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Are Optical Gas Imaging Technologies Effective For Methane Leak Detection? Arvind P. Ravikumar, Jingfan Wang, and Adam R. Brandt Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03906 • Publication Date (Web): 29 Nov 2016 Downloaded from http://pubs.acs.org on December 9, 2016

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Environmental Science & Technology

Are Optical Gas Imaging Technologies Eective For Methane Leak Detection? Arvind P. Ravikumar, 1

∗

Jingfan Wang, and Adam R. Brandt

Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, CA 94305, USA E-mail: [email protected] Phone: +1-650-736-3491. Fax: +1-650-725-2099

2

Abstract

3

Concerns over mitigating methane leakage from the natural gas system have be-

4

come ever more prominent in recent years. Recently, the US Environmental Protection

5

Agency proposed regulations requiring use of optical gas imaging (OGI) technologies

6

to identify and repair leaks. In this work, we develop an open-source predictive model

7

to accurately simulate the most common OGI technology, passive infrared (IR) imag-

8

ing. The model accurately reproduces IR images of controlled methane release eld

9

experiments as well as reported minimum detection limits. We show that imaging

10

distance is the most important parameter aecting IR detection eectiveness. In a

11

simulated well-site, over 80% of emissions can be detected from an imaging distance

12

of 10 m. Also, the presence of `super-emitters' greatly enhance the eectiveness of

13

IR leak detection. The minimum detectable limits of this technology can be used to

14

selectively target `super-emitters', thereby providing a method for approximate leak-

15

rate quantication. In addition, model results show that imaging backdrop controls IR

16

imaging eectiveness: land-based detection against sky or low-emissivity backgrounds

17

have higher detection eciency compared to aerial measurements. Finally, we show 1

ACS Paragon Plus Environment


Page 2 of 23

18

that minimum IR detection thresholds can be signicantly lower for gas compositions

19

that include a signicant fraction non-methane hydrocarbons.

20

Introduction

21

Methane, a signicant component of natural-gas, is a potent greenhouse gas (GHG): its

22

global warming potential is signicantly higher than carbon dioxide, especially over short

23

time periods.

24

of total U.S. methane emissions. Mitigating these emissions would contribute signicantly

25

towards achieving GHG emissions reductions goals outlined in the Paris Agreement.

26

ing methane emissions is especially important if natural-gas-based power generation is to be

27

relied upon to ensure reliability of future grids with high fractions of intermittent renewables.

28

1

Fugitive emissions from the oil and gas industry

A growing number of studies

59

2,3

account for a quarter

4

Reduc-

have found methane emissions that are higher than

29

Environmental Protection Agency's (EPA) estimates in its GHG emissions inventory. In fact,

30

EPA has revised methane emissions estimates upward in its latest inventory.

31

studies have steadily improved our understanding of methane emissions from all sectors of the

32

natural gas supply chain production,

33

identifying and quantifying existing sources of anthropogenic emissions is dicult due to

34

the variety and spatial extent of potential leaks.

35

aircraft still nd emission rates that do not align with expected emissions rates.

5,6,11

processing,

12

transmission,

13

10

While recent

and distribution

9,14

And many regional studies made from

5,15

36

Against this backdrop of rapidly evolving science, a variety of eorts are underway to

37

solve the methane leakage problem with improved regulation and technology. The U.S. EPA,

38

in its recently released updates to the

39

requires oil and gas operators to use optical gas imaging (OGI) as part of structured leak

40

detection and repair (LDAR) program. Endorsing this technique, natural gas operators in

41

the state of Colorado recently acknowledged the cost-eectiveness of the OGI-based LDAR

42

program.

2012

New Source Performance Standards (NSPS),

17

2


16

Page 3 of 23


43

The most common OGI technology for methane detection relies on infrared (IR) imaging.

44

A commonly-used IR camera creates images of a narrow range of the mid-IR spectrum ( 3.3−

45

3.4 µm

46

advanced technologies are under active development. For example, hyperspectral imaging

47

acquires spectrally resolved images, allowing dierentiation between dierent hydrocarbon

48

gas plumes.

