Methane Methane

Industrial Methane Measurement Conference - Abstract


 
methane
 
Methane





Abstract Title: Methane Emissions and Flaring Efficiencies in the North Sea
Presenter Name: Dr Jacob Shaw
Co-authors:Dr Amy Foulds
Prof Grant Allen
Prof James Lee
Dr Stefan Schwietzke
Dr Manchester Team
Dr York Team
Dr Royal Holloway Team
Dr Stephane Bauguitte
Dr Stephen Conley
Dr Ignacio Pisso
Company/Organisation: The University of Manchester
Country: United Kingdom

Abstract Information :

The oil and gas industry is a substantial source of methane CH4 emissions. However, quantifying methane emissions is challenging, particularly for offshore facilities. We present CH4 emission fluxes for 21 oil and gas facilities measured using aircraft over several campaigns in the North Sea in 2018 and 2019. There was excellent agreement within 1 uncertainty between measured and operator-reported emissions for 2019, demonstrating that good quality reporting can be reconciled with accurate top-down measurements. We also found that a global inventory of fossil fuel emissions underestimated CH4 emissions by roughly 42. This underestimation may be a result of the methods used to compile the inventory, and hence, more thorough and up-to-date inventories are needed.rnIn addition, we report CH4, CO2, and nitrogen oxide NOx emission factors for multiple gas flare emissions observed and sampled during the same aircraft campaigns. Gas flaring uses combustion to convert unwanted natural gas into CO2 during production. The efficiency of these flares, including the amount of NOx produced, is important for greenhouse gas budgeting and air quality. These measurements were made as part of a United Nations Climate and Clean Air Coalition UN CCAC project, and the NERC Assessing Atmospheric Emissions from the Oil and Gas Industry AEOG project.rnWe will also present the application of machine-learning tools for the diagnosis and interpretation of onshore industrial methane emissions. Machine-learning tools are able to efficiently process and analyse large datasets for pattern recognition, which can help understanding the incremental impact of a new industrial polluter on an environment.


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