Are Optical Gas Imaging Technologies Effective For Methane LeakDetection?Arvind P. Ravikumar,* Jingfan Wang, and Adam R. Brandt

Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, California 94305, United States

*S Supporting Information

ABSTRACT: Concerns over mitigating methane leakagefrom the natural gas system have become ever more prominentin recent years. Recently, the U.S. Environmental ProtectionAgency proposed regulations requiring use of optical gasimaging (OGI) technologies to identify and repair leaks. Inthis work, we develop an open-source predictive model toaccurately simulate the most common OGI technology,passive infrared (IR) imaging. The model accuratelyreproduces IR images of controlled methane release fieldexperiments as well as reported minimum detection limits. We show that imaging distance is the most important parameteraffecting IR detection effectiveness. In a simulated well-site, over 80% of emissions can be detected from an imaging distance of10 m. Also, the presence of “superemitters” greatly enhance the effectiveness of IR leak detection. The minimum detectable limitsof this technology can be used to selectively target “superemitters”, thereby providing a method for approximate leak-ratequantification. In addition, model results show that imaging backdrop controls IR imaging effectiveness: land-based detectionagainst sky or low-emissivity backgrounds have higher detection efficiency compared to aerial measurements. Finally, we showthat minimum IR detection thresholds can be significantly lower for gas compositions that include a significant fractionnonmethane hydrocarbons.

■ INTRODUCTION

Methane, a significant component of natural-gas, is a potentgreenhouse gas (GHG): its global warming potential issignificantly higher than carbon dioxide, especially over shorttime periods.1 Fugitive emissions from the oil and gasindustry2,3 account for a quarter of total U.S. methaneemissions. Mitigating these emissions would contributesignificantly toward achieving GHG emissions reductionsgoals outlined in the Paris Agreement.4 Reducing methaneemissions is especially important if natural-gas-based powergeneration is to be relied upon to ensure reliability of futuregrids with high fractions of intermittent renewables.A growing number of studies5−9 have found methane

emissions that are higher than Environmental ProtectionAgency’s (EPA) estimates in its GHG emissions inventory. Infact, EPA has revised methane emissions estimates upward in itslatest inventory.10 While recent studies have steadily improvedour understanding of methane emissions from all sectors of thenatural gas supply chain−production,5,6,11 processing,12 trans-mission,13 and distribution9,14 − identifying and quantifyingexisting sources of anthropogenic emissions is difficult due tothe variety and spatial extent of potential leaks. And manyregional studies made from aircraft still find emission rates thatdo not align with expected emissions rates.5,15

Against this backdrop of rapidly evolving science, a variety ofefforts are underway to solve the methane leakage problem withimproved regulation and technology. The U.S. EPA, in itsrecently released updates to the 2012 New Source Performance

Standards (NSPS),16 requires oil and gas operators to useoptical gas imaging (OGI) as part of structured leak detectionand repair (LDAR) program. Endorsing this technique, naturalgas operators in the state of Colorado recently acknowledgedthe cost-effectiveness of the OGI-based LDAR program.17

The most common OGI technology for methane detectionrelies on infrared (IR) imaging. A commonly used IR cameracreates images of a narrow range of the mid-IR spectrum (3.3−3.4 μm wavelength) which methane and other light hydro-carbons actively absorb.18 More advanced technologies areunder active development. For example, hyperspectral imagingacquires spectrally resolved images, allowing differentiationbetween different hydrocarbon gas plumes.19−21 A relatedtechnique called infrared gas-correlation,22,23 have been used toquantify leak rates of gases. A recent simulation study showed asystematic approach to estimating the costs and benefits ofthese different technologies.24

While many facilities already employ OGI-based technologyfor leak detection, systematic scientific analysis of theperformance of this technology is lacking. EPA-commissionedscientific efforts25 show a number of studies by technologydevelopers or users, but little systematic analysis under variousconditions. Part of the reason for this lack of research is the