49

quantify leak rates of gases.

50

estimating the costs and benets of these dierent technologies.

wavelength) which methane and other light hydrocarbons actively absorb.

1921

A related technique called infrared gas-correlation,

22,23

18

More

have been used to

A recent simulation study showed a systematic approach to

24

51

While many facilities already employ OGI-based technology for leak detection, system-

52

atic scientic analysis of the performance of this technology is lacking. EPA-commissioned

53

scientic eorts

54

systematic analysis under various conditions. Part of the reason for this lack of research is

55

the relatively high price of these cameras ( ≈100k USD) and the expense of systematic eld

56

campaigns.

57

25

show a number of studies by technology developers or users, but little

To address these shortcomings we have developed an open-source model that simulates

58

methane leak detection with passive IR imaging.

59

release eld experiments, and reproduce previously published laboratory-based minimum

60

detection limits. After validation, we explore how the eciency of OGI varies with environ-

61

mental parameters like temperature and wind conditions, operator and survey parameters

62

like imaging distance and detection criterion, and nally, the characteristics of the facility.

63

Combining model results and experimental data, we provide recommendations and best-

64

practices guidelines for achieving methane mitigation goals.

65

We validate the model with controlled-

Methods

66

Evaluating OGI for methane leak detection involves modeling of three related physical sys-

67

tems - (1) Infrared absorption, ( 2) Imaging Properties, and ( 3) Leak modeling.

3


A brief


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68

description of the methodology used in this study is presented below. Detailed information

69

and other derivations of key equations can be found in the SI Appendix.

70

Infrared Absorption

71

IR absorption by gas molecules, arising from the rotational-vibrational resonances of molec-

72

ular transitions, is described by the Beer-Lambert Law,

73

absorption cross-section,

74

ted and incident light intensity, respectively.

75

mission (HITRAN) molecular absorption database

76

strengths of the molecular transitions. While the values in the HITRAN database correspond

77

to a reference temperature of

78

line-widths are temperature dependent and should be corrected accordingly (accounted for

79

in our model). The uncertainties in the spectral line-strengths in the HITRAN database is

80

signicantly smaller than the uncertainty from other operator-controlled parameters (see SI

81

Appendix). For heavier hydrocarbons like ethane and propane that often occur in natural

82

gas deposits, line intensity values are used from Pacic Northwest National Laboratory's

83

(PNNL) infrared database.

84

Imaging Properties

85

An infrared camera measures the radiant energy incident on the sensor within the camera's

86

eld of view.

87

sources - (1) direct radiance from the methane plume, ( 2) transmitted radiance from the

88

scene (background), ( 3) scene-reected cold-sky radiance, and nally, ( 4) direct atmospheric

89

radiance. Each of these radiances are in-turn a function of the temperature of the body and

90

its emissivity. At short imaging distances, atmospheric transmission can be assumed to be

91

near unity, and so direct atmospheric radiance can be neglected. Furthermore, except under

92

cases where the scene emissivity is low ( s

ρ

It I0

= e−Kα ρ ,

is the concentration pathlength, and

296

Kα

It

where

and

I0

Kα

is the molar

are the transmit-

is calculated from high-resolution trans-

26

using the spectral line-widths, and line-

K, it should be noted that both the line-intensities and

27

This radiant energy, in a leak-detection setting, comes from four principal

< 0.2),

the scene-reected atmospheric radiance

4


Page 5 of 23


93

is at least an order of magnitude larger than the transmitted radiance from the scene (see

94

SI Appendix Fig. S 4). Finally, we are left with two material contributions to the radiance

95

observed by the IR camera - the direct radiance from the plume, and the transmitted radiance

96

from the scene (see SI Appendix for full equations). This change in observed radiance,

97

with and without the methane plume can be written as,

∆Lobs (ν) = τa (1 − e−Kα ρ )(B(Tp ; ν) − s B(Ts ; ν)) where

98 99

B(T ; ν)

τa

is the atmospheric transmittivity,

ρ

∆Lobs ,

(1)

is the concentration path-length, and

is the Planck blackbody function for a body at temperature

T

and wavenumber

100

ν.

