In this paper, we present an innovative CH
The advent of the techniques of directional drilling, horizontal drilling, 3-D seismic imaging, and hydraulic fracturing have led to rapid increases in oil and gas production throughout the US, especially in basins where production using so-called conventional methods of extraction was not economically viable. Together with the increase of oil and gas production, there has been a concurrent increase in gaseous emissions into the atmosphere. Methane, the primary constituent of natural gas, is a potent greenhouse gas with a global warming potential of up to 86 times that of an equivalent mass of carbon dioxide over a 20-year timescale. With a moderate atmospheric lifetime of 12.4 years, the relative impact of methane on a 100-year timescale is 28 (Myhre et al., 2013). When emissions are kept under control, methane is a clean-burning, high energy content fuel that can reduce carbon dioxide emissions relative to other more carbon-rich fuels. However, when methane emissions are a relatively large fraction of total natural gas production, the climate benefit of natural gas relative to coal (a relatively carbon-intensive fuel) is reduced or even eliminated (Alvarez et al., 2012).
Several recent atmospheric studies using the aircraft mass-balance approach
have focused on quantifying regional emissions from oil and gas production.
In Pétron et al. (2014), the mass-balance approach using aircraft was
used to quantify emissions in the Denver–Julesburg Basin in Colorado,
determining that the methane emissions from fossil fuel extraction
activities are about 4 % of total natural gas production in the basin. This
emission rate is in excess of both the inventory (Pétron et al., 2012)
and the
Top-down measurements of regional emissions provide crucial independent
verification of bottom-up emission inventories. However, the mass-balance
measurements of total methane emissions do not provide a means of
partitioning the emissions i.e., determining the relative fraction of
emissions contributed by the source types within the aircraft footprint.
While the Uintah Basin is fairly simple from the standpoint of methane
emissions, with a small population (
Tracer molecules (i.e., molecules that are co-emitted with methane in
different ratios depending upon the emissions sector) can provide valuable
information to partition regional emissions. In particular, the stable
isotopes of methane and alkanes (ethane, propane, etc.) have been shown to
be valuable in partitioning methane emissions between various sources
(Dlugokencky et al., 2011). It has long been understood that low ethane to
methane ratios (
The use of the atmospheric signals of
These tracers have also been used to infer attribution of emissions on a
regional scale.
Despite their clear utility,
Using a mobile lab equipped with the CRDS analyzer, a high-accuracy GPS, a
sonic anemometer, and an onboard gas storage and playback system, field
measurements were performed in the Uintah Basin (Utah, USA) in the winter
of 2013. The Uintah Basin has about 5000 active gas wells and 3000 active
oil wells in the basin (Utah Well Information Query, 2012). Since 2000, gas
production has increased from 100 BCFE yr
The paper is organized as follows: we first present a detailed description of the CRDS analyzer used in this study, including a thorough discussion of performance, calibration, and cross-interference from other atmospheric constituents. We then describe the mobile laboratory used to perform the measurements, and the methodology employed for characterization of individual sources and regional signals. Results for individual source measurements are presented and compared to studies of the gas composition present in the geologic formations in gas- and oil-producing areas of the basin. These individual source signatures are then used to interpret the regional atmospheric signal using a simple two-end-member model. We conclude the paper with a discussion of the findings and present a future outlook for the measurement technology presented.
The methane and ethane measurements were made with an optical analyzer based
on cavity ring-down spectroscopy (G2132-
CRDS is a method in which laser light is coupled into a resonant optical
cell. The decay rate of the optical power in the cavity is a direct
measurement of the total loss, which includes both absorption loss due to
the gas mixture contained in the optical cell and the loss of the mirrors in
the system. Two separate lasers are used in this spectrometer: one for the
The flow cell has an effective optical path length of 15–20 km; this long path length allows for measurements with high precision (with ppb or even parts per trillion uncertainty, depending on the analyte gas), using compact and highly reliable near-infrared laser sources. The gas temperature and pressure are tightly controlled in these instruments (Crosson, 2008). This stability allows the instrument (when properly calibrated to traceable reference standards) to deliver accurate measurements (Richardson et al., 2012).
