Environmental Issues with Orphaned Wells
Bibliographic record
Abstract
Summary Orphaned oil and gas wells in the United States, abandoned without a responsible party, pose significant and widespread risks to the environment and public health. These wells contribute to methane emissions, groundwater contamination, and chemical migration, yet they received little attention from policymakers until recent decades. Systemic problems, including inadequate funding, insufficient environmental monitoring, and inconsistent regulatory definitions, limit the effectiveness of current risk management and remediation efforts. This study integrates national geospatial data with environmental, infrastructure, and demographic datasets to evaluate proximity-based impacts of more than 117,000 documented orphaned wells. Our analysis shows that more than 4,000 schools, 300 hospitals, and thousands of domestic water wells lie within exposure zones determined using risk-specific buffer distances. For example, we used a 2,000-meter buffer for air quality and buffers from 762 to 3,000 meters for water resources, based on previous studies of air pollution and groundwater migration to provide conservative safety margins. Millions of Americans, with disproportionately high numbers among minorities, older adults, and people with disabilities, live within one mile of an orphaned well. Orphaned wells nearby also threaten environmentally sensitive areas, including national and local parks, major surface water bodies, federally designated critical habitats, and critical infrastructure, for example, power plants and hazardous waste treatment sites. This study also identifies the technical potential to repurpose well sites for geothermal, wind, and solar energy, as well as for subsurface energy storage. It presents new opportunities to address the orphaned well crisis, namely the use of voluntary carbon credit markets to fund plugging activities. Our results offer a comprehensive assessment of both risks and opportunities for remediating orphaned wells with limited funding.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".