Carey W King 1, Gürcan Gülen 2, Stuart M Cohen 3,*, Vanessa Nuñez-Lopez 4

Transcription

1 Supplemental Information: The system-wide economics of a carbon dioxide capture, utilization, and storage network: Texas Gulf Coast w/ pure CO 2 EOR flood Carey W King 1, 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 C9, Austin, TX 78712, USA careyking@mail.utexas.edu 2 Center for Energy Economics, Bureau of Economic Geology, The University of Texas at Austin 181 Allen Parkway, Houston, TX 7719, USA gurcan.gulen@beg.utexas.edu 3 Mechanical Engineering Department, The University of Texas at Austin, 24 E. Dean Keeton St., Stop C22, Austin, TX 78712, USA stuart.cohen@nrel.gov 4 Bureau of Economic Geology, The University of Texas at Austin, 11 Burnet Rd., Bldg 13, Austin, TX 78758, USA vanessa.nunez@beg.utexas.edu * Formerly associated with The University of Texas at Austin during the production of this research. Currently affiliated with National Renewable Energy Laboratory, 1513 Denver West Parkway, Golden, CO 841, USA. S-1

2 Table of Contents Additional Intermediate Tables and Figures of Results and Inputs... 3 Methods for estimating costs for CO 2 -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 Lease Equipment Costs for CO 2 injection wells H 2 O injection wells lease costs CO 2 recycling plant... 2 CO 2 trunk likes for field level distribution... 2 Operating and Maintenance costs for CO 2 -EOR... 2 Annual Operating and Maintenance Costs... 2 Recycle compression, lifting, and other electricity needs General and Administrative (G&A) Costs Percentage of annual O&M costs for personnel Methods for estimating costs for injection of CO 2 into saline reservoirs in the Texas Gulf Coast Saline CO 2 injection costs CO 2 injection well pressure, flow rate and radius Capital costs for drilling saline injection wells Operating and Maintenance costs for saline injection wells Methods for estimating CO 2 pipeline capital costs Carbon Capture Utilization and Storage (CCUS) pipeline network segment description References (Supplemental Information)... 3 S-2

3 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% 5% higher depending upon the total number of patterns. Number 5-spot Number Number water Number of saline CO patterns = oil injection wells 2 Field injection wells number of CO 2 production (4% of CO 2 injection wells wells injection wells) a (Scenarios: 1/2/3/4) Conroe //69/1731 Hastings /47/59/162 Webster //145/434 Tom O Connor /34/588/17 Seeligson //247/38 Oyster Bayou //196/48 East White Point //168/57 Tomball //637/361 Fig Ridge //145/68 Gillock //62/47 Total /81/3387/3953 a: Water injection wells are not modeled as having separate capital costs. They are assumed drilled and then converted to CO 2 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 CO 2 delivered to each oil field (over 2 years of the analysis). The oil recovery is approximately 12 15% of the OOIP of each injection pattern. M = mega (16), t = metric ton, BBL = barrel. Total Field (Scenarios 1 and 2) Total Field (Scenarios 3 and 4) Per Injection Pattern Total Years of Oil Oil Oil Net CO recovery 2 Net CO recovery 2 delivered CO 2 operation recovery delivery delivery CO (1, (1, 2 :oil injection (2 if not (% (MtCO BBL) 2 ) (MtCO BBL) 2 ) ratio max OOIP) (tco 2 /BBL) HCPV) Conroe 62, , (21) East White Point 12, , (12.5) Fig Ridge 5, , (6.1) Gillock 1, , (15.9) Hastings 67, , (21) Oyster Bayou 17, , (19) Tom O Connor 68, , (19) Seeligson 29, , (2) Tomball 18, , (8.2) Webster 52, , (21) S-3