Received: August 3, 2016Revised: November 28, 2016Accepted: November 29, 2016Published: November 29, 2016

Article

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© 2016 American Chemical Society 718 DOI: 10.1021/acs.est.6b03906Environ. Sci. Technol. 2017, 51, 718−724

relatively high price of these cameras (≈100k USD) and theexpense of systematic field campaigns.To address these shortcomings we have developed an open-

source model that simulates methane leak detection withpassive IR imaging. We validate the model with controlled-release field experiments, and reproduce previously publishedlaboratory-based minimum detection limits. After validation, weexplore how the efficiency of OGI varies with environmentalparameters like temperature and wind conditions, operator andsurvey parameters like imaging distance and detection criterion,and finally, the characteristics of the facility. Combining modelresults and experimental data, we provide recommendationsand best-practices guidelines for achieving methane mitigationgoals.

■ MATERIALS AND METHODS

Evaluating OGI for methane leak detection involves modelingof three related physical systems - (1) Infrared absorption, (2)Imaging Properties, and (3) Leak modeling. A brief descriptionof the methodology used in this study is presented below.Detailed information and other derivations of key equations canbe found in the Supporting Information (SI) Appendix.Infrared Absorption. IR absorption by gas molecules,

arising from the rotational−vibrational resonances of moleculartransitions, is described by the Beer−Lambert Law, = ρ− αeI

IKt

0,

where Kα is the molar absorption cross-section, ρ is theconcentration path length, and It and I0 are the transmitted andincident light intensity, respectively. Kα is calculated from high-resolution transmission (HITRAN) molecular absorptiondatabase26 using the spectral line-widths, and line-strengths ofthe molecular transitions. While the values in the HITRANdatabase correspond to a reference temperature of 296 K, itshould be noted that both the line-intensities and line-widthsare temperature dependent and should be corrected accord-ingly (accounted for in our model). The uncertainties in thespectral line-strengths in the HITRAN database is significantlysmaller than the uncertainty from other operator-controlledparameters (see SI Appendix). For heavier hydrocarbons likeethane and propane that often occur in natural gas deposits,line intensity values are used from Pacific Northwest NationalLaboratory’s (PNNL) infrared database.27

Imaging Properties. An infrared camera measures theradiant energy incident on the sensor within the camera’s fieldof view. This radiant energy, in a leak-detection setting, comesfrom four principal sources: (1) direct radiance from themethane plume, (2) transmitted radiance from the scene(background), (3) scene-reflected cold-sky radiance, and finally,(4) direct atmospheric radiance. Each of these radiances are in-turn a function of the temperature of the body and itsemissivity. At short imaging distances, atmospheric trans-mission can be assumed to be near unity, and so directatmospheric radiance can be neglected. Furthermore, exceptunder cases where the scene emissivity is low (ϵs < 0.2), thescene-reflected atmospheric radiance is at least an order ofmagnitude smaller than the transmitted radiance from the scene(see SI Appendix Figure S4). Finally, we are left with twomaterial contributions to the radiance observed by the IRcamera - the direct radiance from the plume, and thetransmitted radiance from the scene (see SI Appendix for fullequations). This change in observed radiance, ΔLobs, with andwithout the methane plume can be written as,

ν τ ν νΔ = − − ϵρ− αL e B T B T( ) (1 )( ( ; ) ( ; ))aK

sobs p s (1)

where τa is the atmospheric transmittivity, ρ is theconcentration path-length, and B(T; ν) is the Planck blackbodyfunction for a body at temperature T and wavenumber ν. Here,we note the scene emissivity corresponds to the compositeemissivity of the “clutter” present in the scene of the methaneleak. Furthermore, we have assumed that the scene emissivity isconstant within the narrow spectral range of the filter used inthe imaging process (see SI Appendix). A pixel registers anintensity change if this difference in radiance, ΔLobs is greaterthan the Noise Equivalent Power (NEP) of the IR cameraafunction of the detector sensitivity, pixel dimensions, andcamera optics and is specified by the manufacturer in terms ofthe Noise Equivalent Temperature Difference.

