Airborne Methane Emission Measurements for Selected Oil and Gas

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Airborne Methane Emission Measurements for Selected Oil and Gas Facilities Across California Shobhit Mehrotra, Ian C. Faloona, Maxime Suard, Stephen A. Conley, and Marc L. Fischer Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03254 • Publication Date (Web): 11 Oct 2017 Downloaded from http://pubs.acs.org on October 12, 2017

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Environmental Science & Technology

Airborne Methane Emission Measurements for Selected Oil and Gas Facilities Across California Shobhit Mehrotra1, Ian Faloona1, Maxime Suard1, Stephen Conley1,2, and Marc L. Fischer3,4* 1

Land Air and Water Resources, University of California, Davis, California 95616

2

Scientific Aviation, Inc., Boulder, Colorado 80301

3

Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720 4

Air Quality Research Center, University of California, Davis, California, 95616

ABSTRACT

We report 65 individual measurements of methane emissions from 24 oil & gas facilities across California. Methane emission rates were estimated using in-situ methane and wind velocity measurements from a small aircraft by a novel Gauss' Theorem flux integral approach. The estimates are compared with annual mean emissions reported to the US-EPA and the California Air Resources Board (CARB) through their respective greenhouse gas reporting programs. The average emissions from 36 measurements of 10 gas storage facilities were within a factor of 2 of emissions reported to US-EPA or CARB, though large variance was observed and the reporting database did not contain all of the facilities. In contrast, average emissions from 15 measurements of the three refineries were roughly an order of magnitude more than reported to

ACS Paragon Plus Environment


the US-EPA or CARB. The remaining measurements suggest compressor emissions are variable and perhaps slightly larger than reported, and emissions from one oil production facility were roughly concordant with a separate (not GHG reporting) bottom-up estimate from other work. Together, these results provide an initial facility-specific survey of methane emissions from California oil and natural gas infrastructure with observed variability suggesting the need for expanded measurements in the future.

* Corresponding Author Marc L. Fischer; Phone (510)-486-5539; Email ([email protected])


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1. INTRODUCTION 1

US Natural Gas (NG) consumption has increased 70% from 16.2 to 27.5 trillion cubic feet per

2

year from 1986 to 2016 [1]. NG is composed of approximately 80-90% methane (CH4), a very

3

potent greenhouse gas (GHG), which has a global warming potential (GWP) of 34 and 86 times

4

that of CO2 on a 100- and 20-year time scales, respectively [2]. Therefore production, storage,

5

transport, and consumption of NG must be carefully monitored to ensure that methane is not lost

6

to the atmosphere, undercutting its environmental benefit as a relatively clean energy source. For

7

example, the above GWP of 86 (gCO2eq/gCH4) suggests that if 3.3% of CH4 is emitted

8

unburned, it will produce warming roughly equivalent to the remaining 97% of CH4 carbon that

9

is combusted to CO2 on a 20 year timescale. Similarly, it has also been estimated that if more

10

than 3.2% of NG is lost before combustion, the global warming impact would be the same as an

11

equivalent output from coal powered plants [3].

12

According to the annual report of Greenhouse Gas Emissions and Sinks released by the US

13

Environmental Protection Agency (US-EPA), methane emissions from oil and gas operations

14

account for about 6% and 25%, respectively, of the national methane emissions inventory [4]. In

15

California, most natural gas is imported and the Air Resources Board (CARB) estimates oil and

16

gas production, processing, transmission, and distribution contribute a total of only 16% of the

17

state's methane emissions [5], with the bulk of this (~10%) coming from the natural gas

18

transmission and distribution system. However, statewide methane emissions might be expected

19

to grow as demand for power generation from NG climbed 30% from 2001 to 2012 [6]. Here,

20

roughly 90% of the natural gas used in California is imported to the state (as of 2013), primarily

21

from the Anardarko and Permian Basins in Texas (38%), the Rocky Mountain Basin (36%), and

22

the Western Canadian Sedimentary Basin (16%) [7,8].



23

In California, total natural gas storage has increased about 24% in the last 25 years as shown in

24

Figure 1 [9]. In addition, there are also both large seasonal variations and smaller inter-annual

25

variations. Seasonally, NG storage typically peaks at the end of the summer when NG utilization

26

and typically reaches a minimum sometime in March following larger winter demand for space

27

heating drops. In the context of this work, it is worth noting that storage facility emissions might

28

depend more on the amount of stored gas (e.g., more emissions at higher pressures) or on the rate

29

at which gas is injected or removed (e.g., leaks in gas handling infrastructure).