101

present in the scene of the methane leak.

102

emissivity is constant within the narrow spectral range of the lter used in the imaging pro-

103

cess (see SI Appendix). A pixel registers an intensity change if this dierence in radiance,

104

∆Lobs

105

detector sensitivity, pixel dimensions, and camera optics and is specied by the manufacturer

106

in terms of the Noise Equivalent Temperature Dierence.

107

Leak Modeling

108

The performance of OGI technology critically depends on the size distribution of the leaks.

109

We model the leaks in a typical upstream production facility (well-pads) using a set of

110

empirically measured leaks from 8 previously published studies (see SI Appendix).

111

distribution has a mean leakage of

112

to about

113

the following categories of leaks:

114

piping; (2) compressor seal vents (very large plumes from sources that are more readily

115

predictable and are easily observed from any distance); (3) leaks from abandoned wells or

Here, we note the scene emissivity corresponds to the composite emissivity of the `clutter' Furthermore, we have assumed that the scene

is greater than the Noise Equivalent Power (NEP) of the IR camera - a function of the

78% of the total

0.25

g CH4 /s, with the top

5%

≈6400 This

of the leaks contributing

leakage. For the creation of the composite leak dataset we remove (1) downstream leaks from distribution systems and

5



116

117

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other abandoned infrastructure.

Model Validation from Controlled Releases

118

We gathered observations of controlled release eld experiments of natural gas leaks near

119

Sacramento, CA in collaboration with Kairos Aerospace. The methane leaks were emitted

120

from an elevated stack of diameter

121

≈ 29

122

imaged at distances from

123

to extract research-grade non-compressed video. Videos were gathered at

124

a total of approximately

125

2.5

g/s (135 mscf/day), for a total of

cm, at rates ranging from

7

≈1

g/s (5 mscf/day) to

dierent constant emissions rates. The leaks were

10 to 60 m by a FLIR GF320 camera using the ResearchIR software

800

16

frames/sec for

frames at each ow rate (SI Appendix).

The methane plumes were dispersed by both ambient winds and a vertical velocity com-

126

ponent resulting from release of pressurized gas.

127

of plume velocity were dependent on leak size. Therefore, an eective wind-speed was used

128

in the simulation, combining horizontal wind-speed arising from local weather, and `wind-

129

speed' from the controlled release.

130

video using feature tracing methods (SI Appendix). While leaks were imaged at up to

131

m distance, a set of

132

analysis due to their relatively short imaging distances (less background interference) and

133

larger plume images (i.e., more pixel coverage).

134

further image processing and analysis; these frames are selected to avoid large wind-gusts or

135

sudden changes to wind direction.

10

The vertical and horizontal components

This eective velocity was directly calculated for each

leaks imaged from distances

20

m or

30

60

m were selected for further

For each video,

50

frames are chosen for

136

In order to compare video of leaks to our simulation results, we measure the number

137

of pixels occupied by the plume in a given frame. We employ two dierent algorithms to

138

accomplish this: (i) direct image enhancement using ltering and thresholding techniques,

139

and (ii) optical ow technique using Lucas-Kanade method

140

methods are used to generate a binary image from which pixel coverage is calculated.

28

6


(SI Appendix). Both of these

50

Page 7 of 23


141

frames are averaged to obtain statistical distributions. Figure 1(a) shows one frame from an

142

unprocessed leak video ( 10 g CH4 /s, imaged from a distance of

143

the extracted plume using direct image enhancement, and (c) using the optical ow method.

144

We can see that both methods reliably extract the plume from the videos.

145

videos are available in supplementary materials.

146

is limited only by the presence of steady wind direction to obtain meaningful averages.

147

While increasing the number of frames averaged might improve pixel count statistics, the

148

uncertainty is often dominated by instantaneous changes to wind speed and direction, and

149

approximations involving the Gaussian plume dispersion model.

(a)

Figure 1:

30

30

(c)

(b)

m from the source.

