using

existing wells)

recycling facility for recycling the ‘produced’ CO2 for reinjection

CO2 distribution trunk lines as estimated from cost data of the Energy Information Administration

Operating and maintenance costs are comprised of the following factors:

Additional O&M costs from EIA data, assuming these meant to be in addition to primary oil

O&M costs.

Recycling, compression, and injection of CO2 at the EOR site (assumed only as electricity:

kWh/tCO2)

Capital costs

Costs for drilling and equipping costs for new wells

The costs estimated using the method in this section can be for injection or production wells: new drilling

and equipping costs. To estimate drilling and equipment costs we use data from the Joint Association

Survey (JAS) drilling cost document, using data from the JAS study for each Railroad Commission

districts in South Texas as needed (primarily RRC 1-4). These data (for the appropriate Railroad

commission district) can also be checked (somewhat) using the “EIA Data” that includes injection well

costs for West Texas Secondary Oil production (Tables A9-A11 from EIA Costs and Indices Data).

Data were taken from the Joint Association Survey on Drilling Costs from 1995 to 2009 excluding 1998

and 2000 due to their unavailability from the IHS Energy Resource Center (ERC). The average depth

between 0 – 12,500ft range and corresponding costs were tabulated. These costs, originally reported in

nominal dollars, were converted to real 2009 US dollars. Due to restrictions on copying data from the

JAS study, no specific drilling cost data are presented here.

The drilling costs reported in the JAS study are:

“In general, the elements contributing to reported cost are the expenditures for drilling dry holes and

productive wells and equipping new productive wells through the “Christmas tree” installation. More

specifically, these cost elements are the costs of labor, materials, supplies, water, fuels, power, and direct

overhead (i.e. field, district, and regional), for such operations as site preparation, road building, erecting

and dismantling derricks and drilling rigs, drilling hole, running and cementing casing, hauling materials,

etc. Include the total cost of water, if purchased, or cost of water well, if drilled and chargeable to oil or

gas well drilling operations. Well costs also include machinery and tool charges and rentals, and

S-8

depreciation charges, where appropriate, for rigs and other equipment and facilities which will be used in

drilling more than one well. Deduct the condition value of materials salvaged after use where appropriate.

Do not report the cost of lease equipment such as artificial lift equipment and downhole lift equipment,

flow lines, flow tans, separators, etc., that are required for production. Do not reduce the costs by test

well, bottom hole, or dry hole contributions.”

Drilling costs are expressed as an exponential function as in Equation (1), where D = depth of well. The

parameters a0 and a1 were determined using linear regression for each year of data from the JAS data set.

We then relate these parameters for the drilling cost equation to the price of oil such that we can use these

as a function of oil price (for future drilling price estimates) if needed.

Da

oea 11000 ($/well)cost drilling (1)

The factors ao ($1000/well) and a1 are determined from the drilling cost data and related to oil price.

Figure S2 (a, c, e, and g) indicate the values of a0 for Texas Railroad Commission districts 1, 2, 3, and 4,

respectively, and Figure S2 (b, d, f and h) indicate the values of a1 for Texas Railroad Commission

districts 1, 2, 3, and 4,

Figure S2a. The drilling cost factor a0 ($1000/well) shown for RRC1 has changed considerably after the oil price

escalations after 2005. We use the new trend of relating a0 to oil price for the years 2006-2009 instead of factoring

the trends from 2005 and earlier.

y = 3.3862x + 5.5327

y = 5.4683x + 98.172

0

100

200

300

400

500

600

700

0 20 40 60 80 100 120

a0 (

$1

00

0s/

we

ll)

Oil price ($2009)

a0 vs. Oil price - RRC1 (wells > 4000')

1995-2005

2006-2009

Linear (1995-2005)

Linear (2006-2009)

S-9

The factor a1 has ranged from approximately 2.0e-4 to 3.0e-4 ft-1, but seems to typically be near 2.5e-4 ft-

1.

Figure S2b. The factor a1 does not follow an obvious trend with respect to oil price for RRC1. Most values are

between 2.0e-4 and 3.0e-4, and a value of 2.5e-4 might be most common for all oil prices.

Figure S2c. The drilling cost factor a0 ($1000/well) shown for RRC2 has changed considerably after the oil price



y = -2.79E-08x + 2.68E-04

y = 9.63E-08x + 2.26E-04

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

4.50E-04

5.00E-04

0 20 40 60 80 100 120

a1 (

1/f

t)

Oil price ($2009)


1995-2005 data

2006-2009 data

Linear (1995-2005 data)

Linear (2006-2009 data)

y = -0.1832x + 118.55

y = 6.472x - 41.081

0

100

200

300

400

500

600

700

0 20 40 60 80 100 120

a0 (

$1

00

0s/

we

ll)

Oil price ($)


ao (1995-2005)

a0 (2006-2009)

Linear (ao (1995-2005))

Linear (a0 (2006-2009))

S-10

Figure S2d. The factor a1 does not follow an obvious trend with respect to oil price for RRC2. Most values are


Figure S2e. The drilling cost factor a0 ($1000/well) shown for RRC3 has changed considerably after the oil price



y = 3E-06x + 0.0002

y = -3E-07x + 0.0003

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

0 20 40 60 80 100 120

a1 (

1/f

t)

Oil price ($2009)

a1 vs. Oil price - RRC2

1995-2005

2006-2009

Linear (1995-2005)

Linear (2006-2009)

y = 0.8044x + 129.83

y = 5.7524x + 85.991

0

100

200

300

400

500

600

700

800

0 20 40 60 80 100 120

a0 (

$1

00

0s/

we

ll)

