Methods and benefits of measuring non-hydrocarbon gases from surface casing vents
Bibliographic record
Abstract
Abstract Surface casing vents divert natural gas migration along oil and gas boreholes to bypass groundwater, with the gas venting to the atmosphere. While this strategy is designed to protect groundwater, it constitutes a source of greenhouse gases to the atmosphere. In instances where gas leakage occurs, the characterization of the molecular and isotopic composition of natural gas emitted from surface casing vent flows can be used to assist in identifying the gas source. We compare concentration measurements of non-hydrocarbon gases (within natural gas) of samples analyzed by laboratory-based gas chromatography (N2, Ar, CO2 and O2) and magnetic sector noble gas mass spectrometry (He, Ar and Kr) with field measurements conducted using a field portable quadrupole mass spectrometer (miniRUEDI). The standard deviation of miniRUEDI concentration results was within plus/minus one standard deviation of samples measured using laboratory-based GC (N2, O2, Ar and He) and magnetic sector noble gas mass spectrometry (He, Ar). Additional laboratory-based determination of isotope ratios of methane and argon (δ13CCH4, δ2HCH4, and 40Ar/36Ar) enabled a comparison between information provided by the analysis of reactive gases compared with noble gas isotopes. Gases from different sources displayed quantifiable differences in δ13CCH4 and δ2HCH4, but these changes may or may not be distinguished if only one sampling event is conducted. By comparison, 40Ar/36Ar further enabled the differentiation of various gas sources. The objective of this paper is to discuss the advantages and trade-offs of the three different analysis methods considered, and the feasibility of their application in different environmental monitoring scenarios.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".