Unprocessed

It should be noted that frame average

(a) A single frame of a controlled leak corresponding to

distance of

m). Figure 1(b) shows

10

g/s imaged from a

(b) Extracted plume using direct image enhancement

techniques, and (c) Extracted plume image using Lucas-Kanade optical ow method.

150

After calculating the observed size of the plume in each frame using the above two

151

algorithms, we compare the observed size to that estimated using our simulation tool, as

152

shown in Fig.

153

dispersion model.

154

atmospheric transport models that better reproduce plume dispersion. Figure 2b is a single

155

frame from a controlled release experiment at a leak rate of

156

of

157

particle tracing methods (see SI Appendix) is used to simulate the plume as shown in the

158

bottom image (see SI Appendix). Only pixels with a signal to noise ratio (SNR)

≈ 30

2.

The model consists of an IR simulator coupled to a Gaussian plume

29

However, the IR simulator could also be coupled with more complex

m from the leak-source.

29

g/s imaged from a distance

The eective wind-velocity of

7


7

m/s, determined from

≥1

were


159

Page 8 of 23

counted as being `detected' (shown in red). Leak Video (Frame: 400, Video: 85)

(a)

Pixels = 5669

V = 7±0.7 m/s (b)

M = 29 g/s D = 29 m Ts = 306 K SNR ≥ 1 Pixels = 5574 Gaussian Plume Model Figure 2:

(a) A single frame of a controlled leak corresponding to

29

g/s imaged from a

29 m from the source. The arrow shows the eective velocity ( V¯ ) of discharge, at 7 m/s. (b) Simulation of the plume in (a) using a Gaussian dispersion model.

distance of estimated

M denotes the mass ow rate, D is the imaging distance, T s is the scene temperature and SNR refers to the signal to noise ratio. We achieve good agreement in pixel coverage between experiments and simulation. (The gure has been rotated clockwise to improve clarity)

Figure 3 compares observed pixel coverage for direct image enhancement (green) and

160 161

optical ow (red) to the simulated coverage for each video (blue range).

162

from about

163

≈

164

50

165

velocities distributed

166

associated with each point estimate corresponds to the leak rate (g CH 4 /s) and distance

167

(m). The videos, frame numbers, and information on imaging distance and leak rates are

168

given in Table S 3. We see that simulated pixel coverage values are in good agreement with

169

experimental data, with both simulation and observations having errors of order 500 to 1000

170

pixels and generally overlapping error bars. This suggests that our model can numerically

171

estimate the leak detection capabilities of IR imaging. In addition to these controlled eld

172

release experiments, we also used the model to reproduce previously published minimum

173

detection limits measured under laboratory conditions as reported by Benson et al.

174

SI Appendix).

21

g/s.

1600

pixels for a leak size of

≈ 1

g/s to over

8000

Coverage varies

pixels for a leak size of

The error bars on experimental symbols are max-min ranges derived from

frames for each video.

The blue range is generated using 100 simulations with wind

±10%

around the video-derived eective wind speed.

8


The numbers

30

(see

Page 9 of 23


Number of Pixels (Video Analysis)

10000

8000

(21.5,20) (16.1,28)

6000

(16.1,20) (21.5,20) (29,28)

(2,2.20)

4000 (10.8,20) (1.1,20)

2000

(10.8,28) (1,1.30)

0 0

Figure 3:

(21.5,28) Simulated Pixels Expt. Pixels (Frame enhancement) Expt. Pixels (Optical flow method)

2000 4000 6000 8000 Number of Pixels (Simulation)

10000

Parity chart of simulated pixel coverage using the model developed here, with

error bars arising from the range of eective wind velocities used in the simulation (blue shaded region), and extracted pixel coverage values using direct image enhancement (solid green triangles), and optical ow (open red squares) methods. The numbers associated with the data points correspond to the leak rate (in g/s) and measurement distsance (m).

