Since 1850 the concentration of atmospheric methane (CH4), a potent
greenhouse gas, has more than doubled. Recent studies suggest that emission
inventories may be missing sources and underestimating emissions. To
investigate whether offshore oil and gas platforms leak CH4 during
normal operation, we measured CH4 mole fractions around eight oil and
gas production platforms in the North Sea which were neither flaring gas nor
offloading oil. We use the measurements from summer 2017, along with
meteorological data, in a Gaussian plume model to estimate CH4
emissions from each platform. We find CH4 mole fractions of between 11
and 370 ppb above background concentrations downwind of the platforms
measured, corresponding to a median CH4 emission of 6.8 g CH4 s-1 for each platform, with a range of 2.9 to 22.3 g CH4 s-1.
When matched to production records, during our measurements individual
platforms lost between 0.04 % and 1.4 % of gas produced with a median
loss of 0.23 %. When the measured platforms are considered collectively
(i.e. the sum of platforms' emission fluxes weighted by the sum of the
platforms' production), we estimate the CH4 loss to be 0.19 % of gas
production. These estimates are substantially higher than the emissions most
recently reported to the National Atmospheric Emission Inventory (NAEI) for
total CH4 loss from United Kingdom platforms in the North Sea. The NAEI
reports CH4 losses from the offshore oil and gas platforms we measured
to be 0.13 % of gas production, with most of their emissions coming from
gas flaring and offshore oil loading, neither of which was taking place at
the time of our measurements. All oil and gas platforms we observed were
found to leak CH4 during normal operation, and much of this leakage has
not been included in UK emission inventories. Further research is required
to accurately determine total CH4 leakage from all offshore oil and gas
operations and to properly include the leakage in national and international
emission inventories.
Introduction
Methane (CH4) is a greenhouse as well as a precursor of tropospheric
ozone, which is widely regulated as a component of photochemical smog. Atmospheric CH4 mixing ratios have increased from 715 ppb in 1850 to 1865 ppb in February 2019 with an annual increase of 10 ppb yr-1 in 2018 (NOAA,
2019). This increase is largely driven by anthropogenic activities though
uncertainties exist in the magnitude of individual source sectors and sinks
(Turner et al., 2019). Observatories have been set up around the world to
track trends in CH4 concentrations (de Coninck et al., 2018).
Between 2012 and 2015 unusually high CH4 enhancements of up to 400 ppb
above background were observed at the University of East Anglia's Weybourne
Atmospheric Observatory (WAO; 52.95∘ N, 1.14∘ E)
during periods of northerly onshore winds and high surface pressures
(Connors, 2015; Staunton-Sykes, 2016; Connors et al., 2018). These elevated
enhancements were unexpected as the air came from the open ocean. However, a
potential source of these CH4 enhancements is leakage from offshore oil
and gas production platforms 80 km away from WAO in the North Sea (OSPAR,
2018; Fig. 1).
Map of the North Sea showing the locations of all UK offshore
oil and gas platforms (the filled yellow circles) and the eight platforms
measured by this study (black crosses) (source: OSPAR, 2012). The map also
shows the location of the University of East Anglia's Weybourne Atmospheric
Observatory (WAO; 52.95∘ N, 1.14∘ E) in Weybourne,
Norfolk, UK.
In 2015 the UK extracted about 32 Tg of natural gas from the North Sea (UK
Oil and Gas Authority, 2018). During this time, a loss of 40 Gg CH4 (0.13 % of natural gas production) was reported by the UK Government
Department for Business, Energy & Industrial Strategy (BEIS), mainly
through venting (24 Gg CH4 yr-1) and flaring (12 Gg CH4 yr-1) activities (BEIS, 2018). However, recent studies indicate public
inventories in the United States underestimate CH4 emissions, including
from the oil and gas supply chain (Alvarez et al., 2018; Zavala-Araiza et
al., 2015; Schwietzke et al., 2017). This leads to the following question. Could CH4 emissions from offshore oil and gas platforms be higher than previously estimated?
