Orphaned oil and gas well stimulus—Maximizing economic and environmental benefits
Bibliographic record
Abstract
Orphaned oil and gas wells are abandoned wells for which the cost of environmental impacts usually falls on governments and the general public. Government agencies responsible for well plugging often face funding shortfalls and many orphaned wells remain unplugged. To address this and support the oil and natural gas industry, federal governments are already spending, or considering spending, billions of dollars to plug orphaned oil and gas wells. Here, we analyze oil and gas data for the United States and Canada and identify policy recommendations that can best address environmental impacts of abandoned and orphaned wells. At least 116,245 wells across 32 states and four Canadian provinces/territories are operated by companies filing for bankruptcy in the first half of 2020, which may be an indication that many wells will be orphaned in the near future. Moreover, there are 4,700,000 historic and active oil and gas wells in the United States and another 790,000 in Canada. Of these, 2,000,000 and 310,000 wells are active in the United States and Canada, respectively. Thus, three of five wells ever drilled in the United States are currently inactive (2,700,000 wells), but only one in three are plugged (1,500,000 wells). Plugging involves isolating zones containing oil, gas, and water and is the main strategy for well abandonment. If the orphaned well stimulus funding comes through, tens of thousands of wells will be plugged within a few years. Well plugging at this scale far exceeds current rates of plugging, and it is important that we work to ensure long-term environmental benefits of well abandonment to water, air, climate, ecosystems, and human health. Minimizing environmental impacts of the millions of abandoned and orphaned wells in the United States, Canada, and abroad will allow for an economically beneficial and environmentally safe transition to a carbon-neutral economy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".