OPGEE v2.0aOil Production Greenhouse Gas Emissions Estimator

Kourosh Vafi,1 MohammadS.Masnadi,1 JacobEnglander,1VinayTripathi1,AdamR.Brandt,1SylviaSleep,3 DianaPacheco,2 AndreaOrellana,2 ZainabDashnadi,3 JouleBergerson,3 Heather

MacLean2

1 DepartmentofEnergyResourcesEngineering,StanfordUniversity2 DepartmentofCivilandEnvironmentalEngineering,UniversityofToronto3 DepartmentofChemicalandPetroleumEngineering,UniversityofCalgary

Photos:Brandt(2006-2011)

Impacts from crude oil productionGoal: Improve modeling, extend capabilities

• We want to improve the modeling of unconventional oil resources• Heavy oil and oil sands• Tight oil via horizontal drilling and HF• CO2 EOR

• Include features and capabilities that are not modeled in old version of OPGEE• Crude oil mining and upgrading• Drilling and fracturing modeling• CO2 storage and recycling

Source: Huffington post

Source: National Geographic

Introduction

ExampleLCAmodel- gasoline

Exploration and field development

Oil production

Separation/reinjection

Crude transport

Crude refining

Product transport

Product consumption

Upstream Refining Consumption

Well-to-tank------------------------------------>

Well-to-wheels---------------------------------------------------------------->

Source:El-Houjeiri etal.2014

OPGEEmodelintroduction

Source:El-HoujeiriandBrandt(2012a,2012b)

El-Houjeiriand

BrandtO

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v1.1D

ocumentation

64

Scrubber

Contactor

350 psi Lean amine

Qamine [gpm]

Power BHPCP [hp]

Power BHPF [hp]

Power BHPBP [hp]

Power BHPF [hp]

Acid gases QCO2 + QH2S [MMscf]

Sweet gas

Charcoal filter

Amine cooler

Charge pump

Booster pump

Heat exchanger

Reflux condenser

Reflux pump

Reflux accum.

Power BHPRP [hp]

Power ∆HR[MMscf/d]

Still

Solid filter

Flash tank Low pressure

Flashed gas

Qg [MMscf/d] Reboiler

Figure 3.12: Amine simple process flow diagram [47, p. 112].

• OilProductionGreenhousegasEmissionsEstimator(OPGEE)

• Modelsemissionsgivenfieldparametersandtechnologies

Thefirst open-sourceGHGtoolforoilandgasoperations

• Anyonecandownload,use,modify,explore

• Documentation(>200pp.)withallsourcesdefined

Upstream modeling goalsFive goals of the upstream modeling effort

1. Build a rigorous, engineering-based model of GHG emissions from oil production operations

2. Use disaggregated data for accuracy and flexibility

3. Use public data where possible

4. Document sources for all equations, parameters, assumptions

5. Maintain model as free to access, use, and modify by any interested party

OPGEEmodelinggoals

1. Oilsandsminingandupgrading2. Drillingandhydraulicfracturing3. CO2 enhancedoilrecovery

Improvementsandaddedfeatures

Upstream modeling goalsGoal: to integrate oil sands production and upgrading more directly into the OPGEE model

• Oil sands mining is included in “production and extraction” worksheet• Oil sands upgrading is included in "surface processing” worksheet

• Use data from a variety of sources• Alberta Energy Regulator (AER) reported energy use• COSIA (Canada’s Oil Sands Innovation Alliance) default

“templates” for mining and in situ operations• AEMERA (Alberta Environmental Monitoring, Evaluation, and

Reporting Agency) data on fugitive emissions

Addedfeature:Oilsandsminingandupgr.

Upstream modeling goalsStand alone mining operations

• Stand alone mines do not upgrade on site but sell diluted bitumen

• Use more modern “paraffinic froth treatment” (PFT) technology

• Somewhat less energy intensive, but lack benefits of integrating upgrading on site

Oilsandsmining:StandaloneEl-Houjeiri and Brandt OPGEE v2.0a Documentation 71

Raw ore in

Electricityimportsand exportsMWh/d

Natural gasimportsmscf/d

DilutedBitumenbbl/d

DieselimportsmBtu/d

Mine

Diluentimportsbbl/d

Figure 4.13: Non-integrated bitumen mining operation

quantity of diesel fuel consumed can then be converted into an energy consump-tion rate:

EMNdi = QMN

di LHVdi (4.47)

where LHVdi is the lower heating value of diesel fuel [mBtu LHV/gallon]. Thisquantity can then be gathered on the ‘Energy Consumption’ gathering sheet andused to compute emissions on the ‘GHG Emissions’ gathering sheet.