4 Table S3. The parameters assumed for each power plant that is modeled to capture CO 2 in the slow or fast scenarios include the fraction of captured CO 2, the heat rate, fixed operating and maintenance costs (FOM), and variable operating and maintenance costs (VOM). Plant Name Rated Capacity (MW) Output Capacity Without CO 2 Capture (MW) Heat Rate Without CO 2 Capture (MMBtu/MWh) CO 2 Emissions Rate Without Capture (tco 2 / MWh) Output Capacity With CO 2 Capture (MW) Heat Rate With CO 2 Capture (MMBtu/ MWh) Emissions Rate With CO 2 Capture (tco 2 / MWh) Big Brown Fayette Power Project Fayette Power Project 2 and J K Spruce J K Spruce J T Deely Limestone Oak Grove Oak Grove San Miguel Sandow No W A Parish 1 thru 6 and WA Parish Plant Name Is CO 2 Capture Installed in Slow scenarios? Fraction of CO 2 Removed With Capture CO 2 Capture Energy (MWh/ tco 2 ) CO 2 Capture FOM Cost ($/MWh) Non- Fuel/CO 2 Capture VOM Cost ($/MWh) Big Brown No Fayette Power Project 1 Yes Fayette Power Project 2 and 3 No J K Spruce No J K Spruce 2 Yes J T Deely No Limestone No Oak Grove 1 No Oak Grove 2 No San Miguel No Sandow No 4 No W A Parish 1 thru 6 and 8 No WA Parish 7 Yes 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 CO 2 capture processes within the power plant. CO 2 Capture FOM cost is additional FOM cost for capture systems only and includes additional labor, maintenance, and administration for capture systems. Non-fuel/CO 2 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 CO 2 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-4

5 TWh TWh TWh TWh TWh TWh TWh 6 ERCOT Generation (Baseline, Scenario 1) 6 ERCOT Generation (Scenario 1) (S1a) ERCOT Generation (Baseline, Scenario 2) 6 ERCOT Generation (Scenario 2) (S1b) ERCOT Generation (Baseline, Scenario 3) ERCOT Generation (Scenario 1) ERCOT Generation (Scenario 3) COAL w/ CAPTURE COAL NUCL WIND + OTH HYDRO GASCHP NGCC NGGT NGST MISCPEAK (S1c) S-5

6 TWh TWh TWh TWh TWh ERCOT Generation (Baseline, Scenario 3) ERCOT Generation (Scenario 3) ERCOT Generation (Baseline, Scenario 4) 6 ERCOT Generation (Scenario 4) 6 ERCOT Generation (Scenario 1) COAL w/ CAPTURE COAL NUCL WIND + OTH HYDRO GASCHP NGCC NGGT NGST MISCPEAK (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 CO 2 capture. All scenarios assume the same amount of generation each year (e.g. 532 TWh in 231). 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. S-6

7 Methods for estimating costs for CO 2 -EOR production in the Texas Gulf Coast This section describes the process for calculating the costs for enhanced oil recovery (EOR) using carbon dioxide (CO 2 ) 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/ CO 2 flows occurring each year). Capital costs are comprised of the following factors: drilling and well equipping for CO 2 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 CO 2 for reinjection CO 2 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 CO 2 at the EOR site (assumed only as electricity: kwh/tco 2 ) 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 29 excluding 1998 and 2 due to their unavailability from the IHS Energy Resource Center (ERC). The average depth between 12,5ft range and corresponding costs were tabulated. These costs, originally reported in nominal dollars, were converted to real 29 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-7

8 a ($1s/well) depreciation charges, where appropriate, for rigs and other equipment and facilities which will be used in drilling more than one well. Deduct the condition value of materials salvaged after use where appropriate. Do not report the cost of lease equipment such as artificial lift equipment and downhole lift equipment, flow lines, flow tans, separators, etc., that are required for production. Do not reduce the costs by test well, bottom hole, or dry hole contributions. Drilling costs are expressed as an exponential function as in Equation (1), where D = depth of well. The parameters a and a 1 were determined using linear regression for each year of data from the JAS data set. We then relate these parameters for the drilling cost equation to the price of oil such that we can use these as a function of oil price (for future drilling price estimates) if needed. drilling cost ($/well) 1a 1 (1) e a D o The factors a o ($1/well) and a 1 are determined from the drilling cost data and related to oil price. Figure S2 (a, c, e, and g) indicate the values of a for Texas Railroad Commission districts 1, 2, 3, and 4, respectively, and Figure S2 (b, d, f and h) indicate the values of a 1 for Texas Railroad Commission districts 1, 2, 3, and 4, a vs. Oil price - RRC1 (wells > 4') y = x Linear ( ) Linear (26-29) y = x Oil price ($29) Figure S2a. The drilling cost factor a ($1/well) shown for RRC1 has changed considerably after the oil price escalations after 25. We use the new trend of relating a to oil price for the years instead of factoring the trends from 25 and earlier. S-8

