Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation

Sun, Shuhui; Ma, Linwei; Li, Zheng

doi:10.3390/su132413895

Cited by 5 publications

(4 citation statements)

References 116 publications

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“…Infrared visualization, a predominant method, detects gases by observing light intensity variations due to gas absorption in the infrared spectrum, a passive sensing approach . The imagery, sequenced into videos, is analyzed by continuous monitoring companies along with their collected meteorological data to identify emissions and estimate the emissions rate …”

Section: Methodsmentioning

confidence: 99%

“…16 The imagery, sequenced into videos, is analyzed by continuous monitoring companies along with their collected meteorological data to identify emissions and estimate the emissions rate. 38 Participants were given the option to evaluate detection performance (reporting binary 0−1 data corresponding to whether gas is emitting) or both detection and quantification performance (reporting the estimated rate of emission in kilograms per hour). It is important to note that the Stanford team did not participate in the analysis of the reports submitted by the teams.…”

Section: Acs Esandt Airmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Chen,

El Abbadi,

Sherwin

et al. 2024

ACS EST Air

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Methane emissions from oil and gas operations are a primary concern for climate change mitigation. While traditional methane detection relies on periodic surveys that yield episodic data, continuous monitoring solutions promise to offer consistent insights and a richer understanding of emission inventories. Despite this promise, the detection and quantification abilities of continuous monitoring solutions remain unclear. To address this uncertainty, our study comprehensively assessed 8 commercial continuous monitoring solutions using controlled release tests to simulate high-volume venting (e.g., uncontrolled tanks, pneumatics, and unlit flares), which accounts for a significant fraction of total emissions from oil and gas systems. The performance of each team varied: when comparing reported results on a second-bysecond basis, all teams reported false positive rates below 10%. For true positive rates, 4 out of 8 systems exceed 80%. In the field test where continuous monitoring solutions identified and reported an emission event, all systems' reliability of identification surpassed 70%. When systems reported that there was no emission event, the reliability of nonemission identification varied from 29.4% to 96.2%. Among 5 systems tested for quantifying the daily average emission rate released by the Stanford team, all were underestimated by an average of 74.38% emissions. This indicates that their application in emissions reporting or regulation may be premature. The variability in monitor performance underscores the importance of understanding systems' strengths and limitations before their broader adoption in methane mitigation approaches or regulatory frameworks.

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Section: Methodsmentioning

confidence: 99%

Section: Acs Esandt Airmentioning

confidence: 99%

Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Chen,

El Abbadi,

Sherwin

et al. 2024

ACS EST Air

View full text Add to dashboard Cite

show abstract

“…16 The imagery, sequenced into videos, is analyzed by continuous monitoring companies along with their collected meteorological data to identify emissions and estimate the emissions rate. 38 Participants were given the option to evaluate detection performance (reporting binary 0-1 data corresponding to whether gas is emitting), or both detection and quantification performance (reporting the estimated rate of emission in kilograms per hour). It is important to note that the Stanford team did not participate in the analysis of the reports submitted by the teams.…”

Section: Description Of Technologies Testedmentioning

confidence: 99%

Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Chen,

El Abbadi,

Sherwin

et al. 2024

Preprint

View full text Add to dashboard Cite

Methane emissions from oil and gas operations are a primary concern for climate change mitigation. While traditional methane detection relies on periodic surveys that yield episodic data, continuous monitoring solutions promise to offer consistent insights and a richer understanding of emission inventories. Despite this promise, the detection and quantification ability of continuous monitoring solutions remain unclear. To address this uncertainty, our study comprehensively assessed 8 commercial continuous monitoring solutions using controlled release tests to simulate high-volume venting (e.g., uncontrolled tanks, pneumatics, unlit flares), which accounts for a significant fraction of total emissions from oil and gas systems. The performance of each team varied: when comparing reported results on a second-by-second basis, all teams reported false positive rates below 10\%. For true positive rates, 4 out of 8 systems exceed 80%. In the field test where continuous monitoring solutions identified and reported an emission event, all systems' reliability of identification surpassed 70%. When systems reported there was no emission event, the reliability of non-emission identification varied from 29.37% to 96.15%. Among 5 systems tested for quantifying the daily average emission rate released by the Stanford team, all underestimated by an average of 74.38% emissions. This indicates that their application in emissions reporting or regulation may be premature. The variability in monitor performance underscores the importance of understanding systems' strengths and limitations before their broader adoption in methane mitigation approaches or regulatory frameworks.

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“…In a comparative review conducted in 2021, Sun et al, 9 investigated how CH 4 emissions estimation technologies, data analysis, and probabilistic tools for emission uncertainty estimation unite to form an efficient system of CH 4 management for the ONG energy sector. The authors found that spatial scale was a primary factor in selecting CH 4 emission measurement technology where bottom-up or top-down methods were applied to calculate emission rates from CH 4 concentrations.…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

Majumder,

Pollard,

Salek

et al. 2024

Environ�Health�Insights

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The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem. The classification model performance for CH4 detection was evaluated using accuracy, F1 score, Matthew’s Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.

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Methane Emission Estimation of Oil and Gas Sector: A Review of Measurement Technologies, Data Analysis Methods and Uncertainty Estimation

Cited by 5 publications

References 116 publications

Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

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