Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS
Bibliographic record
Abstract
Abstract With atmospheric methane concentrations rising, spurring increased social concern, there is a renewed focus in the oil and gas industry on methane emission monitoring and control. In 2019, a methane emission survey at a bp asset west of Shetland was conducted using a closed-cavity methane spectrometer mounted onboard a long-endurance fixed-wing unmanned aerial vehicle (UAV). This flight represents the first methane emissions survey of an offshore facility with a miniature methane spectrometer onboard a UAV with subsequent flights performed. The campaign entailed gathering high-density methane concentration data in a cylindrical flight pattern that circumnavigated the facility in close proximity. A small laser spectrometer was modified from an open-cavity system to a closed-cavity onboard the aircraft and yielded in-flight detection limits (3s) of 1065ppb methane above background for the 2019/2020 sensor version and 150ppb for the 2021 sensor versions. Through simulation, the sensors minimum detection limits in mass flow rate were determined to be 50 kg/h for the 2019/2020 campaign and 2.5kg/h for the 2021 campaigns; translating to an obtainable measurement for 23% and 82% of assets reporting higher than 1 kg/h according to the 2019 EEMS dataset, respectively. To operationalize the approach, a simulation tool for flight planning was developed utilizing a gaussian plume model and a scaled coefficient of variation to invoke expected methane concentration fluctuations at short time intervals. The simulation is additionally used for creation of synthetic datasets to test and validate algorithm development. Two methods were developed to calculate offshore facility level emission rates from the geolocated methane concentration data acquired during the emission surveys. Furthermore, a gaussian plume simulator was developed to predict plume behavior and aid in error analysis. These methods are under evaluation, but all allow for the rapid processing (<24h) of results upon landing the aircraft. Additional flights were conducted in 2020 and 2021 with bp and several UK North Sea Operators through Net Zero Technology Centre (NZTC) funded project, resulting in a total of 18 methane emission survey flights to 11 offshore assets between 2019 and 2021. The 2019 flight, and subsequent 2020/21 flights, demonstrated the potential of the technology to derive facility level emission rates to verify industry emission performance and data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".