Documented Orphaned Oil and Gas Wells Across the United States
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Orphaned oil and gas wells are unplugged nonproducing wells with no solvent owner of record to plug and mitigate them, such that the responsibility often falls on government agencies and the general public. Unplugged wells pose risks to the environment, climate, and human health. To develop a national framework to quantify the environmental benefits of plugging and optimize mitigation, we analyze oil and gas well data from state agencies across the United States to estimate the number of documented orphaned wells over time and evaluate their attributes. We find at least 81,857 documented orphaned wells as of September 2021 and 123,318 as of April 2022, representing 2% and 3%, respectively, of all estimated abandoned wells in the United States. We identify at least 20,286 potentially documented orphaned wells as of September 2021 (0.5% of all estimated abandoned wells in the country), of which 8% became documented orphaned wells as of April 2022. We estimate annual methane emissions to average 0.016 ± 0.001 MMt of CH 4 for the 123,318 documented orphaned wells as of April 2022, corresponding to 5–6% of the total methane emissions estimated by the U.S. EPA for all abandoned wells. Although well type (i.e., oil vs gas) is generally available (83% of the 81,857 documented orphaned wells as of September 2021), only 49% and 16% of the wells have information on depth and last production date, respectively. Overall, documented orphaned wells and their attributes, including location, well type, depth, and last production date, require additional characterization and studies to constrain the uncertainties. Nevertheless, our identification and analysis of documented orphaned wells represent the first steps toward characterizing the full set of wells eligible to be plugged and remediated with the federal funding available in the U.S. via the Infrastructure Investment and Jobs Act. Our results can also be useful for the management of the hundreds of thousands, potentially a million, undocumented orphaned wells likely to exist across the nation.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".