Similar quantities are computed for all main inputs to non-integrated miningoperations by using intensities of electricity use (IMN

el ) and natural gas (IMNng ). For

the case of integrated mining and upgrading operations, the relevant intensities fordiesel, electricity, and natural gas are similarly named (IMI

di , IMIel , and IMI

ng respec-tively). Recall via convention above that consumption of coke or refinery processgas is computed as part of upgrading operations in Section 5.3.

After these mine-type-specific calculations are performed, the overall consump-tion due to mining is then computed using binary variables from the ‘Active Field’sheet. For the case of diesel energy consumption:

Edi = yMNEMNdi + yMI EMI

di (4.48)

where yMN and yMI represent binary variables for mining-non-integrated and mining-integrated, respectively.

Miningflowdiagram(StandAlone)

Upstream modeling goalsIntegrated mining and upgrading operations

• Oil sands mining integrated with upgraders to allow heat recovery for use at mine

• Benefits due to reduced natural gas requirements subtracted from mine use

Oilsandsmining:Integratedwithupgrader

El-Houjeiri and Brandt OPGEE v2.0a Documentation 72

UpgraderRaw ore in SCO out

mBtu/d

Electricity importsand exportsMWh/d

bbl/d

Natural gas importsmscf/d

Coke consumed

Heat recovery (mBtu/d)

Coke exportedt/d

mscf/dProcess gas exports

MineBitumen

bbl/d

(Computed in upgrader calculations)

Internal consumption flowstreated in upgrader calcs.

Process gas consumed

(Computed in upgrader calculations)

Distillate fuel (diesel)mBtu/d

Diesel importsmBtu/d

Figure 4.14: Upgrader-integrated bitumen mining operation

Upstream modeling goalsAddedfeature:Oilsandsminingandupgr.El-Houjeiri and Brandt OPGEE v2.0a Documentation 67

78

107

55

43

144

3

103

45 49 53

74

101

4

120

0

20

40

60

80

100

120

140

160

Suncor (1967)

Syncrude -Mildred lake

(1978)

Syncrude -Aurora (2001)

CNRL -Horizon (2008)

Shell -Muskeg

River (2002)

Shell -Jackpine (2010)

Imperial -Kearl Lake

(2013)

Nat

ural

Gas

Con

sum

ptio

n (m

3 /m3

bit)

Project Name (Startup Year)

2014 IntensityIntensity over Project Life

NFT PFT

COSIA range: 67-118

COSIA range: 54-76

Figure 4.11: Natural gas use in mining operations

Table 4.13: Non-integrated PFT mining energy intensities

Fuel OPGEEvalue

AER PWavg.

COSIAavg.

COSIArange

Notes

Natural gas 85 85 93 67 – 118Electricity cons. 125 125 114 99 – 130Electricity gen. 77 77 114 96 – 132Frac. elect. gen. onsite 0.6 0.6 1.0 1.0 – 1.0Diesel 12.5 - 12.5 9 – 16 aDiluent 25.4% 25.4% - -a COSIA Mine Template ranges presented for low (9%) and high (12%) grade ore.

pine, and Imperial Kearl). Volumetric blending rates over all months aver-aged 25.4% diluent in dilbit. The range over 2014 was from 24.3% to 26.5%Although Kearl dilutes bitumen with SCO (creating “syn-bit”) the dilutionfraction was nearly identical to those of Muskeg River and Jackpine.

Table 4.13 gives results as used in OPGEE, results for the AER production-weighted average, COSIA template average, and COSIA template range.

Proposed modeling of integrated NFT mining operations The integrated mining opera-tion is illustrated in Figure 4.14. The net flows across the process boundary areroughly equivalent to the stand-alone mining operation, with some exceptions.First, large volumes of diluent are not used to reduce the viscosity of bitumen,as upgrading the bitumen to SCO renders it ready for pipeline transport. Also, twonew co-products can be exported from the system: process gas and coke. There-fore, emissions credits should be given for these fuels if they are exported. Lastly,new internal flows between upgrader and mine include heat recovered from up-

El-Houjeiri and Brandt OPGEE v2.0a Documentation 68

145

323

5

52

181

60

122102

161

47

185

105

64

152

0

50

100

150

200

250

300

350

Suncor (1967) Syncrude -Mildred lake

(1978)

Syncrude -Aurora (2001)

CNRL -Horizon (2008)

Shell -Muskeg River

(2002)

Shell -Jackpine

(2010)

Imperial -Kearl Lake

(2013)

Elec

trici

ty C

onsu

mpt

ion

(kW

h/m

3bi

t)

Project Name (Startup Year)

2014 IntensityIntensity over Project Life

NFT PFT

COSIA range: 99-130COSIA value:113

Figure 4.12: Electricity use in mining operations

grader operations that is used in mine ore separations, as well as upgrader productstreams (distillate fuels) that are consumed in mining trucks. New internal con-sumption at the upgrader can include coke and process gas.