Oil price ($2009)

a0 vs. Oil price - RRC3 (wells >4000')

a0 (1995-2005)

a0 (2006-2009)

Linear (a0 (1995-2005))

Linear (a0 (2006-2009))

S-11

Figure S2f. The factor a1 does not follow an obvious trend with respect to oil price for RRC2. Most values are


Figure S2g. The drilling cost factor a0 ($1000/well) shown for RRC4 has changed considerably after the oil price



y = 2E-06x + 0.0002

y = -2E-07x + 0.0003

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

0 20 40 60 80 100 120

a1 (

1/f

t)

Oil price ($2009)


1995-2005

2006-2009

Linear (1995-2005)

Linear (2006-2009)

y = 2.9913x + 18.833

y = 4.4921x + 318.74

y = 10.301x - 208.8

0

200

400

600

800

1000

1200

1400

0 20 40 60 80 100 120

a0 (

$1

00

0s/

we

ll)

Oil price ($2009)


a0 (1995-2005)a0 (2006-2009)a0 (all years)Linear (a0 (1995-2005))Linear (a0 (2006-2009))Linear (a0 (all years))

S-12

Figure S2h. The factor a1 does not follow an obvious trend with respect to oil price for RRC4. Most values are


To consider the cost escalations for drilling costs after 2005, we use an equation for drilling costs that is a

function of the oil price (per Figure S2 a, c, e, g). We assume that the factor a0 is a function of oil price,

but that a1 is constant for any given cost estimate. Equation (1) is modified into Equation (2) by

incorporating the oil price ($2009/BBL) in units of 2009 USD.

Da

o ebBBLb 1

1)/2009$1000 ($/well)cost drilling (2)

The factors b0 and b1 are used to estimate the non-depth dependent cost for drilling as a function of the oil

price. Table S4 indicates the suggested values for b0 and b1 for the three RRC districts of the Gulf Coast.

Table S4. Drilling cost factors for estimating South Texas drilling costs as a function of depth and oil price.

RRC district a1 (ft-1) bo (BBL/well) b1 ($1000/well)

2 ~2.5e-4 6.5 -41

3 ~2.5e-4 5.8 86

4 ~2.5e-4 4.5 120* * The data point for drilling costs of 2007 for RRC4 are much higher than any data point and skew the linear

regression, such that no line seems to represent the cost trend very well. For RRC4, we assume the slope of the line

for the a1 term (e.g. bo in Table S4) but shift the y-intercept in Figure S2g down by 200 $1000s/well to bring it in

line more with the rest of the data.

Lease Equipment Costs for new production wells

Lease equipment costs are not included in the JAS drilling survey, but we need to account for lease

equipment costs. There are lease equipment costs for primary oil recovery wells accounted for in this

section. The “additional lease equipment costs” that we add for secondary oil production, and estimated

as equal those for CO2-EOR tertiary recovery, are described in the next section. The lease equipment

y = -1E-06x + 0.0003

y = -2E-08x + 0.0002

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

0 20 40 60 80 100 120

a1 (

1/f

t)

Oil price ($2009)


1995-2005

2006-2009

Linear (1995-2005)

Linear (2006-2009)

S-13

costs for primary oil production are added to the “additional” lease costs to equal the total lease

equipment costs.

When describing lease equipment costs, the EIA website description of its “Oil and Gas Lease Equipment

and Operating Costs 1994 Through 2009” states:

“Costs were determined for new equipment. Tubing costs are included for the oil wells

but not for the gas wells. Care must be exercised when combining these equipment costs

with drilling costs to obtain total lease development and equipment costs because most

drilling and completion cost estimates also include tubing

costs.” [http://www.eia.gov/pub/oil_gas/natural_gas/data_publications/cost_indices_equ

ipment_production/current/coststudy.html]

Thus, we exclude the “tubing” costs from the EIA data because these are assumed included in the JAS

Drilling Survey costs (to avoid double-counting any costs). Using the line labeled “non-tubing costs”

from Tables B1-B3 (“Lease Equipment Costs and Indices for primary oil production in South Texas”) of

the EIA Oil and Gas Lease Equipment and Operating Costs, the following pattern emerges for 2006-2009

data (See Figure S2). While the trend is not exactly linear, we approximate the cost trend as a linear

function of depth of the form “C0 + C1D”.

Figure S3. Lease equipment costs for “non-tubing” costs for South Texas primary production (in current dollars for

each year).

We need to understand an equation of the form of Equation (3) that models the historical data (from 1994-

2009) as partially represented in Figure S3. These C1 and C0 values have changed over time and this is

captured by the cost indices reported in the EIA tables. We model this cost index as a function of the oil

y = 10.04x + 97170R² = 0.974

0

50,000

100,000

150,000

200,000

250,000

0 2000 4000 6000 8000 10000 12000 14000

$/w

ell

(cu

rre

nt

do

llars

)

Depth of well (feet)

"Non-tubing" lease costs for S. Texas primary production

2006

2007

2008

2009

Linear (2009)

S-14

price. Then we model C1 and C0 as a function of this cost index to ultimately predict the lease equipment

costs as a function of oil price by making the price coefficient as C1($/BBL) and C0($/BBL). Table S5

shows results for estimating a real cost index for the South Texas primary production equipment lease

costs. We then regress the real lease cost indices of Table S5 (an estimated linear curve fit) on real oil

price (see Figure S4).

01 costsequipment lease productionprimary TXSouth cDc (3)

Table S5. Nominal indices for S. Texas primary production ‘non-tubing’ lease costs are reported by EIA. These are

converted to real indices based using $2009 as the real dollar value of interest. The average cost index for all three

depths is then calculated from the real index at each depth for each year.

