Liquid-Liquid Equilibria of Solvent/Heavy Crude Systems: In Situ Upgrading and Measurements of Physical Properties
Bibliographic record
Abstract
Abstract World production of heavy and extra heavy oils has increased as the production of conventional crudes declines. However, conventional oil recovery methods cannot effectively recover heavy oils due to their high viscosities. Different techniques for the recovery of these resources, such as enhanced solvent-steam-assisted-gravity-drainage (ES-SAGD), have been patented. The performance of these methods depends on the amount of solvent dissolved in the oil and its phase behavior properties. Solvent-based recovery processes have lower green house gas emissions and can also contribute to in-situ upgrading of oil. The oil upgrading can be achieved either by the deasphalting or partitioning of the oil mixtures into two liquid phases in which higher grades of oil than the original oil produced. Experimental data on the phase behavior is needed to determine the operating conditions that cause the liquid-liquid system or in situ upgrading. In the present study, the in situ upgrading of heavy oil using propane was experimentally determined at different temperatures and pressures. The effect of the solvent-to-oil ratio on equilibrium compositions and saturated phase properties were measured. The distributions of different components in both phases were observed with simulated distillation (SimDis) analysis. The amount and type of components that was extracted by propane has been investigated. The experimental results showed that propane/oil mixtures partitions into a solvent and asphaltene-enriched phases at specific conditions. The SimDis data demonstrated that the oil was upgraded by the separation of the heavier constitutes with the light components extracted by the propane into a solvent-enriched phase. Recovery processes can, therefore, be designed in such a way that the valuable components are extracted from the heavy crude at specific in situ conditions, to produce higher grades of oil than original heavy oil.
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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".