Atmospheric Methane From a gas well to a hemisphere Christian Frankenberg

and many others

Recent changes in atmospheric methane

Recent papers on methane rise (just a few of them)

From the hemisphere to individual methane plumes (back later)

4 Corners airborne campaign

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners regionChristian Frankenberga,b,1, Andrew K. Thorpeb, David R. Thompsonb, Glynn Hulleyb, Eric Adam Kortc, Nick Vanceb,Jakob Borchardtd, Thomas Kringsd, Konstantin Gerilowskid, Colm Sweeneye,f, Stephen Conleyg,h, Brian D. Bueb,Andrew D. Aubreyb, Simon Hookb, and Robert O. Greenb

aDivision of Geology and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125; bJet Propulsion Laboratory, California Institute ofTechnology, Pasadena, CA 91109; cDepartment of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109; dInstitute ofEnvironmental Physics, University of Bremen, 28334 Bremen, Germany; eCooperative Institute for Research in Environmental Sciences, University ofColorado-Boulder, Boulder, CO 80309; fGlobal Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration,Boulder, CO 80305; gScientific Aviation, Boulder, CO 80301; and hDepartment of Land, Air, and Water Resources, University of California, Davis, CA 95616

Edited by Gregory P. Asner, Carnegie Institution for Science, Stanford, CA, and approved June 17, 2016 (received for review April 10, 2016)

Methane (CH4) impacts climate as the second strongest anthropo-genic greenhouse gas and air quality by influencing troposphericozone levels. Space-based observations have identified the FourCorners region in the Southwest United States as an area of largeCH4 enhancements. We conducted an airborne campaign in FourCorners during April 2015 with the next-generation Airborne Vis-ible/Infrared Imaging Spectrometer (near-infrared) and Hyperspec-tral Thermal Emission Spectrometer (thermal infrared) imagingspectrometers to better understand the source of methane bymeasuring methane plumes at 1- to 3-m spatial resolution. Ouranalysis detected more than 250 individual methane plumes fromfossil fuel harvesting, processing, and distributing infrastructures,spanning an emission range from the detection limit ∼ 2 kg/h to5 kg/h through ∼ 5,000 kg/h. Observed sources include gas process-ing facilities, storage tanks, pipeline leaks, and well pads, as well as acoal mine venting shaft. Overall, plume enhancements and inferredfluxes follow a lognormal distribution, with the top 10% emitterscontributing 49 to 66% to the inferred total point source flux of0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognor-mal emission distribution, this airborne observing strategy and itsability to locate previously unknown point sources in real time pro-vides an efficient and effectivemethod to identify andmitigatemajoremissions contributors over a wide geographic area. With improvedinstrumentation, this capability scales to spaceborne applications[Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578]. Fur-ther illustration of this potential is demonstrated with two detected,confirmed, and repaired pipeline leaks during the campaign.

methane | Four Corners | remote sensing | heavy-tail

Global spaceborne measurements of methane with the Scan-ning Imaging Absorption Spectrometer for Atmospheric

Chartography (SCIAMACHY) instrument (1) revealed a meth-ane anomaly in the Four Corners region, with an estimated regionalemission of 0.59 Tg/y (2). This study explores the role of pointsources that supposedly drive the regional enhancement throughoutthe San Juan Basin in Four Corners.The San Juan Basin is primarily a natural gas production area,

mostly from coal bed methane and shale formations. More than20,000 oil and gas wells operate in the basin, and, for 2009, theUS Energy Information Administration reported an overallannual gas production of 1.3 trillion cubic feet, equivalent to19.2 Tg CH4/y.To estimate methane emissions from oil and gas facilities, the

Environmental Protection Agency uses a process-based approachthat assumes a normal distribution of emissions for each processused in extraction, processing, and distribution. In reality, the fluxdistribution can be heavily skewed, resulting in a heavy-tailed dis-tribution. This suggests that a relatively small percent of thesources in a given field may dominate the overall budget. The roleof heavy-tail distributions has been discussed as a possible reason

for underestimated methane emissions in bottom-up inventories(3–5). Although the heavy-tailed distribution makes it more diffi-cult to estimate emissions using a process-based (or bottom up)approach, it suggests that mitigation of field-wide emissions such asthose estimated for Four Corners will be less costly because it onlyrequires identifying and fixing a few emitters.However, evaluating the distribution and role of point