Due to sharing of waste heat at integrated mining and upgrading projects, someefficiency is gained through process integration. Suncor and Jacobs (2012) estimatethat 30 percent of total natural gas required at a project can be reduced by usinglow-grade waste heat from an integrated upgrader for the bitumen extraction pro-cess.

Updated OPGEE default values for stand-alone NFT mines are taken directlyfrom the COSIA NFT Mine Template [119]. The efficiency factor from Suncor andJacobs [123] is multiplied by the COSIA stand-alone mining natural gas consump-tion to approximate the natural gas consumed solely by the mine at an integratedmining and upgrading facility. These values are compared to the energy consump-tion for integrated mining and upgrading projects in Table 4.14. The remainingenergy for integrated projects not attributed to mining is approximately that con-sumed in the upgrading process. For example, if we compare Suncor and Syncrudenatural gas consumption (total) less that estimated used in upgrading, we get val-ues approximately equalto the COSIA stand-alone NFT mine less a 30% integrationbenefit (40-45 m3 per m3) .

Naturalgas

Electricity

NFT:45sm3/m3 bitumenAccountsfor30%integrationsavings

PFT:85sm3/m3 bitumen

NFT:113kWh/m3 bitumen

PFT:125kWh/m3 bitumen

=OPGEEinput

=OPGEEinput

Upstream modeling goals

Upgrading modeled using variety of fuel sources

• Variety of fuel options• On-site H2 generation• Coke can be

consumed on site, exported, or stockpiled

• Process gas used on site, flared, or exported

• Produces high quality SCO with little residual bottoms fraction

Oilsandsupgrader

El-Houjeiriand

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Upgrader(delayed coking, etc.)

Electricity cogen

Bitumen in

Process gas gen.

SCO out

MWh/d mBtu/d

bbl/d

Electricity importsMWh/d

mscf/d

mscf/d

bbl/d

Cogeneration H2 via SMR

Process gas cons.

Natural gas importsmscf/d

Heatcogen

H2mscf/d

Natural gas to cogenmscf/d

mscf/d

NG to H2

NG to heatmscf/d

Coke gen.t/d

Coke consumedt/d

t/d

Cokestockpiled

Cokeexportedt/d

mscf/d

Process gasexports

PG flaredmscf/d

Figure 4.25: Process flow diagram for OPGEE upgrading module

Oilsandsupgraderworksheet

-10

-5

0

5

10

15

20

25

30

35

Integrated bitumen mining

(DC)

Non-integrated bitumen mining

(DC)

Integrated bitumen mining

(HC)

Non-integrated bitumen mining

(HC)

Low SOR thermal

Med SOR thermal

High SOR thermal

GHG

em

issio

ns [g

CO2e

q/M

J]

Sequestration credit

Offsite

Small sources

Transport

Waste disposal

Maintenance

Surface processing

Crude production

Drilling & Development

Exploration

Total

Oilsandsresults

Electricityco-generationcredit

Upstream modeling goalsDrilling and fracturing now modeled with key engineering relationships

• Oil well drilling and fracturing modeled with open-source toolkit, GHGfrack

• Model is free to access, use and modify

• Not integrated directly into OPGEE due to complexity of drilling modeling

• Integrate into OPGEE using tabulated results for variety of cases

Addedfeature:Detaileddrillingmodule

Upstream modeling goalsOld OPGEE drilling model had increasing consumption per ft.

• Older OPGEE relationship used a steeply increasing function of depth to model energy use per ft.

• Was not based on fundamental engineering model

• Was not extendable to wells with horizontal segments

• Gave erroneously high results for very long lateral wells

OldOPGEE1.1drillingequationEl-Houjeiri and Brandt OPGEE v1.1 Draft E Documentation 38

y = 97.193e0.1215x!R² = 0.58915!

y = 37.169e0.1428x!R² = 0.70849!

