Satellite monitoring of fugitive methane emissions from oil and gas facilities in Australia
Bibliographic record
Abstract
Since 2016, GHGSat has been operating the world’s first and only satellite specifically designed to monitor methane emissions from industrial facilities around the world. The lessons learned through the success of this demonstration satellite have been incorporated into the company’s next two satellites, the first of which was originally scheduled to launch in September 2019 but was delayed as a result of a rocket failure. The satellite’s technology is ideally suited to the oil and gas industry, particularly unconventional developments in which a high density of facilities can be present. This paper introduces the technology briefly and discusses the predictive analytics applications being developed to augment the efficacy of the satellites in detecting methane emissions. An example of successful application of the predictive analytics engine to detect a methane leak in the Delaware Basin in New Mexico is presented. Parallels are drawn between shale basins in the US and the Surat Basin in Australia, highlighting the applicability of the technology for the oil and gas industry in Australia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".