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. 2023 Jul 28;57(32):11823–11833. doi: 10.1021/acs.est.3c01121

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

William S Daniels †,*, Jiayang Lyra Wang ‡,§, Arvind P Ravikumar ‡,§, Matthew Harrison ∥, Selina A Roman-White ⊥, Fiji C George ⊥, Dorit M Hammerling †,§
PMCID: PMC10433519  PMID: 37506319

Abstract

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Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed, site-level emission inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that operator-level top-down methane measurements are lower during the end-of-project phase than during the baseline phase. However, gaps persist between end-of-project top-down measurements and bottom-up site-level inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) validate specific snapshot measurements and determine how they relate to the temporal emission profile of a given site and (ii) create a measurement-informed, site-level inventory that can be validated with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how to reconcile CMS rate estimates and top-down snapshot measurements jointly with bottom-up inventories at the site level. More broadly, it demonstrates the importance of multiscale measurements when creating measurement-informed, site-level emission inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as the Oil and Gas Methane Partnership 2.0, and corporate strategies.

Keywords: methane emissions, oil and gas, measurement-informed site-level inventories, multiscale measurements, continuous monitoring systems

Short abstract

We demonstrate the importance of multiscale measurements, including continuous monitoring systems, when creating measurement-informed, site-level methane emission inventories.

1. Introduction

The oil and gas sector has been the focus of recent regulatory and voluntary corporate initiatives aimed at measuring and mitigating methane emissions.1,2 Specifically, both domestic and international policies have emphasized the importance of developing accurate, measurement-informed methane emission estimates at the site level. In the United States, the Inflation Reduction Act applies a methane fee to oil and gas operators that is a function of site-level emission estimates.3 Internationally, the European Union has adopted quantification, monitoring, reporting, and verification requirements for oil and gas exporters at the site level.4 Furthermore, voluntary methane mitigation initiatives such as the Oil and Gas Methane Partnership (OGMP) 2.0 require source- and site-level reconciliation of measurements with inventory estimates at their strictest reporting levels.2

In the United States, the Environmental Protection Agency’s (EPA) mandatory Greenhouse Gas Reporting Program (GHGRP) Subpart W is the current regulatory standard for estimating and reporting annual methane emissions from covered sites.5 The GHGRP regulations rely on activity-based or bottom-up inventory methods, which have been shown to underestimate total emissions across multiple basins.6−12 Bottom-up inventories often represent month- or year-long averages and hence do not capture complex emission characteristics like temporal variability nor do they account for differences in operational practices between operators.5 Furthermore, a recent National Academies of Sciences report compared the GHGRP to a recommended framework and concluded that certain emission sources may be unaccounted for in the GHGRP.13

Temporal variability is now understood to be an important characteristic of methane emissions from the oil and gas sector, and measurement-informed, site-level emission estimates must be able to account for it. Prior multiscale measurement campaigns have demonstrated the importance of temporal variation in emissions when reconciling estimates from different methods.14−19 For example, Wang et al.8 find that aerial measurements of oil and gas sites taken on the same day can vary by over an order of magnitude, and Vaughn et al.20 show that episodic venting from manual liquid unloadings can cause a factor-of-two variation in emissions. Multiple studies have shown that emissions follow a heavy-tailed distribution,21−23 meaning that a small number of very large emissions represent a large fraction of total emitted volume. This further emphasizes the importance of accurately capturing temporal variability at multiple scales, as short-lived, large volume emission events are often drivers of total emitted volume.

Top-down snapshot measurements taken infrequently are poorly suited for characterizing highly variable emissions at the site level, as any given measurement may or may not capture a short-lived emission event. Note that in this work, we refer to top-down snapshot measurements, such as satellite, aerial, or drone overflights, as simply “top-down” measurements. Schissel and Allen24 quantify the sampling error introduced by top-down measurements, finding that monthly measurements of sites with emissions lasting for 1 h result in an average absolute percent error of 23% in annualized emission estimates. This error assumes zero measurement error and is solely a result of infrequent sampling. Shorter emission events and less-frequent measurements result in larger errors. Schissel and Allen24 also find that quarterly and semiannual measurements result in systematic underestimation of total annual emissions. Hence, measurement technologies that operate at a higher sampling frequency are an important aspect of a measurement portfolio aimed at characterizing site-level emissions that vary temporally.

Continuous monitoring systems (CMS) provide measurements in near real-time and hence are a promising avenue for complementing top-down measurement technologies. Point-in-space CMS measure ambient methane concentrations at fixed sensor locations rather than emission rates and typically require a method for translating concentration data into emission rate estimates. Methods for doing so are available in the published literature25−30 and are also developing rapidly in the private sector. Bell et al.31 evaluate 11 of these proprietary solutions and find that event detection and quantification performance vary widely across solutions, with average quantification errors ranging from −40 to 93% for both single- and multisource emissions > 1 kg/h. Despite the generally poor quantification performance of these 11 proprietary solutions, improvements to quantification algorithms are occurring rapidly both in the private sector and publicly, with Daniels et al.30 having an average quantification error of 4.3% for single-source emissions > 1 kg/h.

In this work, we demonstrate the importance of multiscale measurements when creating measurement-informed, site-level emission inventories. We present top-down measurements from an 11 month field campaign embedded in a multiscale monitoring protocol spanning three U.S. shale basins, finding that high-frequency data is critical for reconciling bottom-up inventories and top-down measurements at the site level. Specifically, we use CMS to (i) validate specific top-down measurements and determine how they relate to the temporal emission profile of a given site and (ii) create a measurement-informed, site-level inventory that can be validated with top-down measurements to update conventional bottom-up inventories.

2. Methods

2.1. Project Design

The field campaign discussed in this work was embedded in the quantification, monitoring, reporting, and verification (QMRV32) protocol that consists of three measurement phases: baseline measurements, enhanced monitoring, and end-of-project measurements. Five upstream oil and gas operators located in the Marcellus, Haynesville, and Permian basins enrolled 39 sites in the QMRV protocol with a total production of about 400 million cubic feet of gas per day. In the Haynesville basin, all enrolled sites produce gas but no oil. In the Marcellus basin, one enrolled site produces both oil and gas, while the others produce only gas. In the Permian basin, all enrolled well pads produce both oil and gas. Enrolled sites in the Permian are a mix of well pads and tank batteries. Across basins, the enrolled sites have distinct equipment groups (e.g., tanks, wellheads, and separators) and range from about 300–700 feet in diameter. Basin and operator names are anonymized to protect data confidentiality. 8 sites were enrolled in “Basin A”, 5 sites were enrolled in “Basin B”, and 26 sites were enrolled in “Basin C”. Only 20 sites in Basin C are used in this analysis, as 5 were enrolled as backup sites in case production stopped at any of the others and 1 was not producing during baseline measurements and hence was excluded from all analysis.

Methane emissions were measured at all sites during the baseline and end-of-project phases and then compared against operator-supplied bottom-up inventories. Emission rates are reported in standard cubic feet per hour (scfh) to be consistent with Wang et al.,8 with 15 °C and 1 atm as the standard conditions. Bottom-up inventories were prepared according to the GHGRP regulations5 and supplemented with additional emission sources not required by the GHGRP or known to be under-reported by the current GHGRP emission factors (e.g., methane slip in compressor engines, produced water tank emissions, vessel blowdowns, compressor blowdowns, and pressure relief valves).8 The operators tailored these inventories to each measurement phase by including operational activity from the time of measurements to enable better comparison to top-down measurements. Analysis of the measurement data was conducted independently from the participating operators and was verified by a third-party scientific team that was not directly involved in the measurement campaign. A list of the supplemental emission sources included in the bottom-up inventories is given in Section S3 of Wang et al.8

Four snapshot measurement technologies were used for the baseline measurements: an optical gas imaging (OGI) camera paired with a Hi-flow sampler that encompasses the entire emission source,33 a drone-based mass balance technology provided by SeekOps Inc. (“SeekOps”), an aerial LiDAR plume identification system provided by Bridger Photonics (“Bridger”), and satellite measurements provided by GHGSat. All four technologies have undergone controlled tests and field trials, with the results made public in peer-reviewed studies.34−39 During the 6 month enhanced monitoring phase, operators conducted weekly audio, visual, and olfactory (AVO) inspections and monthly leak detection and repair (LDAR) surveys using either OGI cameras or Bridger measurements. Additionally, operators submitted monthly bottom-up inventories and installed CMS on some or all of their enrolled sites during this phase. End-of-project measurements were conducted using only SeekOps and Bridger but otherwise followed the same methodology as in the baseline phase. Details on the top-down measurements and CMS are given in Sections 2.2 and 2.3, respectively.

2.2. Top-Down Measurements

Multiple top-down measurements were taken at each site during both baseline and end-of-project phases, with measurements from different technology vendors aligned as closely in time as logistically possible. See Section S1 in the Supporting Information for the full measurement schedule.

SeekOps surveyed each site 1–2 times, taking between 1 and 3 h per site. At each site, SeekOps provides equipment-level measurements that we sum to create a site-level measurement. SeekOps requires site access to take measurements and has a stated quantification uncertainty of ±30% for individual measurements (A. Aubrey, personal communication, July 19, 2021). Bridger surveyed each site 6–11 times over 4–5 days, with each overflight taking less than a minute. Each Bridger overflight provides equipment-level emission measurements. We define a “round” of Bridger measurements as a group of closely timed overflights of a given site (typically within a few minutes of each other), and we average all measurements within each round at the equipment level. We then sum the average equipment-level measurements from each round to create a site-level measurement for that particular round. Bridger does not require site access to take measurements and has a 95% uncertainty interval of −64.1 to 87.0% for individual measurements.40 However, to be consistent with previous publications related to the production QMRV project,8 we use the older 1−σ quantification uncertainty of ±31 to 68% from Johnson et al.37 in this work. Note that the uncertainties listed here are for individual measurements and not for multimeasurement averages, which have smaller uncertainty bounds.

Bridger measurements can be grouped into two categories: below-detection-threshold measurements and above-detection-threshold measurements (henceforth “positive measurements”). We define below-detection-threshold measurements as instances in which Bridger was “successful” in conducting a pass over the site but found no emissions in the flight path, either because there truly were no emissions or because there were emissions below Bridger’s detection threshold. Below-detection-threshold measurements are treated as zeros in this work. This is because Bridger identifies emissions at the equipment level and hence assigning a nonzero value to below-detection-threshold measurements would require an assumption on how many pieces of equipment were emitting at this level. To avoid an arbitrary choice, we treat below-detection-threshold measurements as zeros. Note that this procedure will likely introduce a slight negative bias. However, we expect this bias to be minor given that Bridger’s stated 90% probability of detection threshold is relatively low at 3 kg/h. Future work will develop more sophisticated tools to estimate below-detection-threshold emissions.

To compare the top-down measurements to bottom-up inventories, we create a top-down average (TDA) using site-level measurements from SeekOps and Bridger. Ground-based OGI + Hi-Flow measurements are not included in the TDA because they are poorly suited for capturing total site-level emissions.41−43 Satellite measurements from GHGSat are also not included because of the system’s high detection threshold (100 kg/h38), which we determined to be out of line with the project’s focus on both small and large emissions. We create two levels of TDA: site and operator level. The site-level TDA is the average of all site-level measurements from Bridger and SeekOps for a given site, and the operator-level TDA is the average of all site-level TDAs for a given operator.

2.3. Continuous Monitoring Systems

The CMS technologies deployed during the enhanced monitoring phase all use a network of point-in-space sensors that measure ambient methane concentrations at the sensor locations. These CMS technologies are intended for equipment- or site-level emissions monitoring, with 1–5 sensors typically deployed around the perimeter of each site. Some or all of these sensors, depending on the technology vendor, come equipped with an anemometer that measures wind speed and direction. In this study, there was at least one anemometer per site. Sensors provide concentration and wind measurements every 1–15 min, with the exact frequency depending on the technology vendor. The precision and accuracy of the CMS sensors used in this study are not publicly available nor were they made available to the scientific team.

To provide context to the end-of-project measurements, we use the raw concentration and wind data from the CMS to estimate emission source location (localization) and rate (quantification) on fixed 30 min intervals. This is done using the modeling framework proposed by Daniels et al.,30 which was tested on nonblinded, single-source controlled releases at the Methane Emissions Technology Evaluation Center (METEC). For emissions >1 kg/h, the framework has an average quantification error of 4.3%, and 90% of rate estimates fall within a factor of [−2.0, 1.8] or a percent difference of [−49%, 79%] from the true emission rate. At a high level, the framework simulates methane concentrations from all potential sources on a given site using the Gaussian puff atmospheric dispersion model.44 The simulation predictions are then pattern matched against the actual CMS concentration observations to determine the most likely emission source and rate. Quantification uncertainty is provided by resampling the available data many times, pattern matching on each sample, and then taking the 5th and 95th percentiles of the resulting sample-specific rate estimates. See Section S2 in the Supporting Information for a more detailed description of this framework.

The modeling framework can accommodate concentration data from any CMS technology vendor that uses a network of point-in-space sensors. We reiterate that we do not use the rate estimates created by the CMS technology vendors in this work, which were not made available to the scientific team. The framework assumes that the emission source location and rate are constant within each 30 min interval, which may introduce errors if the true source or rate changes during a given interval. The framework further assumes that all emissions come from a single source within each interval, which may introduce further errors if multiple sources are emitting simultaneously. Note that these errors are discussed fully by Daniels et al.30

3. Results

3.1. Top-Down Measurement Trends from Baseline to End-of-Project

Overall, site-level emission measurements are lower during the end-of-project phase than those during the baseline phase. Figure 1 shows the distribution of positive site-level measurements from Bridger and SeekOps in each basin during baseline (orange) and end-of-project (blue). The average emission rate over all rounds of measurements from each phase (including both below-detection-threshold and positive measurements) is marked with a color-coded vertical line, and bootstrapped 95% confidence intervals are shown with shaded areas. Note that these confidence intervals do not account for uncertainty in the individual measurements. We use the mean rather than the median as a summary statistic to better capture the influence of large, infrequent emissions.

Figure 1.

Figure 1

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Histograms of site-level positive measurements from Bridger and SeekOps during baseline (orange) and end-of-project (blue). Below-detection-threshold measurements are excluded from the histograms but are included in the averages (dashed vertical lines). The percentages of below-detection-threshold measurements are listed for each basin. The horizontal axis shows emission rate on a log scale, and the vertical axis shows the corresponding counts. Note that the vertical axes have different scales. Bootstrapped 95% confidence intervals for the average emission rate are shown as shaded areas.

The average decreases from baseline to end-of-project in all three basins, with nonoverlapping confidence intervals in Basins A and C. Note that including additional uncertainties (e.g., on individual measurements) may cause these confidence intervals to overlap. The average emission rates for baseline and end-of-project are 1011 and 387 (Basin A), 474 and 223 (Basin B), and 440 and 150 scfh (Basin C), respectively. This reduction is driven by more below-detection-threshold measurements and fewer large site-level emission measurements (>1000 scfh). The proportion of below-detection-threshold measurements increased from 22 to 35% in Basin A, 17 to 37% in Basin B, and 46 to 50% in Basin C from baseline to end-of-project. The number of site-level large measurements fell from 18 (20%) to 5 (9%) in Basin A, 7 (19%) to 3 (9%) in Basin B, and 14 (7%) to 1 (1%) in Basin C from baseline to end-of-project.

Equipment-level emission attribution was performed in Basins A and B. This was not possible for Basin C as SeekOps could not fly their drones close enough to distinguish between equipment groups due to operators’ safety requirements. We attribute equipment-level Bridger data to named equipment groups as identified by SeekOps, and hence equipment-level attribution was not possible in this basin from either top-down measurement technology. Figure 2 shows the distribution of positive measurements by the major equipment groups: gas processing unit (GPU)/separator, tanks, and wellheads. The average emission rate during each phase (again including below-detection-threshold measurements) is marked with a color-coded vertical line, and bootstrapped 95% confidence intervals are shown with shaded areas.

Figure 2.

Figure 2

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Histograms of equipment-level positive measurements from Bridger and SeekOps during baseline (orange) and end-of-project (blue). Represented equipment groups include GPU/separators, tanks, and wellheads. Below-detection-threshold measurements are excluded from the histograms but are included in the averages (dashed vertical lines). The percentages of below-detection-threshold measurements are listed for each basin. The horizontal axis shows emission rate on a log scale, and the vertical axis shows the corresponding counts. Note that the horizontal and vertical axes have different scales. Bootstrapped 95% confidence intervals for the average emission rate are shown as shaded areas.

The general decrease in top-down measurements from baseline to end-of-project is seen in almost all equipment groups, implying that this trend is not driven solely by one equipment group. In Basin A, average emissions for all three equipment groups decreased from baseline to end-of-project, with tanks showing the greatest reduction of 68% from 590 to 189 scfh. In Basin B, the average emission rate for tanks and wellheads decreased from baseline to end-of-project by 84 and 92%, respectively, and remained similar for GPU/separators. See Section S3 in the Supporting Information for details.

Finally, we compare individual site-level Bridger measurements from both the baseline and end-of-project phases to bottom-up site-level inventories provided by the operators in Figure 3. See Section S4 in the Supporting Information for the SeekOps measurements. The horizontal axis shows the ratio of Bridger measurements to the corresponding inventories on a log scale, with ratios larger than 1 (100) meaning that the Bridger measurements were larger than the corresponding inventories. The solid vertical line marks a ratio of 1 where measurements and inventories are identical. Individual measurements are both higher and lower than the corresponding bottom-up inventories, depending on the time of measurement and presence of intermittent emission events. The average Bridger measurement to inventory ratio decreased between baseline and end-of-project from 4.8 to 4.3 for Basin A and 208.0 to 30.0 for Basin C. This ratio increased from 1.3 to 4.2 for Basin B.

Figure 3.

Figure 3

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Histograms of the ratio between site-level positive Bridger measurements and the corresponding bottom-up inventory during baseline (orange) and end-of-project (blue). Below-detection-threshold measurements are excluded from the histograms but are included in the averages (dashed vertical lines). The percentages of below-detection-threshold measurements are listed for each basin. The horizontal axis shows the ratio on a log scale, and the vertical axis shows the corresponding counts. Note that the vertical axes have different scales. The solid vertical line marks a ratio of 1 where measured emissions equal inventory. Bootstrapped 95% confidence intervals for the average ratios are shown as shaded areas.

We now consider aggregated measurement trends from baseline to end-of-project at the operator and site level. Operator-level top-down averages (TDAs) decreased by a range of 40–92% from baseline to end-of-project, depending on the operator. Site-level TDAs decreased from baseline to end-of-project at most sites: 6 out of 8 in Basin A, 4 out of 5 in Basin B, and 11 out of 20 in Basin C. However, discrepancies between site-level TDAs and bottom-up inventories persist during end-of-project measurements. In Basin A, the ratio of site-level TDA to inventory ranges from 0.1 to 25 across the eight sites. In Basin B, this ratio ranges from 0.5 to 17 across the five sites. In Basin C, this ratio ranges from 0 (below-detection-threshold) to 104 across the 20 sites. In line with findings from Wang et al.,8 this wide range means that site-level inventories can be both lower and higher than site-level TDAs, even though they consistently underestimate at the operator level.

Despite the reduction in operator-level TDAs from baseline to end-of-project, gaps still persist between top-down measurements and respective bottom-up inventories. This could be the result of (i) the snapshot nature of top-down measurements, meaning that Bridger and SeekOps happened to observe larger than average emissions during end-of-project measurements or (ii) underestimated bottom-up inventories. Disentangling these potential root causes requires high-frequency measurements to characterize intermittent emission events, as this will give an indication of whether a top-down measurement observed unusually large emissions for a given site.

3.2. Reconciling Inventory and Measurements Using High-Frequency CMS Data

To demonstrate how CMS data can be used to reconcile bottom-up site-level inventories with top-down measurements, we present two case studies at sites where the difference between bottom-up inventory and end-of-project TDA was greater than 50%.

3.2.1. Case Study 1

Here, we investigate a gap of 96% between operator-supplied, bottom-up inventory (20.5 scfh) and end-of-project TDA (508 scfh) on a site owned by “Operator 1”. A schematic of this site is shown in Figure 4a. The site-level TDA was highly influenced by a large Bridger measurement of 2814 scfh from the tanks. Bridger was not fully confident in the accuracy of this measurement due to standing water on the site at the time of measurement. However, given the research nature of this project, the measurement was still provided to the scientific team for analysis. Note that additional Bridger measurements were performed on a subsequent day to replace the measurements taken while standing water was present.

Figure 4.

Figure 4

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CMS data is used to validate a large top-down measurement without the use of a quantification algorithm. (a) Schematic of the site with colored rectangles showing potential emission sources and black pins showing CMS sensor locations. (b) Distribution of wind data during the 6 h time period shown in (c). Wind direction indicates the direction the wind is coming from. (c) SSE sensor observations and simulated concentrations assuming a constant 2814 scfh tank emission. Time of the Bridger measurement is shown with a dashed, vertical line.

We can use CMS data collected on this site to validate the large (2814 scfh) Bridger measurement. Specifically, we run the Gaussian puff atmospheric dispersion model using the large Bridger measurement as the presumed emission: the emission source is set to the tanks, and the emission rate is set to 2814 scfh. If the simulated concentrations using these assumptions closely match the actual CMS observations, then it is very likely that the 2814 scfh measurement is valid. Figure 4c shows the simulated concentrations at the SSE sensor along with the actual observations. We select this sensor for the visualization as it was the only downwind sensor during the Bridger measurement and hence the only sensor to observe concentration enhancements. See Figure 4b for the wind data during this time period.