Land-based measurements in West Virginia and Colorado, USA, estimate that
onshore oil and gas extraction activities lose between 0.1 and 10 % of
CH4 produced (Petron et al., 2012; Omara et al., 2016; Schwietzke et
al., 2017; Alvarez et al., 2018; Englander et al., 2018; Riddick et al.,
2019). At present there are no direct, near-source measurements of CH4
emissions from offshore oil and gas production. However, a mass-balance
approach identifies CH4 emissions from offshore oil and gas operations
off the coast of South East Asia as having a large regional median (range)
emission of 99 (4–427) g CH4 s-1 per platform for the Malay Peninsula and 15 (2–46) g CH4 s-1 per platform for Borneo
(Nara et al., 2015). Numerous productive offshore oil and gas fields exist
across the globe, including Saudi Arabia, Brazil, Mexico, Norway and the
United States, making careful measurement and analysis of leakage from these
platforms important for global emission inventories.
Several activities on offshore production platforms in the North Sea are
explicitly identified by the BEIS in the National Atmospheric Emission
Inventory (NAEI) as sources of CH4 emission including combustion
activities such as gas flaring, offshore oil loading and venting directly to
the atmosphere (BEIS, 2018). Leakage during normal operations is not
explicitly included. Oil and gas operators report an annual CH4
emission estimate for each offshore production platform to the NAEI; these
emission estimates are primarily calculated using emission factors
(Butterfield, 2017). Technical guidance on the emission-factor-based
calculations is available through the UK Government's Department of Energy
and Climate Change Environmental and Emissions Monitoring System (DECC EEMS,
2008). The main shortcoming of using emission factors and activity levels to
estimate total emissions is that total emissions can be underestimated if
not all emission sources are identified. For example, leaks not obvious to
platform personnel would not be included, and the total emission would be an
underestimate of CH4 lost. Overall, as emission factor calculations
rely on explicit knowledge of all sources of leakage, current approaches
used by industry could underestimate total CH4 emissions from offshore installations.
In this study we investigate CH4 emissions from offshore oil and gas
installations in the North Sea and determine how they differ from those
currently reported by the BEIS. To investigate the CH4 loss from
offshore oil and gas installations in UK waters, we measure CH4 mixing
ratios downwind of offshore platforms and use these data in a Gaussian plume
model to estimate CH4 emission rates. The CH4 loss is then
presented as a percentage of the CH4 produced by each platform.
Methods – boat-based observations
Oil and gas platforms in UK waters are located between 30 and 500 km from
the UK mainland, with the majority of platforms located to the east of the
UK in the North Sea (Fig. 1; OSPAR, 2018). To investigate possible emissions
from these platforms, sea-level CH4 mole fractions were measured around
eight oil and gas platforms between 6 June and 25 August 2017. Measurements were made during normal operation (i.e. pilot light on
the flare stack was lit, but no flaring or offshore oil loading was
observed). Where possible a full circle was made around the installation to
observe the upwind and downwind methane mole fractions. To determine if
flaring or offshore oil loading was occurring, a visual inspection was made
of the installation. We assumed that venting was not taking place because no
venting was reported in any of the most recent NAEI. Previously published
emissions from the measured platforms in the NAEI are reported to be almost
entirely due to flaring (83 %) and offshore oil loading (17 %), with
reported emissions generated using emission factors (Brown et al., 2017;
BEIS, 2018).
The oil and gas platforms measured here were selected at random, constrained
only by the need for accessibility. Fishing boats were chosen as the
measurement platforms because of budgeting and availability constraints.
Maritime and Coastguard Agency regulations for the available vessels (MCA
category 2) meant that the platforms measured had to be less than 96 km
from a safe haven. Four of the eight platforms only produced natural gas
(nos. 1–4) that was transported to the mainland via pipeline, while
the remaining four produced oil and gas. Two of the oil and gas platforms
(nos. 5 and 6) include floating production storage and offloading
vessels, which receive hydrocarbons, process them and store them until they
can be offloaded by tanker or pipeline, and the other two platforms (nos. 7
and 8) transport oil and gas directly to the mainland by pipeline.
Methane mole fractions, latitude, longitude and meteorological data were
collected as the boat travelled upwind and downwind of the platforms.