California Natural Gas Methane Underground Storage Mass Mass Stored Methane (Gg)

12,000 10,000 8,000 6,000 4,000 2,000 0 Jan-1990

Jan-1994

Jan-1998

Jan-2002

Jan-2006

Jan-2010

Jan-2014

Year

Figure 1. The monthly total mass storage of NG methane in California (assuming 95% CH4 composition.) The black curve is a 12-month running average. Despite a large seasonal variation it is clear that storage masses have been increasing at about 1.5% per annum (~120 Gg/yr) since the turn of the century [1]. 30

Previous work has measured methane emissions from natural gas related facilities, with three

31

studies particularly relevant to our current work. First, a study of methane emissions from 45 US


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32

compressor stations (all outside California), found that roughly 10% of the stations emitted 50%

33

of the methane emissions and the lowest 50% of emitting sites only contributed to 10% of the

34

emissions, citing equipment malfunctions (e.g., leaky isolation valves and compressor vents) as

35

major fugitive leak sources. [10]. Second, a study of three refineries (in Utah, Indiana, and

36

Illinois), average emissions ranged from 360-830 kg/hr, as compared with a range of 4 to 51

37

kg/hr reported by industry to the US-EPA [11]. Third, a natural gas injection and recovery well

38

at the Aliso Canyon gas storage facility failed in November, 2015, releasing approximately 97

39

Gg CH4 before being repaired in February, 2016 [11], equivalent to roughly half of total annual

40

oil and gas related methane emissions (~ 220 Gg CH4/yr) estimated by the California Air

41

Resources Board (CARB) [5].

42

In this study, we describe facility level measurements of CH4 emissions for a subset of natural

43

gas infrastructure in California including gas storage facilities, petroleum refineries (which use

44

NG for power), an oil and gas production field, a gas fired power plant, and several natural gas

45

processing or compression stations. We begin with a methods section describing sites we

46

observed and airborne instrumentation, the mass-balance approach to estimate emissions and

47

sources of uncertainty in the estimates. In results, we describe the emissions estimated for the

48

different facilities and, where we collected multiple measurements over time, estimates of the

49

mean emissions and their variability. As part of the results and discussion, we also compare

50

measured emissions with annual average emissions reported by industry to the US-EPA and

51

CARB for a subset of the facilities, though note that the facility reported emissions should be

52

distinguished from official inventories for national or state level emissions. 2. MEASUREMENT SITES and METHODS



53

2.1 Measurement Sites Observed in this Study

54

A total of 24 facilities were measured in the course of this airborne study on 34 individual days

55

spanning 27 months from June, 2014 to September, 2016 (see table S1). Eight storage facilities,

56

three refineries, and seven compressor stations were measured for methane and ethane (after

57

September, 2014). Ethane measurements provide not only an unambiguous marker of fossil fuel

58

sources, but further establish a quantitative measure of the ratio of ethane to methane that can be

59

compared with pipeline gas supplied by utilities for regional source apportionment. McDonald

60

storage, Rodeo refinery, and Kirby storage which were measured eleven, seven, and seven times

61

respectively, have enough data sets that we can begin to estimate variations over time. These

62

facilities use (storage facilities may use gas to power compressor engines) and emit natural gas

63

during their operation, making them of key interest when attempting to quantify GHG emissions

64

from California’s natural gas infrastructure. The facilities were sampled on randomly chosen

65

days (during favorable meteorological conditions), without communication with facility

66

operators. As such, we are not aware of site status or operations conditions at the time of

67

sampling. In addition, we measured the Elk Hills and Los Medanos power plants and the

68

Belridge South petroleum production field in the Southernwestern San Joaquin valley, though

69

note that the Los Medanos plant is co-located with underground gas storage. As described below,

70

while Belridge South was sampled three times, two samples were considered incomplete due to

71

cloud cover limiting the aircraft's ability to climb (11/3/15), and another with very low and

72

meandering wind (6/17/14). Therefore, we report only one complete sampling of Belridge South

73

and one for Elk Hills power station, preventing us from making any assessment of variability in

74

emissions for those sites. 2.2 Cylindrical Mass-balance Emission Measurements.


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75


To quantify facility emissions a cylindrical flight pattern was employed to measure gas

76

concentration gradients and wind velocities and then calculated facility emissions as the

77

divergence of mass flux within the flight cylinder using Gauss’s Theorem as described by

78

Conley et al. [12]. Using this mass balance (or conservative scalar budget) approach, the

79

emissions from a site, E, can be expressed as: 1 =

′ ∙

80

where the outer integral represents the vertical extent of the cylindrical flight pattern which

81

extends from the lowest safe altitude, zmin, to the maximum flight altitude, zmax, where there is no

82

indication of a plume crossing. Due to FAA regulations, the minimum safe altitude the aircraft

83

can be flown is 500 feet above ground level (AGL) or approximately 150 m. As of October

