Drone-based methane emissions monitoring from orphaned oil and gas wells in Pennsylvania, US
Bibliographic record
Abstract
More than a hundred thousand documented orphaned oil and gas wells are known to exist in the United States, with potentially millions remaining undocumented. Due to funding shortfalls, many orphaned wells remain unplugged and continue to emit methane, a potent greenhouse gas. Drone-based methane emission measurements can help prioritize mitigation efforts for orphaned wells and aid in locating undocumented orphaned wells, which are wells with unknown locations and conditions. In collaboration with the Pennsylvania Department of Environmental Protection (PA DEP) and the US Department of Energy’s (DOE) Orphan Well Program, we will present the results of drone-based methane emission measurements across four regions in Pennsylvania with a high likelihood of containing undocumented orphaned wells. We will share our insights on the potential for detecting methane emissions using drone-based tunable diode laser absorption spectroscopy (TDLAS), an emerging technology for methane monitoring in the oil and gas sector. Additionally, this work explores the foundation of a screening method for providing first-order estimates of methane emission rates at orphaned well sites. We will compare the methane measurements with potential well locations identified using drone-based magnetometry data, historical maps, LiDAR, and atmospheric data. Our results will be helpful for prioritizing plugging and remediation for the hundreds of thousands, and potentially millions, orphaned wells across the US and 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".