0!

100!

200!

300!

400!

500!

600!

700!

800!

900!

1000!

0! 5! 10! 15! 20!

Dril

ling

ener

gy c

onsu

mpt

ion !

(mm

Btu/

1000

ft)!

Measured drilling depth (1000 ft)!

2005 data!2000-2001 data!2002 data!

Figure 4.1: Drilling energy intensity as a function of well depth as measured for Cana-dian drilling operations.

water injection wells. The lifetime productivity of wells varies by orders ofmagnitude, depending on the quality of the oil reservoir and its size. In orderto obtain a central estimate for the productivity of a well, we use historical datafrom California.

California reports the number of producing and shut-in wells, with ⇡ 100,000wells counted in recent years [75]. However, these datasets do not include:

• Wells that are fully abandoned and therefore not classed as “shut-in”,

• Wells that were drilled and plugged in abandoned fields,

• Wells that were drilled before 1915, when reporting began.

To address these shortcomings, wells drilled on a yearly basis were compiledfrom the California Department of Oil, Gas, and Geothermal Resources (DOGGR)annual reports [80]. Production and injection wells drilled per year are com-piled from 1919-2005, while exploration wells drilled per year are compiledfrom 1926 to 2005 (exploratory wells were not reported before 1926). Total ex-ploratory and production/injection drilling activity over these years was equalto 188,508 wells. Due to missing wells (early exploratory wells, all wells priorto 1919, other missing wells) we assume total wells drilled ⇡ 200,000. Cumula-tive production in the entire state of California was ⇡ 25.99 Gbbl at the end of2005. Therefore, average oil produced per well drilled was ⇡ 130,000 bbl/well. Drilling &

Development1.3.1

RelationshipinOPGEEv1.1e

Upstream modeling goalsDrillingschematic

https://www.osha.gov/SLTC/etools/oilandgas/drilling/kickback_final.html

Upstream modeling goalsDrillingmudcirculationenergy

∆Ppump= ∆Pnozzles+∆Pfriction+ ∆Pdownholemotor+ ∆Phydrostatic+∆Pothersources

Hole (annulus)Casing

CementRock

Drill pipe

BHA includingcollar

Bit

Flow ofmud

A B

Upstream modeling goalsVerification:Drillingenergy

100

1,000

10,000

100,000

100 1,000 10,000 100,000

Cal

cula

ted

Fuel

Use

(US

gallo

n)

Reported Fuel Use (US gallon)

VerticalVertical+Horizontal

CreatedadefaultrulebasedonAzarandSamuelranges(e.g.,torqueincreasesasdepthincreases)

Applysameruletoallwells

Goodagreementsuggestsmodelisfeasible

Note:Logarithmicscale

Upstream modeling goalsFracturingmodule

• Newtonianfluidmodel• Singlestageandmultistagefracturing

∆Ppump=∆Pfracturing+∆Pfriction-∆Phydrostatic

Upstream modeling goalsSimulationoffracturingviaAspen-HYSYS

Upstream modeling goalsComparisonofAspen-HYSYSandGHGfrack

Vertical= 8500 ftCurve= 1500 ftHorizontal= 5000 ftCasing ID = 5 inchRelative Error= 0.15-2.15%

Upstream modeling goalsVerificationwithpublisheddata

Wecansetasinglerepresentativeflowrateforoperatortorecreatedata

Lackofdatagivesusonedegreeoffreedom

GHGfrack resultsareconsistent withreported

Upstream modeling goalsSensitivity analysis of both drilling and fracturing

• Drilling energy use most dependent on hole diameter and drill pipe internal diameter

• Fracturing energy use most dependent on casing inner diameter and fluid volume

SensitivityanalysisofGHGfrack results

Upstream modeling goalsModel a typical well design in the Eagle Ford shale of TX

• Casing and hole diameters from published papers

• Geological factors (e.g., fracturing gradient) from published papers

EagleFordcasestudy

Upstream modeling goals[Emphasis]

• List

ComparingEagleFordresults

Emissionsintonnes ofCO2 eq.perwell

Differencesinwellsresultofmudcirculationenergyduringdrilling

Upstream modeling goalsSome aspects of drilling model could be improved in further research

• More validation cases and data desired

• Flowback of gases very difficult to model• Regulations prevent large-scale releases of flowback gas, so

unlikely to be a major issue• Little data available, federally reported data difficult to interpret• Emissions not included in current model

Drillingmodelfrontiers

Upstream modeling goalsCO2-based EOR modeled for possible carbon sequestration benefits

• Includes energy and emissions associated with production of CO2, transport, injection, separation, recycling etc.