84

2015, special permission was granted to Scientific Aviation, Inc. by the FAA to fly as low as 200

85

feet (60 m) AGL over unpopulated areas. This lower limit allows a greater altitude range of

86

measurements which improves accuracy and reduces error as discussed in Section 2.3. The

87

integrand in Eq. 1 is the path integral representing the dot product of the horizontal advective

88

. Here c' represents the fluctuating methane flux, c'uh, and the vector normal of the flight path,

89

or ethane density multiplied by uh, the horizontal wind vector. Details of the method are

90

explicated in Conley et al. [12]. The benefit of using closed loops as opposed to traditional

91

horizontal transects is that emissions can be assumed to be coming from a confined regional

92

source, therefore individual wells, refineries, and storage facilities can be accurately quantified in

93

a manner that minimizes confusion with other surrounding sources. When calculating emissions,

94

a flux divergence profile is generated from altitude binned averages of each flight loop around

95

the source, and the total emission rate is calculated as the integral under this curve (Eq. 1). In



96

order to average over natural turbulent variability of each loop's flux divergence, the

97

measurements are first averaged in altitude bins of approximately 100 m depth. The orange

98

diamonds shown in Figure 2 represent each loop flown and the blue circles represent a binned

99

average that consists of loops in a specified altitude segment. The bottom altitude bin is then

100

extrapolated to the ground with a vertical line, which is shown to be sufficiently accurate by

101

Conley et al. [12] in a certain range of distances downwind of the source. Further discussion of

102

the errors associated with this extrapolation are taken up in Section 2.3.

103

Figure 2. (left) Flux divergence profiles that are created from each loop's flight data (orange

104

diamonds) to estimate the total emission strength and its associated error. Solid blue circles are

105

the binned averages, and the light blue line extending down from the lowest point is the

106

extrapolated flux divergence representing the “ground contribution term”, which is assumed

107

based on LES simulations. (right) The standard deviation measured on each loop around the

108

source. The data is from the McDonald Storage Facility May 18th, 2016.


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109


The aircraft and instrumentation utilized for this study is described in detail by Conley et

110

al., [12] and reviewed briefly here. The aircraft is a Mooney TLS M20 high performance single

111

engine aircraft modified for air sampling. Primary instruments used for this campaign were the

112

Picarro methane and carbon dioxide cavity ring down spectrometer and an Aerodyne ethane gas

113

analyzer. The Picarro samples the air every 0.55 seconds and has a lag time of 9 to 12 seconds,

114

where the Aerodyne samples the air every 0.81 seconds and has a lag time of 5 to 9 seconds. The

115

lag time depends primarily on the size of the inlet lines and the sampling flow rate of the air.

116

This was verified routinely by performing “breath tests,” where a person would abruptly blow

117

into the inlet and the delay in instrument response would be timed. The tubes were modified

118

once during the campaign which altered the lag time and was considered when making estimates.

119

Data from the gas analyzers were interpolated onto a GPS time stamp for analysis. True wind

120

velocities in the meridional and zonal directions are computed from the difference of the aircraft

121

velocity relative to the ground (from the GPS), and the velocity of the aircraft relative to

122

surrounding air (measured with pressure sensors) [12].

123

2.3 Uncertainty Estimation

124

In order to verify the accuracy of the cylindrical mass balance method, controlled releases

125

of known source strength were sampled on several occasions and the analysis is reported by

126

Conley et. al. [12]. From those tests accuracies of the measurements are thought to be 15% or

127

better with an average accuracy of 6%. We note that the controlled release experiments were

128

generally sampled under optimal conditions, and took place over the course of the field results

129

discussed here while continual improvements were being made to the sampling technique. Thus,



130

the average error representative of the estimates reported in this study are likely greater than the

131

results of the controlled releases and reported in Conley et al. [12]. In this paper, we estimate the

132

uncertainty under less ideal flight conditions, and include parameterization for 1) the number of

133

loops flown, 2) the proximity to the source, 3) the variability of the wind during the observation,

134

and 4) a binning error introduced by estimating emissions in a finite set of altitude intervals.

135

Here, the binning error is estimated by varying the number of bins and examining the variation in

136

total emissions for the different number of bins chosen. Figure S1 shows an example of the

137

derived emission estimate as a function of number of bins derived from a controlled release, and

138

shows a standard deviation of approximately 10% of the mean due to different binning.

139

First, we turn to the uncertainty due to not capturing horizontal efflux below the lowest

140

leg of the aircraft profile. In their detailed explanation of the airborne measurement method,

141

Conley et al. [12] show theoretical flux divergence profiles from LES simulations that indicate a

142

nearly constant flux divergence below the lowest altitude leg but only when the distance at which

143

the plume is sampled (in terms of advection time, x/U, distance divided by mean horizontal wind

144

speed) is approximately one-half the large eddy turnover time (zi/w*), where zi is the atmospheric

145

boundary layer (ABL) depth and w* is the convective velocity scale, an estimate of vertical wind

146

vigor in turbulent boundary layers dominated by convective forcing based on the surface

147

buoyancy flux). A non-dimensional distance (X) is derived to normalize the physical distance

148

from the source with the mean wind speed encountered while sampling. This allows us to more

149

accurately determine the fraction of the plume that slipped under the lowest flight leg. 2 =

∗


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150

While the theoretical treatment in Conley et al. [12] shows variations in the slopes of the

151

near surface flux divergence profiles, we do not try to incorporate deviations from the constant

152

vertical extrapolation based on the sampling distance downwind of the source in our error

153

estimates here because the difference appears to be usually less than ~15%, and the average non-

154

dimension downwind distance of 0.56 ± 0.36 is close to 0.45, the downwind distance where

155

vertical extrapolation is considered ideal. Nevertheless, the fraction of the plume that has to be

156

inferred below the minimum flight altitude is a function of how close the downwind leg is to the

157

source, and has some bearing on the uncertainty of the emission estimate. This source of error is

158

parameterized by a fraction of the emissions that are below the lowest leg, FB, scaled by the

159

complement of a normal distribution centered at the optimal non-dimensional distance

160

downwind of the source (X≈0.45), with expected variance of the form

161

(3)

162

where the vertical extrapolation to the surface is found to be most accurate [12]. In short, the

163

fraction below can be estimated with a high degree of certainty if the non-dimensional distance is

164

close to 0.45, but this was not explicitly understood throughout the period of this study and our

165

parameterization is a simple attempt to conservatively estimate this sampling shortcoming.

166

/

1/3"1 − $% ∗ & '|)'*.,-| . ,

Second, we consider how our error varies with the number of loops flown around each

167

facility. Based on the analysis of flight data around controlled releases and large eddy

168

simulations Conley et al. [12] show that estimates asymptotically approach actual values and are

169

within 15-20% after approximately 20 sampling flight loops. In order to capture the general

170

behavior of this sampling limitation we use a simple logistic curve that approximates the

171

diminishing variability of the estimates as the number of loops, NL, increases, saturating after



172

25-30 flight loops, as additional loops do not appear to improve the error estimate significantly.

173

The parameters in the logistic function such as the offset of 8 loops and the multiplier of 0.15

174

were determined 'by eye' from the controlled release data and were chosen to generate a

175

conservative estimate of the error variance associated with this under sampling of the source of

176

the form

1

/ .

177

(4)

178

The third source of uncertainty we include is that due to the lack of consistency of the

123 45.67∗894:

179

wind velocity during the observations. Here, we define a dimensionless wind consistency

180

parameter, WC, as the ratio of the vector mean wind speed to the average wind speed during the

181

flight sampling [12].

(5)

WC =

;∑= > 2∑? > ∑ @=> 2?>

where, ui and vi are the instantaneous meridional and zonal winds speeds respectively. This indicates the degree of variability in the wind direction as described by Stewart et. al [13]. Ideal winds are considered to blow consistently from the same direction during the sampling (which would yield a wind consistency value of 1.) It is generally seen that stronger winds yield a higher wind consistency and this, in principle, leads to greater precision in the emission estimates. For most cases the wind consistency parameter was above 0.8 and thus we do not expect that this was a dominant source of error in the measurements presented herein. 182 183

Taking these three terms together we estimate a generic quality factor for each emission estimate based on the sampling conditions:


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(6)

1

1

1

1

Q = @A 1 − $% ∗ & '|)'*.,-| / + A 123 45.67∗894: / + A CD /

where, Q = Quality Factor WC = Wind consistency FB = Fraction Below (fraction of estimate measured when extrapolating to the surface) X = mean non-dimensional distance from the source LN = complete loops flown around the site 184

Beyond the sampling characteristics, there is an inherent variability in the method that

185

arises from the arbitrary number of altitude bins into which loops are averaged before evaluating

186

the flux integral of Eq. 1. Emission estimates vary 11% on average, depending on the exact

187

number of altitude bins, and we include this variability into the error estimates. This was

188

determined by taking the standard deviation of the emission estimates when varying the number

189

of altitude bins from two to the number of loops flown for all of the data gathered for each

190

facility during this campaign. The binning error (BE) is then averaged to derive the mean

191

emission estimate and the relative standard deviation of this mean is added in quadrature with (1-

192

Q), an approximate measure of the relative error induced by inadequacies of sampling, to

193

determine the overall relative error estimate.

(7)

TC = E;%/ + 1 − F/ G × ||

where,



TC = total uncertainty E = emission estimate 194 195

3. RESULTS Here, we report emission estimates for the facility observations. Individual

196

measurements of methane emissions together with 1 sigma uncertainties from Eq. 7 are reported

197

in Table S-2, and an example image of a cylindrical observation shown in Figure 3. Summary

198

statistics for methane emissions (averages and standard deviations) are then reported for

199

individual facilities in Tables 1, 2, 3. Here, average emissions from some facilities were similar

200

to 2015 emissions reported by industry to the US-EPA Greenhouse Gas Reporting Program [14]

201

and/or the California Air Resources Board Reporting Program [15], while others emitted more

202

during the observation periods than the average annual rates reported.

203

3.1 Storage Facilities. Overall the results indicated a great amount of variability in

204

emissions relative to documented estimates. As shown in Table 1, emissions were reported to the

205

US-EPA and CARB for 3 and 7 facilities, respectively. The largest storage facility of the

206

campaign (82 Bcf working capacity), McDonald Island was visited 11 times and displayed

207

irregular statistically significant variations in emissions. On May 12th, 2015 McDonald was

208

emitting 22 ± 14 kg/hr then a few hours later it was measured at 93 ± 34 kg/hr, while the next

209

day we measured 187 ± 48 kg/hr. Despite the large variance (roughly 50% of the average)

210

observed across all the samples, average emissions were 2.6 times higher than industry reporting

211

to CARB. Kirby, which was visited seven times, had an emission range from essentially 0 to

212

150 +/- 51 kg/h with an average which is nine times higher than reported to CARB. Honor

213

Rancho which was sampled twice measured at 835 ± 215 kg/hr and -20 ± 37 kg/hr in June and


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214

September of 2016 respectively, where we note that the negative value is essentially consistent

215

with zero emissions to within the measurement uncertainty. Wild Goose was sampled four times

216

and on one occasion was not emitting any detectable CH4 (< ~5 kg/hr), though the compressor

217

station was physically separated from the storage field and was observed to emit 167 +/- 42 kg/hr

218

on the one day the compressor station was measured. Due to highly variable temporal changes in

219

emissions for storage facilities, numerous samples such as were sampled from the McDonald

220

Island facility are needed for all the facilities to better understand the role of temporal variability

221

in the overall annual emissions as identified in previous studies [16]. Statistical analysis as well

222

as an in-depth look at the temporal variability for McDonald will be discussed in the following

223

section. Here, we note that the Honor Rancho storage facility is collocated with oil and gas

224

production field and the Los Medanos storage facility also contains a gas fired electricity power

225

plants, leaving some question as to what part of the measured emissions should be attributed to

226

the storage facility infrastructure. Table 1. Storage facility characteristics, and average (with standard deviation and number of measurements when N >1) measured and reported methane emissions (kg CH4/h)

Facility Name Gill Ranch Honor Rancho Kirby La Goleta Lodi Los Medanos* McDonald Pleasant Creek Princeton

Working Storage (Bcf) 20 24 15 22 9 18 82 2 11

Min

Max

Avg. (Std. Dev, N)

US-EPA (2015)

CARB (2015)

5 -21 -10 24 22 8 -

59 835 150 38 461 24 -

33 (27, 3 ) 407 (605, 2) 55 (54, 7) 215 -90 33 (7, 3) 223 (144, 11) 16 (9, 4) 43

55 2 -

63 15 1 3 49 -



Wild Goose** 75 -3 105 35 (61, 3) *Los Medanos contains both gas storage and power generation

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23

51

** Excludes measurement of compressor near Wild Goose storage field 227

3.2 Refineries. By far the greatest discrepancies between reported and measured methane

228

emissions were found for the three refineries that were sampled a total of 15 times, with results

229

shown in Table 2. On average the three refineries emitted roughly an order of magnitude as

230

much methane than reported to US-EPA or CARB. Here, Benicia had average measured

231

emissions of 382 kg/hr over the seven visits, between 12 and 4 times that reported US-EPA and

232

CARB respectively. Martinez refinery, sampled thrice, emitted an average of 260 kg/hr ,

233

between two and four times the reported values. Last, Rodeo, was visited five times had an

234

average emission of 306 kg/hr (albeit with a large) that was roughly 30 times that reported.