Nominal

index of "S.

TX primary

lease equip

costs": non-

tubing total

Real value

index for

choosen

year

Nominal

index of "S.

TX primary

lease equip

costs": non-

tubing total

Real value

index for

choosen

year

Nominal

index of "S.

TX primary

lease equip

costs": non-

tubing total

Real value

index for

choosen

year

Average

Real

Index

170.29 233.70 197.9350 271.64 198.928826 273.01 259.45

176.51 237.29 202.3128 271.98 203.2135 273.20 260.82

182.95 241.36 209.8844 276.89 211.4497 278.96 265.74

200.65 260.11 231.9383 300.68 235.2059 304.92 288.57

211.42 271.02 237.3073 304.20 240.6332 308.46 294.56

212.69 268.69 235.2148 297.15 238.8003 301.68 289.17

224.11 277.12 248.2654 306.99 252.5827 312.33 298.81

231.57 280.02 254.2401 307.43 257.5577 311.44 299.63

235.17 279.84 257.2687 306.13 259.6287 308.94 298.31

238.52 277.85 264.2070 307.77 266.7222 310.70 298.77

225.44 255.36 304.1850 344.56 310.1404 351.31 317.08

246.29 269.97 328.6894 360.29 335.6582 367.93 332.73

265.70 282.06 355.6443 377.54 362.7946 385.13 348.25

287.90 296.89 370.1267 381.68 376.5056 388.26 355.61

321.64 324.59 416.7676 420.59 428.8503 432.78 392.65

309.93 309.93 405.9471 405.95 416.6151 416.62 377.50

INDEX for "lease equipment costs for South Texas Primary production" -

tubing (not including tubing that is assumed to be in drilling and completion

costs already

2000 foot depth 4000 foot depth 8000 foot depth

S-15

Figure S4. The real indices of Table S5 (and estimated linear curve fit) are plotted versus the real price of oil in

$2009/BBL. The linear curve fit of the real index equation is shown as = 1.55×(oil price in $2009/BBL) + 243.

Now that the lease equipment cost index can be estimated as a function of oil price, we can estimate how

C1 and C0 change as a function of the real cost index. Linear correlations are made as a function of the

index as shown in Figure S5a (left) and Figure S5b (right).

Figure S5a and Figure S5b. Relating the coefficients (C1 and C0) of the injection lease equipping cost equation to

the calculated average cost index for all well depths (2000, 4000, and 8000 feet). The coefficient C1 = d11i+d10 and

coefficient C0 = d01i + d00 where i is the real cost index calculated from nominal cost index of the EIA cost data.

Thus, for a given oil price ($2009/BBL), the cost for additional lease equipment for injection wells in

West Texas is as shown in Equations (4) and (5), where i is the real cost index calculated from nominal

cost index of the EIA cost data:

y = 1.5494x + 243.49

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150

aver

age

cost

ind

ex S

. TX

leas

e eq

uip

men

t co

stss

fo

r p

rim

ary

pro

du

ctio

n (

no

n-t

ub

ing

cost

s)

($ real/BBL)

Real cost index

Predicted Real Index values from curvefit

y = 0.0248x - 1.9553R² = 0.9113

0

5

10

15

200 300 400 500 600

Real Cost Index

Slope (C1) of equation: cost ($/well) = C1*D + Co

y = 55.691x + 58423R² = 0.4761

0

50,000

100,000

200 300 400 500 600Real Cost Index

Intercept (Co) of equation ($/well): cost = C1*D + Co

S-16

400,587.5596.1025.0

($2009) costsequipment lease TXSouth

00011011

01

iDi

didDdid

cDc

(4)

where,

243/2009$55.1/2009$ 01 BBLcBBLci (5)

Lease costs are calculated as relating to the number of wells in operation at any given time. In other

words, if 10 wells are drilled in year 1 and these wells operate for 5 years, and 10 wells are drilled in year

6 to operate 5 years, then there will be only 10 wells in operation in any given year. Thus, the lease

equipment costs are only for 10 wells and not for 20 wells.

Lease Equipment Costs for CO2 injection wells

In the case of assuming new injection wells, we calculate the additional lease equipment costs for new

CO2 injection wells as described on pages B-3 to B-4 of ARI (2006). ARI uses an equation of the form:

cost = C0 + C1D, but this is not clear how they get the equation they use in the document. Thus, we obtain

lease equipment costs from the EIA Oil and Gas Lease Operating costs data, Tables A9-A11 for West

Texas secondary oil production. We assume that lease costs for CO2-EOR are the same as those for

secondary oil production, with some additions as described later. We will then scale these costs to South

Texas based upon how much primary production lease costs in South Texas (Tables B1-B4) are larger

than in West Texas (Tables A1-A4). These “Lease Equipment Costs” are only those labeled under the

heading “Injection Equipment” in the EIA data spreadsheet (i.e. we do not include costs of “Producing

Equipment” or “Injection Wells” (accounted for as assumed new drilled wells)) as we already calculate

these costs elsewhere. As seen in Figure S6 below, there is seemingly no depth component to this

equation for < 4000’.

Using only the line of indices for “Injection Equipment” from Tables A9-A11 (“Additional Lease

Equipment Costs and Indices for Secondary Oil Production in West Texas”) of the EIA Costs and Indices

report, the following pattern emerges for 2006-2009 data (See Figure S6). This pattern shows that costs

are not linear from 2000’ to 8000’ depths, but we will assume the trends are linear for > 4000 feet deep

because our 10 candidate EOR fields have reservoir depths > 4000’ and < 8000’. We use the same

information for pure CO2 injection well costs (into saline formations) which can be > 8000 feet deep, and

we assume extrapolating costs for depths slightly beyond 8000 feet is acceptable.

