The Efficacy of Methane Leak Detection and Repair (LDAR) Programs in Practice
Bibliographic record
Abstract
Periodic leak detection and repair (LDAR) surveys are a key part of most modern oil and gas sector methane regulations, however their effectiveness in real-world practice has been difficult to assess. This study analyzes three years of reported data from regulated LDAR surveys in British Columbia, Canada, which suggest that 3×/year optical gas imaging (OGI)-based LDAR surveys reduce detected emissions by half at fully compliant sites. However, independent source-resolved aerial surveys at an identical subset of sites find 12 times more methane emissions overall, and four times more emissions after conservatively excluding potential combustion-related and intentional vent sources not targeted by OGI LDAR surveys. This demonstrates that regulated OGI-based LDAR surveys only capture a small portion of total emissions in practice, raising concerns about overestimated mitigation impacts and potentially misguided expectations when assessing alternative technologies. Further analysis reveals the two methods find complementary subsets of sources, with aerial detections comprising a range of larger combustion, vent, and fugitive sources and LDAR detections dominated by numerous smaller leaks from connectors and valves. This underscores the importance of integrating complementary measurement approaches to capture the full distribution of emissions and the necessity of independent verification frameworks such as OGMP 2.0.
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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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".