235

Unlike storage facilities, the refineries were consistently observed to emit significantly more than

236

the annual average emission rate reported to the US-EPA and CARB. Here, we note that our

237

observations do now allow us to determine what portion of methane emissions come directly

238

from petroleum feed stocks or products and what portion come from natural gas used for heat

239

and power at the facilties. Table 2. Measured and reported methane emissions with statistics as above for refineries (kg CH4/hr)

Facility Name Benicia Martinez Rodeo

Min 220 239 23

Max 700 283 527

Average US(Std.Dev, EPA N) (2015) 382 (185, 7) 30 260 (22, 3) 138 306 (206, 5) 11

CARB (2015)


21 135 11

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Figure 3. Observed CH4 mixing ratios for loops surrounding Benicia refinery, on May 23rd, 2016. Downwind CH4 of the flight pattern show clear enhancements relative to the upwind. The mean wind direction is shown by the black arrow in lower left. 240

3.3 Power Plants, Production Fields, and Processing/Compressor Stations. The

241

majority of natural gas used in California is transported from out of state in transmission lines

242

that require compression and so nine compressor stations were observed once each in this study,

243

with results shown in Table 3. Here, methane emission varied significantly, from barely

244

observable at the Hanford compressor to over 230 kg/hr measured at the Blythe compressor

245

station. On the basis of these limited observations, about half the compressor stations appeared to



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246

emit more than the annual averages reported, but having only one observation each prevents us

247

from drawing strong conclusions about average emissions from any individual facility.

248

Table 3. Measured and reported methane emissions for compressor, oil production, power

249

stations (kg CH4/hr)

250

Min

Max

Avg (Std. Dev)

US-EPA (2015)

CARB (2015)

Compressor

-

-

235

6

58

Burney

Compressor

-

-

73

-

49

Hanford

Compressor

-

-

3

13

-

Mettler

Compressor

-

-

93

10

8

Moreno

Compressor

-

-

43

44

11

Needles

Compressor

-

-

12

-

44

Newberry

Compressor

-

-

72

1

-

Panoche

Compressor

-

-

158

-

1

Wild Goose

Compressor

-

-

167

-

51

Elk Hills

Gas Processing & Power Plant

334

533

433 (141, 2)

16

23

Belridge South

Oil Field

63

1953

827 (996, 3)

-

-

Facility Name

Facility Type

Blythe

In addition to the compressor stations, the Belridge South oil production and processing

251

field was observed three times, but for reasons described below only one set of measurements are

252

valid. Emissions from the combination of a gas processing station and a power plant at Elk Hills

253

were observed twice. Because of its comparatively large size the Belridge South field required

254

15 minutes to loop once, requiring at least three hours to obtain even 12 loops. Here, emissions


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255

ranged from 63 to 1,953 kg/h, with only one of the measurements meeting the above

256

requirements and emissions roughly consistent with an estimate of production and processing

257

emissions derived from a bottom up mapping of oil and gas related methane emissions reported

258

by Jeong et al. [17]. Because of its smaller size both observations of the Elk Hills gas processing

259

and power plant were made with many loops and yielded comparatively consistent methane

260

emissions estimates that were within a factor of two emissions reported to CARB, but appear

261

higher than those reported to US-EPA.

262

Section 3.6 Analysis of Variability. We next examine the significance of observed temporal

263

variability for the four facilities which were measured 5 or more times. Under the assumption

264

that the observations were normally distributed and the average of estimated uncertainties

265

captures the measurement related variation in the observations, we evaluate F statistics, as the

266

ratio of observed variance in emissions to the average of the estimated uncertainties. We then

267

computed the corresponding probabilities, p, that one would exceed the observed values of F,

268

given the number of samples collected for N-1 degrees of freedom. As shown in Table 4, the F-

269

statistics and p-values of exceeding F is significant at the p < 0.05 for three (Benicia, McDonald

270

and Kirby). Table 4. F-test results for facilities sampled more than once

Facility Name

Benicia

Facility Type

Refinery

Average Estimate (kg/hr)

Variance

382

29412

(kg/hr)2

Average Uncertainty squared

Average F ratio

(kg/hr)2

(-)

11080


2.65

prob (-)

0.03


271

Page 20 of 26

Kirby

Storage

55.4

2500

586

4.27

0.00

McDonald

Storage

223

18906

2455

7.70

0.00

Rodeo

Refinery

306

34040

17408

1.96

0.11

Based on the F-test results it appears that emissions at a majority of the sites we sampled

272

repeatedly were more variable than expected based on the estimated errors. McDonald Island

273

was sampled 11 times and was observed to have the largest and most variable emissions

274

observed during this two-year campaign. The cause of such a high variance is uncertain at the

275

time, but is likely due to intentional venting during maintenance operations reported for the site

276

(PG&E private communication).

277 278

Section 3.7 Discussion This work quantifies methane emissions from a sampling of California natural gas

279

facilities. A total of 24 unique sites were sampled, with ten storage facilities, three refineries,

280

seven compressor stations, an oil production field, and the combination of gas processing and

281

nearby power plant.

