NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by 

U.S. Natural Gas System Methane EState of Knowledge from LCAs, Inve

and Atmospheric Measurements

Mechanical Engineering Seminar

Colorado State University (CSU)

Dr. Garvin Heath

National Renewable Energy Laborato

April 7, 2014

Fort Collins, Colorado

NREL/PR‐6A20‐61686
Methane (CH4) Is an Important GHG

Note: Assumes AR

Methane = 2

N2O = 310 (A

So

Source: IPCC AR4 (2007) ‐ 2004 data

Global 

IPCC AR5 (2013): “Of note is an increase in the average growth 

atmospheric CH4 from ~0.5 ppb/yr during 1999–2006 to ~6 ppb

2007 through 2011.”
2011 Methane Emissions – ~587 MMt (Tg) 

Note: Methane is also emitted from a number of natural sources. Wetlands are th

Smaller sources include termites, oceans, sediments, volcanoes, and wildfires.

Source: EPA 

U.S. GHG Inventory: “NG system CH4 emission

1990‐2011 by 16.5 Tg CO2e (‐10%)”
2011 Methane Emissions – ~587 MMt (Tg) 

Note: Methane is also emitted from a number of natural sources. Wetlands are th

Smaller sources include termites, oceans, sediments, volcanoes, and wildfires.

63

32

5

Source: EPA 

Natural Gas (NG) Systems

Coal Mining

Petroleum Systems

Abandoned Underground Coal Mines

U.S. GHG Inventory: “NG system CH4 emission

1990‐2011 by 16.5 Tg CO2e (‐10%)”
Inventory Method

‐ Temporal boundary: typically 1 year

‐ Spatial boundary: global, national, sub‐national.
Challenge: Measurements ≠ Invento

Example: Most recent component/activity mestudy: Allen et al. (2013).

Conclusion: Some sources overestimated by invsome underestimated, with errors compensatin

in similar national estimate.

0

200400600800

100012001400

completion

flowback

chemical

pumps

pnuematic

controllers

equipment

leaks

national

subtotal

Methane Emissions (Gg/ yr) A

E
Measurements ≠ Inventory

• Consistently true at all scales

• Next slides will summarize our recent 

analysis of 20 years of measurements.

• Organized by Novim (UCSB)• Funded by: Cynthia and George Mitchell Fou• Slides in crimson credit to: Adam Brandt, Sta

University.

“Methane leaks from North American natural 

Science, February 14, 2014

Brandt, Heath, et al.
Result: Inventories underestimate CH

4 em

• Evidence from numerous studies suggests that to

emissions are larger than those estimated by EPA 

• National-scale atmospheric studies

1 suggest that 

emissions are 50% higher than EPA estimates (unrange = 25 – 75% higher)

– 14 million tonnes of excess CH

4 per year (range 7 to 21)

2

• Excess CH4 emissions from the NG industry are ve

contribute to the total excess, but exact contributuncertain.

Additional notes:1National-scale studies include Kort et al. (2008), Xiao et al. (2008), Miller et al. (2013), Wang et a2 ~14 Tg (+/- 7 Tg) of excess methane.
Figure 1: What is evidence for excess C

• In Figure 1, we collect and compare e

from all studies we could locate whic

– Generated original observations – Computed an emissions flux and comp

published inventory.

1 kg CH4≈ 50 scf NG≈ 1 day of residential NG consumption for average U.S. citizen

1 Pg CH4≈ 50 TCF NG≈ 2 years of U.S. NG consumption
Ratio < 1 Ratio > 1
Ratio < 1 Ratio > 1

Less CH4 measured

than expected

More CH4 measured

than expected
Bottom-up AdvantagesNo attributioMore precise DisadvantagSample sizes Sampling bia

Allen et al. (2013)
Bottom-up AdvantagesNo attributioMore precise DisadvantagSample sizes Sampling bia
Top-down AdvantagBroad assNot subjecbiasesDisadvantAttributing sourcesRelies on m
Top-down AdvantagBroad assNot subjecbiasesDisadvantAttributing sourcesRelies on m
Challenge of differing baselines

• Nearly all bottom-up studies compar

results to EPA GHGI

1 emissions factor

• Top-down studies compare to a varie

baselines

– Putting studies on consistent basis yield

robust insights

– For each study, we scaled 2013 EPA GHG

region and sectors.

2

Additional notes: 1One exception: Chambers (2004, 2006) compares his results to CAPP methods.2See supplemental worksheet, sheet ‘Calc – Figure 1 – Inset’ for full computation.