Methane mole fraction measurements – Los Gatos UGGA
The Los Gatos Research Ultra-portable Greenhouse Gas Analyzer (UGGA;
http://www.lgrinc.com/, last access: 14 January 2019) was used to measure CH4 concentrations near the
offshore oil and gas platforms. The UGGA is a laser absorption spectrometer
that measures CH4 mole fractions in air (Paul et al., 2001). The UGGA
reports CH4 mole fractions every second, with a stated precision of
<2 ppb (1σ at 1 Hz) over an operating range of 0.1 to 100 ppm. Calibration of the UGGA was conducted before and after deployment using
low (1.93 ppm CH4), target (2.03 ppm CH4) and high (2.74 ppm CH4) mole fraction gases calibrated on the World Meteorological
Organization (WMO) scale. Measurements were taken between the edge of the
exclusion zone (500 m from the platform; HSE, 2018) and 2 km horizontal
distance from the platforms. The inlet line was attached to a mast 2.5 m
above sea level, to avoid contamination from the boat's exhaust, and
protected from water incursion using an aluminium funnel. The air was
filtered using a 2 µm filter. Background CH4 mole fractions were measured while the boat was upwind of the production platform.
Meteorological data
Meteorological data were collected using a wireless weather station (Maplin,
UK) attached to a mast 2 m above sea level. Data were sampled and recorded
at 1 min intervals and included wind speed (u, m s-1), wind
direction (WD, ∘ to North), air temperature at 2 m (Ta, K),
relative humidity (RH, %), rain rate (R, mm h-1), irradiance (I, W m-2) and air pressure (P, Pa). The wind speed used in the emission
modelling was corrected for emission height using a wind profile power law
(Touma, 1977; Hsu et al., 1994).
Gaussian plume model
The Gaussian plume model used in this study calculates the mole fraction of
a gas as a function of distance downwind from a point source (Seinfeld and
Pandis, 2006). As a gas is emitted, it is entrained in the prevailing
ambient air flow and disperses in the y and z directions (relative to a mean
horizontal flow in the x direction) with time, forming a cone. The mole
fraction of the gas as a function of distance downwind depends on the
emission flux at the source, the advective wind speed (u, m s-1) and the rate of dispersion. The mole fraction of the gas (X, µg m-3), at any point x metres downwind of the source, y metres laterally
from the centre line of the plume and z metres above ground level, can be
calculated (Eq. 1) using the source strength (Q, g s-1), the height of
the source (hs, m), the height of the boundary layer (h, m) and the
stability of the air (CERC, 2017;
Hunt, 1982; Hunt et al., 1988). The standard deviations of the lateral
(σy, m) and vertical (σz, m) mixing ratio
distributions are calculated from the Pasquill–Gifford stability class
(PGSC) of the air (Pasquill, 1962; Busse and Zimmerman, 1973; US EPA, 1995).
Even though this modelling method is relatively simple, offshore emission
estimates using the same parameterization of σy and σz were made by Blackall et al. (2008) and were in good agreement
(R2=0.85) with emissions calculated from a concurrent tracer
release experiment. Alternative offshore parameterizations for σy and σz exist and are used in the EPA-recommended
Offshore and Coastal Dispersion model (Hanna et al., 1985). However, these
algorithms require further data on the micrometeorology which are not
available and were therefore not used as they introduce additional
unquantifiable uncertainty.
X(x,y,z)=Q2πuσyσze-y22σy2e-z-hs22σz2+e-z+hs22σz2+e-z-2h+hs22σz2+e-z+2h-hs22σz2+e-z-2h-hs22σz2
The following assumptions are made regarding the Gaussian model: (1) the
source is emitting CH4 at a constant rate, (2) the mass of CH4 is conserved when reflected at the surface of the ocean or the top of the boundary layer, (3) wind speed and vertical eddy diffusivity are constant
with time, (4) there is uniform vertical mixing, and (5) terrain (ocean
surface) is relatively flat between source and detector. The PGSCs were
determined for an offshore flow of air following the parametrizations
described in Erbrink and Scholten (1995), Hanna et al. (1985) and Hsu (1992).
Gaussian plume model parameterization
A Gaussian plume approach was used to infer the CH4 emissions flux from
the oil and gas platforms using the CH4 mole fraction data collected
downwind. We used measurements for the mole fraction, and rearranging
Eq. (1) solved for the source term Q. Data used as input to the Gaussian
plume model are wind speed, wind direction, temperature, minute-averaged
CH4 mole fraction at 2 m, background CH4 mole fraction and the PGSC. For the minute-averaged CH4 mole fraction data, we assume the
1 min averaged data near the centre of the observed instantaneous plume
are representative of the centre of the time-averaged Gaussian plume. The
PGSCs are estimated from wind speed and irradiance data (Turner, 1970;
Seinfeld and Pandis, 2006), as measured by the meteorological station on the
boat. The height of the boundary layer is calculated from the Global
Forecast System's global forecast model archives (GFS, 2019).
