Potential to convert abandoned and orphaned oil and gas well sites for renewable energy production in Canada and the United States
Bibliographic record
Abstract
Millions of oil and gas wells are abandoned and orphaned in Canada and the United States. These well sites can be repurposed for wind and solar energy, while the wells access itself can be redeveloped for geothermal energy production. To identify opportunities for repurposing abandoned and orphaned wells and well sites for renewable energy development, we analyze public oil and gas well data from state, provincial, and territorial agencies to estimate the number and geospatial distribution of abandoned and orphaned wells in Canada and the United States. As of March 2022, we identify 4,724 orphaned wells and 420,113 abandoned wells in Canada and identify 123,318 orphaned wells (as of March 2022) and 3,151,700 abandoned wells (as of August 2022) in the United States. Using this dataset, 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. We then evaluate how the potential to repurpose wells/well sites vary across Canada and the United States. Due to funding shortfalls, many abandoned and orphaned wells remain unplugged and are negatively impacting the environment and contributing to greenhouse gas emissions. Repurposing wells and well sites can provide 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".