Aerial Survey Sample Size Requirements for Robust Methane Inventories in the Upstream Oil and Gas Industry
Bibliographic record
Abstract
Accurate and frequent measurement-based inventories of methane emissions from the upstream oil and gas (UOG) industry are crucial to developing and implementing effective regulations and achieving sustainable mitigation. Recent advances in the analysis of large-scale survey data have enabled measurement-based basin/jurisdiction-level methane inventories from remote surveys of UOG infrastructure. Treating like facility or well types as strata within a larger sample and leveraging analytics that consider measurement uncertainties, probabilities of detection, empirical (non-smooth) source distributions, and sample size effects, specific conclusions can be derived in the context of measurement sensitivities and uncertainties. These analyses provide useful insights to regulators and industry, including but not limited to the individual contributions of equipment types to overall methane emissions. However, a priori design and optimization of surveys to cost-effectively derive measurement-based inventories and ensure survey coverage can achieve acceptable levels of uncertainty remains a key challenge. Here, we present an analysis of data from extensive aerial LiDAR and ground-based surveys of UOG facilities in Western Canada to provide much-needed guidance on source- and facility-specific sampling protocols for the UOG industry. Insights into the temporal intermittency and variability of source rate magnitudes are derived using a statistically robust method that considers the quantification accuracy and probability of detection function of the aerial instrument. Results provide important context regarding the required survey coverage of aerially detectable sources (greater than approximately 1 kg/h) to support the development of accurate inventories, defensible frequencies of regulated inspections, and alternative leak detection and repair programs.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".