Highly Uncertain Methane Leakage from Oil and Gas Wells in Canada Despite Measurement and Reporting
Bibliographic record
Abstract
Leakage of fluids from oil and gas wells is a source of the key greenhouse gas methane, and presents environmental risks, including groundwater contamination. A loss of well integrity can result in fluid leakage into the annular space between subsequent well casings (which is often vented to the atmosphere) or into the surrounding subsurface. In Canada, industry reporting on well integrity is often incomplete, leading government inventories to disagree on emission magnitudes. In this study, we model wellbore methane emissions using industry data in British Columbia and Alberta, Canada, finding that differing model assumptions to handle unclear/missing data have a strong influence on estimated emissions. Considering estimates derived from industry reporting and from independent measurement, wellbore emissions in the two provinces range anywhere from 23 to 176 kt of methane, representing 1.7-11.4% of their upstream sector methane emissions. Further, finding over 130 examples of measured leaks seemingly missing from industry reporting, we conclude that wellbore emissions, groundwater contamination, and broader environmental risks are underestimated. We provide recommendations to improve well integrity tracking through data quality assurance measures and increased testing. Finally, we find that ongoing optical gas imaging camera surveys could be an effective tool to augment wellbore testing requirements to minimize industry burden.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".