S-17

Figure S6. Lease equipment costs for water injection wells in West Texas (in current dollars for each year).

Thus we need to understand an equation of the form of Equation (6) that models the historical data (from

1994-2009) as partially represented in Figure S6. These C1 and C0 values have changed over time and

this is captured by the cost indices reported in the EIA tables. Thus, we model this cost index as a

function of the oil price. Then we will model C1 and C0 as a function of this cost index to ultimately

predict the additional injection costs as a function of oil price, or have C1($/BBL) and C0($/BBL).

Table S6 shows results for estimating a real cost index for the additional injection equipment lease costs.

We then regress the real lease cost indices of Table S6 (and estimated linear curve fit) on real oil price

(see Figure S7).

01 costsequipment lease additional cDc (6)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0 2000 4000 6000 8000 10000

$/w

ell

(cu

rre

nt

do

llars

)


"Additional lease costs, W. Texas, for secondary oil recovery: Injection equipment ONLY"

2006

2007

2008

2009

S-18

Table S6. Nominal indices are reported by EIA, and these are converted to real indices based using $2009 as the

real dollar value of interest. The average cost index for all three depths is then calculated from the real index at each

depth for each year.

Figure S7. The real indices of Table S6 (and estimated linear curve fit) are plotted versus the price of oil. The linear

curve fit of the real index equation is shown as = 3.36*oil price + 235.

Nominal

index of

"additional

lease equip

costs, W. TX

H2O flood"

Real value

index for

choosen

year

Nominal

index of

"additional

lease equip

costs, W. TX

H2O flood"

Real value

index for

choosen

year

Nominal

index of

"additional

lease equip

costs, W. TX

H2O flood"

Real value

index for

choosen

year

Average

Real

Index

222.63 305.53 211.3746 290.09 216.14614 296.64 297.42

235.88 317.11 223.9593 301.09 230.8780 310.39 309.53

236.65 312.20 224.7338 296.48 230.6423 304.28 304.32

247.45 320.79 234.9952 304.64 242.1626 313.93 313.12

283.94 363.98 269.4579 345.41 282.7932 362.51 357.30

253.67 320.46 240.6583 304.03 247.4367 312.59 312.36

271.61 335.86 257.6960 318.65 266.7649 329.86 328.12

278.29 336.51 264.0368 319.28 276.2817 334.08 329.96

285.02 339.15 270.7164 322.14 282.7048 336.40 332.56

309.58 360.63 294.0949 342.58 310.2829 361.44 354.88

348.42 394.67 331.6070 375.62 345.3742 391.22 387.17

396.43 434.55 377.6864 414.00 393.8126 431.68 426.74

456.32 484.42 434.6563 461.42 456.1874 484.28 476.70

488.43 503.68 465.1985 479.72 487.9493 503.19 495.53

550.36 555.40 524.0561 528.86 553.9481 559.03 547.76

536.14 536.14 510.0194 510.02 543.0466 543.05 529.73

2000 foot depth 4000 foot depth 8000 foot depth

INDEX for "additional injection lease equipment costs for W.TX secondary

production"

y = 3.355x + 235.04

0

100

200

300

400

500

600

700

0 20 40 60 80 100 120 140

aver

age

cost

ind

ex (

add

tio

nal

leas

e eq

uip

. co

st f

or

inje

ctio

n o

nly

, W.

Texa

s)

($2009 real/BBL)

Real cost index

S-19

Now that the lease equipment cost index can be predicted as a function of oil price, we can estimate how

C1 and C0 change as a function of the real cost index. Linear correlations are made as a function of the

index as shown in Figure S8a and Figure S8b.

Figure S8a and Figure S8b. Relating the coefficients (C1 and C0) of the injection lease equipping cost equation to

the calculated average cost index for all well depths (2000, 4000, and 8000 feet).

Thus, for a given oil price ($2009/BBL), the cost for additional lease equipment for injection wells in

West Texas is as shown in Equations (7) and (8), where i is the real cost index calculated from nominal

cost index of the EIA cost data:

550,53.3716.1036.0

($2009) costsequipment lease additional

00011011

01

iDi

bibDbib

cDc

(7)

where,

235/2009$36.3/2009$ 01 BBLcBBLci (8)

To obtain estimated costs in South Texas, we multiply the final cost for West Texas by the calculated

factor that describes capital cost differences of South versus West Texas oil production (described

elsewhere as South Texas is estimated to have lease costs 1.15 times larger than West Texas).

H2O injection wells – lease costs

We are approximating the enhanced oil recovery method as discussed by Denbury Resources Inc. for their

operations in the Hastings oil field (Davis et al., 2011). Thus, there are some necessary number of water

injection wells to create the ‘water curtain’ around the CO2-injectoring and oil-producing wells. Figure 5

of the Denbury SPE paper indicates 14 water-alternating-gas (WAG) injection wells as water curtain

wells around 23 CO2 injection wells. Thus, there are 14/23 ~ 0.6 WAG wells for every one CO2 injection

well, and this ratio is for a relatively confined system (fault block “A” of Hastings) with the WAG wells

on the outside of a “pie wedge” with an included angle of 90o-110o. The geologic faults restrict the fluid

flows to this ~ 100o wedge (or slightly larger than one quarter of a circle).