• Includes loss rate to account for produced CO2 or CO2 lost to env.

Addedfeature:CO2 enhancedoilrecovery

Upstream modeling goalsGoals of modeling CO2 EOR

•What is CO2-enhanced oil recovery (EOR)?•Inject CO2 into an oil field, CO2 advances to producer wells inducing more oil production

•CO2 is separated from produced fluids, recompressed, and reinjected

•Oil reservoirs could be long-term CO2 sequestration sites

•CO2-EOR modeling can be applied to stand-alone CCS analysis using OPGEE compression and injection results

•Want to be able to rigorously compare CO2 EOR to other oil technologies

Objectives:CO2EORmodeling

Upstream modeling goalsSome additional modeling features were included

1. Incorporate additional CO2 separation processes into OPGEE

2. Account for non-ideality of CO2 for modeling of compression costs

3. Model CO2 sequestration and leakage estimates for carbon crediting and debiting

4. Include emissions associated with producing purpose-drilled CO2

AdditionstomodelforCO2 EOR

Upstream modeling goalsA large driver of CO2-EOR costs is separation

• CO2 used for CO2-EOR is purchased by operating companies• Operators are incentivized to separate and reinject CO2

from produced fluids

• Separation processes incur a significant GHG cost• In most cases, simple bulk separation by phase is not

sufficient (mixed with saleable gases)

• Wide variation in separation processes and practices depending on properties of mixed gases

1.CO2separation

Upstream modeling goalsGoal is not “reinvent the wheel”

• U.S. Department of Energy / National Energy Technology Laboratory (DOE/NETL) life-cycle analysis of CO2-EOR emissions• “An Assessment of Gate-to-Gate

Environmental Life Cycle Performance of Water-Alternating-Gas CO2-Enhanced Oil Recovery in the Permian Basin.” DOE/NETL-2010/1433. 2010.

• “Gate-to-Gate Life Cycle Inventory and Model of CO2-Enhanced Oil Recovery.” DOE/NETL-2013/1599. 2013

Approach:DOE/NETLCO2 EORmodeling

Upstream modeling goalsExpanded gas balance sheet has multiple processing paths

Expanded“gasbalance”sheet

Majorgaspathways

Upstream modeling goalsGasprocessingoptions

Sequence Description Active Gas Processing Steps1 None No processing2 Minimal Dehydrator3 Acid Gas Dehydrator + Amine4 Wet Gas Dehydrator + Demethanizer5 Complete Dehydrator + Amine + Demethanizer

6 CO2-EOR MembraneDehydrator + Chiller + Compressor + Membrane Separation + Amine Process + Demethanizer

7 CO2-EOR Ryan Holmes Dehydrator + Ryan Holmes

Upstream modeling goalsMembraneseparation

Source: Department of Energy / National Energy Technology Laboratory, 2013

http://www.mtrinc.com/co2_removal.html

Upstream modeling goals[Emphasis]

• List

Ryan-Holmesprocess

http://pilotllc.com/wp/wp-content/uploads/2011/02/0213-Pilot-Energy-Svcs-Eprint.pdf

Upstream modeling goals2.Non-idealgascompression

• Compression is a major source of GHG emissions in oil extraction

• Expand OPGEE’s modeling of compression to account for non-ideal gas behavior– Important for CO2 compression

• “Z-Factors” capture behavior at a temperature/pressure– Z-Factor > 1: Real gas occupies more volume than ideal gas

– Z-Factor < 1: Real gas occupies less volume than ideal gas

– Z-Factors affect gas compression energy requirement calculations

Upstream modeling goalsNon-idealcompressionandwork

• OPGEE’s ideal isentropic horsepower equation now includes Z-factors. applied at each stage of compression (documentation, p. 52, originally from Jarrell, et al., 2002):

• To allow for deviation from ideality, insert “Z-factor”:

Upstream modeling goalsZ-factor also affects compressor temperatures

• OPGEE’s equation for ratio of compressor discharge temperature to suction temperature now includes Z-factors (originally from Jarrell, et al., 2002)

• The Z-factor has a cascading impact on compressor calculations, affecting each stage’s temperature and work requirements

Non-idealcompressionandtemperatures

Upstream modeling goalsCO2 balance is tracked throughout life of EOR project

• OPGEE will assume a “closed-loop” approach

• CO2 injected into the reservoir is either:

1. Sequestered in the reservoir pores and fluids or produced at the wellhead

2. Produced CO2 is separated, recompressed, and reinjected

3. At each recycling step, some CO2 is lost as fugitives, and additional CO2 is emitted due to work of separation and compression

• Following termination of EOR activity, CO2 either:

1. Remains sequestered in the reservoir (less a leakage parameter)

2. Is extracted and shipped to another oilfield for further EOR operation

3.CO2 accounting

OPGEE implementation • OPGEE models GHG emissions per barrel of total (gross)

oil produced

• OPGEE considers oil production per day

• OPGEE’s gas flooding module tracks gas injected:– Injection ratio: scf injected per bbl

– Production: bbl gross oil produced per day

– Injection is therefore modeled in scf per day (gross)

• The gross CO2 utilization ratio: Gross CO2 Injected (scf, fresh + recycled)

Gross Oil Produced (bbl per day)

CO2 accounting for recycling

• Begin with gross utilization ratio to account for injection of fresh and recycled CO2

• CO2 sequestered in the reservoir should include only total amount of “fresh” CO2 injected

CO2 seq.day = Oil prod

bblday

Gross CO2 inj. [scf]Gross oil prod[bbl]

Fresh CO2 Injected [scf]Gross CO2 Injected[scf]

• Ratio of fresh/injected CO2 varies: One example from DOE/NETL (2014) indicates a ratio of 41% at an offshore Louisiana field

How much CO2 injected per bbl?

• A variety of data from literature on CO2utilization– LostSoldierField(WY)grossutilization:11mscf/bbl (CookandMason)

– “5to10mscf”perbbl grossutilization(BruleandWitson 2000)

– WeeksIslandReservoir (LA)grossutilization:7.9mscf/bbl oil(NETL2014)

– LittleCreekField (MS)grossutilization:27mscf/bbl (Matthews1989)

– JoffreVikingPool(AB)grossutilization:10.8mscf/bbl.Individualpatternsrangefrom3.5to24.9mscf/bbl (Pyo etal.2003)

• ProposedOPGEEdefault:10mscf perbbl (grossutilization)

Modeling long-term CO2 leakage

• Difficult to predict long-term CO2 migration and leakage following EOR

• Some degree of CO2 leakage from wellbores is expected

• CO2 EOR projects typically in mature oil provinces with many abandoned wells – potential leakage conduits

Sequestration Mechanisms

Image Source: Intergovernmental Panel on Climate Change, 2005

• Structural/Stratigraphic Trapping: CO2 istrapped by impermeable caprock

• Residual Trapping: Oil and CO2 remain behind stuck in “nooks and crannies”

• Solubility Trapping: CO2 dissolves in aqueous reservoir fluids and sinks due to density

• Mineral Trapping: Occurs over geological time periods. In water, CO2 + H2O à H2CO3 (carbonic acid) à 2H+ + CO3

- Carbonate anions react with magnesium, calcium, iron cations to form carbonate minerals.

Modeling long-term CO2 leakage• DOE/NETL (2013) results

– Default leakage: 0.5% over 100 years to atmosphere

– Range of 0.1% to 1% leakage over 100 years

• Many simulation studies

– Example: Celia, et al. (2011) simulate CO2 leakage during 50-year injection period in AB, Canada

– Monte Carlo simulation suggested leakage rates below 1%

Modeling long-term CO2 leakage

• Newly published experimental results

• Kang et al. (2015) experimental study of 30 abandoned wells in PA– Wells were abandoned poorly, often before formal plugging rules in place

– Use gas flow rates to estimate a range in effective vertical permeability

– “Overall, the calculated K values for the AOG wells...are within the scale of previous effective permeability estimates. This is true even if we consider variations due to assumptions on parameter values. Therefore, the leakage risk posed by the presence of these wells in geologic storage of CO2 remains small.”