282

The storage facilities were measured frequently and differed by less than a factor of 2

283

from available reporting. One exception was the McDonald Island storage facility that exhibited

284

large and variable emissions, likely associated with maintenance operations at the site. We note

285

that although the average of measurements was within a factor of two of the average reported,

286

high variability was observed, suggesting the need for additional measurements, perhaps at other

287

times of day when operations might differ. Here we also note that the CO2 emissions were


Page 21 of 26


288

generally below the limit of detection, consistent with there being much smaller combustion than

289

for the refineries or power stations.

290

Three petroleum refineries were found to be the largest methane emitters, with emissions

291

roughly an order of magnitude larger than that reported to the US-EPA or CARB during the 15

292

samplings. These observations are similar to those reported previously where three refineries in

293

three different states showed emissions that were on average eight-fold higher than reported [16].

294

Because of the large discrepancies, we also compared measured and reported carbon dioxide

295

emissions CO2 emissions, which for the refineries are roughly 1,000 times greater than the

296

methane emissions. Unlike methane, Table 5 shows that the average CO2 emissions measured

297

during the flights agree with reporting to within 50% for all three facilities, suggesting that the

298

measurements might reasonably be expected to approximate average from these facilities and

299

hence that methane emissions are likely under-reported. In addition, CO2 emissions from the Elk

300

Hills power station were measured to be quite similar on both days of observation, and quite

301

consistent with annual reporting to CARB, though lower than annual reporting to US-EPA. To

302

the best of our understanding, differences in which components are included in reporting may

303

provide an explanation for the differences in emissions reporting to the two agencies. Table 5. Measured and reported carbon dioxide emissions (Mg CO2 /hr) for the subset of facilities with clearly measurable emissions.

Facility Benicia Martinez Rodeo Elk Hills

Min -4 155 152 133

Max

Average

445 275 407 140

285 223 235 137

Std.Dev 157 62 105 5

304


US EPA (2015) 341 386 168 231

CARB (2015) 323 468 150 231


305

The emissions measured at the nine compressor stations were in the same range as

306

storage facilities. While we were generally not able to separate compressor emissions from

307

subsurface leakage, this raises the likelihood that a significant fraction of methane emitted from

308

storage facilities may be associated with compressors. In one case, emissions measured at Wild

309

Goose subsurface storage were physically separated from the Wild Goose compressor and

310

smaller than the single measurement of Wild Goose compressor emissions. Since compressor

311

stations are expected to emit variable amounts of methane depending on their operation and

312

maintenance operations, this suggests a value in coordinating some fraction of measurements

313

with site operators.

314

This work is an initial sampling and only captures total emissions without resolving the sub-

315

facility components responsible for the majority of emissions. In order to improve annual

316

average emission estimates and capture variations, future measurements of California natural gas

317

facilities are needed. Here, it will be important to both measure facilities either more frequently

318

using the methods described here, continuously (e.g., fenceline monitoring), and apply methods

319

that provide sufficient spatial resolution (e.g., infrared plume imaging) to identify and remediate

320

emissions from the components responsible for emissions. ACKNOWLEDGEMENTS

321

This analysis was supported by the California Energy Commission’s Natural Gas Research

322

Program under contracts 500-12-006 and 500-13-005. IF is supported in part by the California

323

Agricultural Experiment Station, USDA National Institute of Food and Agriculture project #CA-

324

D-LAW-2229-H. Work by MLF was performed at LBNL under U.S. Department of Energy

325

Contract No. DE-AC02-05CH11231. The statements and conclusions in this paper are those of


Page 22 of 26

Page 23 of 26


326

the authors and not necessarily those of the California Energy Commission. The views and

327

opinions of authors expressed herein do not necessarily state or reflect those of the United States

328

Government or any agency thereof, or The Regents of the University of California. Ernest

329

Orlando Lawrence Berkeley National Laboratory is an equal opportunity employer nor do

330

references herein to any specific commercial product, process, or service by its trade name,

331

trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement,

332

recommendation, or favoring by the United States Government or any agency thereof, or The

333

Regents of the University of California.

334

Supporting Information: Supporting information includes a table summarizing all the facilities

335

measured, including facility type, location, and number of times each was measured, a second

336

table reporting measured data for each facility and observation date, and a figure showing the

337

variation in estimated emission as a function of the number of vertical bins used to average

338

observations for an example measurement during a controlled release test.