The assumption for this present work is that there will be 5 water injection wells for every 10 CO2

injection wells as if the development of the oil field might progress in a linear step-wise manner from one

y = 0.0356x - 1.1644R² = 0.9971

0

5

10

15

20

200 300 400 500 600

Real Cost Index

Slope (C1) of equation: cost ($/well) = C1*D + Co

y = 37.29x + 5550.6R² = 0.9369

0

10,000

20,000

30,000

200 300 400 500 600Real Cost Index

Intercept (Co) of equation ($/well): cost = C1*D + Co

S-20

side of the oil field to another and the water injection wells would always be ‘ahead’ of the CO2 injection

wells and oil production wells.

CO2 recycling plant

As estimated in ARI (2006) pg. B-9, the capital costs for CO2 recycling plant is estimated as

$700,000/(MMcf/day of capacity) of peak CO2 recycling capacity needed. We estimate capital costs of

CO2 recycling equipment using this value.

CO2 trunk likes for field level distribution

We do not include the cost for CO2 trunk lines as separate cost items for EOR. The reason is that both the

“Primary production Lease Equipment Costs” and “Additional Lease Equipment Costs” of the EIA Costs

and indices data include “Distribution lines.” By already accounting for these distribution lines from

these two sources of data, we inherently assume that CO2 distribution lines would be of similar cost.

Although the EIA Costs and indices data for “Additional Lease Equipment Costs” for secondary

production in West Texas have the line item for “Distribution Lines” assuming they distribute water, we

assume the costs are the same for CO2 distribution (even though CO2 is delivered at much higher

pressure). However, there could be appreciably higher costs for CO2 distribution lines versus water

distribution lines. In contrast, ARI (2006) estimates CO2 distribution costs (at the EOR field) as =

$150,000 + CD×Distance (see page B-10).

Operating and Maintenance costs for CO2-EOR The method for estimating O&M costs for CO2-EOR come primarily from ARI (2006) and DOE (2009).

Annual Operating and Maintenance Costs

Cost data from EIA Costs and Indices data are modified per the Appendix pages B-7 to B-10 of ARI

(2006). Tables A12-A14 of the EIA Costs and Indices data have direct annual O&M cost data for West

Texas secondary oil production which we modify for CO2-EOR and for South Texas as compared to West

Texas. The modification of EIA cost data is to subtract “Fuel, Water, and Power” and multiply both

“surface maintenance” and “subsurface maintenance” categories by 2 to account for higher costs for CO2-

EOR versus H2O-EOR operations. We derive these costs as a function of oil price to project into the

future. These costs are then scaled by a factor indicating how much O&M costs are higher in South Texas

versus West Texas (using data in EIA Tables A5-A8 and Tables B5-B8 that compare primary production

O&M costs in West and South Texas, respectively).

Figure S9 shows the O&M costs for secondary oil production in West Texas and that these costs can be

expressed as a linear function of the depth of the well of a form “C1D + C0”. By converting the nominal

dollar cost indices to real dollar indices (in $2009), we plot the full O&M cost index for all depths (2000’,

4000’, and 8000’) as single cost index (see Figure S10). The cost index as a function of oil price is of the

form “C1D + C0”.

S-21

Figure S9. The annual O&M costs for West Texas secondary oil production show a cost equation of the form “C1D

+ C0”.

Figure S10. The average real cost index (averaged for 2000’, 4000’, and 8000’ wells) for O&M costs as a function

of real oil price ($2009/BBL). The linear curve fit relation is also shown to predict this index as a function of

(projected) oil prices.

y = 2.8189x + 25215R² = 0.9948

0

10,000

20,000

30,000

40,000

50,000

60,000

0 2000 4000 6000 8000 10000

$/w

ell/

yr (

curr

en

t d

olla

rs)


"Annual O&M costs, for W. Texas secondary oil recovery"

2006

2007

2008

2009

Linear (2009)

y = 2.3962x + 299.34R² = 0.9251

0

100

200

300

400

500

600

700

0 50 100 150

aver

age

cost

ind

ex (

O&

M c

ost

s, W

. Te

xas

seco

nd

ary

pro

du

ctio

n)

($ real/BBL)

Real cost index

Predicted Real Index values from curve fit

S-22

Figure S11. The trends for the O&M cost for secondary production in West Texas, using costs scaled to real dollars

($2009) show linear trends for the slope and intercept factor of the cost equation as a function of cost index.

Figure S11 shows the curve fit equations that are used to calculate the O&M cost equation factors (of the

form “C1D + C0” as in Figure S9) for West Texas secondary oil production as a function of the cost index.

Again, Figure S10 shows how the cost index is a function of oil price, effectively making O&M costs a

function of oil price. Thus, for future costs, the assumed future oil price is input into the equation (linear

trend) in Figure S10 to output a cost index. Then the output cost index is used as an input to the equations

of Figure S11 to estimate the factors C1 and C0. These C1 and C0 then used in an equation for O&M costs

per well as a function of depth: $/well/yr = C1D + C0.

To scale West Texas CO2-EOR estimated costs to those for South Texas, we use data on the primary

production O&M costs for each. That is to say find the C1 and C0 values (values corresponding to Figure

S9 indicating O&M costs as a function of depth) for each year of data (1994-2009) for West Texas

primary O&M and also for South Texas primary O&M ($/well/yr) and compare. Figure S11 shows that

the factors describing the ratio of O&M costs in S. Texas : W. Texas has changed considerably over the

last two decades. From 1994-2001 the costs in S. Texas coming closer into parity with those in W. Texas,

and C1 and C0 ratios following the same downward trend. Then from 2001-2009, the fixed cost factor

(C0) ratio increased while the depth-dependent cost factor (C1) ratio continued to decrease such that by

2005 depth dependent costs in S. Texas were lower than in W. Texas. Per the calculations shown in

Figure S12, predicting South Texas CO2-EOR O&M costs from those in West Texas is very approximate.

