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Article
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: arvindr@stanford.edu 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
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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
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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.
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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
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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
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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
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(SI Appendix). Both of these
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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
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m/s, determined from
≥1
were
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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.
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(see
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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
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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
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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
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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
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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.
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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
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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
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Environmental Science & Technology
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
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Environmental Science & Technology
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.
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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.
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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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Graphical TOC Entry Leak Video (Frame: 400, Video: 85)
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SNR ≥ 1 V = 7±0.7 m/s
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