Leak Modeling. The performance of OGI technologycritically depends on the size distribution of the leaks. Wemodel the leaks in a typical upstream production facility (well-pads) using a set of ≈6400 empirically measured leaks from 8previously published studies (see SI Appendix). Thisdistribution has a mean leakage of 0.25 g CH4/s, with thetop 5% of the leaks contributing to about 78% of the totalleakage. For the creation of the composite leak data set weremove the following categories of leaks: (1) “downstream”leaks from distribution systems and piping; (2) compressor sealvents (very large plumes from sources that are more readilypredictable and are easily observed from any distance); (3)leaks from abandoned wells or other abandoned infrastructure.

■ MODEL VALIDATION FROM CONTROLLEDRELEASES

We gathered observations of controlled release field experi-ments of natural gas leaks near Sacramento, CA in collaborationwith Kairos Aerospace. The methane leaks were emitted froman elevated stack of diameter 2.5 cm, at rates ranging from ≈1g/s (5 mscf/day) to ≈29 g/s (135 mscf/day), for a total ofseven different constant emissions rates. The leaks were imagedat distances from 10 to 60 m by a FLIR GF320 camera usingthe ResearchIR software to extract research-grade noncom-pressed video. Videos were gathered at 16 frames/sec for a totalof approximately 800 frames at each flow rate (SI Appendix).The methane plumes were dispersed by both ambient winds

and a vertical velocity component resulting from release ofpressurized gas. The vertical and horizontal components ofplume velocity were dependent on leak size. Therefore, aneffective wind-speed was used in the simulation, combininghorizontal wind-speed arising from local weather, and “wind-speed” from the controlled release. This effective velocity wasdirectly calculated for each video using feature tracing methods(SI Appendix). While leaks were imaged at up to 60 m distance,a set of 10 leaks imaged from distances 20 or 30 m wereselected for further analysis due to their relatively short imagingdistances (less background interference) and larger plumeimages (i.e., more pixel coverage). For each video, 50 framesare chosen for further image processing and analysis; theseframes are selected to avoid large wind-gusts or sudden changesto wind direction.In order to compare video of leaks to our simulation results,

we measure the number of pixels occupied by the plume in agiven frame. We employ two different algorithms to accomplishthis: (i) direct image enhancement using filtering andthresholding techniques, and (ii) optical flow technique usingLucas-Kanade method28 (SI Appendix). Both of these methods

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are used to generate a binary image from which pixel coverageis calculated. 50 frames are averaged to obtain statisticaldistributions. Figure 1(a) shows one frame from an

unprocessed leak video (10 g CH4/s, imaged from a distanceof 30 m). Figure 1(b) shows the extracted plume using directimage enhancement, and (c) using the optical flow method. Wecan see that both methods reliably extract the plume from thevideos. Unprocessed videos are available in SI. It should benoted that frame average is limited only by the presence ofsteady wind direction to obtain meaningful averages. Whileincreasing the number of frames averaged might improve pixelcount statistics, the uncertainty is often dominated byinstantaneous changes to wind speed and direction, andapproximations involving the Gaussian plume dispersionmodel.After calculating the observed size of the plume in each frame

using the above two algorithms, we compare the observed sizeto that estimated using our simulation tool, as shown in Figure2. The model consists of an IR simulator coupled to a Gaussianplume dispersion model.29 However, the IR simulator couldalso be coupled with more complex atmospheric transportmodels that better reproduce plume dispersion. Figure 2a is asingle frame from a controlled release experiment at a leak rateof 29 g/s imaged from a distance of ≈30 m from the leak-source. The effective wind-velocity of 7 m/s, determined fromparticle tracing methods (see SI Appendix) is used to simulatethe plume as shown in the bottom image (see SI Appendix).Only pixels with a signal-to-noise ratio (SNR) ≥ 1 werecounted as being “detected” (shown in red).Figure 3 compares observed pixel coverage for direct image

