Methane emissions from oil and gas platforms in the North Sea
Bibliographic record
Abstract
Since 1850 the concentration of atmospheric methane (CH 4 ), a potent greenhouse gas, has more than doubled. Recent studies suggest that emission inventories may be missing sources and underestimating emissions. To investigate whether offshore oil and gas platforms leak CH 4 during normal operation, we measured CH 4 mole fractions around eight oil and gas production platforms in the North Sea which were neither flaring gas nor offloading oil. We use the measurements from summer 2017, along with meteorological data, in a Gaussian plume model to estimate CH 4 emissions from each platform. We find CH 4 mole fractions of between 11 and 370 ppb above background concentrations downwind of the platforms measured, corresponding to a median CH 4 emission of 6.8 g CH 4 s −1 for each platform, with a range of 2.9 to 22.3 g CH 4 s −1 . When matched to production records, during our measurements individual platforms lost between 0.04 % and 1.4 % of gas produced with a median loss of 0.23 %. When the measured platforms are considered collectively (i.e. the sum of platforms' emission fluxes weighted by the sum of the platforms' production), we estimate the CH 4 loss to be 0.19 % of gas production. These estimates are substantially higher than the emissions most recently reported to the National Atmospheric Emission Inventory (NAEI) for total CH 4 loss from United Kingdom platforms in the North Sea. The NAEI reports CH 4 losses from the offshore oil and gas platforms we measured to be 0.13 % of gas production, with most of their emissions coming from gas flaring and offshore oil loading, neither of which was taking place at the time of our measurements. All oil and gas platforms we observed were found to leak CH 4 during normal operation, and much of this leakage has not been included in UK emission inventories. Further research is required to accurately determine total CH 4 leakage from all offshore oil and gas operations and to properly include the leakage in national and international emission inventories.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".