175

Model Results and Recommendations

176

After model validation, we use the simulator to explore the ecacy of leak detection us-

177

ing previously reported empirical leak distributions and a simple gaussian plume dispersion

178

model (SI Appendix). The IR camera properties used in this model are similar to that of the

179

FLIR GasFind-IR cameras, a commonly used model in industry and the model used above

180

in eld verication (GF 320 series, see SI Appendix Table S 1). A lateral cross-section of the

181

plume, parallel to the center-line, is simulated at distances ranging from

182

from the leak source. Atmospheric transmission is assumed to be

183

and wind-speeds and associated atmospheric stability classes (see SI Appendix) have been

184

randomized based on empirical distributions suggested by ARPA-E in the MONITOR chal-

185

lenge.

186

and compared to the NEP of the camera. A plume is considered detected if more than 400

187

pixels have a SNR

188

width and height). It should be noted that the coverage criterion of

189

is approximate and depends on the visual acuity of the camera operator. Dierences in this

31

0.9

10 m to 200 m away

in these simulations,

For each leak, the change in observed radiant power is calculated for every pixel

≥1

(e.g., a square of size 20 by 20 pixels, or about 10% of viewnder

9


400

pixels for detection


190

value will obviously aect detection eciency, dened as the fraction of emissions detected

191

(SI Appendix). It should be noted that imaging leaks parallel to the plume center-line can be

192

easily accomplished with a rotation of the camera. However, imaging at angle will reduce the

193

concentration-path length and hence, the detection eectiveness. This is further discussed

194

in the SI Appendix. Each simulation run presented below consists of

195

≈ 6400

100

leaks drawn randomly from an

196

empirical dataset of

197

(Methods, and SI Appendix). The fraction of total leakage detected and minimum detectable

198

leak rates (MDLR) are identied in each case. To ensure statistical representativeness, a total

199

of

200

Imaging distance is the most important parameter for eective leak

201

detection

202

Figure 4 shows a grid (A through L) of normalized color maps of the fraction of total

203

leakage detected as a function of imaging distance (vertical grids) and background emissivity

204

(horizontal grids). Warmer colors represent a larger fraction of gas detected. Each color-map

205

in turn shows variations over a range of scene temperatures (x-axis) and plume temperature

206

deviation (y-axis), with a positive deviation reecting a plume that is hotter than the scene

207

temperature. Consider an emissivity of

208

eciency for a given plume and scene temperature decreases with increasing imaging distance

209

- at

210

about

211

inversely proportional to the square of imaging distance, resulting in a progressive reduction

212

in eciency. Eective leak detection requires short imaging distances.

50

previously measured leaks at upstream natural gas facilities

simulations per scenario gives statistical distributions.

10

m (grid A), approximately

90%

0.1 (rst-column, grids A through D). The detection

of emissions are detected (on-average), dropping to

40% at 200 m (grid D). For a given leak, the number of pixels occupied by the plume is

10


Page 10 of 23

Page 11 of 23


213

Measurement against sky tends to improve detection

214

For a given scene temperature ( Ts

215

detected increases from under

216

over

217

portional to temperature, a hotter plume emits more infrared radiation compared to the

218

relatively cooler scene (B(T p )>B(Ts )), thereby increasing the change in radiance observed

219

by the camera. Although plume temperature cannot be controlled by the operator, this in-

220

creased detection capability from temperature contrast is directly applicable to leak detection

221

surveys. The temperature of the atmosphere, as imaged by an infrared camera, is typically

222

20 to 50 ◦ C cooler than surface temperatures due to the energy balance between the Sun and

223

the Earth. This cooler atmospheric radiance is often termed as `cold-sky radiation'. As a

224

result, images of leaks taken from the ground-up with the cold-sky radiation as background

225

tend to have higher contrast than images taken looking down (as from a low-ying aircraft

226

or helicopter), since surface temperatures are closer to plume temperature. This eect has

227

been observed in recent aerial surveys,

228

It should be noted however that the water-vapor (humidity) will reduce atmospheric trans-

229

mission and consequently, detection eciency. The eect of humidity will be enhanced at

230

longer imaging distances (see SI Appendix for detailed discussion).

