Performance of continuous emission monitoring solutions under single-blind controlled testing protocol.

Jia

Hammerling

2022

Preprint

We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas facilities that uses concentration data collected by point-in-space continuous emissions monitoring systems (CEMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and end times (event detection) and pattern matches simulated and observed concentrations to estimate emission source location (localization) and rate (quantification). We test the framework on a month of single-source controlled releases ranging from 0.50 to 8.25 hours in duration and 0.18 to 6.39 kg/hr in size conducted at the Methane Emissions Technology Evaluation Center in Fort Collins, Colorado. All controlled releases are identified and 82% are localized correctly. For emissions < 1 kg/hr, the framework underestimates by 37.2% on average, with 90% of rate estimates within a factor of [-4.6, 2.8] or a percent difference of [-78.1%, 178.6%]; for emissions > 1 kg/hr, the framework overestimates by 1.5% on average, with 90% of rate estimates within a factor of [-2.0, 1.8] or a percent difference of [-49.6%, 77.4%] from the true rates. Potential uses for the proposed framework include near real-time alerting for rapid emissions mitigation and emission quantification for data-driven inventory estimation on production-like facilities.

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Methane emission detection, localization, and quantification using continuous point-sensors on oil and gas facilities

Jia

Hammerling

2022

Preprint

“…15 CMS provide high frequency measurements, but localization and quantification capabilities are still in development and require additional testing before being fully trusted by industry and regulators. 22 OGI with Hi-Flow for quantification is poorly suited for site-level quantification [24][25][26] 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).…”

Section: Discussionmentioning

confidence: 99%

“…Methods for doing so are available in the published literature [16][17][18][19][20][21] and are also developing rapidly in the private sector. Bell et al 22 evaluate 11 of these proprietary solutions and find that event detection and quantification performance varies widely across solutions, with average quantification errors ranging from -40% to 93% for both single-and multi-source emissions >1 kg/hr. Despite the generally poor quantification performance of these 11 proprietary solutions, improvements to quantification algorithms are occurring rapidly in both the private sector and in the published literature, with Daniels et al 21 having an average quantification error of 1.5% for single-source emissions >1 kg/hr.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Ravikumar

et al. 2023

Preprint

Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed emissions 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 basin- and operator-level top-down measurements show lower methane emissions during end-of-project than during baseline 9-months earlier. However, gaps persist between end-of-project top-down measurements and bottom-up inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) assess the validity of snapshot measurements and determine how they relate to the temporal emissions profile of a given site and (ii) create a near-real time, measurement-informed inventory that can be cross-checked with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how CMS can be used to reconcile top-down snapshot measurements with bottom-up inventories at the site-level. More broadly, it demonstrates the importance of multi-scale measurements when creating measurement-informed emissions inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as OGMP 2.0, and corporate strategies.

“…Colorado during the Advancing Development of Emissions Detection (ADED) research program (see Bell et al27 for details). The layout of the METEC site and the configuration of the ADED experiment are shown in Figure2.…”

mentioning

confidence: 99%

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

Jia

Hammerling

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.