Close alignment between sensor observations and simulated concentrations in Figure 4c provides evidence that the 2814 scfh measurement can be trusted. In fact, it reveals that the emission likely started at least 2 h before the Bridger measurement and lasted at least an hour after. Given this analysis, we include the 2814 scfh measurement in the site-level TDA despite concerns over standing water.

We have shown an example of how CMS data, even without quantification capabilities, can be used to validate top-down measurements. However, we have not yet assessed the root cause of the gap between bottom-up inventory and end-of-project TDA, namely, if it is due to inaccurate extrapolation of the top-down measurements given the temporal variability of emissions or due to an underestimated inventory. We can do so by applying the localization and quantification framework described in Section 2.3 to 6 months of CMS data collected on the site. This means that we no longer assume the source location (tanks) and rate (2814 scfh) of the Bridger measurement discussed earlier but instead allow the framework to pick between the three sources shown in Figure 4a and estimate an emission rate during periods of enhanced concentrations.

Figure 5a shows a short time series of the actual CMS observations and the simulated concentrations scaled by the estimated emission rates shown in Figure 5b. Intervals without a rate estimate were deemed unsuitable for quantification by the framework due to poor alignment between simulated concentrations and actual observations. The Bridger measurement discussed earlier (2814 scfh) is shown with a green triangle in Figure 5b and overlaps with the CMS rate estimate when considering the confidence intervals of both measurements. This again illustrates the complementary nature of multiscale measurements. The Bridger and CMS rate estimates are derived using different measurement data and analytical techniques, and hence their alignment provides evidence that both technologies are creating reasonable estimates. The full time series of CMS localization and quantification estimates spanning the 6 month monitoring period is provided in Section S7 of the Supporting Information.

Figure 5.

Figure 5

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Quantification using CMS data reveals an unusually large top-down measurement. (a) SSE sensor observations (black) and simulated concentrations (yellow, red, or blue) scaled by the corresponding rate estimate in (b). Colored rectangles in (a,b) show the start and end times of the 30 min intervals during which quantification is performed, with color corresponding to the localization estimate. (c) Histogram of all CMS rate estimates over the 6 month study period with Bridger measurements shown as vertical lines. (d) Same histogram as (c) but zoomed in to show detail. Operator-supplied bottom-up inventory is shown as a dashed line and average of CMS rate estimates is shown as a solid, magenta line. Note that histograms in (c,d) show counts in thousands of emission rate estimates.

Figure 5c shows a histogram of the CMS rate estimates over the 6 month monitoring period. All of the end-of-project Bridger measurements are shown with vertical lines. The 2814 scfh Bridger measurement falls above the 99th percentile of all CMS rate estimates, providing evidence that temporal variability and the snapshot nature of top-down measurements had a large impact on the gap between bottom-up inventory and TDA on this site. See Section S5 in the Supporting Information for a more detailed comparison of top-down and CMS rate estimates for Operator 1.

Figure 5d shows the same histogram as Figure 5c but zooms in on [0, 300] scfh. The average CMS rate estimate is shown with a solid, magenta line, and the operator-supplied bottom-up inventory is shown with a dashed, black line. Comparing these values indicates that an underestimated inventory may have also contributed to the gap between inventory and TDA as the average CMS rate estimate is nearly 3 times larger than the inventory. Note that the average CMS rate estimate is influenced by a small number of very large rate estimates, which is a desirable effect, as long-term inventories should capture not only the small routine emissions but also the large infrequent emissions.

This case study illustrates the benefit of multiscale methane measurements, as each measurement technology can be cross-referenced against the others. Furthermore, it demonstrates how high-frequency CMS data can be used to reconcile bottom-up inventories with top-down measurements by placing the top-down measurements within the temporal emission profile of a given site. In the following case study, we demonstrate how CMS rate estimates can be used to create a measurement-informed, site-level inventory that can then be used to update conventional bottom-up inventories.