enhancement (green) and optical flow (red) to the simulatedcoverage for each video (blue range). Coverage varies fromabout 1600 pixels for a leak size of ≈1 g/s to over 8000 pixelsfor a leak size of ≈21 g/s. The error bars on experimentalsymbols are standard deviations derived from 50 frames foreach video. The blue range is generated using 100 simulationswith wind velocities distributed ±10% around the video-derivedeffective wind speed. The numbers associated with each pointestimate corresponds to the leak rate (g CH4/s) and distance(m). The videos, frame numbers, and information on imagingdistance and leak rates are given in SI Table S3. We see thatsimulated pixel coverage values are in good agreement with

experimental data, with both simulation and observationshaving errors of order 500−1000 pixels and generallyoverlapping error bars. This suggests that our model cannumerically estimate the leak detection capabilities of IRimaging. In addition to these controlled field release experi-ments, we also used the model to reproduce previouslypublished minimum detection limits measured under laboratoryconditions as reported by Benson et al.30 (see SI Appendix).

■ MODEL RESULTS AND RECOMMENDATIONSAfter model validation, we use the simulator to explore theefficacy of leak detection using previously reported empiricalleak distributions and a simple Gaussian plume dispersionmodel (SI Appendix). The IR camera properties used in thismodel are similar to that of the FLIR GasFind-IR cameras, acommonly used model in industry and the model used above infield verification (GF320 series, see SI Appendix Table S1). A

Figure 1. (a) A single frame of a controlled leak corresponding to 10g/s imaged from a distance of 30 m from the source. (b) Extractedplume using direct image enhancement techniques, and (c) Extractedplume image using Lucas-Kanade optical flow method.

Figure 2. (a) A single frame of a controlled leak corresponding to 29g/s imaged from a distance of 29 m from the source. The arrow showsthe effective velocity (V̅) of discharge, estimated at 7 m/s. (b)Simulation of the plume in (a) using a Gaussian dispersion model. Mdenotes the mass flow rate, D is the imaging distance, Ts is the scenetemperature and SNR refers to the signal-to-noise ratio. We achievegood agreement in pixel coverage between experiments andsimulation. (The figure has been rotated clockwise to improve clarity).

Figure 3. Parity chart of simulated pixel coverage using the modeldeveloped here, with error bars arising from the range of effective windvelocities used in the simulation (blue shaded region), and extractedpixel coverage values using direct image enhancement (solid greentriangles), and optical flow (open red squares) methods. The numbersassociated with the data points correspond to the leak rate (in g/s) andmeasurement distance (m).

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lateral cross-section of the plume, parallel to the center-line, issimulated at distances ranging from 10 to 200 m away from theleak source. Atmospheric transmission is assumed to be 0.9 inthese simulations, and wind-speeds and associated atmosphericstability classes (see SI Appendix) have been randomized basedon empirical distributions suggested by ARPA-E in theMONITOR challenge.31 For each leak, the change in observedradiant power is calculated for every pixel and compared to theNEP of the camera. A plume is considered detected if morethan 400 pixels have a SNR ≥ 1 (e.g., a square of size 20 × 20pixels, or about 10% of viewfinder width and height). It shouldbe noted that the coverage criterion of 400 pixels for detectionis approximate and depends on the visual acuity of the cameraoperator. Differences in this value will obviously affect detectionefficiency, defined as the fraction of emissions detected (SIAppendix). It should be noted that imaging leaks parallel to theplume center-line can be easily accomplished with a rotation ofthe camera. However, imaging at angle will reduce theconcentration-path length and hence, the detection effective-ness. This is further discussed in the SI Appendix.Each simulation run presented below consists of 100 leaks

drawn randomly from an empirical data set of ≈6400 previouslymeasured leaks at upstream natural gas facilities (Materials andMethods and SI Appendix). The fraction of total leakagedetected and minimum detectable leak rates (MDLR) areidentified in each case. To ensure statistical representativeness,a total of 50 simulations per scenario gives statisticaldistributions.Imaging Distance Is the Most Important Parameter

for Effective Leak Detection. Figure 4 shows a grid (A

through L) of normalized color maps of the fraction of totalleakage detected as a function of imaging distance (verticalgrids) and background emissivity (horizontal grids). Warmercolors represent a larger fraction of gas detected. Each color-map in turn shows variations over a range of scenetemperatures (x-axis) and plume temperature deviation (y-axis), with a positive deviation reflecting a plume that is hotterthan the scene temperature. Consider an emissivity of 0.1 (first-column, grids A through D). The detection efficiency for agiven plume and scene temperature decreases with increasingimaging distance, at 10 m (grid A), approximately 90% ofemissions are detected (on-average), dropping to about 40% at200 m (grid D). For a given leak, the number of pixels occupiedby the plume is inversely proportional to the square of imagingdistance, resulting in a progressive reduction in efficiency.Effective leak detection requires short imaging distances.

Measurement against Sky Tends to Improve Detec-tion. For a given scene temperature (Ts = 300 K, grid D), wenote that fraction of emissions detected increases from under10% when the plume is 10 degrees cooler than the scene toover 60% when it hotter by 20 degrees. This is because radiantemissions are directly proportional to temperature, a hotterplume emits more infrared radiation compared to the relativelycooler scene (B(Tp) > B(Ts)), thereby increasing the change inradiance observed by the camera. Although plume temperaturecannot be controlled by the operator, this increased detectioncapability from temperature contrast is directly applicable toleak detection surveys. The temperature of the atmosphere, asimaged by an infrared camera, is typically 20−50 °C cooler thansurface temperatures due to the energy balance between theSun and the Earth. This cooler atmospheric radiance is oftentermed as “cold-sky radiation”. As a result, images of leaks takenfrom the ground-up with the cold-sky radiation as backgroundtend to have higher contrast than images taken looking down(as from a low-flying aircraft or helicopter), since surfacetemperatures are closer to plume temperature. This effect hasbeen observed in recent aerial surveys,3 which exhibit highminimum detectable leak rates. It should be noted however thatthe water-vapor (humidity) will reduce atmospheric trans-mission and consequently, detection efficiency. The effect ofhumidity will be enhanced at longer imaging distances (see SIAppendix for detailed discussion).

Warmer Days Are Preferable for Improved DetectionEffectiveness. It is preferable to conduct leak detection surveyunder higher scene temperature conditions (warm days),although it should be noted that this does not significantlyaffect detection effectiveness compared to other factorsdiscussed above. Under all measurement conditions (A throughL), we can observe an ≈10 percentage point increase in fractionof emissions detected as the scene temperature increases from270 to 310 K, for a given temperature contrast. The radiancecontrast, ΔLobs, can be written as, ΔLobs = B(Ts + ΔT) −ϵsB(Ts), where ΔT = Tp−Ts is the temperature contrast, Tp isthe plume temperature, Ts is the scene temperature, ϵs is thescene emissivity, and B(T) is the spectral Planck body radiation.To fi r s t o rde r , t h i s c an be app rox ima t ed a s

Δ ≈ + − ϵΔ( )L B T( ) 1 TTobs s s

s, which increases as Ts increases.

In physical terms, the radiance from the plume (B(Ts + ΔT))increases faster than the radiance from the scene (B(Ts)) as Tsincreases, thereby improving contrast.

Low Emissive Scenes Provide Better Contrast thanHigh Emissive Scenes. Emissivity of the scene plays an

Figure 4. Color maps of the fraction of total leakage detected as afunction of imaging distance (vertical grid-axis), and scene emissivity(horizontal grid-axis). Each color-map in turn shows variation over arange of scene temperatures (x-axis) and plume temperaturedeviations (y-axis). Best practices for maximum leak detectionefficiency include a short imaging distance, higher scene temperatures,and large temperature contrast between the plume and the scene. Ofall factors, we can see that imaging distance strongly affects detectioncapabilities.

