A Demonstrated Reconciliation of Top-Down and Bottom-Up Methane Measurements to Derive Verified Emission Intensities
Bibliographic record
Abstract
Oil and gas companies striving to attain the OGMP2.0 “Gold Standard” for methane Measurement, Reporting, and Verification (MRV) are required to find and measure individual methane sources across all their operating assets, and in particular, to verify total emissions using an independent "top-down" measurement. The process of comparing source-level and top-down measurements is termed “reconciliation” and is an essential part of meeting the “Gold Standard”. However, to date, there is no prescriptive OGMP 2.0 protocol on how to reconcile site and source-level measurements, and there are key knowledge gaps regarding calculation methods, required sample sizes, and uncertainty protocols.This study demonstrates a novel framework for reconciling on site source measurements with independent source-resolved aerial survey data to derive corporate methane emissions and intensity data sufficient to meet or exceed the OGMP 2.0 Gold Standard certification requirements. Critically, the protocol allows for direct analysis of measurement uncertainties. In partnership with an oil and gas producer operating in Canada, a source-level inventory is first created based on extensive ground measurements and company-specific emission factors. Independent source-resolved measurements, covering 100% of the company’s operating assets, are then conducted using Bridger Photonics Inc.’s Gas-Mapping LiDAR (GML). Multi-pass aerial data are analyzed using detailed probability of detection models, which consider the conditions of each pass, and integrated with the bottom-up data to account for unmeasured sources. The result is a comprehensive, verified inventory of total methane emissions for the company. As part of this demonstration, the analysis is repeated using up to four independent aerial surveys, providing real-world insights to the importance of temporal variability in emissions and its influence on required sample sizes for accurate reconciliation. The results have important implications for creating MRV protocols that ensure reported emissions adequately represent the variable nature of emissions, particularly when sample sizes are small because measurements are limited to assets from a single operator as is the case under OGMP2.0 reporting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".