3.2.2. Case Study 2

We now investigate a gap of 94% between operator-supplied, bottom-up inventory (43.4 scfh) and end-of-project TDA (726 scfh) on a site owned by “Operator 2.” A schematic of this site is shown in Figure 6a. We apply the localization and quantification framework described in Section 2.3 to 7 months of CMS data collected on the site. Sections S6 and S8 in the Supporting Information show the wind data and full time series of localization and quantification estimates, respectively, during this time period.

Figure 6.

Figure 6

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CMS rate estimates reveal a slightly overestimated bottom-up inventory until a GPU swap in February. (a) Schematic of the site with colored rectangles showing potential emission sources and black pins showing CMS sensor locations. (b) Monthly operator-supplied bottom-up inventory (rectangles) and average of CMS rate estimates (circles). Color corresponds to the equipment groups in (a). Note that CMS rate estimates localized to the sale line are excluded in (b) as Operator 2 does not own the sale line and is not responsible for its emissions.

The end-of-project TDA for Operator 2 is 1.8 times larger than the average CMS rate estimate, compared to 4.0 times larger for Operator 1. This suggests that temporal variability of emissions is also contributing to the gap between inventory and TDA for Operator 2, as the top-down measurements happened to capture larger emissions than average for this site. However, because the difference between TDA and average CMS rate estimate is smaller for Operator 2 than for Operator 1, it is likely that temporal variability is having less of an effect for Operator 2 than Operator 1. This points to an underestimated inventory as a potential driver of the gap between inventory and TDA for Operator 2. See Section S5 in the Supporting Information for a more detailed comparison of top-down and CMS rate estimates for Operator 2.

To investigate the potentially underestimated bottom-up inventory, we compare monthly inventory estimates to monthly averages of the CMS rate estimates separated by equipment group, which we call the “CMS-based inventory estimates”. Figure 6b reveals that the bottom-up inventory is actually overestimating emissions from October to February, with the GPUs representing the largest share of emissions in both the monthly inventories and the CMS averages (except for the tank inventory in January). However, we begin to see notable underestimation starting in March and persisting through April, the month in which end-of-project measurements were taken. This shift aligns closely with an operator-reported GPU swap on February 23, 2022, during which several 2 MMBtu/h GPUs were replaced with 1 MMBtu/h GPUs. In fact, much larger concentration enhancements can be seen in Figure S15 in the Supporting Information starting on February 23, the day of the GPU swap. Note that the CMS-based inventory estimates for the other equipment groups are also higher after the GPU swap. This could be due to truly elevated emissions associated with the GPU swap or to incorrect localization estimates attributing GPU emissions to other equipment groups.

The quantification results shown in Figure 6 provide evidence that the gap between bottom-up inventory and end-of-project TDA is largely driven by an underestimated inventory that did not adjust to notably higher emissions after a GPU swap. This case study again illustrates the complementary nature of multiscale measurements. We have used CMS to reconcile top-down measurements with a bottom-up inventory, showing that the top-down measurements observed large emissions from a GPU swap that were not fully captured in the bottom-up inventory. Furthermore, this case study serves as a proof of concept for using high-frequency CMS data to create measurement-informed, site-level inventories.

4. Discussion

We have presented end-of-project results from a multiscale measurement campaign embedded within the QMRV protocol.8 Field data from this campaign has demonstrated the following:

  • 1.

    Top-down measurements decrease at the operator level from baseline measurements taken in Summer 2021 to end-of-project measurements taken in Spring 2022. This decrease could be due to temporal variability or to emission reduction practices implemented by the operators during the enhanced monitoring phase, which included monthly LDAR inspections.

  • 2.

    On average, bottom-up inventories prepared according to the GHGRP regulatory guidelines and supplemented with sources that are not included in the GHGRP underestimate top-down measurements at the operator-level. Individual site-level measurements may or may not capture short-lived, high-volume emission events and therefore can be higher or lower than bottom-up inventories.

  • 3.

    High-frequency CMS data enabled two key developments necessary for reconciling top-down measurements with bottom-up inventory estimates: (i) validating top-down measurements and (ii) determining how top-down measurements relate to the temporal emissions profile of a given site. Detailed record keeping of one-time events, maintenance activities, and upset conditions by the operator can help interpret CMS findings when attempting to reconcile bottom-up inventories with top-down measurements.

  • 4.

    Even though quantification algorithms and uncertainty quantification are still in development, high-frequency CMS data can be used to create measurement-informed, site-level emission inventories that can be validated with top-down measurements to update conventional bottom-up inventories on oil and gas production sites. Furthermore, they have the potential to support mitigation activities envisioned by traditional leak-detection and repair programs.