However, to predict South Texas CO2-EOR O&M costs from those estimated for West Texas, we assume

values typical of 2008 and 2009 for the C1 and C0 ratios 0.85 and 1.55, respectively.

y = 0.0104x - 0.623R² = 0.9383

0

2

4

6

200 300 400 500 600

C1

, usi

ng

real

$ a

lrea

dy

scal

ed f

or

CO

2-E

OR

in W

. Tex

as

Real Cost Index (for W.Texas secondary production)

Slope (C1) of equation: cost ($/well/yr) = C1*D + Co

y = 47.109x - 2104.4R² = 0.9723

0

5,000

10,000

15,000

20,000

25,000

200 400 600

C0

, usi

ng

real

$ a

lrea

dy

scal

ed f

or

CO

2-E

OR

in W

. Tex

as

Real Cost Index (for W.Texas secondary production)

Intercept (Co) of equation ($/well/yr): cost = C1*D + Co

S-23

Figure S12. Ratios of C1 and C0 when comparing O&M costs for primary production in South Texas to West Texas

where O&M costs are expressed using an equation of the form “= C1D + C0.”

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600

1.800

1990 1995 2000 2005 2010

Ratio of "S. Texas/W.Texas" O&M curve fit parameters over time (real cost = C1*D + C0)

C1

C0

S-24

Figure S13. Predicted O&M costs for CO2-EOR in South Texas ($2009/well/yr) at depths of 2000, 4000, and 8000

feet as a function of oil price ($2009/BBL).

Recycle compression, lifting, and ‘other’ electricity needs

The power requirements for compressing and pressurizing CO2 for EOR come from McCullom and

Ogden (2006) as in Equation (9). There is assumed to be only one stage of compression required for

recycling CO2 that is produced from EOR operations as the injection pressure is high enough to produce

the oil/water/CO2 mixture at pressure significantly above the ambient pressure (per Keshgi et al. (2010)).

Thus, there is only on stage of compression

11360024

10001

,s

s

k

k

s

s

is

ins

is CRk

k

M

RTmZW

(9)

where,

Ws,i = compression power for a given stage of compression (in kW)

M = mass flow rate of CO2 in tonnes per day

R = 8.314 kJ/kmol-K

M = 44.01 kg/kmol

Tin = 313.14 K

is = 0.75 (efficiency of compression stage)

1000 = kg per tonne (conversion)

24 = hours per day (conversion)

3600 = seconds per hour (conversion)

For an assumed single (‘final’) stage of compression (from pressures of 3.1 MPa to supercritical at Pcut-off

= 7.4 MPa), Zs = 0.845 and ks = 1.704 and CR = (Pcut-off – Pmin).

$0

$20

$40

$60

$80

$100

$120

$140

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1990 2000 2010 2020 2030

Oil

pri

ce (

$re

al/B

BL)

O&

M C

ost

s fo

r S.

Tex

as C

O2

-EO

R

(no

t in

clu

din

g C

O2

rec

om

pre

ssio

n

and

inje

ctio

n)

($2

00

9/w

ell/

yr)

Year

South Texas

2000

4000

8000

oil price

S-25

The power requirement for compressing CO2 to the injection pressure, Pfinal, (up to 15 MPa) higher than

supercritical CO2 pressure (e.g. Pcut-off = 7.4 MPa) is as in Equation (10):

pCO

offcutfinal

pump

PPmW

2

3624

101000 (10)

where

CO2 = density of CO2= 630 kg/m3

p = 0.75 (efficiency of pump)

10 = represents the number of bar per MPa

36 = conversion factor of (m3bar/hr/kW)

The annual electricity (kWh/yr) for recompressing and pumping, Epumping, recycled CO2 for EOR is as in

Equation (11). Costs of electricity are then the assumed electricity price ($/kWh) multiplied by Epumping.

36524, pumpsipumping WWE (11)

Additional electricity costs for lifting CO2 come from an Advanced Resources international report to

DOE (ARI, 2009). For lifting costs, we assume that there are no specific pumps, but that CO2 is injected

to such a pressure as to make the CO2 and oil flow readily from production wells. From Table 5, pg. 24 of

ARI (2009), they approximate 3 kWh/BBL of oil produced each year for lifting, and we also use this

value. “Other” power costs (from Table 5, pg. 24 of ARI (2009)) are 5 kWh/BBL of oil produced each

year such that total ‘lifting + other’ electricity costs are 8 kWh/BBL of oil produced each year.

General and Administrative (G&A) Costs

Per ARI (2006) on page B-10, general and administrative (G&A) costs of 20% are added to well O&M

(and lifting costs). We use this as a straight calculation for each EOR field based upon total field O&M

costs.

Percentage of annual O&M costs for personnel

Here we estimate the fraction of annual O&M costs that are for personnel costs (e.g. administration,

supervisory, and labor). We use the data from the EIA Cost and Indices Tables A12-A14 for West Texas

secondary oil production along with the aforementioned augmentation for converting these H2O-EOR

operating costs for CO2-EOR. When considering only these augmented direct annual operating costs for

secondary oil production in West Texas, the percentage of these costs for personnel is approximately 31%

for wells at 2000’ (Table A12), 29% for wells 4000’ (Table A13), and 21% for wells 8000’ (Table A14).

