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

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.

Section: Discussionmentioning

confidence: 99%

Section: Cems Sensor Network At the Methane Emissions Technology Eval...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Jia

Hammerling

2023

Preprint

“…Many 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 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.…”

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.

Comparison of the Gaussian plume and puff atmospheric dispersion models for methane modeling on oil and gas sites

Jia

Hammerling

2023

Preprint

Self Cite

Characterizing methane emissions on oil and gas sites 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 monitoring systems (CMS) 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 in terms of pattern and amplitude, respectively. 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 provide computationally efficient and scalable implementations of the Gaussian puff model. Compared to regulatory-grade, Gaussian puff-based models like CALPUFF, our implementations have higher spatial and temporal resolution and require only essential and practically available meteorological information. These features enable near real-time methane mitigation applications on oil and gas sites and might be useful for near-field atmospheric transport modeling applications more broadly.