Reducing methane emissions from documented abandoned and orphaned oil and gas wells in Canada and the United States 
Bibliographic record
Abstract
Millions of oil and gas wells are abandoned and orphaned around the world. Due to funding shortfalls, many abandoned and orphaned wells remain unplugged and are negatively impacting the environment and contributing to greenhouse gas emissions, such as methane. To reduce emissions and environmental impacts, the wells are required to be plugged, but the well sites can be repurposed for wind and solar energy and/or the wells itself can be redeveloped for geothermal energy production. To quantify methane emissions and identify opportunities for repurposing abandoned and orphaned wells and well sites for renewable energy development, we analyze public oil and gas well data from governmental agencies of documented abandoned and orphaned wells in Canada and the United States. We estimate the total number of abandoned and orphaned wells in Canada and the United States to be 3,500,602, of which 4% are orphaned and in need of government funding. We estimate plugging costs for orphaned wells in the United States to exceed federal funding by 30%-80%. For abandoned and orphaned wells, we quantify methane emissions at the national and state/provincial/territorial level and potential emission reductions achieved through plugging. Furthermore, to evaluate mitigation and redevelopment opportunities, we analyze geographic locations of abandoned and orphaned wells with national maps of renewable energy potential (geothermal, wind, and solar) and land cover/land use in Canada and the United States. Mitigating oil and gas wells can help fulfill national energy transition goals and emission reduction targets, while providing an additional funding stream to manage the millions of abandoned and orphaned wells around the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".