Large-Scale Controlled Experiment Demonstrates Effectiveness of Methane Leak Detection and Repair Programs at Oil and Gas Facilities
Bibliographic record
Abstract
The importance of reducing methane emissions from oil and gas operations as a near-term climate action is widely recognized. Most jurisdictions around the globe using leak detection and repair (LDAR) programs to find and fix methane leaks. In this work, we empirically evaluate the efficacy of LDAR programs using a large-scale, bottom-up, randomized controlled field experiment across ~200 oil and gas sites in Canada. We find that tanks are the single largest source of emissions, contributing to nearly 60% of total emissions. The average number of leaks at treatment sites that underwent repair reduced by ~50% compared to control sites. Although control sites did not see a reduction in the number of leaks, emissions reduced by approximately 36% suggesting potential impact of routine maintenance activities to find and fix large leaks. By tracking tags on leaking equipment over time, we find a high degree of persistence – leaks that are repaired remain fixed in follow-up surveys, while non-repaired leaks remain emitting. We did not observe any significant growth in emission rate for non-repaired leaks, suggesting that any increase in observed leak emissions following LDAR surveys are likely from new leaks. Vent emissions reduced by 38% without a significant reduction in the average number of vents across control and treatment sites, showing the importance of both anomalous vents and temporal variations in vent emissions. Our results show that a focus on equipment and sites that are prone to high emissions such as tanks and oil sites are key to cost-effective mitigation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".