Measurement-Informed Methane Inventories for Colorado’s Upstream Oil and Gas Sector: Two Independent Modeling Approaches
6 affiliations
- 1Energy Institute
- 2Colorado State University
- 3Dept. of Applied Mathematics and Statistics
- 4Colorado School of Mines
- 5Dept. of Environmental Health and Engineering
- 6Johns Hopkins University
Abstract
The state of Colorado is recognized as a leader in regulating oil and gas methane emissions, with Colorado requirements often influencing subsequent federal policy. Building on existing efforts, Colorado published a greenhouse gas intensity verification rule in 2023, requiring upstream operators to either create their own measurement-informed emissions inventory or multiply their bottom-up inventory by a state-developed default factor. This factor is intended to address known deficiencies in bottom-up emissions reporting by increasing the bottom-up inventory to match measured emissions. In this paper, we describe the aerial measurement campaign and subsequent modeling approaches used to create the 2026 default factor. Three aerial technology vendors (Bridger Photonics, GHGSat, and Insight M) performed over 30,000 scans of production facilities across Colorado, covering roughly 94% of the state’s oil and gas production facilities that were operating or partially operating. Using these aerial measurements as inputs, two independent models were developed to estimate state-wide annual methane emissions. One model uses a discrete event simulation tool, Mechanistic Air Emissions Simulator (MAES), to estimate total emissions, while the other model, referred to as the statistical model, is purely data-driven and estimates a facility-level emission rate distribution from the measurement data. MAES and the statistical model estimate upstream oil and gas emissions of 38.7 (35.3, 44.6) and 97.7 (84.4, 114.7) Gg/y, respectively, which are 1.47 (1.34, 1.69) and 3.70 (3.19, 4.34) times higher than the state’s bottom-up inventory.
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BibTeX
@misc{brown2026measurementinformed,
title = {{Measurement-Informed Methane Inventories for Colorado’s Upstream Oil and Gas Sector: Two Independent Modeling Approaches}},
author = {Jenna A. Brown and Callan Okenberg and Michael Moy and William S. Daniels and Arthur Santos and Ethan Rimelman and Olga Khaliukova and Winrose Mollel and Daniel Zimmerle and Dorit M. Hammerling and Anna L. Hodshire},
howpublished = {ChemRxiv},
year = {2026},
month = aug,
doi = {10.26434/chemrxiv.15008067/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008067/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Measurement-Informed Methane Inventories for Colorado’s Upstream Oil and Gas Sector: Two Independent Modeling Approaches
AU - Jenna A. Brown
AU - Callan Okenberg
AU - Michael Moy
AU - William S. Daniels
AU - Arthur Santos
AU - Ethan Rimelman
AU - Olga Khaliukova
AU - Winrose Mollel
AU - Daniel Zimmerle
AU - Dorit M. Hammerling
AU - Anna L. Hodshire
PY - 2026
DA - 2026/08/31
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008067/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008067/v1
AB - The state of Colorado is recognized as a leader in regulating oil and gas methane emissions, with Colorado requirements often influencing subsequent federal policy. Building on existing efforts, Colorado published a greenhouse gas intensity verification rule in 2023, requiring upstream operators to either create their own measurement-informed emissions inventory or multiply their bottom-up inventory by a state-developed default factor. This factor is intended to address known deficiencies in bottom-up emissions reporting by increasing the bottom-up inventory to match measured emissions. In this paper, we describe the aerial measurement campaign and subsequent modeling approaches used to create the 2026 default factor. Three aerial technology vendors (Bridger Photonics, GHGSat, and Insight M) performed over 30,000 scans of production facilities across Colorado, covering roughly 94% of the state’s oil and gas production facilities that were operating or partially operating. Using these aerial measurements as inputs, two independent models were developed to estimate state-wide annual methane emissions. One model uses a discrete event simulation tool, Mechanistic Air Emissions Simulator (MAES), to estimate total emissions, while the other model, referred to as the statistical model, is purely data-driven and estimates a facility-level emission rate distribution from the measurement data. MAES and the statistical model estimate upstream oil and gas emissions of 38.7 (35.3, 44.6) and 97.7 (84.4, 114.7) Gg/y, respectively, which are 1.47 (1.34, 1.69) and 3.70 (3.19, 4.34) times higher than the state’s bottom-up inventory.
ER -