The well IDs in Figure 3 are sorted by the smallest measuredmethane flow rate at a given well location. Figure 3 shows thatthe Keff values are strongly correlated with the measuredmethane flow rate, m. For example, the lowest Keff value isobtained with the lowest m, and the highest Keff value isobtained with the highest m. Figure 3 also shows that the largeorders of magnitude variations in Keff values are found at wellswith relatively low methane flow rates. For higher flow rates,the Keff values stay within 2 orders of magnitude.We categorize the Keff values by their order of magnitude and

present the corresponding histogram in Figure 4. Figure 4

shows that the distribution of Keff is bimodal with the first peakat 10−1 mD and the second peak at 102 mD. Such bimodaldistributions in Keff values have previously been assumed torepresent “bad cement” and “good cement”.35 However, thereare other potential contributing factors, which are evaluated inthe next section.4.2. Role of Plugging Status, Geographical Location,

and Well Type. To understand factors that may be predictorsof Keff values and to determine the cause of the bimodaldistribution observed in Figure 4, we explore the role of wellplugging, geographic location, and well type. These factors wereselected since ref 24 found them to have a major impact onleakage potential and since sufficient data are available toanalyze these factors. The plugging status follows the definitionin ref 3, where a plugged well was identified on the basis of

surface evidence. Geographic location is considered at thecounty level. Well type is assumed on the basis of the oil, gas, orcombined oil and gas field in which the well is located or, ifpossible, as specified in the PA DEP’s list of AOG wells. Weperform the two-sample t test with unequal sizes and unequalvariances at the 95% confidence level (Supporting Informa-tion).Figure 5 shows Keff values based on plugging status. Plugged

wells have a mean Keff value of 0.4 mD, while unplugged wells

have a mean Keff value of 17 mD. However, this difference isnot statistically significant (Supporting Information). Weobserve that the mean for unplugged wells is governed by thelarge Keff values (>10 mD). Both smallest and largest Keff valuesare associated with unplugged wells. Keff values associated withplugged wells vary by 5 orders of magnitude, reflecting the widerange in plugging techniques and cement conditions.Figure 6 shows the relationships of Keff to well locations, with

the location identified by the county in which the measuredwell is located. The measurements from McKean County,where we have the largest number of data points, give Keff

Figure 3. Box plots of effective permeabilities (Keff) considering one toseven m values, three A values, two ρg,bot values, and one or two dvalues. The well IDs are based on the smallest measured methane flowrate at a given well location and are assigned in terms of increasingflow rates. The box represents values within the 25th and 75thpercentiles and the whiskers represent approximately ±2.7 standarddeviations. The red pluses represent the outliers. Note that the scalefor the Keff is logarithmic.

Figure 4. Histogram of effective permeabilities (Keff) using the mostlikely depths and assuming a “cool” basin and A based on the casingarea.

Figure 5. Effective permeabilities (Keff) and the role of well plugging.The most likely depths are used. A “cool” basin and A based on thecasing area are assumed. The well IDs are based on the smallestmeasured methane flow rate at a given well location and are assignedin terms of increasing flow rates. Note that the scale for the Keff islogarithmic.

Figure 6. Effective permeabilities (Keff) and geographical variationpresented in terms of the five counties in which the measured wells arelocated. The most likely depths are used. A “cool” basin and A basedon the casing area are assumed. The well IDs are based on the smallestmeasured methane flow rate at a given well location and are assignedin terms of increasing flow rates. Note that the scale for the Keff islogarithmic.

Environmental Science & Technology Article

DOI: 10.1021/acs.est.5b00132Environ. Sci. Technol. 2015, 49, 4757−4764

4761

The well IDs in Figure 3 are sorted by the smallest measuredmethane flow rate at a given well location. Figure 3 shows thatthe Keff values are strongly correlated with the measuredmethane flow rate, m. For example, the lowest Keff value isobtained with the lowest m, and the highest Keff value isobtained with the highest m. Figure 3 also shows that the largeorders of magnitude variations in Keff values are found at wellswith relatively low methane flow rates. For higher flow rates,the Keff values stay within 2 orders of magnitude.We categorize the Keff values by their order of magnitude and

present the corresponding histogram in Figure 4. Figure 4

shows that the distribution of Keff is bimodal with the first peakat 10−1 mD and the second peak at 102 mD. Such bimodaldistributions in Keff values have previously been assumed torepresent “bad cement” and “good cement”.35 However, thereare other potential contributing factors, which are evaluated inthe next section.4.2. Role of Plugging Status, Geographical Location,

and Well Type. To understand factors that may be predictorsof Keff values and to determine the cause of the bimodaldistribution observed in Figure 4, we explore the role of wellplugging, geographic location, and well type. These factors wereselected since ref 24 found them to have a major impact onleakage potential and since sufficient data are available toanalyze these factors. The plugging status follows the definitionin ref 3, where a plugged well was identified on the basis of