An unknown variable used in the Gaussian plume model in this study is the
height at which emissions are released. The emissions could have come from
the working deck of the platform, the top of the flare or somewhere in
between. For the purposes of the emission estimates calculated and presented
here, we assume CH4 is emitted from the working deck only, which results
in the smallest emissions possible for a given measurement. As a sensitivity
study, emissions were also calculated assuming the source was at the top of
the flare stack only (see Sect. S1 in the Supplement). The height of
the working deck and the height of the flare stack at each of the platforms
were determined using platform characteristics data from each oil and gas
platform available on the internet.
Uncertainties
Of the Gaussian plume model assumptions presented in Sect. 2.3, two may
not be valid – uniform vertical mixing and a constant wind speed. The
uncertainty in uniform vertical mixing is discussed in Sect. 3.4. To
investigate how uncertainties in the measurements and modelling affect the
calculated emission, we ran Gaussian plume model scenarios using data that
reflect the input values' uncertainty bounds. The scenarios run using the
Gaussian plume approach were varying wind speed (based on measurement),
UGGA precision (±2 ppb), thermometer precision (±0.1 ∘C), the PGSC (+1 PGSC) and distance from detector to
emission source (±50 m). The uncertainties of the UGGA and
thermometer were taken from literature. The uncertainty in the PGSC used
reflects the possibility that the temperature of the natural gas leaving the
subsurface could be hotter than air and therefore less stable. The
uncertainty in distance from the emission source to the detector results
from not knowing where gas is leaking; here we assume the leak could be from
anywhere on a production platform that is 100 m long.
Data sources
The UK Department for Business, Energy & Industrial Strategy (BEIS) keeps
the Environmental and Emissions Monitoring System (EEMS) which is the
environmental database of the UK oil and gas industry. Methane emission data
are uploaded to this by industry partners. These data form the basis for
emissions reported under category 1B2 within the National Atmospheric
Emissions Inventory (NAEI; BEIS, 2018). For details of how these data are
incorporated into the NAEI, see Brown et al. (2017). The most recent
point-source emission database from the NAEI available at the time of
writing was for the year 2015. Individual platform production data for both
2015 and 2017 were taken from the Petroleum Production Reporting System
published by the UK Oil and Gas Authority (OGA, 2018).
Meteorological, position and mole fraction data taken during the
boat-based measurement campaign around oil and gas production platforms in
the North Sea between 6 June and 25 August 2017. Emissions
were calculated assuming the source of the emissions was at the working deck
level. The calculations of the median, mean and total only use
data from platforms nos. 4–8. Platforms nos. 1–2 did not have
production data available for the time of measurement. During the
measurement of platform no. 3 the height of the PBL was calculated as zero
(GFS, 2019), making the Gaussian plume modelled emission estimate ambiguous.
MeasurementHeightPeak enhancement at theEmissionPlatform CH4ID no.dateType(m)centre of the plume (ppb)(g s-1)production (g s-1)Loss (%)16 JuneGas40504.9N/A26 JuneGas40514.9N/A35 JulyGas40341.16720.17416 AugustGas50305.715 2300.04524 AugustOil/gas5037022.315851.41624 AugustOil/gas5031218.118450.98725 AugustOil/gas50356.829520.23825 AugustOil/gas50112.980470.04Median (nos. 4–8) 6.80.23Mean (nos. 4–8) 11.20.54Total (nos. 4–8) 55.829 662Loss of CH4 produced (%) (nos. 4–8) 0.19ResultsMethane mole fractions around North Sea oil and gas platforms
Our sea-level surveys indicate CH4 mole fraction enhancements can be
measured near all of the production platforms observed, when upwind CH4
mole fractions ([CH4]bgd, ppb) are compared with downwind mole
fractions ([CH4], ppb; Table 1). The largest enhancement of 370 ppb was
observed downwind of platform no. 6 on 24 August 2017, while the
lowest enhancement of 11 ppb was observed downwind of platform no. 8 on
25 August 2017. The median CH4 enhancement downwind of the eight
platforms was 43 ppb (mean: 112 ppb; range: 11–370 ppb). While
measurements were being conducted, a maximum variability in wind speed of
±0.6 m s-1 was measured at platform no. 3 on 6 July 2017; during no measurements did an observable change in wind direction
occur. Complete circles of all installations were not possible due to access
restrictions; i.e. the measurement vessel could not get between some
platforms and maintain the 500 m clearance required of each platform, and
there were occasions when the measurement boat was actively blocked by the
platform's standby vessel (Sect. S2).
