Fugitive methane gas migration around Alberta's petroleum wells
Bibliographic record
Abstract
Abstract Methane emission quantification from gas migration (GM) and surface casing vent flow (SCVF) is needed to support strategic methane reduction targets and mitigate explosion and groundwater quality risks. This paper assessed which of 451 990 Alberta oil and gas wells should have been (or will be) tested for SCVF and/or GM according to regulations, and compared the results with the provincial GM testing database. As of 2017, GM testing was required on 3.5%, and reported for 0.75%, of Alberta’s energy wells. Similarly, SCVF testing was required on 58.2%, and reported for 6.2%, of all wells. An estimated 14.5% of all wells were legally abandoned before GM and SCVF testing regulations existed. All of the remaining wells will require SCVF testing prior to legal abandonment, and an estimated 32.9% to 75.5% of the total will not require GM testing before abandonment based on current regulations. The cumulative number of ‘serious’ GM reports that have remained open since submission has continuously been increasing each year, which contradicts the requirement for repair within 90 days, suggesting regulations are not enforced. The GM testing procedure is inadequate for quantitative testing. We conclude that fugitive methane emissions, and in particular gas migration, are not well constrained in Alberta. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".