the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of current methane emissions quantification techniques for natural gas midstream applications
Yunsong Liu
Jean-Daniel Paris
Gregoire Broquet
Violeta Bescós Roy
Tania Meixus Fernandez
Rasmus Andersen
Andrés Russu Berlanga
Emil Christensen
Yann Courtois
Sebastian Dominok
Corentin Dussenne
Travis Eckert
Andrew Finlayson
Aurora Fernández de la Fuente
Catlin Gunn
Ram Hashmonay
Juliano Grigoleto Hayashi
Jonathan Helmore
Soeren Honsel
Fabrizio Innocenti
Matti Irjala
Torgrim Log
Cristina Lopez
Francisco Cortés Martínez
Jonathan Martinez
Adrien Massardier
Helle Gottschalk Nygaard
Paula Agregan Reboredo
Elodie Rousset
Axel Scherello
Matthias Ulbricht
Damien Weidmann
Oliver Williams
Nigel Yarrow
Murès Zarea
Robert Ziegler
Jean Sciare
Mihalis Vrekoussis
Philippe Bousquet
Abstract. Methane emissions from natural gas systems are increasingly scrutinized and accurate reporting requires site- and source-level measurement-based quantification. We evaluate the performance of ten available, state-of-the-art CH4 emission quantification approaches against a blind controlled release experiment at an inerted natural gas compressor station in 2021. The experiment consisted of 17 blind, 2-hour releases at single or multiple simultaneous exhaust points. The controlled releases covered a range of methane flow rates from 0.01 kg h-1 to 50 kg h-1. Measurement platforms included aircraft, drones, trucks, van, and ground-based stations, as well as handheld systems. Herewith, we compare their respective strengths, weaknesses, and potential complementarity depending on the emission rates and atmospheric conditions. Most systems were able to quantify the releases within an order of magnitude. The level of errors from the different systems was not significantly influenced by release rates larger than 0.1 kg h-1, with much poorer results for the 0.01 kg h-1 release. It was found that handheld OGI cameras underestimated the emissions. In contrast, the ‘site-level’ systems, relying on atmospheric dispersion, tended to overestimate the emission rates. We assess the dependence of the emission quantification performance against key parameters such as wind speed, deployment constraints and measurement duration. At the low windspeeds encountered (below 2 m s-1), the experiments did not reveal a significant dependence on wind speed. The ability to quantify individual sources was degraded during multiple-source releases. Compliance with the Oil and Gas Methane Partnership (OGMP2.0) highest level of reporting may require a combination of the specific advantages of each measurement technique and will depend on reconciliation approaches. Self-reported uncertainties were either not available, or based on standard deviation in a series of independent realizations or fixed value from expert judgement or theoretical considerations. For most systems, site-level overall relative errors estimated in this study are higher than self-reported uncertainties.
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Yunsong Liu et al.
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RC1: 'Comment on amt-2023-97', Andrew Feitz, 29 Aug 2023
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The manuscript describes the results of a large blind controlled methane release experiment. Ten different quantification techniques were assessed over an intensive release campaign, where emission rates were varied over several orders of magnitude in 2 hour blocks. More details on the setup of the measurement system are required including the location of the sensors (and reflectors) from the release point for each of the releases, details on the quantification method employed, elevation of the helicopter or drone flights, whether the technique was mobile or fixed during the experimental run, etc. This information could be included as supplementary information and is necessary for the reader make sense of the high errors.
The study found high measurement errors across almost all techniques employed but the authors appear have omitted the obvious conclusion – 2 hours is far too short to accurately quantify a leak for many of the techniques employed. Having the majority of techniques able to estimate the emission within an order of magnitude (i.e. a long way from 10%) is not encouraging. More time is required to average the plume and optimize wind direction. This could potentially reduce the errors from approximately 100% and see them closer to 10%, as has been observed in comparable studies. The drones disturbing the plume profiles is also not helpful for the techniques that require estimates of plume shape.
