Effect of Naphthenic Acid and Metal Ions on Emulsification of Heavy Oil
Bibliographic record
Abstract
The emulsification of heavy oil is universal in various stages of heavy oil extraction, processing, and transportation. In this study, molecular dynamics simulations were employed to reveal the emulsification of heavy oil from different regions. When the water mass fraction is low, metal ions in the Karamay emulsion exist at the oil–water interface and interact with naphthenic acid (NA), which results in strong emulsion stability. The dominant interactions between resin and water molecules cause weak emulsion stability for Liaohe and Canada heavy oils. On the basis of the thermodynamic analysis, we found that the interaction between the metal ion and NA in the Karamay emulsion is much stronger than the interaction between the asphaltene/resin molecules and water molecules in Liaohe and Canada emulsions, which is the main reason for stronger emulsion stability in the Karamay region. As the water concentration increases, the emulsion stability of Karamay heavy oil will have a significant change as a result of the lower metal ion concentration. The water-phase structures and emulsion viscosity were investigated, and the water aggregations present spherical, worm-like, stick, net, and honeycomb structures with the increase of the water concentration. The breakup of the emulsion structure leads to a sudden decrease in the viscosity of the Karamay heavy oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".