Satellite methane observations from the Sentinel-5P TROPOMI instrument consistently show basin-level methane concentrations that exceed what operator-reported inventories predict. [1] The gap is not small. TROPOMI-derived estimates put US oil and gas methane about 80% above the national inventory, and more than two times above bottom-up estimates in the Permian Basin (EPA is reconsidering Subpart W reporting requirements for RY2025–2034; future data availability is uncertain). [2] For operations and engineering teams, this raises a specific question: what emission sources exist at the facility level that bottom-up inventories structurally miss? For sustainability leaders and data analysts, the question is more specific. How defensible are current inventory numbers when a publicly funded satellite contradicts them at basin scale?

TROPOMI Measures Basin-Scale Methane That Bottom-Up Inventories Cannot Capture

TROPOMI (TROPOspheric Monitoring Instrument) aboard ESA’s Sentinel-5P satellite retrieves column-averaged methane dry-air mole fractions (XCH4) at approximately 5.5 km x 7 km spatial resolution with daily global coverage. [3] The instrument detects methane in the shortwave infrared band, producing gridded concentration maps that researchers invert into emission flux estimates using atmospheric transport models. [3]

Zhang et al. (2020) used TROPOMI observations from May 2018 through March 2019 to estimate Permian Basin methane emissions at 2.7 ± 0.5 Tg/yr. [4] That was the largest methane flux reported from a US oil and gas producing region at the time, and more than two times higher than bottom-up inventory-based estimates for the basin. [4] Production in that basin keeps rising, as covered in Southern Permian oil jumped 14% in 2024.

The satellite does not identify individual facilities. [3] It sees the aggregate plume from all sources within each grid cell, including oil and gas operations, agriculture, landfills, and natural seeps. Researchers use spatial and temporal correlation to attribute observed enhancements to specific source sectors. [5] In basins dominated by oil and gas activity, published inversions attribute the large majority of the observed enhancement to O&G sources. [5]

What TROPOMI Cannot Do at Facility Scale

TROPOMI cannot pinpoint which specific facilities are responsible for the excess emissions it observes. [3] Its 5.5 km pixel size means a single observation may contain dozens of wellpads, compressor stations, and gathering system components. Attribution to individual operators or equipment categories requires higher-resolution instruments (aircraft, drones, continuous monitors) or mechanistic models that disaggregate basin totals into facility-level probability distributions.

Non-O&G Methane Sources Explain Only a Small Fraction of the Permian Gap

Published inversions account for wetlands, livestock, and landfills when attributing basin methane to source sectors. [5] In the Permian Basin, non-O&G sources explain only a small fraction of the total observed methane. [4] The remaining gap points to O&G emissions present in the atmosphere but absent from reported inventories.

What Drives the Gap: Abnormal Process Emissions and Factor-Based Undercounting

Abnormal process emissions that factor-based inventories structurally exclude drive the satellite-inventory gap.

Equipment malfunctions, stuck dump valves, malfunctioning flares, and compressor seal failures occur intermittently but contribute disproportionately to total facility emissions. Legacy factor methods did not capture these events. The 2024 Subpart W revisions added an “other large release events” category with a 100 kg/hr threshold aimed at exactly this kind of episodic emission. [6] Those revisions are among the reporting requirements EPA is now reconsidering.

Mollel et al. (2024) modeled prototypical Denver-Julesburg production facilities with MAES and showed that traditional bottom-up methods miss emission behavior driven by throughput changes, gas composition differences, and failure conditions such as stuck dump valves. [7] Factor-based methods multiply equipment counts by static emission factors, many of which trace to 1990s-era GRI/EPA study data. [8] Those factors do not capture how throughput changes, gas composition variability, and equipment degradation affect real-time emissions.

The operational data that production engineers already track (throughput rates, separator pressures, compressor run hours, flare status) contains emission variability information. That information never reaches the inventory because the calculation method cannot use it. MAES, developed at CSU and UT Austin as part of the EEMDL initiative, uses this operational data to generate facility-level emission distributions that include failure-mode categories. [7] TetraSoft offers MAES as software with a guided interface, so a team without an emissions-modeling background can build these facility-level estimates directly.

What the Satellite-Inventory Gap Means for Reporting Credibility

For sustainability leaders and ESG teams, TROPOMI data creates a specific credibility problem. The data is public. Anyone can check. Investors, NGOs, and European gas buyers can access TROPOMI-derived basin emission estimates from peer-reviewed publications and compare them against operator-reported methane intensity figures. [9] When the satellite consistently shows roughly twice the methane that operators collectively report, operators face pressure to explain the discrepancy and to show whether their specific facilities contribute to it. What operators are still required to report federally is covered in GHGRP Subpart W reporting in 2026.

Higher-resolution aerial instruments, such as the MethaneAIR airborne survey, produce sharper facility-level findings than a basin-scale satellite. [10]

Why Does OGMP 2.0 Level 4/5 Require Measurement-Based Approaches?

OGMP 2.0 Level 4 and Level 5 reporting explicitly require measurement-based or measurement-informed approaches that go beyond factor-based inventories. [11] Level 3 methods (source-level quantification using generic emission factors) cannot resolve the TROPOMI gap. The methodology structurally excludes the emission categories responsible for it.

A Measurement-Informed Inventory (MII) combines mechanistic simulation (MAES) with field measurement data to produce facility-level emission estimates that capture both normal-operating and abnormal process emissions. [12] The MII framework calibrates MAES against operator-reported inventory data, then uses measurement campaigns (aerial surveys, continuous monitors) to identify emissions exceeding the modeled normal-operation range. [12] Those excess observations, statistically characterized, feed back into MAES to produce a final inventory that includes failure-mode emissions. [12]

Frequently Asked Questions

Can TROPOMI identify which specific operator is responsible for excess emissions?