9 a ($1s/well) a1 (1/ft) The factor a 1 has ranged from approximately 2.e-4 to 3.e-4 ft -1, but seems to typically be near 2.5e-4 ft E-4 a1 vs. Oil price - RRC1 (wells > 4') 4.5E-4 4.E-4 3.5E-4 3.E-4 y = -2.79E-8x E-4 2.5E-4 2.E-4 y = 9.63E-8x E-4 1.5E-4 1.E-4 5.E data data Linear ( data) Linear (26-29 data).e Oil price ($29) Figure S2b. The factor a 1 does not follow an obvious trend with respect to oil price for RRC1. Most values are between 2.e-4 and 3.e-4, and a value of 2.5e-4 might be most common for all oil prices a vs. Oil price - RRC2 (wells > 4') ao ( ) a (26-29) Linear (ao ( )) y = 6.472x Linear (a (26-29)) y = x Oil price ($) Figure S2c. The drilling cost factor a ($1/well) shown for RRC2 has changed considerably after the oil price escalations after 25. We use the new trend of relating a to oil price for the years instead of factoring the trends from 25 and earlier. S-9

10 a ($1s/well) a1 (1/ft) 4.E-4 3.5E-4 a1 vs. Oil price - RRC2 y = 3E-6x E-4 2.5E-4 2.E-4 y = -3E-7x E-4 1.E-4 5.E Linear ( ) Linear (26-29).E Oil price ($29) Figure S2d. The factor a 1 does not follow an obvious trend with respect to oil price for RRC2. Most values are between 2.e-4 and 3.e-4, and a value of 2.5e-4 might be most common for all oil prices. 8 a vs. Oil price - RRC3 (wells >4') a ( ) a (26-29) Linear (a ( )) Linear (a (26-29)) y = x y =.844x Oil price ($29) Figure S2e. The drilling cost factor a ($1/well) shown for RRC3 has changed considerably after the oil price escalations after 25. We use the new trend of relating a to oil price for the years instead of factoring the trends from 25 and earlier. S-1

11 a ($1s/well) a1 (1/ft) 3.5E-4 a1 vs. Oil price - RRC3 (wells > 4') 3.E-4 y = 2E-6x E-4 2.E-4 y = -2E-7x E-4 1.E-4 5.E-5.E Linear ( ) Linear (26-29) Oil price ($29) Figure S2f. The factor a 1 does not follow an obvious trend with respect to oil price for RRC2. Most values are between 2.e-4 and 3.e-4, and a value of 2.5e-4 might be most common for all oil prices a ( ) a (26-29) a (all years) Linear (a ( )) Linear (a (26-29)) Linear (a (all years)) a vs. Oil price - RRC4 (wells > 4') 8 y = x y = 1.31x y = x Oil price ($29) Figure S2g. The drilling cost factor a ($1/well) shown for RRC4 has changed considerably after the oil price escalations after 25. We use the new trend of relating a to oil price for the years instead of factoring the trends from 25 and earlier. S-11

12 a1 (1/ft) 4.E-4 a1 vs. Oil price - RRC4 (wells > 4') 3.5E-4 3.E-4 y = -1E-6x E-4 2.E-4 y = -2E-8x E-4 1.E-4 5.E-5.E Linear ( ) Linear (26-29) Oil price ($29) Figure S2h. The factor a 1 does not follow an obvious trend with respect to oil price for RRC4. Most values are between 2.e-4 and 3.e-4, and a value of 2.5e-4 might be most common for all oil prices. To consider the cost escalations for drilling costs after 25, 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 a is a function of oil price, but that a 1 is constant for any given cost estimate. Equation (1) is modified into Equation (2) by incorporating the oil price ($29/BBL) in units of 29 USD. a1d b $29 / BBL) b e drilling cost ($/well) 1 (2) o The factors b and b 1 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 b and b 1 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 a 1 (ft -1 ) b o (BBL/well) b 1 ($1/well) 2 ~2.5e ~2.5e ~2.5e * * The data point for drilling costs of 27 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 a 1 term (e.g. b o in Table S4) but shift the y-intercept in Figure S2g down by 2 $1s/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 CO 2 -EOR tertiary recovery, are described in the next section. The lease equipment 1 S-12

13 $/well (current dollars) 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 29 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. [ 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 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 C + C 1 D. 25, 2, "Non-tubing" lease costs for S. Texas primary production y = 1.4x R² = , , 29 Linear (29) 5, Depth of well (feet) 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 ) as partially represented in Figure S3. These C 1 and C 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 S-13

14 price. Then we model C 1 and C 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 C 1 ($/BBL) and C ($/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). South TXprimary production lease equipment costs c D c (3) 1 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 $29 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. 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 2 foot depth 4 foot depth 8 foot depth Nominal index of "S. TX primary lease equip costs": nontubing total Real value index for choosen year Nominal index of "S. TX primary lease equip costs": nontubing total Real value index for choosen year Nominal index of "S. TX primary lease equip costs": nontubing total Real value index for choosen year Average Real Index S-14

15 average cost index S. TX lease equipment costss for primary production (non-tubing costs) y = x Real cost index Predicted Real Index values from curve fit ($ real/bbl) Figure S4. The real indices of Table S5 (and estimated linear curve fit) are plotted versus the real price of oil in $29/BBL. The linear curve fit of the real index equation is shown as = 1.55 (oil price in $29/BBL) Now that the lease equipment cost index can be estimated as a function of oil price, we can estimate how C 1 and C change as a function of the real cost index. Linear correlations are made as a function of the index as shown in Figure S5a (left) and Figure S5b (right) Slope (C1) of equation: cost ($/well) = C1*D + Co y =.248x R² = , 5, Intercept (Co) of equation ($/well): cost = C1*D + Co y = x R² = Real Cost Index Real Cost Index Figure S5a and Figure S5b. Relating the coefficients (C1 and C) of the injection lease equipping cost equation to the calculated average cost index for all well depths (2, 4, and 8 feet). The coefficient C1 = d11i+d1 and coefficient C = d1i + d where i is the real cost index calculated from nominal cost index of the EIA cost data. Thus, for a given oil price ($29/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: S-15

16 where, South TX lease equipment costs($29) c D c 1 d11i d1d d 1i d.25i 1.96D 55.7i 58,4 29/ BBL c 1.55 $29/ BBL 243 i c (5) 1 $ Lease costs are calculated as relating to the number of wells in operation at any given time. In other words, if 1 wells are drilled in year 1 and these wells operate for 5 years, and 1 wells are drilled in year 6 to operate 5 years, then there will be only 1 wells in operation in any given year. Thus, the lease equipment costs are only for 1 wells and not for 2 wells. (4) Lease Equipment Costs for CO 2 injection wells In the case of assuming new injection wells, we calculate the additional lease equipment costs for new CO 2 injection wells as described on pages B-3 to B-4 of ARI (26). ARI uses an equation of the form: cost = C + C 1 D, 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 CO 2 -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 < 4. 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 data (See Figure S6). This pattern shows that costs are not linear from 2 to 8 depths, but we will assume the trends are linear for > 4 feet deep because our 1 candidate EOR fields have reservoir depths > 4 and < 8. We use the same information for pure CO 2 injection well costs (into saline formations) which can be > 8 feet deep, and we assume extrapolating costs for depths slightly beyond 8 feet is acceptable. S-16

