Estimating Methane Emissions from Non-Producing Oil and Gas Wells in British Columbia Using a Helicopter-Based Methane Detection System
Bibliographic record
Abstract
Non-producing oil and gas wells emit methane, a greenhouse gas with approximately 80 times the warming potential of carbon dioxide over a 20-year period. Reducing methane emissions from the oil and gas industry is crucial in assuring Canada reaches its pledge of cutting greenhouse gas emissions by 40% below 2005 levels by 2030. Currently, British Columbia (BC) hosts approximately 20,000 non-producing oil and gas wells. The British Columbia Energy Regulator (BCER) has been conducting annual LiDAR-based helicopter surveys of methane emissions, with 1,334 non-producing oil and gas wells surveyed from 2017 to 2024. To estimate methane emissions rates using BCER's helicopter survey data, we performed a controlled release test of the Lasen Airborne LiDAR Pipeline Inspection System to evaluate the detection range. The controlled-release testing involved multiple helicopter flyovers over a single site, during which various methane flow rates, ranging from 0.05 to 1.8 kg/hr, were released. We used our test results to combine available BCER aerial survey data and ground-based measurements and estimate total methane emissions from non-producing oil and gas wells across BC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".