Source of leaks
Although the production platforms measured in this campaign were not
actively flaring gas (i.e. burning gas to reduce pressure during oil
extraction), the pilot light on the top of the flare stack was actively
burning gas. As an example, Fig. 2 shows the minute-averaged CH4
enhancements upwind and downwind of a production platform on 24 August. This example was chosen as it was the only platform that had an
offset flare stack (i.e. not centred in the platform). Figure 2 indicates the
largest enhancement was downwind of the flare stack. The width of the plume
(< 200 m) suggests a compact CH4 source. This could be
associated with incomplete combustion of natural gas feeding the pilot light
at the top of the platform, or it could be associated with gas being emitted
at the working deck level.
Minute-averaged CH4 enhancements made upwind and downwind of
production platform no. 6, on 24 August 2017.
Estimating methane emissions
Using Gaussian plume modelling and assuming all emissions came from the
working deck, the highest emission of 22.3 g s-1CH4 was observed
from platform no. 5 on 24 August 2017 while the lowest emission,
2.9 g s-1, was observed on 25 August 2017 from platform no. 8. During the measurement of platform no. 3, the calculated boundary layer
height was 0 m (GFS, 2019), making the emission estimate ambiguous, and, even though presented in Table 1, has not been used further in the analysis.
Using emission data from the five platforms with available production data
and with a non-zero calculated PBL (platforms nos. 4–8), the
median CH4 emission was 6.8 g s-1 (mean 11.2 g s-1). As a
sensitivity study, the median modelled emission is 2658 g s-1 (mean
1892 g s-1) when we assume all CH4 is emitted from the highest
point of the platform, i.e. the flare.
When normalized against natural gas production data (OGA, 2018), the highest
CH4 loss rate corresponded to 1.4 % of production at platform no. 5
while the lowest loss rate corresponded to 0.04 % of production at
platforms nos. 4 and 8. We estimate the median CH4 loss from
platforms nos. 4–8 to be 0.23 % of production. When weighted
by production, i.e. the collective emission from the measured platforms (56 g s-1; Table 1) as a fraction of the collective production of the
measured platforms (29 662 g s-1; Table 1), the average loss from all
measured platforms was 0.19 % of their total production.
For comparison, we have also calculated the reported loss rates for 2015
using the most recent NAEI emissions data (Brown et al., 2017; BEIS, 2018).
We find the median reported loss rate from NAEI was 0.23 % for the six
platforms we measured where production data were available, with a
production-weighted average of 0.19 %. These values are close to those we
calculated. However, this apparent consistency is misleading as the NAEI
emissions are dominated by CH4 emissions from flaring and offshore-oil-loading activities, neither of which was occurring during our measurement
periods; this is discussed further in Sect. 4.
Uncertainties/shortcomings of Gaussian plume modelling
A range of scenarios were run using the Gaussian plume model to estimate
uncertainty in average CH4 emissions resulting from UGGA instrument
precision, thermometer precision, varying wind speed, assessment of the PGSC,
and uncertainty in distance between the emission source and the detector.
Uncertainty in the UGGA and the thermometer has little effect on the
average emission estimate (Sect. S3). The largest
variability in wind speed was recorded during measurement of platform no. 3
on 6 July 2017 at 4.4±0.6 m s-1 (Sect. S1); using this variability in wind speed in the Gaussian
plume model results in an uncertainty in average emission of ±12 %. Uncertainty in estimating the distance between the emission source and
the detector results in an uncertainty in average emissions of ±8 %. The Gaussian plume model has the greatest response to the uncertainty
in estimating the PGSC, resulting in an uncertainty of ±41 %. We
estimate the overall uncertainty in the average CH4 emission,
calculated as the root of the sum of the individual uncertainties squared,
to be ±45 %.
As mentioned in Sect. 2.5, the uniform vertical mixing assumption made in
the Gaussian plume model may not hold here as the data we collected provide
no information on vertical mixing. However, the Gaussian plume model only
assumes a constant vertical mixing rate between the source and the detector.
In most cases this distance is relatively short and unlikely to
significantly affect the calculation of emissions. In future experiments,
the vertical mixing rate could be calculated by measuring the vertical
gradient of wind speeds to make an accurate thermodynamic profile.