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important role in improving contrast, especially when there islittle to no temperature contrast between the plume and thescene. Such situations are fairly typical when the plume is inthermal equilibrium with the surroundings, except in cases likeextreme winter conditions or high pressure exhausts. Tomaximize the change in observed radiance, lower values foremissivity are preferable for Tp ≥ Ts. From grid C, G, and K inFigure 4, we see that the fraction of emissions detecteddecreases from about 70% to 40% as scene emissivity increasesfrom 0.1 to 0.9 (ΔT = 10 K, Ts = 290 K). In practice, low-emissivity backgrounds correspond to reflective metallicsurfaces, while high emissivity background could be soil,forests, etc. It should be noted that scene-reflected atmosphericradiance cannot be neglected if the scene emissivity is low (ϵs ≤0.2) - this decreases the temperature-emissivity contrast, whichreduces detection efficiency faster than expected from modelsthat neglect reflected radiance (SI Appendix). At the otherextreme, for plumes colder than the surroundings where B(Tp)< B(Ts), maximum contrast is reached for ϵs = 1. A highlyemissive background increases radiation incident on the cameracompared to the relatively cooler plume, thereby increasingcontrast. This can be clearly seen by comparing grids C and K,where plumes that are colder by 20 K compared to the scenecan be detected only when the emissivity is high (grid K), whilethey are undetected under low emissivity conditions (grid C).In a typical facility, operators will encounter scenes of mixedemissivity. In such cases, the number of pixels on the camerawhere a plume is registered will be lower compared to sceneswith constant emissivity. In particular, we can see that high-emissive objects in the background of a leak will tend to reducecontrast.OGI Provides Approximate Leak Quantification to

Selectively Target the Biggest Leaks. A useful metric fromthe standpoint of leak detection surveys is the minimumdetectable leak rate (MDLR) - a low MDLR may result in thedetection of a large number of small leaks, while a large MDLRmay not detect large leaks. An appropriate balance required todesign surveys will depend on a working knowledge oftechnology limitations. Figure 5 shows the MDLR as a functionof operating distance at a scene temperature of 310 K and ascene emissivity of 0.5, for three different temperature contrasts

between the plume and the scene. For a plume in thermalequilibrium with its surroundings (Tp = Ts), the MDLR variesfrom about 1 g/s at 10 m to about 20 g/s at 200 m imagingdistance. Such a large dynamic range in detection thresholdresults in leakage detection of about 70% at 10 m, to under 5%at 200 m (grid A through D in Figure 4). Therefore, given a setof environmental conditions, the minimum leak-size detectedcan be approximately quantified by choosing an imagingdistance.At 10 m imaging distance, an MDLR of about 2 g/s allows us

to remove 70% of the volume of leaked gas by only fixing some5% of the leaks. This clearly illustrates the importance ofconsidering leak size distributions when designing rules foracceptable imaging distance. Because of the strong dependenceof MDLR on distance, choosing distance to design an effectiveMLDR can be used to selectively target the “superemitters”defined by a predetermined leak rate cutoff.The inset of Figure 5 shows the MDLR at 50 m imaging

distance as a function of scene temperature, going from about 7g/s at 270 K, to about 3 g/s at 310 K, a reduction of only afactor of 2. Thus, for practical applications, imaging distanceplays a stronger role in determining the effectiveness of OGIbased leak detection techniques compared to scene temper-atures. Any policy that does not specify a maximum operatingdistance for infrared imaging will result in a wide variety inreported leak rates.

