Methane cycling at buried abandoned wells in a peat-rich area in northern Germany – curse or blessing for atmospheric emissions and emission studies?
Bibliographic record
Abstract
In the worldwide effort to reach the 1.5-degree target, governments try to mitigate anthropogenic methane emissions. One example is the oil & gas sector, which is responsible for the second most anthropogenic methane emission source after agriculture. Abandoned oil and gas wells are seen as a promising target, as they can in some cases emit up to several tons of methane per year. However, only the USA includes emissions from such wells into their yearly greenhouse gas emission inventory and only for a few other countries like Canada, the United Kingdom, Romania and the Netherlands measured data on methane emissions from abandoned gas wells are available. Most countries do not even have sufficient data regarding numbers, positions, and status of their abandoned wells let alone the related methane emissions. Germany has about 20,000 abandoned wells, which are generally filled and buried, however, it is unclear, whether they are emitting methane or not.Here, we present our approach and first data to fill this knowledge gap for Germany regarding methane emissions from onshore-abandoned oil and gas wells. For our first measuring campaign we focused on five regions in Lower Saxony (Federal State in Northern Germany) measuring 29 wells, covering both backfilled exploration and abandoned production wells of oil and gas fields. We will present our preliminary results including rates of soil methanotrophy focusing on one region with both, shallow oil wells and industrial peat production. Our data demonstrate the necessity for detailed knowledge on background methane emissions and cycling particularly in such methane-laden settings.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".