Spectra of key species in the frequency ranges employed in the
spectrometer, displaying loss on a log scale vs. optical frequency in
wavenumbers for the low-frequency region (left panel) and high-frequency
region (right panel). The spectra for methane (both isotopologues,
concentration of 2 ppm), water vapor (1 %), and carbon dioxide (400 ppm)
are obtained from the HITRAN spectral database (Rothman et al., 2013), for a
pressure of 148 Torr and
The instrument employs precise monitoring and control of the optical
wavelength, which delivers sub-picometer wavelength targeting on a
microsecond timescale. When the laser is at the proper wavelength and is in
resonance with the optical cell, the laser is turned off. The resulting
decay of optical power, called a ring-down, is measured with a fast
photodetector. From the ring-down decay time the total absorbance of the
system is derived using the equation
The right panel of Fig. 1 displays the spectra of key gas species, which
includes the five analytical species and other atmospheric constituents that
absorb in the 6057 wavenumber region, generated with the HITRAN database
(Rothman et al., 2013) for CH
We note that the spectral region and the analysis algorithms for
The left panel of Fig. 1 displays the spectra of key gas species (the
analytes and other key atmospheric constituents) in the 6029 wavenumber
region. The individual spectra were generated from the HITRAN database
(Rothman et al., 2013) for CH
The
The amplitudes of the
From this nonlinear fit, the water concentration is also determined using
the peak height of the water feature centered at 6028.79 wavenumbers. In
addition, a second independent nonlinear fit is performed in which the model
function of C
Allan deviation for
There are two modes of operation in this instrument. In one mode, called the
“high-precision” mode, the
We have performed a basic assessment of the instrument by measuring gas from
a high-pressure cylinder containing 1.78 ppm CH
In this expression,
The spectroscopic line used to quantify
The operating pressure differs by 8 Torr i.e., 148 Torr in this instrument vs. 140 Torr in the other models. The absorption line used to quantify water vapor is different between this model and the other instrumentation
These two differences lead to small differences in the
The output of the spectroscopic fitting algorithm is a peak height of a line
(or lines) associated with the analyte gas. The function relating the mole
fraction
The constant of proportionality ppb cm
The
In principle, with careful measurements, we could determine calibration
constants for
It is experimentally more straightforward to generate a constant
Dilution by other gas species, such as water vapor, oxygen, or argon,
occurs to both species equally (as a percentage of each species' dry mole fraction),
which means that Spectral line shape effects due to other gas species are likely to have
similar effects on the two methane species.
Throughout this paper, we will consider
Equation (5) relates the spectroscopic measurements of absorption loss at
the peak of the two isotopologues (
First, we consider the case of an ideal spectrometer, for which the
calibration coefficients are constants (i.e.,
Equation (6) can be used to calibrate the instrument, as it relates the
spectroscopically measurable quantities (i.e., the peak height ratio
Isotope calibration experiment, using samples prepared for this
work and then analyzed at a commercial isotope laboratory. The
Setup for measuring dilute mixtures of 2500 ppm standards. The
MFC was set to flows ranging from 2 to 20 sccm. The needle valve was set to
Results of the validation calibration experiment. Top panel: blue
circles: measured isotope ratio (
To validate the calibration over a wider range of delta, we used four
isotope standards at a concentration of 2500 ppm CH
This calibration has a 7 % difference in slope from the initial
calibration. The bottom panel shows the residuals of the linear fit (green
circles), as well as the difference between the original calibration and the
standard values (red values). The two calibration functions give results
that are in reasonably good agreement in the
Note that it is recommended that each instrument of this model be calibrated separately and independently, over the relevant range of delta that will be encountered in the experiment, and with sufficient frequency in time (daily or even more frequently) to track any drift in the analyzer. This topic will be discussed in greater detail below.
The calibration slope parameter
To design an effective calibration and drift correction routine, it is
important to understand how the optical spectrometer drifts, and how best to
correct for that drift. In Sect. S1 in the Supplement, we present the nonlinearity
of the instrument as a function of methane concentration in detail. We
begin by inspecting Eq. (S5, in the Supplement), reproduced below (where we
have not included the nonlinear term
As has been discussed in the Supplement, the net loss offset term
If we substitute in this equation the calibration terms
Equation (12) shows that as the spectrometer drifts, changes in
Setup for drift correction testing.