I would encourage the authors to consider including ensemble estimates (i.e. averaging multiple techniques) for the emission rate. This may yield a better estimate for the blind releases and it would be interesting to see how this could be optimised for the minimum number of the techniques to achieve a reasonable emission rate estimate.
Line 55 - please include a statement about the short measurement time and its potential influence on the error through insufficient dispersion modelling.
Line 61 - remove significantly - it depends on the technology used to burn gas or coal.
Line 106/107 - is the underground gas storage facility defunct, i.e. not working? I was under the impression this is the source of the methane used in the experiments.
Line 125 – Can the authors clarify the source (and purity) of the methane used in the experiments? Presumably it is from the nearby underground storage site but it is not explicitly stated anywhere.
Line 126 - The compressor is not in …
Line 167 - “… across the releases a threshold will emerge between … with instrumental accuracies) and “high concentration ….”
Line 174 - not sure what this short sentence means. Please clarify.
Line 185/186 - a single anemometer at 5m could be contributing to the significant errors for many of the techniques. It would be have been preferable to measure the wind properties at multiple heights, particularly given the large range in release heights (1.5 to 28m).
Line 191 - please include the release heights with the Node labels for easy reference.
Line 226 – Table 2. The quantification algorithm column does not adequately describe the techniques employed for the measurements, providing the reader little confidence in the techniques employed. Please include a more fulsome description in this table and as supplementary information (or link to relevant published techniques). For example, do the OGI techniques use wind direction/speed information? Is LIDAR 2 mobile or fixed? Additional information required is the location of the sensors and reflectors, were the techniques mobile or fixed during the 2hr experiment, did the location of the measurement equipment change for each experiment, how far away where the handheld, fixed sensor, truck, drones, etc from the release points? A more useful summary could be included in the table with the measurement setup for each technique described in the supplementary information.
Line 249 - “This consideration is essential when evaluating …”
Line 253 – Please expand on this - 2 hours is a short window for the experiments and would rule out several (and possibly more accurate) quantification techniques, i.e. most techniques that require plume simulation. The sensors may not be in the optimal location downwind of the release point during the 2 hour period or experience a range of wind directions (i.e. null results) that would be helpful for Bayesian analysis. Such an approach could significantly improve the emission rate and error.
Line 270, 290 – the plots in Figure 3 and Figure 4 show that many of the techniques had large errors – higher than observed in similar studies. This is most likely due to the short timeframe of the experiments. The tracer (and LIDAR) techniques stand out as the most accurate techniques by far. Why is this? Are these techniques not dependent on describing the shape of the horizontal plume?
Line 320 – The errors are not correlated with time over the 2 hour period, but given longer periods of measurement time (e.g. days/weeks) some of the techniques would have significantly lower errors for the emission rates.
Line 325 - internal review comment?
Line 326 - replace didn’t with did not
Line 341 - Can’t read y-axis text at bottom of plot. What does this represent? Please describe in the figure caption.
Line 346 - the role of the wind needs a more thorough consideration, e.g. how it affects plume shape estimates for some of the techniques, measurements not at the right height, etc
Line 354 – “do not reveal any clear relationship between the wind speed and direction and errors”. Given the errors are so high, I’m not sure this is an appropriate conclusion.
Line 360 – improved wind measurement protocols would be very beneficial.
Line 374 - Table 4. This is not particularly useful - remove.
Line 396 - Figure 6 can be removed - it is virtually identical to figure 4.
Line 424 - I’m not sure you can make this conclusion given the very high errors, i.e. performance is not good for either.
Line 458 - “Most systems can report within an order of magnitude of the controlled release rate”. This not an encouraging outcome, given the experiment was under constant flow rate and controlled conditions. The authors need to reframe their results. Why are the errors so high and what recommendations can be made to reduce them? Not having enough measurements at optimal wind orientations (enough time) and a good model of wind dispersion are likely to be key issues.
Citation: https://doi.org/10.5194/amt-2023-97-RC1
Yunsong Liu et al.
Yunsong Liu et al.
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