Improving the efficiency of diluents for heavy oil pipeline transportation
Bibliographic record
Abstract
With the projected increase in the Canadian production of heavy oil and bitumen, the transportation issues are likely to come to the forefront. The occurrence of asphaltene association in heavy oil is a contributing factor in high viscosity of heavy oils. The asphaltene aggregates formed by asphaltene self-association can also precipitate by the variation of temperature, pressure or oil compositions. Precipitated asphaltenes can form deposits on the wall of pipelines so as to reduce the effective transportation volume. Therefore, if the asphaltene association can be inhibited, it will improve the efficiency of heavy oil transportation significantly. In this research, different solvents and their combinations were tested to evaluate their dilution effects on heavy oils and their abilities for inhibiting asphaltene association. Diluents were formulated by combining aromatic and polar solvents. Analysis of experimental results showed that polar solvents can be beneficial, when combined with non-polar diluents, in inhibiting asphaltenes association and reducing oil viscosity. The ability of a solvent for inhibiting asphaltene association improves mildly with increasing its aromaticity. Several surfactants were evaluated as additives to inhibit the asphaltenes' association. These surfactants were tested with heavy oil diluted with a common solvent by measuring their impact on the diluted oil viscosity. The surfactants were also tested on solution of asphaltenes in m-xylene to isolate their abilities for inhibiting asphaltene association. Our results suggested that generalization on optimal molecular structure of surfactants for inhibiting asphaltene association in heavy oil pipeline transportation has to account for the presence of resins and other oil constituents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".