17 $/well (current dollars) 18, 16, 14, "Additional lease costs, W. Texas, for secondary oil recovery: Injection equipment ONLY" 12, 1, 8, 6, , 2, Depth of well (feet) Figure S6. Lease equipment costs for water injection wells in West Texas (in current dollars for each year). Thus we need to understand an equation of the form of Equation (6) that models the historical data (from ) as partially represented in Figure S6. These C 1 and C values have changed over time and this is captured by the cost indices reported in the EIA tables. Thus, we model this cost index as a function of the oil price. Then we will model C 1 and C as a function of this cost index to ultimately predict the additional injection costs as a function of oil price, or have C 1 ($/BBL) and C ($/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). additional lease equipment costs c D c (6) 1 S-17

18 average cost index (addtional lease equip. cost for injection only, W. Texas) Table S6. Nominal indices are reported by EIA, and these are converted to real indices based using $29 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. INDEX for "additional injection lease equipment costs for W.TX secondary production" 2 foot depth 4 foot depth 8 foot depth 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 y = 3.355x Real cost index ($29 real/bbl) 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 S-18

19 Now that the lease equipment cost index can be predicted as a function of oil price, we can estimate how C 1 and C 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 Slope (C1) of equation: cost ($/well) = C1*D + Co y =.356x R² = , 2, 1, Intercept (Co) of equation ($/well): cost = C1*D + Co y = 37.29x R² = Real Cost Index Real Cost Index Figure S8a and Figure S8b. Relating the coefficients (C 1 and C ) of the injection lease equipping cost equation to the calculated average cost index for all well depths (2, 4, and 8 feet). Thus, for a given oil price ($29/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: where, additional lease equipment costs($29) c D c 1 b11i b1d b1i b.36i 1.16D 37.3i 5,55 (7) 29/ BBL c 3.36 $29/ BBL 235 i c (8) 1 $ 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). H 2 O 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., 211). Thus, there are some necessary number of water injection wells to create the water curtain around the CO 2 -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 CO 2 injection wells. Thus, there are 14/23 ~.6 WAG wells for every one CO 2 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 9 o -11 o. The geologic faults restrict the fluid flows to this ~ 1 o 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 1 CO 2 injection wells as if the development of the oil field might progress in a linear step-wise manner from one S-19

20 side of the oil field to another and the water injection wells would always be ahead of the CO 2 injection wells and oil production wells. CO 2 recycling plant As estimated in ARI (26) pg. B-9, the capital costs for CO 2 recycling plant is estimated as $7,/(MMcf/day of capacity) of peak CO 2 recycling capacity needed. We estimate capital costs of CO 2 recycling equipment using this value. CO 2 trunk likes for field level distribution We do not include the cost for CO 2 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 CO 2 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 CO 2 distribution (even though CO 2 is delivered at much higher pressure). However, there could be appreciably higher costs for CO 2 distribution lines versus water distribution lines. In contrast, ARI (26) estimates CO 2 distribution costs (at the EOR field) as = $15, + C D Distance (see page B-1). Operating and Maintenance costs for CO 2 -EOR The method for estimating O&M costs for CO 2 -EOR come primarily from ARI (26) and DOE (29). Annual Operating and Maintenance Costs Cost data from EIA Costs and Indices data are modified per the Appendix pages B-7 to B-1 of ARI (26). 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 CO 2 -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 CO 2 - EOR versus H 2 O-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 C 1 D + C. By converting the nominal dollar cost indices to real dollar indices (in $29), we plot the full O&M cost index for all depths (2, 4, and 8 ) as single cost index (see Figure S1). The cost index as a function of oil price is of the form C 1 D + C. S-2

21 average cost index (O&M costs, W. Texas secondary production) $/well/yr (current dollars) 6, "Annual O&M costs, for W. Texas secondary oil recovery" 5, 4, 3, 2, y = x R² = Linear (29) 1, Depth of well (feet) Figure S9. The annual O&M costs for West Texas secondary oil production show a cost equation of the form C 1 D + C. 7 6 y = x R² = Real cost index Predicted Real Index values from curve fit ($ real/bbl) Figure S1. The average real cost index (averaged for 2, 4, and 8 wells) for O&M costs as a function of real oil price ($29/BBL). The linear curve fit relation is also shown to predict this index as a function of (projected) oil prices. S-21

