Using Prototypical Oil and Gas Sites to Model Methane Emissions in Colorado’s Denver-Julesburg Basin Using Mechanistic Emission Estimation Tool

Traditional bottom-up (BU) methods estimate methane emissions from oil and gas facilities by multiplying activity data with emission factors. Top-down (TD) methods measure methane emissions from all sources across an entire site and often use BU estimates to attribute emissions specifically to natural gas, often through ethane-to-methane ratio analysis. However, traditional BU methods do not adequately account for variations in throughput and failure conditions, which can significantly impact gas composition and emission rates. The Mechanistic Air Emissions Simulator (MAES) is a computer model developed to estimate methane and other hydrocarbon emissions from oil and gas facilities. MAES employs two distinct modeling approaches: mechanistic models and traditional methods. MAES uses mechanistic models to estimate emissions based on fluid flow through equipment and equipment states, offering a detailed, process-oriented emissions representation. In contrast, the traditional methods utilized by MAES estimate emissions by applying activity data multiplied by emission factor distributions, offering a statistical approach grounded in empirical data. This study applies MAES to assess emission impacts on three vintages of production wellpads in the Denver-Julesburg (DJ) basin: a wellpad with two stages of separation ("old" facility), one with three stages of separation ("current" facility), and a tankless wellpad ("future" facility). The study found that increased throughput led to higher methane emissions in older wellpads but not in tankless future facilities. Additionally, MAES showed that failure conditions, like stuck dump valves, increased emission rates and affected the ethane-to-methane ratio, which could vary by 2.15 times depending on facility configuration. These findings underscore the importance of incorporating variability in facility operations into emissions estimates to improve accuracy and guide effective mitigation strategies.