A43F-0353
Impact of routine episodic emissions on the expected frequency distribution of emissions from oil and gas production sources.
Impact of routine episodic emissions on the expected frequency distribution of emissions from oil and gas production sources.
Thursday, 17 December 2015
Poster Hall (Moscone South)
Abstract:
In coordination with oil and gas operators, we developed a high resolution (< 1 min) simulation of temporal variability in well-pad oil and gas emissions over a year. We include routine emissions from condensate tanks, dehydrators, pneumatic devices, fugitive leaks and liquids unloading. We explore the variability in natural gas emissions from these individual well-pad sources, and find that routine short-term episodic emissions such as tank flashing and liquids unloading result in the appearance of a skewed, or ‘fat-tail’ distribution of emissions, from an individual well-pad over time. Additionally, we explore the expected variability in emissions from multiple wells with different raw gas composition, gas/liquids production volumes and control equipment. Differences in well-level composition, production volume and control equipment translate into differences in well-level emissions leading to a fat-tail distribution of emissions in the absence of operational upsets. Our results have several implications for recent studies focusing on emissions from oil and gas sources.- Time scale of emission estimates are important and have important policy implications.
- Fat tail distributions may not be entirely driven by avoidable mechanical failures, and are expected to occur under routine operational conditions from short-duration emissions (e.g., tank flashing, liquid unloading).
- An understanding of the expected distribution of emissions for a particular population of wells is necessary to evaluate whether the observed distribution is more skewed than expected.
- Temporal variability in well-pad emissions make comparisons to annual average emissions inventories difficult and may complicate the interpretation of long-term ambient fenceline monitoring data. Sophisticated change detection algorithms will be necessary to identify when true operational upsets occur versus routine short-term emissions.