Experimental Viscosity Data for Binary and Ternary Systems of Arab Heavy Crude Oil and Diluents
Bibliographic record
Abstract
To meet the global exponential demand for crude oil, development of unconventional crude oil deposits is essential but the major limitation is seen in pipeline transportation, given its exceptionally high viscosity. Saudi Aramco is the world’s leading crude oil producer with a significant contribution of Arab Heavy crude oil in the total production mix. In this paper, we have measured the viscosities of both binary and ternary mixtures of this heavy oil crude for the first time, using toluene and natural gas condensate fluids over temperatures encountered in pipeline transportation. Our study shows that both binary and ternary systems show excellent viscosity reduction characteristics with increase in viscosity index with temperature. The degree of viscosity reduction shows polynomial trends with excellent fits that facilitated the determination of the wax appearance temperature using differential calculus approach. The range of experimental temperature in this study and the excellent viscosity reduction characteristics of the systems testify to the suitability of our experimental data for pipeline transportation planning
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".