FT‐IR measurements of petroleum fluid inclusions: methane, <i>n</i>‐alkanes and carbon dioxide quantitative analysis
Bibliographic record
Abstract
Abstract A recent advancement in petroleum geochemistry is to model fossil oil composition using microthermometric and volumetric data acquired from individual fluid inclusion analysis. Fourier transform infrared (FT‐IR) microspectroscopy can record compositional information related to gas (CH4 and CO2) and alkane contents of petroleum inclusions. In this study, a quantitative procedure for FT‐IR microspectrometry has been developed to obtain, from individual fluid inclusions, mol percentage concentrations of methane, alkanes and carbon dioxide as constraints to thermodynamic modelling. A petroleum inclusion in a sample from the Québec City Promontory nappe area was used as standard to record a reference spectrum of methane. The analytical procedure is based on the measurement of CH4/alkane and CH4/CO2 band area ratios. CH4/alkane infrared band area ratio is obtained after spectral subtraction of the reference methane spectrum. This area ratio, affected by absolute absorption intensities of methane, methyl and methylene, provides a molar CH4/alkane ratio. Methyl/methylene ratio (CH2/CH3) ratio is obtained following procedures established in previous work. CO2/CH4 concentration ratio is estimated from relative absolute absorption intensities. Application to natural inclusions from different environments shows good correlation between FT‐IR quantification and PIT (petroleum inclusion thermodynamic) modelling.
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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 | 0.000 | 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 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".