TROPOMI cannot attribute emissions to individual operators or facilities. [3] The instrument observes column-averaged methane over grid cells that may contain dozens of facilities from multiple operators. [3] Higher-resolution instruments (aircraft-mounted spectrometers like MethaneAIR, or satellite platforms like Carbon Mapper, which offers sub-kilometer resolution) can narrow attribution to facility clusters. [13] Operator-level attribution requires either direct measurement at the facility or mechanistic modeling that disaggregates basin totals.

Does the TROPOMI-inventory gap mean operators are violating reporting requirements?

A basin-scale measurement gap does not, by itself, establish that any individual operator is violating reporting requirements, and it is not evidence that none are; an aggregate flux cannot be attributed to specific compliant or non-compliant sources. Current US reporting frameworks (GHGRP Subpart W, state programs like Colorado ONGAEIR) require operators to calculate emissions using prescribed methodologies, typically factor-based. [14] An operator who correctly applies the prescribed emission factors is compliant with the reporting requirement, even when actual atmospheric emissions from the facility exceed the calculated value. Peer-reviewed work attributes much of the top-down/bottom-up gap to the structural limits of factor-based methods, including abnormal process conditions that those methods do not represent. [15] That structural explanation accounts for a large share of the gap, but it does not rule out that some portion reflects unreported events or reporting errors; the measurement data alone cannot separate the two.

How frequently does TROPOMI update its methane observations?

TROPOMI provides daily global coverage with an overpass time of approximately 13:30 local solar time. [3] Cloud cover blocks retrievals on overcast days, so effective temporal resolution depends on regional cloud frequency. [3] Published basin-level emission estimates typically aggregate many months to two years of valid retrievals to achieve statistically robust flux estimates. [5]

Interested in building a Measurement-Informed Inventory for your operations? Contact us to learn about our MAES-based estimation services.

This post is for informational purposes only and does not constitute legal or compliance advice. Consult qualified legal counsel or a compliance professional for guidance specific to your operations and jurisdiction.

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References

  1. Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., et al. (2020). “Quantifying methane emissions from the largest oil-producing basin in the United States from space.” Science Advances, 6(17), eaaz5120.; Shen, L., et al. (2022). “Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins.” Atmospheric Chemistry and Physics, 22, 11203–11215.; Lorente, A., et al. (2021). “Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements.” Atmospheric Measurement Techniques, 14(1), 665–684.
  2. Shen, L., et al. (2022). “Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins.” Atmospheric Chemistry and Physics, 22, 11203–11215.; Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., et al. (2020). “Quantifying methane emissions from the largest oil-producing basin in the United States from space.” Science Advances, 6(17), eaaz5120.
  3. Lorente, A., et al. (2021). “Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements.” Atmospheric Measurement Techniques, 14(1), 665–684.
  4. Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., et al. (2020). “Quantifying methane emissions from the largest oil-producing basin in the United States from space.” Science Advances, 6(17), eaaz5120.
  5. Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., et al. (2020). “Quantifying methane emissions from the largest oil-producing basin in the United States from space.” Science Advances, 6(17), eaaz5120.; Shen, L., et al. (2022). “Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins.” Atmospheric Chemistry and Physics, 22, 11203–11215.
  6. US EPA (2024). Greenhouse Gas Reporting Rule: Revisions and Confidentiality Determinations for Petroleum and Natural Gas Systems (Subpart W final rule), published May 14, 2024; effective for reporting year 2025.
  7. Mollel, W., Zimmerle, D., Santos, A., & Hodshire, A. (2024). “Using prototypical oil and gas sites to model methane emissions in Colorado’s Denver-Julesburg Basin using a mechanistic emission estimation tool.” ACS ES&T Air. DOI: 10.1021/acsestair.4c00168.
  8. US EPA, 40 CFR Part 98, Subpart W (Greenhouse Gas Reporting Rule, petroleum and natural gas systems); 1996 GRI/EPA study lineage for Subpart W emission factor tables, updated in the 2024 revisions.
  9. European Space Agency (ESA), Copernicus Sentinel-5P TROPOMI data products, publicly available; peer-reviewed emission estimates published in open-access journals.
  10. Staebell, C., Sun, K., Samra, J., Franklin, J., Chan Miller, C., Liu, X., et al. (2021). “Spectral calibration of the MethaneAIR instrument.” Atmospheric Measurement Techniques, 14(5), 3737–3753.; Chulakadabba, A., et al. (2023). “Methane point source quantification using MethaneAIR: a new airborne imaging spectrometer.” Atmospheric Measurement Techniques, 16, 5771–5785.
  11. UNEP International Methane Emissions Observatory (IMEO), OGMP 2.0 Framework, 2022.
  12. Santos, A., Mollel, W., Duggan, J., Hodshire, A., Vora, P., & Zimmerle, D. (2025). “Using measurement-informed inventory to assess emissions in the Denver-Julesburg Basin.” ACS ES&T Air, 2, 1598–1611.
  13. Staebell, C., Sun, K., Samra, J., Franklin, J., Chan Miller, C., Liu, X., et al. (2021). “Spectral calibration of the MethaneAIR instrument.” Atmospheric Measurement Techniques, 14(5), 3737–3753.; Carbon Mapper, program documentation.
  14. US EPA, 40 CFR Part 98, Subpart W (Greenhouse Gas Reporting Rule, petroleum and natural gas systems); Colorado Air Quality Control Commission (AQCC), Regulation Number 7.
  15. Zavala-Araiza, D., et al. (2017). “Super-emitters in natural gas infrastructure are caused by abnormal process conditions.” Nature Communications, 8, 14012.