Origins of Oil and Gas Sector Methane Emissions: On-Site Investigations of Aerial Measured Sources.
Matthew R JohnsonDavid R TynerBradley M ConradPublished in: Environmental science & technology (2023)
Success in reducing oil and gas sector methane emissions is contingent on understanding the sources driving emissions, associated options for mitigation, and the effectiveness of regulations in achieving intended outcomes. This study combines high-resolution, high-sensitivity aerial survey data with subsequent on-site investigations of detected sources to examine these points. Measurements were performed in British Columbia, Canada, an active oil- and gas-producing province with modern methane regulations featuring mandatory three times per year leak detection and repair (LDAR) surveys at most facilities. Derived emission factors enabled by source attribution show that significant methane emissions persist under this regulatory framework, dominated by (i) combustion slip (compressor exhaust and also catalytic heaters, which are not covered in current regulations), (ii) intentional venting (uncontrolled tanks, vent stacks or intentionally unlit flares, and uncontrolled compressors), and (iii) unintentional venting (controlled tanks, unintentionally unlit/blown out flares, and abnormally operating pneumatics). Although the detailed analysis shows mitigation options exist for all sources, the importance of combustion slip and the persistently large methane contributions from controlled tanks and unlit flares demonstrate the limits of current LDAR programs and the critical need for additional monitoring and verification if regulations are to have the intended impacts, and reduction targets of 75% and greater are to be met.
Keyphrases
- anaerobic digestion
- municipal solid waste
- carbon dioxide
- sewage sludge
- drinking water
- high resolution
- room temperature
- climate change
- life cycle
- fatty acid
- randomized controlled trial
- cross sectional
- south africa
- systematic review
- public health
- transcription factor
- big data
- weight loss
- metabolic syndrome
- insulin resistance
- loop mediated isothermal amplification
- risk assessment
- artificial intelligence
- deep learning