Discussion
From boat-based observations we observed elevated CH4 mole fractions,
between 11 and 370 ppb above background, downwind of eight oil and gas
production platforms in the North Sea when none of the platforms was engaged
in either gas flaring or oil transfer and unloading. This suggests that all
observed oil and gas platforms leak CH4 during normal operations.
Using the near-source CH4 measurements in a simple Gaussian plume model
(where the CH4 emissions are calculated from the minute-averaged peak
enhancement at the centre of the plume), we found the median of the
calculated CH4 emissions from offshore oil and gas installations to be
589 kg CH4 d-1, with individual platforms' CH4 emissions
ranging from 98 to 1928 kg CH4 d-1. Matching production data to
our measurements we estimate (1) a median loss of CH4 from the six
platforms, unweighted by production, of 0.23 % (mean 0.54 %); and (2) the cumulative loss of CH4, weighted by total production, of 0.19 %.
These results indicate that, of the platforms measured, those producing more
gas leaked proportionally less of what they produced. Also, the two higher
emitting platforms (nos. 5 and 6) include floating production storage and
offloading vessels; we find these to have much larger loss rates than the
three fixed platforms (nos. 4, 7 and. 8). However, we also acknowledge
our sample size is small and the five platforms may not be indicative of the
overall performance of platforms in the North Sea.
The 2015 emission-factor-based NAEI emissions are within the ranges
calculated in this study, i.e. a median loss rate of 0.23 % and a
production-weighted loss of 0.19 %, and also show larger losses come from
lower-producing platforms. However, the NAEI provides the main source of
emission for each installation, and their reported emissions from the six
platforms are almost entirely due to flaring (83 %) and offshore oil
loading (17 %), neither of which was taking place during our measurements.
Typically, these activities are not continuous on North Sea platforms;
consequently, emission rates are likely to be much higher at certain times
than others. As flaring and oil loading did not coincide with our
measurement campaign, the measured emissions presented here represent
leakage only and do not account for intermittent emissions due to venting,
flaring or oil-loading activities. This suggests a potentially large missing
source of CH4 emissions in the national UK CH4 emission
inventory.
The emission estimates presented here are from a pilot study and further
work is needed to establish total CH4 leakage rates from offshore oil
and gas platforms. We have established, however, that CH4 enhancements
can be detected downwind of all production platforms during normal
operations when neither venting, flaring or oil-loading activities are
taking place. Our measurements used in a Gaussian plume model indicate
leakage from offshore installations is likely larger than previously
estimated. However, these emission estimates come with large uncertainties
as they are based on relatively few measured platforms; assume values for
the height of emission, lateral and vertical mixing ratio distributions; and
may not meet all the Gaussian plume model assumptions.
When the CH4 emissions are calculated for two different emission
heights, the importance of identifying the source location and height above
the sea becomes apparent. The median CH4 emission from the five
platforms is 6.8 g s-1 when the emissions are all assumed to come from
the working deck, while the median emission is 2658 g s-1 (47 % of
production) when all CH4 is assumed to be emitted from the flare, i.e.
the highest point of the platform. This analysis indicates that the median
emission presented here, based on the assumption that the emissions occur
from the working deck, is a conservative estimate. However, without further
measurements the height of the emission source cannot be definitively
determined, and this leaves the possibility that leakage is higher during
normal operations than our results indicate. The other input variables that
cannot be determined without further measurement are the lateral and
vertical mixing ratio distributions, but we feel that following the study of
Blackall et al. (2007) the estimates used in this study are sufficient to
establish leakage from oil and gas platforms and to provide a rough estimate
of their emissions. As with the emission height, mixing can be resolved with
further measurement, including the use of aircraft to resolve the vertical
and horizontal mixing of the plume.
It is clear that further studies are needed to provide additional data that
will yield more definitive emission estimates. Using the near-source
(< 1 km) observations of this paper (Fig. 2; Sect. S2, platform nos. 5 and 6) we can see that plumes from the leaks
are compact (< 200 m wide) and in some cases difficult to detect
from sea-level measurements (Sect. S2, platforms
nos. 7 and 8). Making three-dimensional observations downwind of the
platforms and using a sonic anemometer would help identify some of the
unknowns presented here. Also, measuring more platforms over a longer time
frame would improve the understanding of ambient leakage.
