Multi-scale Methane Measurements at Oil and Gas Facilities Reveal Necessary Framework for Improved Emissions Accounting

Abstract

Methane mitigation from the oil and gas (O&G) sector represents a key near-term global climate action opportunity. The effectiveness of mitigation strategies rests on the ability to quantify spatially and temporally varying methane emissions more accurately than existing approaches. Advances in technologies have enabled improvements in methane emissions measurements and monitoring, In this work, we demonstrate a quantification, monitoring, reporting, and verification framework that pairs snapshot measurements with continuous emissions monitoring systems (CEMS) to reconcile measurements with inventory estimates and account for intermittent emissions events. We find site-level emissions exhibit significant intra-day and daily emissions variation. Snapshot measurements of methane can span over three orders of magnitude and may have limited application in developing annualized inventory estimates. Consequently, while official inventories underestimate methane emissions on average, emissions at individual facilities can be lower than inventory estimates. Using CEMS, we characterize distributions of frequency and duration of intermittent emission events. Technologies that allow high sampling frequency such as CEMS, paired with a mechanistic understanding of facility-level events is key to accurate accounting of short-duration, episodic, and high-volume events that are often missed in snapshot surveys, and to scale snapshot measurements to annualized emissions estimates.

Towards multi-scale measurement-informed methane inventories: reconciling bottom-up site-level inventories with top-down measurements using continuous monitoring systems

“…Bottom-up inventories were prepared according to the GHGRP regulations 5 and supplemented with additional emission sources not required by the GHGRP. 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.…”

“…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, 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.…”

“…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][7][8][9][10][11][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.…”

“…Prior multi-scale measurement campaigns have demonstrated the importance of temporal variation in emissions when reconciling estimates from different methods. [14][15][16][17][18][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][22][23] meaning that a small number of very large emissions represent a large fraction of total emitted volume.…”

Methods for spatial extrapolation of methane measurements in constructing regional estimates from sample populations

“…Other high emission rate events are due to equipment malfunctions, for which counts of events may be difficult to obtain and which may have emission rates that vary significantly from event to event. Recent studies using direct methane measurements observe skewed distributions of methane emissions where a small number of sources are responsible for a significant portion of total emissions [10,11,12,13,14,15]. The complexity of emission behavior, and the difficulty in estimating emissions due to malfunctions, are responsible for part of the difficulty in accurate emissions inventories.…”