sources in large geographical areas with limited road access istoo time-consuming without prior knowledge of suspected lo-cations. We conducted an intensive airborne campaign in April2015 to overcome this shortcoming and directly measure thesource distribution, identify strong emitters, and provide real-time feedback to ground teams. We flew two NASA/Jet Pro-pulsion Laboratory airborne imaging spectrometers, namely, thenext-generation Airborne Visible/Infrared Imaging Spectrometer(AVIRIS-NG) (6) and the Hyperspectral Thermal EmissionSpectrometer (HyTES).Recent studies have shown that both can retrieve methane

quantitatively using methane absorption features in the short-wave infrared around 2.3 μm [AVIRIS-NG (7, 8)] and in thethermal infrared around 7.65 μm [HyTES (ref. 9 or refs. 10 and11)]. Here, we report on the experiment design as well resultsfrom both instruments, having successfully identified more than250 individual point sources, for which quantitative flux esti-mates are derived.

Significance

Fugitive methane emissions are thought to often exhibit a heavy-tail distribution (more high-emission sources than expected in anormal distribution), and thus efficient mitigation is possible ifwe locate the strongest emitters. Here we demonstrate airborneremote measurements of methane plumes at 1- to 3-m groundresolution over the Four Corners region. We identified more than250 point sources, whose emissions followed a lognormal distri-bution, a heavy-tail characteristic. The top 10% of emitters ex-plain about half of the total observed point source contributionand ∼1/4 the total basin emissions. This work demonstrates thecapability of real-time airborne imaging spectroscopy to performdetection and categorization of methane point sources in ex-tended geographical areas with immediate input for emissionsabatement.

Author contributions: C.F. designed research; C.F., A.K.T., D.R.T., E.A.K., J.B., T.K., K.G., C.S.,A.D.A., S.H., and R.O.G. performed research; C.F., A.K.T., D.R.T., G.H., N.V., J.B., T.K., S.C., andB.D.B. analyzed data; and C.F., A.K.T., and E.A.K. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.1To whom correspondence should be addressed. Email: cfranken@caltech.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1605617113/-/DCSupplemental.

9734–9739 | PNAS | August 30, 2016 | vol. 113 | no. 35 www.pnas.org/cgi/doi/10.1073/pnas.1605617113

How it all got started SCIAMACHY Methane Anomalies

Kort, Frankenberg et al, GRL, 2014

How it all got started SCIAMACHY Methane Anomalies

Kort, Frankenberg et al, GRL, 2014

—> Estimated to be about 0.5Tg/yr, almost 10% of US total Oil&Gas

d"

SCIAMACHY"xCH4"enhancements" Scaled"Model""xCH4"enhancements"

Potential Sources of CH4 in Four Corners Region• Total Production

rate in San Juan Basin about 1000 billion cubic feet (20Tg/yr)

• 0.5Tg/yr would be about 2.5%

• Largest Coalbed methane production area in US

Methane Airborne Remote Sensing AVIRIS-NG (5nm sampling, 400-2500nm)

Campaign Area

Airborne operations

AVIRIS-NG real time methane detection (Thompson et al, AMT)

Real-Time CH4 display

Native resolution examples (background is 2.3µm radiance in gray, meter axis)

Up-scaling to fluxes (integrate methane amount in plume)

0 10 20 30 40 50 60 70 80

IME (kg CH4)

0

500

1000

1500

2000

2500

3000

3500

4000

Flux

esti

mat

e(k

gCH

4/h

r)

r2= 0.97r2= 0.96

Gaussian Integral Flux InversionIn-situ airborne Flux Inversion

>200 plumes detected

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution — Log-normal

Det

ectio

n th

resh

old

Porter Ranch 60,000kg/hr

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution — Wellhead

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution — ???

Methane plume

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution — underground storage tank

Methane plume from tank

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution - Wellhead

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution — Guesses welcome

102 103 104 105 106

Flux rates (scfh)

0.0

0.2

0.4

0.6

0.8

1.0

Freq

uenc

y

1 10 100 1000 10000Flux rates (kg/hr)

Plume distribution — Unclear (multiple sources, maybe well completion? Near coal-mine

What does the log-normal distribution imply?

0 20 40 60 80 100

Percentile (%) of sources (large to small)

0

20

40

60

80

100

tota

lflux

perc

enta

ge

Measured plumes95% confidence interval

101

102

103

104

Flux

rate

(kg/

hr)

Flux distribution;P

=0.30 Tg/yr (95% confidence=0.23–0.39)

measured flux rates