the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment of methane emissions from US onshore oil and gas production using MethaneAIR measurements
Abstract. Mitigation of methane emissions from the oil and gas sector is an effective way to reduce the near-term climate warming and losses of a valuable energy resource. The oil and gas value chain contributes at least 25 % of anthropogenic methane emissions globally and is the second-largest methane-emitting sector in the US. Here, we assess methane emissions in regions accounting for 70 % of US onshore oil and gas production in 2023 using data collected by the MethaneAIR airborne imaging spectrometer. We quantify total methane emissions across all observed regions to be ~9 (7.8–10) Tg/yr, with ~90 % of emissions estimated from the oil and gas sector (~8 Tg/yr, equivalent to a methane loss rate of 1.6 % of gross gas production), which is about five times higher than reported by the US EPA. Both oil and gas emissions and gas production-normalized methane loss rates varied considerably by basin. Highly productive basins such as the Permian, Appalachian, and Haynesville-Bossier had the highest emissions (95–314 t/hr), whereas lower producing basins possibly associated with older infrastructure such as the Uinta and Piceance had higher loss rates (>7 %). We found good agreement across total emissions quantified by MethaneAIR and other empirical and remote sensing estimates at national/basin/target-level scales. This work underscores the increasing value of remote sensing data for quantifying methane emissions, characterizing methane intensities across the oil/gas sector, and mapping inter-basin emissions variability, which are all critical for tracking methane mitigation targets set by industry and governments.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3008', Anonymous Referee #1, 21 Aug 2025
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RC2: 'Comment on egusphere-2025-3008', Anonymous Referee #2, 30 Aug 2025
The authors present results from many MethaneAIR flights performed in the United States, primarily to quantify oil&gas emissions across major basins. Overall this represents a tremendous body of work to execute the flights, process data, and analyze for fluxes and the authors should be commended for this effort. The manuscript succinctly summarizes the results and shows general consistency in emission rates derived from previous observation studies (mass-balance, and satellite remote sensing). The authors employ what appears to be a novel way to calculate methane fluxes from remote sensing observations, but the details are extremely light. Before I can accept for publication, considerable more detail needs to be included and justified. My comments are as follows:
1. Table 1. Is Basin Area the total area of the basin or total area flown? If total area, can you express in the same table how much of that area you flew with MethaneAIR?
Section S1. STILT.
2. How do you simulate columns with STILT? How many layers? Interpolate between layers? Use an averaging kernel? What is the averaging kernel?Section S1. The calculation of the background is unclear.
3. Can you restate in terms of an equation, figure, or additional clarifying language? - "The background concentrations are given by a model..." - what model? STILT?
4. "The boundary inflow is modeled using the Jacobian and emission rates outside the domain of observed concentrations." Where do you get these emissions? An inventory? Proper background quantification is so vital to robust inversions, this section needs to be much clearer.Section S1. Point Sources.
5. Is the divergence integral method to calculate point sources applied at the 0.01 binning or at the native resolution? If you are binning, then you are certainly subtracting out more than point sources, as you are aggregating all true emission sources within that ~1km domain.
6. If you are not binning, how do assess that model transport error correctly subtracts the influence of point sources? Do you have a quality control approach that ensures this? If the transport is wrong, then you risk not subtracting the point source component in your concentration field, which I can imagine will throw the inversion haywire and produce flux artifacts.
7. How do you determine an origin of the point source via the divergence integral method? It seems quite critical that you get the origin correct if you are are forward simulating a concentration field with STILT.
8. It's not obvious to the reader that subtracting a forward model simulated concentration field produces a preferred result, especially assuming some level of spatial aggregation (e.g., were you to run these inversions at 10-20km, scales that others perform satellite inversions, would this still be required?).Section S1. Inverse Problem
9. What is the formulation of the inversion problem? An SI is a good place to put these equations to paper. The way it currently reads, the mass-balance constraint would just be the inverse of the Jacobian - i.e., s = (H^-1)y and the non-negativity constraint would be some sort of gradient descent (or something else?) algorithm that stops at zero. Not obvious from what's written. Are there other parameters that keep the solution from an overfit? They say there is no need for a prior, so not Bayesian I guess?
10. The authors do not provide any sort of metric of goodness of fit (e.g., H * s_hat plotted against y) or information content from the retrieval. It's fairly common practice to show how well your model around the optimal emission state compares to observations. It's also common to show information content metrics (e.g., degrees of freedom for signal, model-resolution matrices, etc) for inversions, but given there's not an explicit inverse formulation in paper, it's hard to know if that would be feasible.11. Table S5. Please provide citations for literature based estimates, perhaps as an additional column in the table or a footnote.
Citation: https://doi.org/10.5194/egusphere-2025-3008-RC2
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The authors quantify methane emissions from 12 US oil and gas basins using methane column observations from 32 MethaneAIR flights in 2023. These 12 basins accounted for 70% of total onshore oil and gas production in the contiguous United States in 2023. The authors estimate both total and sector-specific (oil + gas) emissions for each basin. They use a novel two-step regional flux inversion approach that first quantifies large point sources and then diffuse area emissions via Bayesian inverse analysis with the Stochastic Time-Inverted Lagrangian Transport (STILT) model. Emission contributions from non-oil and gas sources are estimated using sectoral emission estimates from a collection of previous top-down and bottom-up studies. The authors compare their regional estimates of methane emissions and loss rates with 16 previous studies and find generally good agreement.
The manuscript is well-written and a good fit for ACP. I recommend that it be accepted for publication with revisions to address the following comments and questions: