Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Accidental blowouts in oil and gas wells can result in large and prolonged methane emissions, which are often unreported when happening in remote places. The rapid advancement of space-based methods for detecting and quantifying methane plumes provides an essential tool for uncovering these superemission events. We use a number of methane-sensitive satellite missions, including the Sentinel-5P/TROPOMI global mapper and several high-resolution instruments, to document a methane leak from a well blowout happening in Kazakhstan’s Karaturun East oil field in 2023. A dense time series of plume detections from those satellites shows that the leak was active during 205 days and that most of the emissions were in the range 20–50 t/h. Using 48 high-quality emission rate estimates, we calculate that a total of 131 ± 34 kt of methane was released to the atmosphere during this leak, which exceeds the total emissions from all previously documented accidents. Our study characterizes the evolution and magnitude of the 2023 Karaturun East methane leak and showcases how different types of satellite instruments can be combined to document and quantify methane leaks active during long time periods.
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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 | 0.001 | 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".