“Superemitters” Greatly Enhance the Effectiveness ofOGI Based Mitigation Strategies. It is important to re-emphasize the crucial difference between the fraction ofemissions detected from the fraction of total leaks foundusing OGI technology. Under a policy scenario where everydetected leak has to be repaired, the immediate concernbecomes: is there an MDLR that could result in the detectionof a large number of inconsequential leaks? Let us consider thebest case scenario represented in this study - grid A in Figure 4.At a temperature contrast of 0 K with an MDLR of about 0.5 g/s, OGI technology detects approximately 85% of the totalemissions, which corresponds to only the largest 10% of theleaks. Under less favorable imaging conditions, the number ofleaks detected will be well below 10% of the total leaks in thefacility under study.For leak size distributions that are less heavy-tailed compared

to the empirical distribution used in this study, the fraction ofemissions detected by OGI drops significantly. To illustrate,Figure 6 shows the fraction of emissions detected from a facilitywith different underlying leak-size distribution. Each distribu-tion has a mean leakage of 0.22 g/s, with total fugitiveemissions contribution from the top 5% of the leaks varyingfrom 75% (most skewed) to 6% (least skewed, see SIAppendix). Correspondingly, we see that the fraction ofleakage detected drops from about 70% to under 5%. Thisshows that any leak mitigation policy should consider theunderlying characteristics of the facility or basin to estimatecosts and expected emissions reductions.

Minimum Detection Threshold Can Be up to 3−4Times Lower for Wet-Gas Compared to Dry-GasCompositions. Oil and gas operations often result in leakagethat contains a mixture of gases, most notably higher molecularweight hydrocarbons like ethane and propane. Recent measure-ments in the Bakken32 indicate elevated ethane concentrationin the atmosphere around oil and gas facilities. Because ofstructural similarity of methane and other hydrocarbons,heavier hydrocarbons like ethane and propane also absorb in

Figure 5. Minimum detectable leak rate (MDLR) as a function ofimaging distance at a scene temperature of 300 K, and an emissivity of0.5 for three different temperature contrasts: 0 K (red), 10 K (blue),and 20 K (green). The MDLR increases by about 25 times across therange of distances considered, resulting in significant change indetection efficiency. (inset) MDLR as a function of scene temperatureat 50 m and ΔT = 0. Note that MDLR is more strongly dependent onimaging distance than scene temperatures.

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the 3.2−3.4 μm region of the IR camera. Model results suggestthat the MDLR for a pure propane stream (0.7 g/s) is about 4times smaller than that of a pure methane leak (2.7 g/s). Thishas been cited as a possible reason for elevated fugitiveemissions seen in some aerial measurements.3 Further detailson how typical nonmethane hydrocarbon composition (2−20%) affects emissions detection in a natural gas-field can befound in the SI Appendix. Field measurements using OGItechnology at facilities where nonmethane hydrocarbons areknown to occur in significant quantities should be carefullycalibrated to reduce the effect of false positives.In this paper, we have developed a systematic model to assess

the effectiveness of OGI technologies for methane leakdetection in the oil and gas industry. Pixel coverage measuredfrom controlled release field experiments agree well with modelpredictions. Based on experimental data and model results, weconclude with the most important take-aways: (1) Shortimaging distances are crucial for effective leak detection, (2)Land-based IR imaging typically provides better contrast thanaerial imaging, (3) OGI based surveys provide pseudoquanti-fication to target the biggest leaks, thereby providing a way tofix potential “superemitters”, and finally (4) detection limits willvary significantly based on the gas composition being observed.Such predictive capability for the efficiency of leak detection

using IR cameras can be used by businesses and policy makersto develop smart leak detection protocols. The model can alsobe tailored to specific facilities and leak size distributions,helping businesses to assess this technology without under-taking expensive trials. Because pixel coverage identification isbased on simple and rapid algorithms, this tool can be furtherdeveloped to provide real-time information on leak quantifica-tion. In light of the recently proposed EPA standards for use ofOGI techniques for leak detection, it becomes crucial to be ableto identify the right conditions to render such regulationseffective.Future work will involve coupling the infrared imaging

simulator with more complex plume dispersion models thatdescribe the turbulent flow of gases around complex surfacefeatures.