231

Warmer days are preferable for improved detection eectiveness

232

It is preferable to conduct leak detection survey under higher scene temperature conditions

233

(warm days), although it should be noted that this does not signicantly aect detection

234

eectiveness compared to other factors discussed above. Under all measurement conditions

235

(A through L), we can observe an

236

detected as the scene temperature increases from

237

contrast. The radiance contrast,

238

where

60%

when it hotter by

20

10%

= 300

K, grid D), we note that fraction of emissions

when the plume is

10

degrees cooler than the scene to

degrees. This is because radiant emissions are directly pro-

3

which exhibit high minimum detectable leak rates.

≈ 10

∆Lobs ,

percentage point increase in fraction of emissions

270

K to

can be written as,

310

K, for a given temperature

∆Lobs = B(Ts + ∆T ) − s B(Ts ),

∆T = Tp −Ts is the temperature contrast, Tp is the plume temperature, Ts is the scene 11



0.5

−20 A −10 0 10 20

E

I

−20 B −10 0 10 20

F

J

−20 C −10 0 10 20

G

K

H

L

100 m 50 m ∆T (plume - ambient) (K)

10 m

0.1

0.9 Emissivity 1

0 1

0 1

0 1

200 m

−20 D −10 0 10 20 Distance 270

Page 12 of 23

Figure 4:

290

310 270 290 310 270 Scene Temperature (Ts)

290

310

0

Color maps of the fraction of total leakage detected as a function of imaging

distance (horizontal grid-axis), and scene emissivity (vertical grid-axis). Each color-map in turn shows variation over a range of scene temperatures (x-axis) and plume temperature deviations (y-axis).

Best practices for maximum leak detection eciency include a short

imaging distance, higher scene temperatures, and large temperature contrast between the plume and the scene.

Of all factors, we can see that imaging distance strongly aects

detection capabilities.

s

is the scene emissivity, and

B(T )

239

temperature,

is the spectral Planck body radiation. To

240

rst order, this can be approximated as

241

Ts

242

than the radiance from the scene ( B(Ts )) as

243

Low emissive scenes provide better contrast than high emissive scenes

244

Emissivity of the scene plays an important role in improving contrast, especially when there

245

is little to no temperature contrast between the plume and the scene. Such situations are

246

fairly typical when the plume is in thermal equilibrium with the surroundings, except in

∆Lobs ≈ B(Ts )(1 +

∆T Ts

− s ),

increases. In physical terms, the radiance from the plume ( B(Ts

Ts

which increases as

+ ∆T ))

increases faster

increases, thereby improving contrast.

12


Page 13 of 23


247

cases like extreme winter conditions or high pressure exhausts. To maximize the change in

248

observed radiance, lower values for emissivity are preferable for

249

and K in Fig. 4, we see that the fraction of emissions detected decreases from about

250

to

251

low-emissivity backgrounds correspond to reective metallic surfaces, while high emissivity

252

background could be soil, forests, etc. It should be noted that scene-reected atmospheric

253

radiance cannot be neglected if the scene emissivity is low ( s

254

temperature-emissivity contrast, which reduces detection eciency faster than expected from

255

models that neglect reected radiance (SI Appendix).

256

colder than the surroundings where

257

A highly emissive background increases radiation incident on the camera compared to the

258

relatively cooler plume, thereby increasing contrast. This can be clearly seen by comparing

259

grids C and K, where plumes that are colder by

260

only when the emissivity is high (grid K), while they are undetected under low emissivity

261

conditions (grid C). In a typical facility, operators will encounter scenes of mixed emissivity.

262

In such cases, the number of pixels on the camera where a plume is registered will be lower

263

compared to scenes with constant emissivity. In particular, we can see that high-emissive

264

objects in the background of a leak will tend to reduce contrast.

265

OGI provides approximate leak quantication to selectively target

266

the biggest leaks

267

A useful metric from the standpoint of leak detection surveys is the minimum detectable

268

leak rate (MDLR) - a low MDLR may result in the detection of a large number of small

269

leaks, while a large MDLR may not detect large leaks.

270

to design surveys will depend on a working knowledge of technology limitations. Figure 5

271

shows the MDLR as a function of operating distance at a scene temperature of

272

a scene emissivity of

40%

as scene emissivity increases from

0.5,

0.1

to

0.9 (∆T = 10

Tp ≥ Ts .

