On Asphaltene and Resin Association in Athabasca Bitumen and Maya Crude Oil
Bibliographic record
Abstract
The mass fraction of resins in asphaltene-rich aggregates, present in crude oils, and the physics and chemistry associated with them are poorly defined because of the variability of the definitions of asphaltenes and resins but remain a subject of significant interest to practitioners who model asphaltene behavior in hydrocarbon resources. In this contribution, the mass fraction of resins in pentane−asphaltene-rich aggregates in nanofiltered Athabasca bitumen and Maya crude oil samples is evaluated using a mass balance model and data regression fits to saturates, aromatics, resins, and asphaltenes (SARA) analyses of permeate and retentate samples obtained by filtering these hydrocarbon resources directly through 5, 10, 20, 50, 100, and 200 nm filters. Solvents are not employed in the filtration experiments. At 473 K, the mass fraction of resins in pentane−asphaltene-rich aggregates and the fraction of resins present in aggregates are both found to be at or below the threshold for resin mass fraction measurement error. The aggregates present in both of these hydrocarbon resources may be treated as resins and pentane-free asphaltenes at this temperature. Nanofiltration measurements with Maya crude oil at lower temperatures, namely, 338−373 K, suggest that ∼16% of resins comprise ∼10 wt % of pentane−asphaltene-rich aggregates. Even at lower temperatures, the resins content of pentane−asphaltene-rich aggregates appears to be limited to a small fraction of resins that comprise a small fraction of asphaltene-rich aggregates. On a heptane asphaltene basis, aggregates would comprise a minimum of 20 wt % resins. Because the definitions of asphaltenes and resins are fungible, these results provide directional and qualitative guidance with respect to asphaltene modeling and highlight limitations in terminology and ambiguity in analytic approaches used in this field.
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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) | 0.000 | 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".