339



REFERENCES [1] U.S. Energy Information Administration, U.S. Natural Gas Total Consumption. Web. 02 June, 2017. https://www.eia.gov/dnav/ng/hist/n9140us2a.htm [2] "Global Warming Potentials." Global Warming Potentials. United Nations, 2014. Web. 04 July 2015. http://unfccc.int/ghg_data/items/3825.php [3] Alvarez, R. A.; Pacala, S. W.; Winebrake, J. J.; Chameides, W. L.; Hamburg, S. P. Greater focus needed on methane leakage from natural gas infrastructure. Proceedings of the National Academy of Sciences 2012, 109 (17), 6435–6440. [4] U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and Sinks from 1990-2015. https://www.epa.gov/ghgemissions/inventory-us-greenhousegas-emissions-and-sinks (accessed 14 June 2017). [5] "Methane (CH4)." CH4. Air Resources Board, 6 May 2015. Web. 03 Feb. 2016. https://www.arb.ca.gov/cc/inventory/background/ch4.htm [6] "Supply and Demand of Natural Gas in California." California Natural Gas Data, Facts, & Statistics. California Energy Commission, n.d. Web. 03 Feb. 2016. http://www.energy.ca.gov/almanac/naturalgas_data/overview.html [7] Brandt, A. R.; Heath, G. A.; Kort, E. A.; O'sullivan, F.; Petron, G.; Jordaan, S. M.; Tans, P.; Wilcox, J.; Gopstein, A. M.; Arent, D.; et al. Methane Leaks from North American Natural Gas Systems. Science 2014, 343 (6172), 733–735. [8] "California Underground Natural Gas Storage Capacity." California Underground Natural Gas Storage Capacity. US Energy Administration, n.d. Web. 03 Feb. 2016. https://www.eia.gov/dnav/ng/ng_stor_cap_dcu_SCA_a.htm [9] Subramanian, R.; Williams, L. L.; Vaughn, T. L.; Zimmerle, D.; Roscioli, J. R.; Herndon, S. C.; Yacovitch, T. I.; Floerchinger, C.; Tkacik, D. S.; Mitchell, A. L.; et al. Methane Emissions from Natural Gas Compressor Stations in the Transmission and Storage


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Sector: Measurements and Comparisons with the EPA Greenhouse Gas Reporting Program Protocol. Environmental Science & Technology 2015, 49 (5), 3252–3261. [10] Lavoie, T. N.; Shepson, P. B.; Cambaliza, M. O. L.; Stirm, B. H.; Karion, A.; Sweeney, C.; Yacovitch, T. I.; Herndon, S. C.; Lan, X.; Lyon, D. Aircraft-Based Measurements of Point Source Methane Emissions in the Barnett Shale Basin. Environmental Science & Technology 2015, 49 (13), 7904–7913. [11] Conley, S. A.; Franco, G.; Faloona, I. C.; Blake, D. R.; Peischl, J.; Ryerson, T. B. Methane emissions from the 2015 Aliso Canyon blowout in Los Angeles, CA. Science, 2016, 351, 6279, 1317–1320. [12] Conley, S., I. Faloona, S. Mehrotra, M. Suard, D. H. Lenschow, C. Sweeney, S. Herndon, S. Schwietzke, G. Pétron, J. Pifer, E. A. Kort and R. Schnell (2017). Application of Gauss's theorem to quantify localized surface emissions from airborne measurements of wind and trace gases. Atmos. Meas. Tech. 10(9): 3345-3358. DOI: 10.5194/amt-10-33452017. [13] Stewart, J. Q.; Whiteman, C. D.; Steenburgh, W. J.; Bian, X. A Climatological Study of Thermally Driven Wind Systems of the U.S. Intermountain West. Bulletin of the American Meteorological Society 2002, 83 (5), 699–708. [14] United States Environmental Protection Agency, Greenhouse Gas Reporting Program (GHGRP). FLIGHT: Facility Level Information on GreenHouse Gases Tool. Web. 26 Feb 2016. https://ghgdata.epa.gov/ghgp/main.do [15] California Air Resources Board (CARB), Regulation for the Mandatory Reporting of Greenhouse Emissions (MRR), 2014 GHG Facility and Entity Emissions Data, from CARB Pollution Mapping Tool. Web. 26 Feb 2016. https://www.arb.ca.gov/ei/tools/pollution_map/pollution_map.htm [16] Lavoie, T. N., P. B. Shepson, C. A. Gore, B. H. Stirm, R. Kaeser, B. Wulle, D. Lyon and J. Rudek (2017). Assessing the Methane Emissions from Natural Gas-Fired Power



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Plants and Oil Refineries. Environmental Science & Technology 51(6): 3373-3381. DOI: 10.1021/acs.est.6b05531. [17] Jeong, S.; Millstein, D.; Fischer, M. L. Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California. Environmental Science & Technology 2014, 48 (10), 5982–5990.

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Airborne Methane Emission Measurements for Selected Oil and Gas

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