K,

Ts = 290

≤ 0.2)

From grid C,G,

70%

K). In practice,

- this decreases the

At the other extreme, for plumes

B(Tp ) < B(Ts ), maximum contrast is reached for s = 1.

20 K compared to the scene can be detected

An appropriate balance required

310

K and

for three dierent temperature contrasts between the plume and

13



Page 14 of 23

273

the scene. For a plume in thermal equilibrium with its surroundings ( Tp

274

varies from about

275

dynamic range in detection threshold results in leakage detection of about

276

under

277

conditions, the minimum leak-size detected can be approximately quantied by choosing an

278

imaging distance.

279

At

5%

10

at

200

1

g/s at

10

m to about

20

g/s at

200

= Ts ),

the MDLR

m imaging distance. Such a large

70%

at

10

m, to

m (grid A through D in Fig. 4). Therefore, given a set of environmental

m imaging distance, an MDLR of about

5%

2

g/s allows us to remove

70%

of the

280

volume of leaked gas by only xing some

281

portance of considering leak size distributions when designing rules for acceptable imaging

282

distance.

283

design an eective MLDR can be used to selectively target the `super-emitters' dened by a

284

predetermined leak rate cut-o.

285

of the leaks. This clearly illustrates the im-

Because of the strong dependence of MDLR on distance, choosing distance to

50

The inset of Fig. 5 shows the MDLR at

7

270

K, to about

3

g/s at

310

286

temperature, going from about

287

only a factor of

288

in determining the eectiveness of OGI based leak detection techniques compared to scene

289

temperatures. Any policy that does not specify a maximum operating distance for infrared

290

imaging will result in a wide variety in reported leak rates.

291

`Super-emitters' greatly enhance the eectiveness of OGI based mit-

292

igation strategies

293

It is important to re-emphasize the crucial dierence between the fraction of emissions de-

294

tected from the fraction of total leaks found using OGI technology. Under a policy scenario

295

where every detected leak has to be repaired, the immediate concern becomes: is there an

296

MDLR that could result in the detection of a large number of inconsequential leaks? Let us

297

consider the best case scenario represented in this study - grid A in Fig. 4. At a temperature

298

contrast of

2.

g/s at

m imaging distance as a function of scene K, a reduction of

Thus, for practical applications, imaging distance plays a stronger role

0 K with an MDLR of about 0.5 g/s, OGI technology detects approximately 85% 14



Ts = 310 K

1 10

ΔT = +10 K

ΔT = +20 K 8

0 10

6

εs = 0.1 d = 50 m

4

ΔT = 0 K 2 270 290 310 Ambient Temperature (K)

−1 10 0

Figure 5:

ΔT = 0 K

εs = 0.5

MDLR (g/s)

Minimum Detectable Leak Rate (g/s)

Page 15 of 23

50

100 150 Distance (m)

200

Minimum Detectable Leak Rate (MDLR) as a function of imaging distance at a

300 K, and an emissivity of 0.5 for three dierent temperature contrasts: 0 K (red), 10 K (blue), and 20 K (green). The MDLR increases by about 25 times across the scene temperature of

range of distances considered, resulting in signicant change in detection eciency. (inset) MDLR as a function of scene temperature at

50

m and

∆T= 0.

Note that MDLR is more

strongly dependent on imaging distance than scene temperatures.

10%

299

of the total emissions, which corresponds to only the largest

of the leaks. Under less

300

favorable imaging conditions, the number of leaks detected will be well below

301

total leaks in the facility under study.

10%

of the

For leak size distributions that are less heavy-tailed compared to the empirical distribu-

302 303

tion used in this study, the fraction of emissions detected by OGI drops signicantly.

304

illustrate, Fig. 6 shows the fraction of emissions detected from a facility with dierent un-

305

derlying leak-size distribution. Each distribution has a mean leakage of

306

fugitive emissions contribution from the top

307

to

308

detected drops from about

309

should consider the underlying characteristics of the facility or basin to estimate costs and

310

expected emissions reductions.