■ ASSOCIATED CONTENT*S Supporting InformationThis material is available free of charge via the Internet athttp://pubs.acs.org/. The Supporting Information is availablefree of charge on the ACS Publications website at DOI:10.1021/acs.est.6b03906.

Simulation code, controlled release leak-videos andadditional analyzes and Supporting Information (PDF)Matlab code (ZIP)Controlled leak videos (ZIP)

■ AUTHOR INFORMATIONCorresponding Author*Phone: +1-650-736-3491; fax: +1-650-725-2099; e-mail:arvindr@stanford.edu.ORCIDArvind P. Ravikumar: 0000-0001-8385-6573Adam R. Brandt: 0000-0002-2528-1473NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSWe acknowledge the assistance of Kairos Aerospace forallowing collaboration and measurement on controlled releaseevents. We also thank Jacob Englander for help with fieldexperiments.

■ REFERENCES(1) Pachauri, R. K.; Allen, M.; Barros, V.; Broome, J.; Cramer, W.;Christ, R.; Church, J.; Clarke, L.; Dahe, Q.; Dasgupta, P. et al. ClimateChange 2014: Synthesis Report. Contribution of Working Groups I, IIand III to the Fifth Assessment Report of the Intergovernmental Panelon Climate Change. 2014.(2) Brandt, A.; et al. Methane leaks from North American natural gassystems. Science 2014, 343, 733−735.(3) Lyon, D. R.; Alvarez, R. A.; Zavala-Araiza, D.; Brandt, A. R.;Jackson, R. B.; Hamburg, S. P. Aerial surveys of elevated hydrocarbonemissions from oil and gas production sites. Environ. Sci. Technol.2016, 50, 4877−4886.(4) Adoption of the Paris Agreement Vol. FCCC/CP/2015/L.9/Rev.1;UNFCCC, 2015;.(5) Karion, A.; Sweeney, C.; Kort, E. A.; Shepson, P. B.; Brewer, A.;Cambaliza, M.; Conley, S. A.; Davis, K.; Deng, A.; Hardesty, M.; et al.Aircraft-based estimate of total methane emissions from the BarnettShale region. Environ. Sci. Technol. 2015, 49, 8124−8131.(6) Lavoie, T. N.; Shepson, P. B.; Cambaliza, M. O.; Stirm, B. H.;Karion, A.; Sweeney, C.; Yacovitch, T. I.; Herndon, S. C.; Lan, X.;Lyon, D. Aircraft-based measurements of point source methaneemissions in the Barnett Shale basin. Environ. Sci. Technol. 2015, 49,7904−7913.(7) Lyon, D. R.; Zavala-Araiza, D.; Alvarez, R. A.; Harriss, R.;Palacios, V.; Lan, X.; Talbot, R.; Lavoie, T.; Shepson, P.; Yacovitch, T.I. Constructing a spatially resolved methane emission inventory for theBarnett Shale region. Environ. Sci. Technol. 2015, 49, 8147−8157.(8) Lan, X.; Talbot, R.; Laine, P.; Torres, A. Characterizing fugitivemethane emissions in the Barnett Shale area using a mobile laboratory.Environ. Sci. Technol. 2015, 49, 8139−8146.(9) McKain, K.; Down, A.; Raciti, S. M.; Budney, J.; Hutyra, L. R.;Floerchinger, C.; Herndon, S. C.; Nehrkorn, T.; Zahniser, M. S.;Jackson, R. B. Methane emissions from natural gas infrastructure anduse in the urban region of Boston, Massachusetts. Proc. Natl. Acad. Sci.U. S. A. 2015, 112, 1941−1946.(10) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990−2014; US Environmental Protection Agency, 2016.

Figure 6. Fraction of emissions detected (red bars) with differentunderlying leak-size distribution. The skewness of the distribution isdenoted by the contribution of the largest 5% of the leaks to the totalemissions (shown in parentheses). The simulation was performed at ascene and plume temperature of 300 K, and scene emissivity of 0.5.The parameters of the distributions can be found in the SI Appendix.

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