Sevenfold Underestimation of Methane Emissions from Non-producing Oil and Gas Wells in Canada
Bibliographic record
Abstract
Millions of non-producing oil and gas wells around the world are leaking methane and other contaminants, contributing to increased greenhouse gas emissions and polluting our water, soil, and air. Quantifying methane emissions and understanding the attributes driving these emissions are important for evaluating the scale of the environmental risks and informing mitigation strategies. With our national-scale direct measurement database of 494 non-producing wells across Canada, we find total annual methane emissions from non-producing wells in Canada to be 230 kt/year (51-560 kt/year) for 2023, which is 7 (1.5-16) times higher than estimated in Canada's National Inventory Report (34 kt/year) and accounts for 13% of total fugitive emissions from oil and natural gas systems in Canada. We show that the role of well attributes in methane emissions is best evaluated by considering the emitting component (wellhead/surface casing vent) and the spatial scale (e.g., national, provincial, subprovincial). Large uncertainties in methane emissions from non-producing wells can be reduced not only with additional measurements but also with detailed well attribute analysis using direct measurements. Identifying attributes linked to high emitters can also be used to prioritize mitigation, thereby reducing methane emissions and broader environmental risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".