This work presents a real-world demonstration of how CMS can be used to reconcile top-down measurements and bottom-up inventories at the site level. The Inflation Reduction Act requires accurate emission estimates at this scale, and voluntary methane mitigation initiatives such as OGMP 2.0 require source- and site-level reconciliation of measurements with inventory estimates. While accurate basin-level emission estimates can be obtained through aerial surveys with large sample sizes,45,46 site-level estimates for individual operators require detailed temporal characterization of intermittent emission events. Information on such emission events, obtained through CMS in this work, will be critical for effectively creating site-specific, measurement-informed inventories.

Note that when interpreting the CMS quantification results presented in this study, it is important to consider the limitations of current CMS solutions. First, the production sites studied in this work are relatively simple, and hence it is reasonable to model the transport of methane using the Gaussian puff atmospheric dispersion model. Large buildings or obstructions introduce blockage and downwash effects, both of which are not captured by the Gaussian puff model, and hence extending this work to more complicated sites will likely require a more nuanced monitoring and modeling approach. Second, accurate measurement-informed, site-level inventories that rely on CMS data require a sensor configuration that provides full coverage of the site, meaning that concentration enhancements can be detected by at least one sensor regardless of the emission source and wind direction. If full site coverage is not possible, then assumptions about the persistence of emissions during periods when the wind is not blowing toward the sensors may be required. Finally, the quantification framework used in this study has only been evaluated on single-source controlled releases.30 Additional development and testing are required to obtain rate estimates from this framework under more complex emission scenarios, which will be addressed in future work.

It is also important to note that the emission rate estimates provided by the various measurement modalities discussed here have associated measurement uncertainty, methodological or algorithmic uncertainty, and sampling uncertainty. The methods used to quantify these uncertainties vary across technologies and published studies, and we leave a comprehensive synthesis of uncertainty quantification to future work.

This study provides two lessons for future measurement campaigns that aim to improve methane emission estimates. First, large unplanned emissions are unpredictable and therefore challenging to incorporate into conventional bottom-up inventories. Top-down measurements varied by over 3 orders of magnitude across the field campaign described in this work, but there was not one source or operational event that dominated this variability. Equipment-level distributions of top-down measurements reveal a similar spread across GPUs/separators, tanks, and wellheads.

Second, there are advantages and limitations to all current measurement technologies. Satellite measurements can capture super-emitters on a global scale but have a high detection threshold38 and only quantified emissions on 1 of 40 overflights performed during this field campaign. Drone and aerial measurements have reasonably well-characterized quantification accuracy40,47 but are subject to sampling error, which has been found to introduce negative bias on cumulative emission estimates.24 CMS provide high-frequency measurements, but localization and quantification capabilities are still in development and require additional testing before being fully trusted by the industry and regulators.31 OGI with Hi-flow for quantification is poorly suited for site-level quantification41−43 but can be used to check if specific components are emitting and differentiate between nearby sources (e.g., as a follow-up to top-down measurements).

Each of these measurement modalities use different data and analytical techniques to produce emission rate estimates. Therefore, when deployed simultaneously, each modality produces an independent rate estimate that can be cross-referenced against the others to provide a more complete picture of the true emission characteristics. As such, multiscale measurements are a critical aspect of building public trust and moving toward measurement-informed methane emission reporting.

Acknowledgments

The authors thank the participating oil and gas operators, including Aethon Energy, and the measurement technology vendors, including Montrose, SeekOps, Bridger, and GHGSat, for their participation in this program. The authors also thank Robert Fee and Greg Ross for discussion and feedback and Kaylyn Burmaster and Curtis Rice for administrative and logistical support.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c01121.

  • Additional details about the QMRV protocol, field campaign, and measurement results (PDF)

This work was funded by Cheniere Energy, Inc.

The authors declare the following competing financial interest(s): F.C.G. and S.R.W. are employees of Cheniere Energy Inc. A.P.R. has current research support from multiple natural gas operators, Cheniere, and the Environmental Defense Fund and has worked as a consultant for SLR International in recent years. D.M.H. has current research support from Cheniere and a CMS vendor. SLR International performs work for Cheniere, other oil and gas industry clients, academic institutions, and industry research organizations.

Supplementary Material

es3c01121_si_001.pdf (3.1MB, pdf)

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Supplementary Materials

es3c01121_si_001.pdf (3.1MB, pdf)

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