Summary results: Figure 1

• Normalized top-down studies suggest o

emissions of CH

4 of ~1.5 (1.25-1.75) time

EPA estimates

• NG-specific studies also find excess

– Both top-down and bottom-up tend > 1– Wider range and more scatter 

• We cannot say how much of the overall 

due to the NG system.
Causes of the Gap – Some ExampleMany possible reasons for inventory underestimat

o Inventories not from same time period as measuremo Emission factors low/unrepresentative of current teco Inaccurate countso Missing sourceso Average emission factors do not consider fat tails dis

(“super‐emitters”)

Also challenges for top‐down studies

o Mixture of methane sources – how attribute?

– Oil and gas– Agriculture– Natural seeps

o Meteorologyo Measurements at specific times compared to annua
Results: Variability and “super-emitte

• Bottom-up studies

1 suggest that unintentio

leakage rates vary greatly between devices

– Most devices do not leak– A small fraction of devices leak excess gas–

A very small fraction (<<1%) leak a large amounsources often contribute a large fraction of the leakage.

Additional notes: 1See Table S6 in paper SM for tabular evidence of “super-emitters”, and Figure S2.

Alvarez et al. (2012)Well pad emissions

Harrison et al. (2012)
Results: Variability and “super-emitte

• Bottom-up studies

1 suggest that unintentio

leakage rates vary greatly between devices

– Most devices do not leak– A small fraction of devices leak excess gas–

A very small fraction (<<1%) leak a large amounsources often contribute a large fraction of the leakage.

Additional notes: 1See Table S6 in paper SM for tabular evidence of “super-emitters”, and Figure S2.

Alvarez et al. (2012)Well pad emissions

Harrison et al. (2012)

An example: 50 out of 75,000 source points 

resulted in 60% of all emissions.
Research Needs

Top‐down:• Source se• Represent

• Accuracy

• Gas comp

• Downscal

Reconcile GaBottom‐up:• “Fat tails” 

distributio

• Missing so

• Represent

• Activity da• Uncertain

Source: NREL
Results: NG fuel substitution

• Leakage rates from the NG system are unlikely to 

enough to disfavor coal to NG substitution for elegeneration (100 year global warming potentials)

– To favor coal: high estimate of undercounted emissions 

excess CH

4 from NG industry.

– We know other sources contribute (e.g., livestock).

• Climate benefits from using NG in transportation 

(for gasoline cars) or unlikely (for heavy-duty diesbut this conclusion is based on key assumptions be updated. 

Additional notes: 1Fuel switching benefits defined using cutoff percentages from Alvarez et al. (2012).
Opportunities to solve the problem

• Leakage detection and repair progra

been shown to be profitable

• Super-emitting sources: if we can find 

cheaply and quickly, they are profitab

• EPA Inventory is a critical resource th

be improved to provide better policy 
Collaborators:

• Science paper co‐authors

• Proposal team (CSM

(Nummedal), NOAA (Petron), CSU(Zimmerle), ENVIRON (Bar‐Ilan), WRAP

(Moore))

• NREL/JISEA (Doug Arent, Morgan

Bazilian, Jeff Logan, Ethan Warner,

Maggie Mann, Dan Steinberg).

Fundin

• JISE• DO• Nov

Thank you!
Garvin.Heath@nrel.gov

www.nrel.gov/harmonizatiowww.jisea.org/natural_gas.cwww.novim.org/projects/na

Contact and links
Supplementary Slides
Source: Larsen (2013)

Changing EPA Inventory Estimates
Fuel switching analysis caveats• We use 100‐year climate assessment period

• We rely on Alvarez et al. (2012) analysis and ass

o Leakage rates assumed for NG vehicle refueling and ve

efficiencies were from a single, 1992 study

o Global warming potentials for CH4 have since increased

• System boundary alignment between inventorie

poor

o EPA GHGI methods attribute CH4 emissions from copro

systems to the NG sector, rather than apportion some 

liquids

o EPA GHGI methods attribute CH4 emissions from assoc

production to petroleum sector.

• Does not consider technological change or redu

leakage
Figure 2: What might be causing excess CH

• In Figure 2, we perform a thought exp

on some possible causes of excess C

evidence available

– What sources might be responsible for 

• We focus on NG sources and sources 

might be mistaken for NG

– Sources that could “look like” NG by ch

isotopic signature.

