CO<sub>2</sub>-Miscible Flooding for Three Saskatchewan Crude Oils: Interrelationships between Asphaltene Precipitation Inhibitor Effectiveness, Asphaltenes Characteristics, and Precipitation Behavior
Bibliographic record
Abstract
Studies were conducted to determine the asphaltene precipitation inhibition effectiveness of three carefully chosen chemicals (dodecylbenzenesulfonic acid (DDBSA), nonyl phenol (NP), and toluene) during CO 2 flooding of three Saskatchewan crude oils, as well as to evaluate the interrelationships between the chemicals' inhibition effectiveness, crude oil/asphaltenes characteristics, and asphaltene precipitation behavior (in terms of kinetic and equilibrium parameters). Results showed that both the asphaltene precipitation rate dependence on asphaltene content and apparent rate constant for asphaltene precipitation were strong functions of the paraffin fraction of the asphaltenes and the propensity of the asphaltene molecules for aggregation. On the other hand, the precipitation rate dependence on the amount of CO 2 added was a strong function of the heteroatoms (nitrogen, sulfur, and oxygen) content of the oil and asphaltenes, the aromatic carbon fraction, and the degree of branching of the asphaltene molecules. The equilibrium parameter (onset point) increased with the paraffin fraction of the asphaltene molecule but decreased with the propensity of the asphaltene molecule for aggregation. In terms of kinetic parameters, NP with the −OH functional group in its molecule was most effective with the more-aromatic (and more-substituted and more-polycondensed) shorter-alkyl-chain-length oil, whereas toluene (the most-aromatic additive) was most effective with the least-aromatic oil. In terms of the onset point, all three chemical additives showed maximum effectiveness with the least-stable oil that had the lowest metal content, and in the asphaltenes molecules that had the lowest paraffin fraction, highest degree of condensation, highest aromatic carbon fraction, and highest propensity for aggregation.
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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) | 0.000 | 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 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".