6%

0.22

To

g/s, with total

5% of the leaks varying from 75% (most skewed)

(least skewed, see SI Appendix). Correspondingly, we see that the fraction of leakage

70%

to under

5%.

This shows that any leak mitigation policy

15


Fraction of Emissions Detected


0.8

Top 5% leaks = 75% emissions (5-75)

0.7 (5-62) (5-50)

0.6 0.5 0.4

(5-37)

0.3 (5-22)

0.2

(5-11)

0.1 0

Figure 6:

Page 16 of 23

(5-6) 1

2 3 4 5 6 7 Leak Size Distribution (see Table S4)

Fraction of emissions detected (red bars) with dierent underlying leak-size dis-

tribution. The skewness of the distribution is denoted by the contribution of the largest

5%

of the leaks to the total emissions (shown in parenthesis). The simulation was performed at a scene and plume temperature of

300 K, and scene emissivity of 0.5.

The parameters of the

distributions can be found in the SI Appendix

311

Minimum detection threshold can be up to

312

wet-gas compared to dry-gas compositions

313

Oil and gas operations often result in leakage that contains a mixture of gases, most notably

314

higher molecular weight hydrocarbons like ethane and propane.

315

the Bakken

316

facilities. Because of structural similarity of methane and other hydrocarbons, heavier hy-

317

drocarbons like ethane and propane also absorb in the

318

Model results suggest that the MDLR for a pure propane stream ( 0.7 g/s) is about

319

smaller than that of a pure methane leak ( 2.7 g/s). This has been cited as a possible reason

320

for elevated fugitive emissions seen in some aerial measurements.

321

typical non-methane hydrocarbon composition ( 2−20%) aects emissions detection in a nat-

322

ural gas-eld can be found in the SI Appendix. Field measurements using OGI technology

323

at facilities where non-methane hydrocarbons are known to occur in signicant quantities

324

should be carefully calibrated to reduce the eect of false positives.

325 326

32

3−4

times lower for

Recent measurements in

indicate elevated ethane concentration in the atmosphere around oil and gas

3.2 − 3.4 µm region of the IR camera.

3

4

times

Further details on how

In this paper, we have developed a systematic model to assess the eectiveness of OGI technologies for methane leak detection in the oil and gas industry.

16


Pixel coverage mea-

Page 17 of 23


327

sured from controlled release eld experiments agree well with model predictions.

Based

328

on experimental data and model results, we conclude with the most important take-aways:

329

(1) Short imaging distances are crucial for eective leak detection, ( 2) Land-based IR imag-

330

ing typically provides better contrast than aerial imaging, ( 3) OGI based surveys provide

331

pseudo-quantication to target the biggest leaks, thereby providing a way to x potential

332

`super-emitters', and nally ( 4) detection limits will vary signicantly based on the gas com-

333

position being observed.

334

Such predictive capability for the eciency of leak detection using IR cameras can be used

335

by businesses and policy makers to develop smart leak detection protocols. The model can

336

also be tailored to specic facilities and leak size distributions, helping businesses to assess

337

this technology without undertaking expensive trials. Because pixel coverage identication

338

is based on simple and rapid algorithms, this tool can be further developed to provide real-

339

time information on leak quantication. In light of the recently proposed EPA standards for

340

use of OGI techniques for leak detection, it becomes crucial to be able to identify the right

341

conditions to render such regulations eective.

342

Future work will involve coupling the infrared imaging simulator with more complex

343

plume dispersion models that describe the turbulent ow of gases around complex surface

344

features.

345

Acknowledgement

346

We acknowledge the assistance of Kairos Aerospace for allowing collaboration and mea-

347

surement on controlled release events. We also thank Jacob Englander for help with eld

348

experiments.

17



349

Page 18 of 23

Supporting Information Available

350

Simulation code, controlled release leak-videos and additional analyzes and supporting in-

351

formation.

352

353

This material is available free of charge via the Internet at

http://pubs.acs.org/ .

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Are Optical Gas Imaging Technologies Effective ... - ACS Publications

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