22 C1, using real $ already scaled for CO2-EOR in W. Texas C, using real $ already scaled for CO2-EOR in W. Texas Slope (C1) of equation: cost ($/well/yr) = C1*D + Co y =.14x R² = Real Cost Index (for W.Texas secondary production) Intercept (Co) of equation ($/well/yr): cost = C1*D + Co 25, 2, 15, 1, 5, y = 47.19x R² = Real Cost Index (for W.Texas secondary production) Figure S11. The trends for the O&M cost for secondary production in West Texas, using costs scaled to real dollars ($29) 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 C 1 D + C as in Figure S9) for West Texas secondary oil production as a function of the cost index. Again, Figure S1 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 S1 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 C 1 and C. These C 1 and C then used in an equation for O&M costs per well as a function of depth: $/well/yr = C 1 D + C. To scale West Texas CO 2 -EOR estimated costs to those for South Texas, we use data on the primary production O&M costs for each. That is to say find the C 1 and C values (values corresponding to Figure S9 indicating O&M costs as a function of depth) for each year of data ( ) for West Texas primary O&M and also for South Texas primary O&M ($/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 the costs in S. Texas coming closer into parity with those in W. Texas, and C 1 and C ratios following the same downward trend. Then from 21-29, the fixed cost factor (C ) ratio increased while the depth-dependent cost factor (C 1 ) ratio continued to decrease such that by 25 depth dependent costs in S. Texas were lower than in W. Texas. Per the calculations shown in Figure S12, predicting South Texas CO 2 -EOR O&M costs from those in West Texas is very approximate. However, to predict South Texas CO 2 -EOR O&M costs from those estimated for West Texas, we assume values typical of 28 and 29 for the C 1 and C ratios.85 and 1.55, respectively. S-22

23 1.8 Ratio of "S. Texas/W.Texas" O&M curve fit parameters over time (real cost = C1*D + C) C1 C Figure S12. Ratios of C 1 and C 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 = C 1 D + C. S-23

24 O&M Costs for S. Texas CO2-EOR (not including CO2 recompression and injection) ($29/well/yr) Oil price ($real/bbl) South Texas $14 $12 $1 4 $ $ $2 oil price $ Year Figure S13. Predicted O&M costs for CO 2 -EOR in South Texas ($29/well/yr) at depths of 2, 4, and 8 feet as a function of oil price ($29/BBL). $8 Recycle compression, lifting, and other electricity needs The power requirements for compressing and pressurizing CO 2 for EOR come from McCullom and Ogden (26) as in Equation (9). There is assumed to be only one stage of compression required for recycling CO 2 that is produced from EOR operations as the injection pressure is high enough to produce the oil/water/co 2 mixture at pressure significantly above the ambient pressure (per Keshgi et al. (21)). Thus, there is only on stage of compression where, W s, i 1 mzsrt Mis in k s CR ks 1 ks 1 k s 1 W s,i = compression power for a given stage of compression (in kw) M = mass flow rate of CO 2 in tonnes per day R = kj/kmol-k M = 44.1 kg/kmol T in = K is =.75 (efficiency of compression stage) 1 = kg per tonne (conversion) 24 = hours per day (conversion) 36 = seconds per hour (conversion) (9) For an assumed single ( final ) stage of compression (from pressures of 3.1 MPa to supercritical at P cut-off = 7.4 MPa), Z s =.845 and k s = 1.74 and CR = (P cut-off P min ). S-24

