Methane emission rate estimate using airborne measurement at offshore oil platforms in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Abstract Methane (CH4) measurements are needed to better understand emissions from oil and gas sources. While many CH4 measurement studies have been done in Canada, they have not yet targeted offshore production from which low emission intensities are reported by industry. For this study, a Twin Otter aircraft was equipped with a Picarro 2210-i gas analyzer and an Aventech wind measurement system (AIMMs_30) to measure CH4 emissions from three oil production facilities in offshore Newfoundland and Labrador. Each facility was visited three times to account for daily variability. Measured concentrations were used to estimate emission rates and production-weighted intensities using two different methods, Top-down Emission Rate Retrieval Algorithm (TERRA), a mass conservation technique developed by Environment and Climate Change Canada, and a Gaussian Dispersion method (GD). Overall, TERRA mass balance-derived emission rates from our measurements were 2,890 ± 3,027 m3 CH4 day− 1(GD = 7,721 m3 CH4 day− 1), 3,738 ± 7,199 m3 CH4 day− 1 (GD = 13,131 m3 CH4 day− 1) and 7,975 ± 4,453 m3 CH4 day− 1 (GD = 7,242 m3 CH4 day− 1), respectively for SeaRose, Hibernia and Hebron. Based on results from both TERRA and Gaussian dispersion the weighted average was (considering number of samples in each method) 5,000 m3 CH4 day− 1 (3.35 tonnes CH4 day− 1), which is comparable to the federally reported estimate of 8,037 m3 CH4 day− 1 of federal estimates, reported in 2019. Production-weighted methane intensities calculated using measured emission rates and reported oil production show that Canadian offshore production ranges from 0.4–2.2 MJ emitted/MJ produced, making it among the least methane-intensive oil produced in Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".