Any further measurements would be significantly easier with the cooperation
of the oil and gas industry, which could benefit from the findings. If the
emissions are as low as the industry currently estimates, further
measurements confirming low leakage rates would improve consumer confidence
in oil and gas extraction activities. Alternately, if emissions are higher
than currently reported, additional measurements would give the industry an
opportunity to identify common issues such as incomplete combustion at the
flare (Fig. 2), reduce leakage, and improve the efficiency of platforms, thus
potentially increasing profits from the extracted gas.
The continuous leakage of CH4 from offshore production platforms
observed here is consistent with observations of similar onshore operations
(Omara et al., 2016; Riddick et al., 2019). Ambient leakage is not
unexpected as these offshore production platforms are located in the
inhospitable conditions of the North Sea, where wind speeds regularly exceed
hurricane force and waves can reach the working deck. However, it is
surprising that ambient leakage has not been explicitly factored into the UK
national emissions inventory, which relies solely on operator self-reported
emissions calculated using emission factors combined with specific processes
like flaring. Without direct measurement, operators can remain unaware of
small emissions that occur during normal operation.
The CH4 lost as ambient leakage measured here may not
be economically important, but when extrapolated to a global scale the loss
of 0.19 % of gas production (the production-weighted average loss) from
offshore oil and gas production corresponds to a global emission of 0.8 Tg CH4 yr-1 (IEA, 2018). Currently, the Oil and Gas Climate
Initiative (OGCI) estimates the global CH4 emission from the oil and
gas sector to be 1.6 Tg CH4 yr-1, based on the OGCI's own estimate
that 0.32 % of CH4 extracted is lost (OGCI, 2018). This estimate
represents data from 13 of the largest oil and gas producers and accounts
for upstream CH4 emissions from flaring, venting and offshore oil
loading for all operated gas and oil assets. If a global CH4 emission
from ambient leakage of 0.19 % estimated by this study (0.8 Tg CH4 yr-1) is added to the current global estimate from flaring, venting and
offshore oil loading (1.6 Tg CH4 yr-1), the total CH4 emission
from offshore oil and gas production would increase approximately 50 %. It should
be noted that the value of 0.19 % is based on a very small sample size
using a method that comes with significant uncertainty. Moreover, the median
value of this study (6.8 g s-1) is much smaller than the regional
median emission estimate of 99 g s-1 for the Malay Peninsula and 15 g s-1 for Borneo (Nara et al., 2015), which suggests that
the ambient leakage rate may be lower in the North Sea than other regions of
the world. This study does highlight the shortcomings of using emission
factors which rely on a priori knowledge of the source, in contrast with direct
measurements that account for all emissions and better estimate total
emissions. In conclusion, we suggest that additional measurements of
offshore oil and gas production platform operations (e.g. Saudi Arabia,
Brazil, Mexico, Norway and the United States) be conducted to better inform
leakage estimates and that these measurements be used to improve the UK and
global CH4 emission inventories.
Data availability
Data can be accessed from the CEE Research Data Set collection on the Princeton University DataSpace server: http://arks.princeton.edu/ark:/88435/dsp015999n6220 (last access: 22 July 2019, Riddick, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-9787-2019-supplement.
Author contributions
JSS, SNR and NRPH designed the
experiment; SNR, JSS, GA, JP and GLF prepared equipment and calibrated the instruments; SNR, JSS and GLF carried out the measurements; and JSS, AJM, DL and EGN provided the analysis.
DLM and MC were the project leaders and provided
scientific oversight and guidance throughout the planning, implementation,
collection and data analysis processes. SNR and DLM
wrote the paper with help from MC, MK and NRPH and
with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The US National Oceanic and Atmospheric Administration
and the UK Natural Environment Research Council (NERC) through the
Greenhouse gAs UK and Global Emissions (GAUGE) project supported this research. We thank Glen Thistlethwaite (Ricardo)
for his help understanding the BEIS Environmental and Emissions Monitoring
System and the National Atmospheric Emissions Inventory. We also thank the
reviewers for their valuable suggestions and especially Reviewer 1 for the
data provided.
Financial support
This research has been supported by the National Oceanic and Atmospheric Administration (grant no. AWD1004141) and the UK Natural Environment Research Council (grant no. NE/K002570/1), as well as by funding to Stuart N. Riddick by the Science, Technology and Environmental Policy program at Princeton University.
Review statement
This paper was edited by Eliza Harris and reviewed by Daniel Varon and one anonymous referee.
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