In this section we describe an experiment in which we track the drift of the
instruments using two bottles. In this experiment, we will ignore the
contribution of the term
There are two bottles in the experiment we performed, shown in Fig. 6: a
high concentration bottle (HI) of about 10 ppm CH
Measured isotope ratio on three bottles, HI (10.1 ppm CH
Figure 7 shows the isotope ratios measured by the instrument for each of the
three bottles over time. Clearly, there is significant drift in the
instrument. The lower concentration bottles drift much more than the high
concentration (about 5 vs. 1 ‰),
indicating that
For each cylinder (HI and LO) measurement during each hour, we may derive an
equation based on Eq. (12) that describes the terms
In the above expressions,
In addition, we define
Using these definitions and Eq. (13), we can derive the time-dependent drift
parameters
Measurements of the drift correction parameters
Hourly measurements of
In other words, for each hour, we can determine the calibration factors
Summary of cross-interference due to direct absorption on
Allan standard deviation of
Using these calibration constants, we can calculate
It is clearly important that for the instrument to be of practical use, it
must precisely and accurately report the analytic quantities (
These two gas species do not have any direct spectroscopic absorption in the spectral regions used in the analyzer to quantify methane. They affect the reported methane as a shift in the reported isotope ratio that is proportional to the concentration of the gas. This shift is independent of the methane concentration. Note that the balance gas is nitrogen in these experiments. We note that while it is possible for argon or other inert gases (e.g., helium) to be present in natural gas plumes, we expect that the low concentrations that would be present in the dilute downwind plume to affect the isotope ratio measurement negligibly.
These two gas species have direct absorption features that are in the
vicinity of the
We have investigated the effect of these gases on the measurement of
The effect of carbon monoxide was not measured (as was the case of the above gases), but estimated from the spectral database HITRAN (Rothman et al., 2013). The spectroscopy of this simple molecule is extremely well understood. Our spectroscopic modeling shows that at the same concentration as methane, the effect on the measured isotope ratio is expected to be negligible. The effect could be more pronounced in an atmosphere rich in carbon monoxide.
For the experiments in the Uintah Basin, a small consumer sport utility vehicle is used as the mobile platform. An instrument (G2132-i, SN FCDS2004) is placed in the rear compartment of the vehicle. Power is supplied directly from the vehicle's 12V DC electrical system via heavy gauge cables attached directly to the terminals of the vehicle battery. A 1000 W capacity DC to AC modified sine-wave inverter (Power Inverter 1000W, West Marine, Watsonville, CA, USA) is used to supply power to the instrument, pumps, and other equipment in the vehicle. For safety, 40 A DC fuses are placed in line at the battery under the hood of the vehicle, and at the inverter, and care is taken to ensure that all the equipment is properly grounded to the vehicle frame.
A high-precision GPS (R100, Hemisphere GPS, Scottsdale, Arizona, USA) is used for geolocation. The receiver antenna is affixed to the vehicle roof, and the 1 Hz positional data is integrated into the CRDS instrument data stream. A 2-D sonic anemometer is mounted 1.0 m above the roof to be out of the slipstream of the vehicle. Measurements of the wind, transverse and longitudinal to the car's orientation, are also integrated into the instrument data stream. Positional information from the GPS is used to remove the vehicle motion from the measured wind to determine the ground wind velocity.
Apparatus for measuring the isotope ratio and ethane-to-methane ratio of single plumes in the mobile lab. For survey mode, all three-way valves are in “normally open” (N.O.) position (white), and the instrument measures the real-time signal at the input to the 770 cc storage tube. The flow through the tube is set by the pump and needle valve. When a plume is detected at the instrument, the three-way valves are switched to “normally closed” (N.C.) position (gray) and the instrument slowly re-analyzes the gas stored in the long tube.
The mobile laboratory was used to measure the source signatures (i.e., of
To quantify the isotopic signature of each individual plume, we have
followed the analysis described by Miller and Tans (2003), which is
analogous to the Keeling analysis performed in Pataki et al. (2003). Both
analysis methods rely on a simple two-member mixing model for source and
background gas. The data from each plume measurement are analyzed with the
following expression:
Over a period of 5 days, 56 separate plume analyses were made in the
Uintah Basin. Of the 56 individual source measurements, we have discarded
measurements for which the uncertainty of the measurement was greater than
The emissions sources of methane and ethane in the Uintah Basin together with the background concentrations of these gases determine the observed atmospheric signals. We first derive expressions relating the observed atmospheric concentrations and ratios given the background signals and the emissions quantities.
Consider first the emission of methane into the atmosphere. The observed
concentration of methane is given by the expression below:
At any given point in space and time, the term
We can write a similar expression to Eq. (17) for the tracer gas (in this
case, either ethane or
Solving to remove atmospheric dilution from these expressions, we find
Apparatus for making regional isotope and ethane ratio
measurements. The top schematic shows the mobile sampling system. The flow
through the long storage tube is set by the pump and needle valve. The two
manual valves (V
In this expression,
Using Eq. (21), if we can measure
How can we determine the source emission ratio
Note that the source emission ratio
These expressions can be used to quantify the source signatures (
To quantify the emission rates of oil wells relative to gas wells, we need a
method for collecting a representative sample of the air in the Uintah
Basin. We have designed a system that samples gas over long periods of time
from the mobile lab. This gas sample is analyzed in a stationary laboratory,
where careful calibration and longer measurement times can be brought to
bear to improve the precision and accuracy of the measurements
of
To ensure that the gas sampled in the storage tube is representative of the
regional air, it is important to have a good understanding of the inlet flow
of the system. Under constant pressure conditions at the inlet of the
system, the flow at the inlet
The bottom panel of Fig. 13 shows the laboratory reanalysis system. The
reanalysis can occur at a much slower flow rate (about 17 sccm for
experiments described here), leading to improved precision on the isotope
and ethane analysis, and the overall accuracy and drift of the system is
improved by using one or more calibration standards. For the measurements
described here, we used a single cylinder at 1.85 ppm CH
To associate a particular measurement made in the laboratory during reanalysis to a specific location on the drive, it is necessary to properly resynchronize the time axes, and to account for gas diffusion in the tube. This is accomplished using the following procedure:
The time axis during the “recording” phase is never adjusted. The reanalysis time axis (called “replay” time) is first shifted by the
time delay between the end of the recording and the beginning of the reanalysis. The flow in the storage tube is reversed during reanalysis; for this reason,
the replay time axis is reversed. The time axis is compressed by the ratio of reanalysis flow to recording
flow (or To compare the methane signal measured during reanalysis to the recorded
methane signal, a smoothing function is convolved with the recorded methane time
series. This smoothing function is simply the Green's function for 1-D diffusion:
Finally, to account for flow differences in different parts of the drive,
the replay time axis is compressed or expanded using a cubic spline function with
15 knots across the time axis (every 300 s), minimizing the time mismatch between
the smoothed recorded methane signal and the reanalyzed signal.
Top panel: cyan denotes the methane signal recorded in the vehicle over about 1.6 h on 3 February 2013. Some individual plumes are as large as 30 ppm. Black denotes the methane signal recorded in the vehicle, after applying the smoothing function derived from gas diffusion in the storage tube. Red denotes the methane signal obtained during reanalysis of the gas stored in the tube over about 24 h of analysis time. The time axis of the reanalyzed signal has been adjusted according to the ratio of the flows during recording and reanalysis. To account for flow differences in different parts of the drive, the replay time axis is compressed or expanded using a cubic spline function with 15 knots across the time axis (every 300 s), optimizing the time mismatch between the recorded methane signal and the reanalyzed signal. These time shifts are shown in the bottom panel (blue points) (where a positive value indicates that the reanalysis time should be shifted later, indicative of a reduced flow into the tube). The purple line in the bottom panel is the time shift predicted by the inlet flow model for this drive described in the Supplement – the only free parameter was an overall offset to the modeled time shift.
The result of this procedure is shown in Fig. 14. The reanalyzed data
reproduces the smoothed in-vehicle measurements well, except for the most
narrow methane plumes observed during the drive (e.g., at
Dozens of individual narrow plumes (< 10 s in duration) are
visible in Fig. 14. These plumes are due to sources that are relatively
close to the vehicle (
A total of three regional drives were performed in the winter of 2013, with
a total distance travelled of 314 km. The regional fractions for these
drives were 90, 92, and 91 %, for an average of 91 %. For each
drive, data from the reanalysis are averaged in 5 min blocks,
corresponding to a time resolution of about 1 min, or 1 km along the path
of the vehicle. In this way, we obtain values for CH
The reanalysis algorithm provides a functional relationship between the time axis of the recorded data to that of the reanalyzed data. Using this relationship, we associate the isotope and ethane signatures measured in the laboratory with the latitude and longitude recorded in the vehicle. A map of the Uintah Basin, showing the locations of gas and oil wells is shown in Fig. 15, along with the recorded isotope signatures for the three drives. A clear geographic dependence of the isotope ratio can be seen, with the heaviest values observed in the primary gas production area in the southeast of the basin, and the lightest values observed in the oil-producing region in the west. The concentration signatures were also highest in the gas-producing area, although, because the data were collected on different days with different atmospheric conditions, direct comparison is difficult.