Range of total excess CH4 (based on normalized Figure 1 results)

= 50% (25-75%) above EPA GHGI= 14 Tg CH4/y (7- 21 Tg CH4/y ).
Broad industry sectors• Top-down studies give us emissions estimates for a region (or NG ind• They do not tell us how common these emissions rates are• So we construct “illustrative prevalence scenarios”

• Take leakage rate observations from the literature• Apply these to a set fraction of activity (a = 1%, b = 10%, c = 25%

activity remains at EPA average.
Broad industry sectors• Example: NG production and processing: 

• Leakage rate of 9% (Karion et al. (2013) data from Uintah Basin U• Apply this to 1%, 10% and 25% of U.S. gas production, compute 

• Result: High emissions rates from recent atmospheric studies (UT, 

norm. If so, we would see more methane from the continental/nat

studies.
Hydraulic fracturingEmissions rates during flowback are uncertainShale gas HF:• Examine “Big 5” shale gas plays, use method from O’Sullivan and Pal

• Low (f) is “current practice” emissions rate: 56 Mg CH4/well

• High (g) is “all vented” emissions rate: 234 Mg CH4/well

Result: HF may contribute if uncontrolled flowback is the norm. suggests that HF is not the dominant contributor to overall exces
Sources not included in inventory• Petroleum HF not likely a major source. • Abandoned wells could contribute, but requires assuming high • Geologic seeps could confound, but some studies (e.g., Miller e

already account for this.
Emissions share by emissions rate

a 

Number of sources Emissio

Footnotes: 

1See Figure S3 in paper SM. Data from NGML, IES, Clearstone (2006) report for EPA. Measuremen

other sources.
The challenge of attribution

• How do we attribute what we see in t

atmosphere to the NG industry?

– Focus on regions with natural gas prod– Model atmospheric transport (wind) to 

sources

– Use chemical signatures to determine f– Use chemical signatures to assign to a g

sector (e.g., processed gas or raw gas).
Misalignment: inventories, LCAs, mea

• A still-entangled problem: methodolo

(and often do) have different system boundaries

• Ideal comparisons require understan

congruence (or lack thereof) between inventories, and experimental eviden

• Examples: 

– Peischl et al. (2013) gas vs. petroleum– EPA sector divisions vs. LCA methods.
Life cycle assessment (LCA) – quantifies resource

consumption, energy use, and emissions, from

cradle‐to‐grave.

• Practiced for 40 years• Methods codified in standards (e.g., ISO) and

guidelines, though some methodological issues

persist.

Selected by IPCC as most appropriate basis forconsistent comparison of renewable and

conventional energy technologies in Special Report

on Renewables.

Metrics

• GHG emissions

• Water consumption and discharges

• Energy use• Petroleum use• Raw material consumption

• Air pollutant emissions

• Solid waste.

Quantifying Attributable Impacts

Source
LCA Harmonization ProjectSix articles in Special Issue of Journal of Industrial Ecolog

Analysis of LCA (May, 2012)

– Coal– Nuclear– Wind– Concentrating solar power

– Crystalline silicon PV

– Thin‐film PV.

• In press: Conventional Natural Gas

• In review: Unconventional Natural Ga

www.nrel.gov/harmonization
NG Life Cycle GHG Emissions in Context(pre‐2011 published LCAs)

C
Shale (Unconventional) Gas LCAs Em

Study1. Howarth et al. (2011)2. Jiang et al. (2011)

3. Skone et al. (2011)

(since updated, adding Marcellus)

4. Hultman et al. (2011)

5. Stephenson et al. (2011)

6. Burnham et al. (2012)7. Heath et al. (2012)

Headline result1. NG > coal

2. Marcellus < do

(conv. + unconv

3. Barnett Shale > 

(except onshor

4. Unconv. < conv

5. Shale > conven

both < coal

6. Conv. > shale, b

7. Shale ~ conv., b

All rely heavily on inventory results, data s

assumptions
Preliminary Results of Gas LCA Meta‐analysis

(Harmonization)

Sources: Figure from JISEA (2013). Conventional results – O’Donoughue, P.; Heath, G.; Dolan, S. Vorum M.; (2014). Jou

Unconventional results – Heath. G.; O’Donoughue, P.; Arent, D.; Bazilian, M. (in review). Coal: Whitaker et al. (2012).

NGCC = natural gas combined cycle.

Subject to cpublication 
Maximum “well‐to‐wheels” natural gas leak rate as a function of th

needed to achieve net climate benefits [from fuel switchin

Alvarez et al. (2012). PNAS. (109); pp. 6435‐6440.

©2012 by National Academy of Sciences

Dotted lines: single emissions pulses

Dashed lines: the service life of a vehicle or a power plant, 15 or 50 years, resp

Solid lines: a permanent fleet conversion.

Methane Leakage Rate: A Framing Conce
Next Step: Contemporaneous, State of the S

Bottom‐up Plus Top‐down Empirical Assessm
Reconciling Top‐down and Bottom‐up GHG and Air Pollutant Emissio

Unconventional Gas Development in the Denver‐Julesburg Basin

Colorathe w

instituresea

aspec

challe