Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Heltzel, Robert; Johnson, Derek; Zaki, M. T.; Gebreslase, A. K.; Abdul‐Aziz, Omar I.

doi:10.1016/j.heliyon.2022.e11962

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…A wide range of technologies have been deployed to quantify emissions from the oil and gas industry, including quantitative optical gas imaging (QOGI) using mid-wavelength infrared (MWIR) cameras; − stationary and mobile − methane concentration sensors; and airborne , and satellite-based measurements. All of these systems utilize a measurement model that relates direct observations and auxiliary inputs to the methane emission rate.…”

Section: Introductionmentioning

confidence: 99%

“…Deep and rapid reductions in methane emissions from leading anthropogenic sources, especially upstream oil and gas activities, are crucial in order to avoid the worst outcomes of climate change [1], but doing this requires instrumentation that can reliably detect and quantify these emissions. Technologies for doing this include: quantitative optical gas imaging (QOGI) using mid-wavelength infrared (MWIR) cameras [2][3][4]; stationary [5] and mobile [6][7][8] methane concentration sensors; and airborne [9,10] and satellite-based [11] measurements. All of these systems utilize a measurement model that relates direct observations and auxiliary inputs to the methane emission rate.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

Wigle,

Béliveau,

Blackmore

et al. 2024

ACS EST Air

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An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from the upstream oil and gas sector in a variety of contexts, from component-level measurements to yearly industry-wide inventories. One possibility is to derive an uncertainty estimate from the physical model that connects the measurement data to the emission estimates directly, but this information is often proprietary and thus unavailable to end users. Instead, we provide a method to develop probability distributions of measurements given a true emission rate empirically using controlled release data. This method is completely technology-agnostic, and provides a route to summarise uncertainty without the need to release proprietary modelling or data. To demonstrate the wide applicability of the method, we introduce an algorithm that can be used to synthesize the uncertainty model and measurement-based surveys to produce an uncertainty range for new measurements in the field.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

Wigle,

Béliveau,

Blackmore

et al. 2024

ACS EST Air

View full text Add to dashboard Cite

show abstract

Deep Learning-Based quantifications of methane emissions with field applications

Jahan,

Mehana,

Matheou

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Understanding the Accuracy Limitations of Quantifying Methane Emissions Using Other Test Method 33A

et al. 2022

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Researchers have utilized Other Test Method (OTM) 33A to quantify methane emissions from natural gas infrastructure. Historically, errors have been reported based on a population of measurements compared to known controlled releases of methane. These errors have been reported as 2σ errors of ±70%. However, little research has been performed on the minimum attainable uncertainty of any one measurement. We present two methods of uncertainty estimation. The first was the measurement uncertainty of the state-of-the-art equipment, which was determined to be ±3.8% of the estimate. This was determined from bootstrapped measurements compared to controlled releases. The second approach of uncertainty estimation was a modified Hollinger and Richardson (H&R) method which was developed for quantifying the uncertainty of eddy covariance measurements. Using a modified version of this method applied to OTM 33A measurements, it was determined that uncertainty of any given measurement was ±17%. Combining measurement uncertainty with that of stochasticity produced a total minimum uncertainty of 17.4%. Due to the current nature of stationary single-sensor measurements and the stochasticity of atmospheric data, such uncertainties will always be present. This is critical in understanding the transport of methane emissions and indirect measurements obtained from the natural gas industry.

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Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Cited by 3 publications

References 31 publications

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

Deep Learning-Based quantifications of methane emissions with field applications

Understanding the Accuracy Limitations of Quantifying Methane Emissions Using Other Test Method 33A

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