From Data to Actionable Insight: Monitoring Fugitive Methane Emissions at Oil and Gas Facilities Using Satellites
Bibliographic record
Abstract
Abstract Up until recently, the monitoring of greenhouse gases with satellites had been limited to a regional or global scale. Because of the low spatial resolution of scientific satellites looking at gases, attributing emissions to specific facilities had so far not been possible. GHGSat changed that narrative with its first satellite GHGSat-D in June 2016, the first and only in the world specifically designed to monitor emissions directly from industrial sites, with a spatial resolution of less than 50m. The system makes it possible for oil and gas companies to keep a frequent eye on their facilities scattered across vast areas at the lowest cost possible since all measurements are performed remotely with no need to access the sites. We present recent single pass measurements taken with our demonstration satellite in the Short-Wave Infrared (SWIR) band, showing evidence of point source emission plumes at facilities such as underground coal mine vents and oil and gas facilities. The lessons learned from GHGSat-D in the last three years making over 4,000 measurements at hundreds of facilities around the world have been incorporated into our second satellite scheduled for launch in August 2019. As a result, GHGSat-C1 is expected to improve on the performance of its predecessor by an order of magnitude. We will present some of the first results from this second satellite. Finally, we introduce some of the innovative products and applications we are developing using analytics, artificial intelligence and machine learning to better serve our customers with actionable insight and optimize the operation of our system. The ability of the technology to work together with other sources of data (such as other satellites, drones or ground measurements) in an effective tiered monitoring system will also be demonstrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".