A Rheological and Chemical Investigation of Canadian Heavy Oils From the McMurray Formation
Bibliographic record
Abstract
The prediction of viscosity in the extraction of heavy and viscous oil resources is essential for the economically viable production of these resources. A rheological and chemical investigation of oils from the McMurray formation produced at different depths was undertaken. Chemical analysis using high-resolution time-of-flight mass spectrometry (TOF MS), Fourier transform infrared spectroscopy (FTIR), and nuclear magnetic resonance spectroscopy (NMR) suggested specific compounds representative of the compound classes observed in these heavy oils: [1] water, [2] sec-hexadecyl naphthalene, [3] 2,2′,5,5′-tetramethyl-1,1′-biphenyl, [4] 1-methylanthracene, and [5] cyclopentylcyclopentane. All three analytical techniques detected the monoaromatic, diaromatic, and triaromatic ring hydrocarbons as being the most abundant species in this heavy oil. Specific molecules with intense FTIR modes near 1600 cm –1 and 1380 cm –1 were not identified, and these may account for unknown species in asphaltene fractions. Correlations between heavy-oil chemistry and its viscosity were built using a partial linear square fit (PLS) regression from vibrational modes in the FTIR spectra, predicting an inverse correlation between water and viscosity.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 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".