Related Experiment Video
Updated: Jun 12, 2025
08:18
Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
Published on: June 12, 2016
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Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach
Augustine Wigle1, Audrey Béliveau1, Daniel Blackmore2
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
ACS ES&T Air
|September 19, 2024
Summary
Understanding measurement uncertainty is crucial for accurate methane emission estimates from oil and gas operations. New methods using controlled release data improve uncertainty quantification for various measurement technologies.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Oil and Gas Industry
Background:
- Accurate methane emission estimates are vital for upstream oil and gas operations.
- Interpreting these estimates requires a robust understanding of measurement uncertainty.
- Existing methods may not fully capture the complexities of measurement error.
Purpose of the Study:
- To characterize measurement uncertainty for methane emission estimation technologies.
- To develop novel methods for quantifying uncertainty using controlled release data.
- To provide a framework for synthesizing measurements with external information.
Main Methods:
- Examined controlled release (CR) data from five technology providers (e.g., quantitative gas imaging, TDLAS, NIR HS imaging).
- Developed an empirical method to create probability distributions of measurements given true emission rates.
- Created an algorithm to determine the distribution of true emission rates based on measurements and CR data.
Main Results:
- Flexible models accommodating nonlinear behavior are necessary for accurate error modeling.
- Measurement error can vary significantly under different conditions.
- Repeated measurements can effectively reduce measurement uncertainty.
Conclusions:
- The developed methodology provides improved uncertainty quantification for methane emission estimates.
- The models highlight the need for flexible approaches to account for complex measurement errors.
- Future work can extend the methodology to diverse industrial settings and conditions.
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