25 The power requirement for compressing CO 2 to the injection pressure, P final, (up to 15 MPa) higher than supercritical CO 2 pressure (e.g. P cut-off = 7.4 MPa) is as in Equation (1): where W pump 11 m Pfinal P CO 2 p CO2 = density of CO 2 = 63 kg/m 3 p =.75 (efficiency of pump) 1 = represents the number of bar per MPa 36 = conversion factor of (m 3 bar/hr/kw) cutoff The annual electricity (kwh/yr) for recompressing and pumping, E pumping, recycled CO 2 for EOR is as in Equation (11). Costs of electricity are then the assumed electricity price ($/kwh) multiplied by E pumping. W W E (11) pumping i, s pump Additional electricity costs for lifting CO 2 come from an Advanced Resources international report to DOE (ARI, 29). For lifting costs, we assume that there are no specific pumps, but that CO 2 is injected to such a pressure as to make the CO 2 and oil flow readily from production wells. From Table 5, pg. 24 of ARI (29), 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 (29)) 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 (26) on page B-1, general and administrative (G&A) costs of 2% 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 H 2 O-EOR operating costs for CO 2 -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 2 (Table A12), 29% for wells 4 (Table A13), and 21% for wells 8 (Table A14). (1) S-25

26 Methods for estimating costs for injection of CO 2 into saline reservoirs in the Texas Gulf Coast Saline CO 2 injection costs CO 2 injection well pressure, flow rate and radius We calculate the CO 2 injection rate by assuming a uniform reservoir and injection at constant pressure per (Lee, 27): Pkh q (12) re 141.2B g g ln rw where, q = injection rate (MCF/day) P = change in pressure, psi (P = P wf P i ) P i = initial reservoir pressure, psi (P i =.465 psi/ft) P wf = injection pressure, psi P f = fracture pressure of reservoir, psi (P f =.7 psi/ft) k = reservoir permeability, md h = reservoir thickness, ft B g = gas formation volume factor, RCF/MCF g = CO 2 gas viscosity, cp r e = external injection radius, ft r w = well bore radius, ft and the external injection radius of the CO 2 is: where 876kt r e (13) 948 g t = time, years = porosity The number of CO 2 injection wells needed for any given year is equal to the quantity of CO 2 for injection that year (the CO 2 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 CO 2 injection wells, then more wells are assumed drilled in that year as needed. If for any given year there are more than enough CO 2 injection wells for the quantity of CO 2 for injection, then no wells are drilled during that year. S-26

27 Capital costs for drilling saline injection wells Based upon the previous description of estimating the CO 2 injection rate, we estimate the total number of saline wells needed in operation for any given year. The drilling costs for saline CO 2 injection wells are assumed as 1% of that of a new oil well. Lease costs for saline CO 2 injection wells are assumed equal to those additional lease costs required for CO 2 -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 CO 2 injection wells are assumed equal to those additional O&M lease costs required for CO 2 -EOR operations and are applied only to the number of saline CO 2 injection wells that are needed in operation for a given year. Monitoring and verification costs are assumed the same for saline CO 2 injection operations as for EOR operations. The additional on-site pumping needs for injecting CO 2 into saline reservoirs is 5 kwh/bbl of CO 2 per ARI (29) pumping needs for CO 2 -EOR. The CO 2 is assumed already in supercritical state when arriving at the sequestration site. S-27

