Asphaltene Nanoaggregates Measured in a Live Crude Oil by Centrifugation
Bibliographic record
Abstract
Asphaltene nanoaggregates have recently been observed in live crude oil by observation of gravitationally induced asphaltene gradients in four different reservoir sands with oil columns up to 1000 m vertical. When the liquid phase is invariant, these gradients can be fit using Archimedes buoyancy in the Boltzmann distribution; the only adjustable parameter in data fitting is the size of the asphaltene nanoaggregate; ∼2 nm is obtained in four reservoir sands and is similar to laboratory results for asphaltene nanoaggregates in toluene. Here, a live crude oil (with dissolved gases) has been spun at modest g forces for long times designed to create a large, equilibrium asphaltene gradient for the presumed 2 nm aggregates. Elevated temperatures (∼91 °C) were employed during centrifugation to mimic reservoir conditions for asphaltene aggregation and prevention of a possible wax phase. Elevated pressures were employed on the hot, live crude oil to maintain dissolved gas concentrations. A total of 13 alliquots of crude oil were removed after centrifugation, and the asphaltene concentrations were determined by optical spectroscopy. Indeed, a large asphaltene gradient was observed, and a 2.6 nm diameter nanoaggregate was obtained using Archimedes buoyancy in the Boltzmann distribution. In addition, a solubility model accounting for the gas/oil ratio (GOR) gradient was used to analyze the asphaltene gradient, giving an asphaltene particle size of 2.0 nm, thus, the same as field observations. In addition, the gradient in bulk resins was shown to be quite small, showing the stark contrast of asphaltene versus bulk resin aggregation. The heaviest resins (or lightest asphaltenes) do show some gradient. These observations allow for the determination of the maximum and minimum asphaltene aggregation number; the range is roughly 3−8. Some modest resin association with asphaltenes, one resin molecule in every asphaltene nanoaggregate, is consistent with our data. These results are discussed within the increasingly successful modified Yen model of asphaltenes.
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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".