S-26

Methods for estimating costs for injection of CO2 into saline

reservoirs in the Texas Gulf Coast

Saline CO2 injection costs

CO2 injection well pressure, flow rate and radius

We calculate the CO2 injection rate by assuming a uniform reservoir and injection at constant pressure per

(Lee, 2007):

w

egg

r

rB

Pkhq

ln2.141

(12)

where,

q = injection rate (MCF/day)

P = change in pressure, psi (P = Pwf – Pi)

Pi = initial reservoir pressure, psi (Pi = 0.465 psi/ft)

Pwf = injection pressure, psi

Pf = fracture pressure of reservoir, psi (Pf = 0.7 psi/ft)

k = reservoir permeability, md

h = reservoir thickness, ft

Bg = gas formation volume factor, RCF/MCF

g = CO2 gas viscosity, cp

re = external injection radius, ft

rw = well bore radius, ft

and the external injection radius of the CO2 is:

g

e

ktr

948

8760 (13)

where

t = time, years

= porosity

The number of CO2 injection wells needed for any given year is equal to the quantity of CO2 for injection

that year (the CO2 is assumed injected at the same rate for each day in a given year) divided by the

injection rate as in Equation (12). If for any given year there are not enough saline CO2 injection wells,

then more wells are assumed drilled in that year as needed. If for any given year there are more than

enough CO2 injection wells for the quantity of CO2 for injection, then no wells are drilled during that

year.

S-27

Capital costs for drilling saline injection wells

Based upon the previous description of estimating the CO2 injection rate, we estimate the total number of

saline wells needed in operation for any given year. The drilling costs for saline CO2 injection wells are

assumed as 100% of that of a new oil well. Lease costs for saline CO2 injection wells are assumed equal

to those additional lease costs required for CO2-EOR operations.

To be conservative and not underestimate the number of needed saline wells, all wells in operation for a

given year are assumed to operate at an injection rate of a well that would be assumed to have operating

continuously at maximum injection rate from the beginning of the analysis. Thus, the injection rate

declines over time the same for each well.

Operating and Maintenance costs for saline injection wells

The operating and maintenance costs for saline CO2 injection wells are assumed equal to those additional

O&M lease costs required for CO2-EOR operations and are applied only to the number of saline CO2

injection wells that are needed in operation for a given year. Monitoring and verification costs are

assumed the same for saline CO2 injection operations as for EOR operations. The additional on-site

pumping needs for injecting CO2 into saline reservoirs is 5 kWh/BBL of CO2 per ARI (2009) pumping

needs for CO2-EOR. The CO2 is assumed already in supercritical state when arriving at the sequestration

site.

S-28

Methods for estimating CO2 pipeline capital costs

Carbon Capture Utilization and Storage (CCUS) pipeline network segment

description

Equation (14) describes the assumed cost function for pipeline construction where:

f, is a cost escalation factor that increases from 1 depending upon the local geography and

landscape (no units), assumed as 1.1 for an overall average;

α, is the construction cost per unit diameter and length ($/in/mile), assumed as 102,000 $/in/mile

at 3 times the cost of pipelines modeled between 1989 and 1998 as described in (Herzog and

Javedan, 2010) ;

D, is the diameter of the pipeline (in), calculated based upon Equation (15)

L, is the length of the pipeline segment (miles), calculated based upon geometry of connecting

coal-fired power plants and EOR reservoirs.

DLfonconstructiC (14)

Equation (15) describes the assumed function for calculating the diameter of a CO2 pipeline based upon

the maximum flow rate (Q, in MtCO2/yr) of CO2 in the pipe. Equation (15) is from (Herzog and Javedan,

2010) using assumptions of pipeline operation and physical properties as made in (Heddle et al., 2003).

After calculating D as the “Ideal pipe diameter” using Equation (15), we assumed that the next highest

pipeline diameter (8, 12, 16, and 20 inches) would be used. Additionally, if we calculated D > 20 inches

(as occurs in the “fast” scenarios), then we modeled the pipeline segment as multiple pipelines of 20

inches or less.

4.025.7 QD (15)

Table S7. Pipeline segment description for “fast” EOR scenarios.

Pipeline

segment

Pipeline

length

(miles)

Max CO2 flow

(MtCO2/yr) for

EOR only

Ideal pipe

diameter (in)

Chosen

standard pipe

diameter (in)

Max CO2 flow in

standard chosen

pipe size

(MtCO2/yr)

Construction

Cost ($)

Name Name

1 JK Spruce Tom O'Connor 96 4.5 13.2 16.0 7.2 173,000,000$

2 Tom O'Connor Portilla 26 3.0 11.3 12.0 3.5 34,000,000$

3 Portilla East White Point 12 2.7 10.8 12.0 3.5 17,000,000$

4 East White Point Seeligson 53 2.3 10.1 12.0 3.5 71,000,000$

5 Tom O'Connor WA Parish 120 4.5 13.2 16.0 7.2 216,000,000$

6 WA Parish Hastings 23 12.0 19.6 20.0 12.6 52,000,000$

7 Hastings Webster 6 4.3 13.0 16.0 7.2 10,000,000$

8 Hastings Gillock 15 1.0 7.3 8.0 1.3 13,000,000$

9 Webster Point N of Galveston Bay 35 1.1 7.5 8.0 1.3 32,000,000$

10 Point N of Galveston Bay Fig Ridge 17 1.1 7.5 8.0 1.3 15,000,000$

11 Fig Ridge Oyster Bayou 7 0.9 6.9 8.0 1.3 6,000,000$

12 Fayette Tomball 68 3.7 12.2 16.0 7.2 122,000,000$

13 Tomball WA Parish 40 3.7 12.2 16.0 7.2 73,000,000$

14 Tomball Conroe 20 3.1 11.4 12.0 3.5 26,000,000$

TOTAL 860,000,000$

From To

S-29

Table S8. Pipeline segment description for “fast” EOR scenarios.

