Near‐field atmospheric inversions for the localization and quantification of controlled methane releases using stationary and mobile measurements

et al. 2022

Environ. Sci. Technol.

Methane mitigation from the oil and gas (O&G) sector represents a key near-term global climate action opportunity. Recent legislation in the United States requires updating current methane reporting programs for oil and gas facilities with empirical data. While technological advances have led to improvements in methane emissions measurements and monitoring, the overall effectiveness of mitigation strategies rests on quantifying spatially and temporally varying methane emissions more accurately than the current approaches. 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 emission events. We find that site-level emissions exhibit significant intraday and daily emission variations. Snapshot measurements of methane can span over 3 orders of magnitude and may have limited application in developing annualized inventory estimates at the site level. Consequently, while official inventories underestimate methane emissions on average, emissions at individual facilities can be higher or 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, are key to an 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.

Section: Resultsmentioning

confidence: 99%

Multiscale Methane Measurements at Oil and Gas Facilities Reveal Necessary Frameworks for Improved Emissions Accounting

Wang

et al. 2022

Environ. Sci. Technol.

“…These sensors provide near-continuous concentration measurements without needing a human operator. While reliable site-level or equipment-specific emission quantification is still an open problem, current CEMS can act as an indicator for methane emission events [47]- [49]. See Figure S13 in the SI for an example of emission events on an enrolled asset that were captured by the CEMS.…”

Section: Using Continuous Emissions Monitoring System (Cems) To Estim...mentioning

confidence: 99%

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

Wang

et al. 2022

Preprint

Methane mitigation from the oil and gas (O&G) sector represents a key near-term global climate action opportunity. Recent legislation in the United States requires updating of current methane reporting programs for oil and gas facilities with empirical data. While technological advances had led to improvements in methane emissions measurements and monitoring, the overall effectiveness of mitigation strategies rests on quantifying spatially and temporally varying methane emissions more accurately than current approaches. 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 at site-level. Consequently, while official inventories underestimate methane emissions on average, emissions at individual facilities can be higher or 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, are 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.

“…13 For point-in-space sensors, inferring emission source information (i.e., location and rate) from concentration measurements can be framed as an inverse problem, which requires a forward model to simulate the transport of methane. Many inversions use the Gaussian plume atmospheric dispersion model, 14,15 which is easy to implement and computationally inexpensive but relies on a steady state assumption that is violated in practice during variable wind conditions. On the other hand, averaging wind data over longer time periods to meet the steady state assumption often eliminates the pertinent high frequency elements of the signal.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Gaussian plume and puff atmospheric dispersion models on oil and gas facilities

Jia

2023

Preprint

Characterizing methane emissions on oil and gas facilities often relies on a forward model to describe the atmospheric transport of methane. Here we compare two forward models: the Gaussian plume, a commonly used steady-state dispersion model, and the Gaussian puff, a time varying dispersion model that approximates a continuous release as a sum over many small "puffs". We compare model predictions to observations from a network of point-in-space continuous emissions monitoring systems (CEMS) collected during a series of controlled releases. Specifically, we use the Pearson correlation coefficient and mean absolute error (MAE) as metrics to assess the fit of the model predictions to the observed concentrations, with the former assessing the fit of pattern and the latter the fit of amplitude. The Gaussian puff outperforms the Gaussian plume using both metrics with average correlation coefficients of 0.38 and 0.31 and average MAEs of 0.70 and 0.74, respectively. We also investigate how the frequency at which puffs are generated affects the accuracy and computational cost of the Gaussian puff and propose guidelines for choosing an appropriate value. Finally, we provide open-source implementations of the Gaussian puff model in Python and R that are tailored for use on oil and gas facilities.