Methane emissions at buried abandoned wells in Northern Germany: sampling strategies, pitfalls, and silver linings
Bibliographic record
Abstract
As we are risking to exceed the 1.5-degree target in the next decade, effective measures to cut back greenhouse gas emission are necessary. With this in mind, the global community expressed the necessity to shift away from fossil fuels at COP 28 for the first time in its history. During this transition process, millions of oil and gas wells will be abandoned. However, recent studies found substantial methane emissions from old abandoned wells for example in the USA and Canada. Hence, the establishment of a proper abandonment procedure is necessary. To achieve this, further research of the current situation of abandoned wells in different countries, reasons for well integrity failure and best abandonment practices are inevitable. So far, only about a dozen countries have measured data on methane emissions and even less include it in their yearly greenhouse gas inventory. Germany has about 20,000 abandoned wells, which are generally plugged and buried, however, it is unclear, whether they are emitting methane or not.Here, we present an overview of two years of closed-chamber methane emission measurements at 59 onshore oil and gas wells in Northern Germany, covering both abandoned exploration and production wells. As the majority of well sites showed no methane leakage, we focus on two oil fields (“Steimbke Nord” and “Nienhagen (-Elwerath)”) with sample sites that showed minor methane emissions of up to ~540 nmol m-2 s-1. Based on a combination of soil gas hydrocarbon concentrations (methane, ethane, and propane) and isotopic methane compositions, we were able to link the methane emissions at three well sites at “Steimbke-Nord” to regionally occurring natural methanogenesis in the overburden (peat). One well at “Nienhagen (-Elwerath)”, however, was characterized by high δ13C-CH4 and a composition of higher hydrocarbons, typical for oil-associated gases and/or biodegraded oil. We will discuss two possible emission sources: (1) well integrity failure and (2) microbial degradation of oil residues from an old oil spill or a drilling mud pit. Furthermore, we will examine the mitigation potential of microbial methane oxidation for methane emissions to the atmosphere. In summary, our data demonstrates the complexity of emission studies on buried abandoned wells and underlines the necessity for a combination of soil gas sampling and flux measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".