Using Measurement-Informed Inventory to Assess Emissions in the Denver-Julesburg Basin

Abstract

Site-level aerial surveys, while effective in detecting CH₄ emissions from upset conditions, face challenges to provide comprehensive long-term emission estimates due to their snapshot measurements, emissions variability, and minimum detection limits. Conversely, annual inventories submitted by operators often exclude emissions from failure events and unregulated sources, leading to incomplete emission estimates. This study introduces a novel methodology that utilizes the Mechanistic Air Emissions Simulator (MAES) to integrate two highly variable estimation methods: inventory and aerial methods. The proposed methodology identifies and characterizes failure events with site-specific information, thereby enhancing the accuracy of inventory programs through the so-called measurement-informed inventories (MIIs). Furthermore, it emphasizes the importance of carefully comparing instantaneous emission measurements from aerial surveys with annual average emissions reported in inventories, as they have distinct timeframes. Colorado State University (CSU) collaborated with the Colorado Department of Public Health and Environment (CDPHE) to utilize this approach to enhance reported emissions from the upstream sector in Colorado Denver-Julesburg (DJ) basin. This initiative is part of Colorado's Upstream greenhouse gas (GHG) Intensity Program, a regulatory initiative that requires oil and gas (O&G) operators to monitor, report, and reduce GHG emissions. The goal was to incorporate measured emissions from failure events conducted by Carbon Mapper (CM) in the simulations to derive a multiplier that rectifies for potential omissions of emissions from abnormal conditions within the O&G sector. To simplify the simulation process, prototypical sites were defined in conjunction with operators and are used to represent groups of O&G facilities in the basin with similar configuration. The outcomes of this work indicate that inventories are likely underestimating total emissions, as an additional 16.4% of total emissions from abnormal events is estimated for the basin. This estimate may represent a lower bound, as the survey technologys detection limit may exclude most CH₄ emission events below 50 kg/h.

Aerial and inventory methods often miss methane emissions from abnormal oil and gas site events. This study improves estimates by integrating both methods, revealing higher total emissions with regulatory implications.