Effects of Asphaltene Content and Solvent Concentration on Heavy-Oil Viscosity
Bibliographic record
Abstract
Abstract During a solvent-based heavy oil recovery process, significant viscosity reduction is achieved through sufficient solvent dissolution and possible asphaltene precipitation. In the second viscosity reduction mechanism, the heavy oil is in-situ upgraded as the asphaltene content in the produced heavy oil is greatly lowered. In this paper, the respective and synergistic effects of asphaltene content and solvent concentration on heavy oil viscosity are studied by measuring viscosities of heavy oil samples with different asphaltene contents and/or solvent concentrations. More specifically, a deasphalted heavy oil sample is obtained by using the standard ASTM method to extract asphaltenes from a crude heavy oil. Then reconstituted heavy oil sample is prepared by adding the extracted asphaltenes into the deasphalted heavy oil at a different asphaltene content each time and its corresponding viscosity is measured at the atmospheric pressure. It is found that the viscosity of such prepared heavy oil is sensitive to the asphaltene content. In particular, the viscosity of the deasphalted heavy oil is reduced by 13.7 times in comparison with that of the original heavy oil at T=23.9°C. This indicates that high asphaltene content results in high viscosity of the heavy oil. On the other hand, the viscosities of propane-saturated heavy oil samples with different asphaltene contents are measured at high pressures. As the equilibrium propane concentration reaches certain value, the viscosity of the crude heavy oil-propane system is reduced by almost two orders. This result shows that solvent dissolution plays a dominant role in heavy oil viscosity reduction. When the equilibrium propane concentration is high enough, all the three reconstituted heavy oil-propane systems have extremely low viscosities. In this case, effect of asphaltene content on heavy oil viscosity becomes negligible.
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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 | 0.000 | 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 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".