Figure 16 shows a Keeling plot of the observed isotope ratios (left panel) and the ethane-to-methane ratio (right panel) from these drives.
Map of the Uintah Basin, showing the locations of oil wells
(blue) and gas wells (red). The axes are in degrees of longitude (
Left panel: Keeling plot of
We can make three clear observations from these data:
The regional isotope signatures are consistent with the gas wells as
the predominant emissions source. The regional ethane-to-methane ratio is about 9.6 %, which is close
to the gas source average ratio of 11.8 % (the centroid of the gas well
ethane-to-methane ratio from Fig. 12), and significantly different than the oil
well signature of 22.3 % (also from Fig. 12), although we note that the uncertainty
in the ethane ratios is very high for both populations (7.5 and 13 %, respectively). Even in the predominantly oil-producing western region (1 February drive),
the observed isotope ratio, which shows significant deviation to lighter isotope
values, implies that the observed signal is a mixture of nearby oil sources and the more distant gas sources.
Taken together, these observations provide strong evidence that the gas wells are the predominant source of methane in the Uintah Basin. We note that these measurements were purposely made during a period of time when an atmospheric inversion was present in the Uintah Basin. There is only one opportunity for air to flow out of the basin (the Green River valley to the south), and the methane accumulated in the basin atmosphere over several days. It is therefore not surprising that the dominant emissions source signature (in this instance, the gas wells) would be visible throughout the basin, even in the western region.
To quantify the emission ratio, we apply the following algorithm. First, we
consider each point in the left panel of Fig. 16, which corresponds to the
isotope ratio at a specific location on one of the drives. From this isotope
ratio, we can calculate a “local” isotope ratio at any position
If we define the fraction of emissions from gas wells observed at
location
To derive the total regional ratio
It is difficult to quantify the local emissions
If we continue to use the assumption that the emissions-weighted end members
of gas and oil sources are
It is not a surprising result that the majority of methane emissions in the Uintah Basin are from natural gas wells. The 2012 emission study performed in the Uintah (Karion et al., 2013) focused on the gas-producing eastern portion of the basin, but about 1000 of the 3000 oil wells in the basin. The western portion of the flight path used in that study traverses the western oil-producing region. This segment of the flight path does not show a significant methane enhancement, in support of the conclusion that the gas field is the dominant source of methane in the region.
To quantify the uncertainty in the fractional emissions estimate, we perform
a Monte Carlo uncertainty analysis, by varying the following parameters
(normally distributed) around their nominal values:
If the isotope ratios of the sources sampled are not representative of the
emissions-weighted distribution of well signatures, then uncertainty in the
mean of the sampled distribution underestimates the uncertainty in the
centroid of the full population. The uncertainty in the end members should
be increased to encompass the populations that were not sampled. Assuming
that the population is at least somewhat representative, and noting that the
observed range in isotope values and ethane ratios is typical of oil- and
gas-producing geological formations, then the standard deviation of the sampled
distribution should represent a reasonable upper limit for the uncertainty.
In this situation, a substantial fraction of the Monte Carlo realizations
are nonphysical i.e., the isotope ratios observed in the regional sampling
can exceed the simulated gas well end member, leading to calculated emission
ratios that exceed 1 at times. However, even with these highly uncertain end
member populations, > 98 % of the realizations predict that
Finally, we note that the three drives that contributed to this study are
not necessarily representative of the total air mass in the basin. We can
obtain one estimate of this sampling uncertainty by noting that the drive in
the oil-producing region lead to an estimate of 73 % for the emission
ratio, and drives in the gas-producing region lead to estimates of 93
and 91 %, for a total span of about
In this paper, we present a comprehensive approach to emissions attribution,
using an innovative CH
As an example of the type of research that can be performed with this
instrument, field measurements were performed in the Uintah Basin (Utah,
USA) in the winter of 2013, using a mobile lab equipped with the CRDS
analyzer, a high-accuracy GPS, a sonic anemometer, and an onboard gas
storage and playback system. With an extremely small population and almost
no other sources of methane and ethane other than oil and gas extraction
activities, the Uintah Basin represents an ideal location to investigate and
validate new measurement methods of atmospheric methane and ethane. We
present the results of measurements of the fugitive emissions from 23
natural gas wells and six oil wells in the region. The
The authors would like to thank Colm Sweeney, Gaby Petron, Anna Karion, Sonja Wolter, Tim Newberger, and Bruce Vaughn for experimental support and helpful scientific discussions during this project. Edited by: M. Hamilton