Methane Emissions Quantification and Resulting Methane Emissions Reduction in the Permian Basin Enabled by Automated Unmanned Systems

Traditional methods for monitoring emissions from production operations have typically used optical gas imaging cameras or Method 21 systems, based on an intermittent basis to determine and document methane gas leaks, which are then subsequently identified for repair under the US Code of Federal Regulations (2017). These optical gas imaging emissions monitoring surveys can have a subjective bias, are highly conditional on the skill of the camera operator, and are an inexact method of measuring the quantity of the leak rate. With a renewed industry emphasis on methane emissions measurement and reduction, this paper describes a case study using a high- sensitivity sensor technology (laser absorption spectrometry) specifically targeting methane emissions, the unique capabilities engendered by its deployment on unmanned aerial systems (UAS), the leveraging of automation in field- operation and data analysis, and the system's successful utilizationin enabling emissions limitations over several production sites in the Permian Basin. The use of automation enabled categorization of the leak type and intensity, and triage according to leak rate, facilitating prompt remedial action and directly limiting emissions. By automating the comprehensive flight paths specific to equipment groups (e.g., compressors, tanks, and flares), targeted repeat surveys confirmed that specific leaks were fixed, emphasizing a general downward trend in overall siteand asset- level emissions. These surveys were completed in 22.5 minutes, on average, at each of the five sites. Additionally, the use of high- resolution UAS- generated orthomosaic maps enabled the direct placement of emissions data into the context of the operations at the time of the survey, facilitating the generation of automated actionable reports, helping direct repair teams, and resulting in effective and necessary fixes. Furthermore, the campaign validated that following the set up of the initial survey, subsequent regular, repeat surveys could be commissioned at the push of a button, yielding reliable, actionable emissions data, with a direct impact on both environmental (6% reduction in emissions) and financial impact.

Automated summary

Traditional methods for monitoring emissions from production operations have limitations in terms of subjective bias, reliance on operator skills, and inaccuracy. This paper presents a case study on using high-sensitivity sensor technology deployed on unmanned aerial systems (UAS) to target methane emissions, leveraging automation in field operation and data analysis. The results show that this method enables prompt remedial actions and effective emissions limitations.