A Constellation of Satellites Hunting Methane Leaks Is Launching Soon
Bibliographic record
Abstract
A blowout in Ohio in 2018 was the first ever where the emissions could be measured from space, though it was at best a rough estimate based on data gathered on the 13th day after the XTO Energy well control event began. A year later, a blowout of a Devon well near Victoria, Texas, was measured starting the day after it occurred, with data collected on 3 days over the next 2 weeks. Using the measurement of carbon dioxide, it was estimated that the flare was 87% effective in burning about 4,800 metric tons of the leaking methane gas. Emission estimates varied wildly, and both the Ohio (Pandey et al. 2019) and Texas (Cusworth, Duren, Thorpe et al. 2020) efforts to use satellites led to technical papers to consider how they addressed this challenge. For those with blowouts next year, chances are a lot better methane-emission data would be available because of the launch of a constellation of specialized methane-measurement satellites by the two groups that played a key role in the earlier tests. In presentations at CERAWeek by IHS Markit, GHGSat said it has two methane-detection satellites in orbit and plans the launch of eight more, and the Environmental Defense Fund (EDF) said it is moving forward with the launch of its first one next year. Both are aiming to cover the lion’s share of oil and gas operations and measure the flow rate of the gas rather than concentrations in the atmosphere. They said they can do that far more accurately than was possible with the general-purpose climate observation satellites by focusing their equipment on the wavelength of methane. GHGSat said its satellites, which are about the size of a microwave oven, can measure the potent greenhouse gas from an elevation of 500 km and up. They are placed in polar orbit, which allows them to cover the globe every 2 weeks as the Earth rotates. Launching more satellites will allow more frequent looks. There are differences in the GHGSat and EDF designs, reflecting their contrasting missions. The Canadian company GHGSat, whose satellite initiative was initially supported by Schlumberger and the Oil and Gas Climate Initiative, is building tiny satellites with extremely high resolution to serve clients in the oil and mining businesses. During the presentation, Stéphane Germain, chief executive officer of GHGSat, displayed an image and said its satellites can tell if the methane is “coming from a particular facility and even tell what part of the facility it is coming from.” The company also sells the services of similarly equipped planes that can create more-detailed images using similar equipment at elevations of 3000 m and higher. EDF raised $100 million from donors, including Elon Musk, and has hired Raytheon to build a satellite equipped with a detector from Ball Aerospace. It can survey an area that is 260 km wide. That is far wider than the GHGSat satellites, which have the advantage of being able to zero in on smaller details when looking for leaks. The environmental group points out its device is more sensitive to methane emissions, detecting levels down to two parts per billion.
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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".