surface evidence. Geographic location is considered at thecounty level. Well type is assumed on the basis of the oil, gas, orcombined oil and gas field in which the well is located or, ifpossible, as specified in the PA DEP’s list of AOG wells. Weperform the two-sample t test with unequal sizes and unequalvariances at the 95% confidence level (Supporting Informa-tion).Figure 5 shows Keff values based on plugging status. Plugged

wells have a mean Keff value of 0.4 mD, while unplugged wells

have a mean Keff value of 17 mD. However, this difference isnot statistically significant (Supporting Information). Weobserve that the mean for unplugged wells is governed by thelarge Keff values (>10 mD). Both smallest and largest Keff valuesare associated with unplugged wells. Keff values associated withplugged wells vary by 5 orders of magnitude, reflecting the widerange in plugging techniques and cement conditions.Figure 6 shows the relationships of Keff to well locations, with

the location identified by the county in which the measuredwell is located. The measurements from McKean County,where we have the largest number of data points, give Keff

Figure 3. Box plots of effective permeabilities (Keff) considering one toseven m values, three A values, two ρg,bot values, and one or two dvalues. The well IDs are based on the smallest measured methane flowrate at a given well location and are assigned in terms of increasingflow rates. The box represents values within the 25th and 75thpercentiles and the whiskers represent approximately ±2.7 standarddeviations. The red pluses represent the outliers. Note that the scalefor the Keff is logarithmic.

Figure 4. Histogram of effective permeabilities (Keff) using the mostlikely depths and assuming a “cool” basin and A based on the casingarea.

Figure 5. Effective permeabilities (Keff) and the role of well plugging.The most likely depths are used. A “cool” basin and A based on thecasing area are assumed. The well IDs are based on the smallestmeasured methane flow rate at a given well location and are assignedin terms of increasing flow rates. Note that the scale for the Keff islogarithmic.

Figure 6. Effective permeabilities (Keff) and geographical variationpresented in terms of the five counties in which the measured wells arelocated. The most likely depths are used. A “cool” basin and A basedon the casing area are assumed. The well IDs are based on the smallestmeasured methane flow rate at a given well location and are assignedin terms of increasing flow rates. Note that the scale for the Keff islogarithmic.

Environmental Science & Technology Article

DOI: 10.1021/acs.est.5b00132Environ. Sci. Technol. 2015, 49, 4757−4764

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Modeling long-term CO2 leakage

• Proposed OPGEE default leakage rate is 0.5% over 100 years, following DOE/NETL

• Life-time leakage rates are “collapsed” in time and a composite GHG cost accrues to the field– Simply lessen current net storage by amount assumed to be leaked

Upstream modeling goalsMuch current CO2 injection comes purpose-drilled CO2

• Model offsite emissions associated with producing and transporting CO2 using GREET 2016 model for natural gas production

• This approach is consistent with the offsite accounting elsewhere in OPGEE

• Modeled as 100% “conventional” gas.

• Sheet 'NG' modified as follows: • No CH4 leakage from production• No energy for acid gas cleanup or other processing• No venting of CO2 from AGR unit• No methane leakage or slip during transport

• Resulting off-site emissions intensity 1/3 that of NG

4.EmissionsfromdrillingforCO2

Upstream modeling goalsCO2 EORresults

-40

-30

-20

-10

0

10

20

30

40

CO2 default (Mem)

CO2 high

recycle (Mem)

CO2 low recycle (Mem)

CO2 seq (Mem)

CO2 seq 50% credit (Mem)

CO2 seq 100% credit (Mem)

CO2 default (R-H)

CO2 high

recycle (R-H)

CO2 low recycle (R-H)

CO2 seq (R-H)

CO2 seq 50% credit (R-H)

CO2 seq 100% credit (R-H)

GHG

em

issio

ns [g

CO2e

q/M

J]

Sequestration credit

Offsite

Small sources

Transport

Waste disposal

Maintenance

Surface processing

Crude production

Drilling & Development

Exploration

Total

Upstream modeling goalsA number of smaller improvements made throughout the model

• Emissions from embodied energy is able to be modeled (not included in LCFS calculations)

• Transport module adds trucking from GREET model• Good for use in tight oil plays like Bakken

• Usability improvements• Data sheet is now where all fields are entered• Can run select subsets of fields from “Inputs” sheet

• Better compilation sheets• More clear compilation of sources from each sheet• Units of reported summary emissions are easier to use (tonnes

CO2/d)

Miscellaneouschanges

Accessing OPGEE and GHGfrack

https://eao.stanford.eduEnvironmental Assessment and Optimization Group