28 Methods for estimating CO 2 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 12, $/in/mile at 3 times the cost of pipelines modeled between 1989 and 1998 as described in (Herzog and Javedan, 21) ; D, is the diameter of the pipeline (in), calculated based upon Equation (15) L, is the length of the pipeline segment (miles), calculated based upon geometry of connecting coal-fired power plants and EOR reservoirs. C fdl (14) constructi on Equation (15) describes the assumed function for calculating the diameter of a CO 2 pipeline based upon the maximum flow rate (Q, in MtCO 2 /yr) of CO 2 in the pipe. Equation (15) is from (Herzog and Javedan, 21) using assumptions of pipeline operation and physical properties as made in (Heddle et al., 23). After calculating D as the Ideal pipe diameter using Equation (15), we assumed that the next highest pipeline diameter (8, 12, 16, and 2 inches) would be used. Additionally, if we calculated D > 2 inches (as occurs in the fast scenarios), then we modeled the pipeline segment as multiple pipelines of 2 inches or less..4 D 7.25Q (15) Table S7. Pipeline segment description for fast EOR scenarios. Pipeline segment From To Pipeline length (miles) Max CO2 flow (MtCO2/yr) for EOR only Ideal pipe diameter (in) Chosen standard pipe diameter (in) Max CO2 flow in standard chosen pipe size (MtCO2/yr) Construction Cost ($) Name Name 1 JK Spruce Tom O'Connor $ 173,, 2 Tom O'Connor Portilla $ 34,, 3 Portilla East White Point $ 17,, 4 East White Point Seeligson $ 71,, 5 Tom O'Connor WA Parish $ 216,, 6 WA Parish Hastings $ 52,, 7 Hastings Webster $ 1,, 8 Hastings Gillock $ 13,, 9 Webster Point N of Galveston Bay $ 32,, 1 Point N of Galveston Bay Fig Ridge $ 15,, 11 Fig Ridge Oyster Bayou $ 6,, 12 Fayette Tomball $ 122,, 13 Tomball WA Parish $ 73,, 14 Tomball Conroe $ 26,, TOTAL $ 86,, S-28

29 Table S8. Pipeline segment description for fast EOR scenarios. Pipeline segment From To 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 $ 173,, 2 Tom O'Connor Portilla $ 46,, 3 Portilla East White Point $ 22,, 4 East White Point Seeligson $ 95,, 5 Tom O'Connor WA Parish $ 81,, 6 WA Parish Hastings $ 156,, 7 Hastings Webster $ 26,, 8 Hastings Gillock $ 27,, 9 Webster Point N of Galveston Bay $ 64,, 1 Point N of Galveston Bay Fig Ridge $ 31,, 11 Fig Ridge Oyster Bayou $ 6,, 12 Fayette Tomball $ 153,, 13 Tomball WA Parish $ 272,, 14 Tomball Conroe $ 44,, 15 San Miguel Tom O'Connor $ 113,, 16 Oak Grove Tomball $ 623,, 17 Sandow Oak Grove $ 98,, 18 Limestone Oak Grove $ 93,, 19 Big Brown Limestone $ 62,, TOTAL $ 1,925,, S-29

30 References (Supplemental Information) ARI (26), 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 (23), Economic Evaluation of CO2 Storage and Sink Enhancement Options, TVA Public Power Institute. EIA (29). Oil and Gas Lease Equipment and Operating Costs 1994 Through 29, data available February 8, 212 at: n/current/coststudy.html Kheshgi, H. S., Bhore, N. A., Hirsch, R. B., Parker, M. E., Teletzke, G. F. & Thomann, H. (21) Perspectives on CCS Cost and Economics, SPE SPE International Conference on CO2 Capture, Storage, and Utilization. New Orleans, LA, Society of Petroleum Engineers. Lee, J., 27, 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. (26) Techno-Economic Models for Carbon Dioxide Compression, Transport, and Storage & Correlations for Estimating Carbon Dioxide Density and Viscosity, Report UCD ITS RR Institute of Transportation Studies, University of California, Davis. ARI (29) Electricity Use of Enhanced Oil Recovery with Carbon Dioxide (CO 2 -EOR), DOE/NETL- 29/1354. National Energy Technology Laboratory, U.S. Department of Energy. Davis, D., Scott, M., Roberson, K. & Robinson, A. (211) Large Scale CO2 Flood Begins Along Texas Gulf Coast, Paper SPE PP. SPE Enhanced Oil Recovery Conference. Kuala Lumpur, Malaysia, SPE International. Heddle, G., Herzog, H. & Klett, M. (23) The Economics of CO2 storage. Laboratory for Energy and the Environment of the Massachusetts Institute of Technology. Herzog, H. & Javedan, H. (21) Development of a Carbon Management Geographic Information System (GIS) for the United States: Final Report. DOE Award No. DE-FC26-2NT IN MASSACHUSETTS INSTITUTE OF TECHNOLOGY (Ed. S-3