Automated Unmanned Systems Quantify Emissions in the Permian
This case study highlights the effectiveness of unmanned aerial systems in enabling land-based operators to assess the relative seriousness of leaks efficiently by both localizing and quantifying their methane emission rates.
Traditional methods for monitoring emissions from production operations typically have used optical gas imaging (OGI) cameras or Method 21 systems, based on an intermittent basis to determine and document methane gas leaks, which subsequently are identified for repair. These OGI 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 specifically targeting methane emissions. Its unique capabilities are engendered by its deployment on unmanned aerial systems (UAS), specifically leveraging automation in field-operation and data analysis, and its successful use in enabling emissions limitations over several production sites in the Permian.
By leveraging the automation capabilities of modern enterprise-grade drones, and by the use of pre-programmed flight plans based on prepared waypoints, consistency can be achieved, ensuring that accurate temporal emissions profiles can be established.
Automation also allowed for categorization of leak types and intensities and triage according to leak rate, facilitating prompt remedial action and directly limiting emissions. Being able to triage leaks based on accurate quantification enabled repair crews to be optimally scheduled so that larger leaks are prioritized. With the flightpaths already programmed into the drones, the effectiveness of the repairs can be quickly and easily evaluated—in a matter of minutes.
By automating the comprehensive flight paths, specific to equipment groups (e.g., compressors, tanks, flares), targeted repeat surveys confirmed that specific leaks were fixed, emphasizing a general downward trend in overall site- and asset-level emissions. Additionally, the use of high-resolution UAS-generated orthomosaic maps enabled the direct placement of emissions data into the context of the actual operations at the time of the survey. This also facilitated the generation of automated actionable reports, enabling repair teams to be directed to the problem, resulting in effective and necessary fixes. Furthermore, the campaign validated that, following the setup of the initial survey, regular, repeat surveys could be commissioned at the push of a button, yielding reliable, actionable emissions data, with a direct effect on both environmental and financial impact.