Pipeline

segment

Pipeline

length

(miles)

Maximum estimate

CO2 flow in

pipeline segment

(MtCO2/yr) for EOR

Ideal pipe

diameter (in)

Pipeline

diameter (in)

Number of

pipelines

side by side

Maximum

estimated CO2

flow in pipeline

(MtCO2/yr)

Construction Cost

($)

Name Name

1 JK Spruce Tom O'Connor 96 7.22 16.0 16.0 1.0 7.2 173,000,000$

2 Tom O'Connor Portilla 26 7.10 15.9 16.0 1.0 7.2 46,000,000$

3 Portilla East White Point 12 4.50 13.2 16.0 1.0 7.2 22,000,000$

4 East White Point Seeligson 53 3.60 12.1 16.0 1.0 7.2 95,000,000$

5 Tom O'Connor WA Parish 120 35.00 19.4 20.0 3.0 12.6 810,000,000$

6 WA Parish Hastings 23 38.10 20.0 20.0 3.0 12.6 156,000,000$

7 Hastings Webster 6 19.00 17.8 20.0 2.0 12.6 26,000,000$

8 Hastings Gillock 15 3.60 12.1 16.0 1.0 7.2 27,000,000$

9 Webster Point N of Galveston Bay 35 4.70 13.5 16.0 1.0 7.2 64,000,000$

10 Point N of Galveston Bay Fig Ridge 17 4.70 13.5 16.0 1.0 7.2 31,000,000$

11 Fig Ridge Oyster Bayou 7 1.10 7.5 8.0 1.0 1.3 6,000,000$

12 Fayette Tomball 68 8.21 16.8 20.0 1.0 12.6 153,000,000$

13 Tomball WA Parish 40 26.91 17.4 20.0 3.0 12.6 272,000,000$

14 Tomball Conroe 20 14.20 20.9 20.0 1.0 12.6 44,000,000$

15 San Miguel Tom O'Connor 84 2.30 10.1 12.0 1.0 3.5 113,000,000$

16 Oak Grove Tomball 93 32.90 18.9 20.0 3.0 12.6 623,000,000$

17 Sandow Oak Grove 55 4.50 13.2 16.0 1.0 7.2 98,000,000$

18 Limestone Oak Grove 21 20.10 18.2 20.0 2.0 12.6 93,000,000$

19 Big Brown Limestone 31 8.30 16.9 18.0 1.0 9.7 62,000,000$

TOTAL 1,925,000,000$

From To

S-30

References (Supplemental Information)

ARI (2006), Basin Oriented Strategies for CO2 Enhanced Oil Recovery: East & Central Texas, in A. R.

International, ed., Washington, D.C., U.S. Dept. of Energy Office of Fossil Energy - Office of Oil

and Natural Gas, p. 77.

Bock, B., R. Rhudy, H. Herzog, M. Klett, J. Davison, D. G. D. L. T. Ugarte, and D. Simbeck (2003),

Economic Evaluation of CO2 Storage and Sink Enhancement Options, TVA Public Power

Institute.

EIA (2009). Oil and Gas Lease Equipment and Operating Costs 1994 Through 2009, data available

February 8, 2012 at:

http://www.eia.gov/pub/oil_gas/natural_gas/data_publications/cost_indices_equipment_productio

n/current/coststudy.html

Kheshgi, H. S., Bhore, N. A., Hirsch, R. B., Parker, M. E., Teletzke, G. F. & Thomann, H. (2010)

Perspectives on CCS Cost and Economics, SPE 139716. SPE International Conference on CO2

Capture, Storage, and Utilization. New Orleans, LA, Society of Petroleum Engineers.

Lee, J., 2007, Fluid Flow Through Permeable Media, in E. D. Holstein., ed., Reservoir

Engineering and Petrophysics: Petroleum Engineering Handbook, v. 5: Richardson, TX, Society of

Petroleum Engineers.

McCullom, D. L. & Ogden, J. M. (2006) Techno-Economic Models for Carbon Dioxide Compression,

Transport, and Storage & Correlations for Estimating Carbon Dioxide Density and Viscosity,

Report UCD—ITS—RR—06-14. Institute of Transportation Studies, University of California,

Davis.

ARI (2009) Electricity Use of Enhanced Oil Recovery with Carbon Dioxide (CO2-EOR), DOE/NETL-

2009/1354. National Energy Technology Laboratory, U.S. Department of Energy.

Davis, D., Scott, M., Roberson, K. & Robinson, A. (2011) Large Scale CO2 Flood Begins Along Texas

Gulf Coast, Paper SPE 144961-PP. SPE Enhanced Oil Recovery Conference. Kuala Lumpur,

Malaysia, SPE International.

Heddle, G., Herzog, H. & Klett, M. (2003) The Economics of CO2 storage. Laboratory for Energy and

the Environment of the Massachusetts Institute of Technology.

Herzog, H. & Javedan, H. (2010) Development of a Carbon Management Geographic Information System

(GIS) for the United States: Final Report. DOE Award No. DE-FC26-02NT41622. IN

MASSACHUSETTS INSTITUTE OF TECHNOLOGY (Ed.

http://www.eia.gov/pub/oil_gas/natural_gas/data_publications/cost_indices_equipment_production/current/coststudy.html

http://www.eia.gov/pub/oil_gas/natural_gas/data_publications/cost_indices_equipment_production/current/coststudy.html

Documents

Supplemental Information: The system-wide economics of a ...iopscience.iop.org/1748-9326/8/3/034030/media/erl468459...-EOR production in the Texas Gulf Coast This section describes