Partitioning of Bitumen-Solvent Systems Into Multiple Liquid Phases
Bibliographic record
Abstract
Abstract Both gravity-based and cyclic processes for heavy oil/bitumen recovery may involve the use of hydrocarbon (n-alkane) solvent at relatively high solvent/oil ratios. Previous work at ARC has shown that at high solvent loadings, the oil/solvent mixture partitions into a solvent-rich oil phase and a heavy-ends-rich (mostly asphaltene) oil phase. The liquid phases have significantly different densities and viscosities. The partitioning phenomenon could have a significant impact on the performance of gravity-based processes such as Vapex involving solvents, where the low-viscosity liquid phase carries the bulk of the oil production, and the heavier liquid phase consisting of mostly asphaltene is essentially immobile. The solvent-rich phase will consist of the upgraded (de-asphalted) oil. Production of upgraded oil thus would not only enhance the production rate, but also have both economic and pipelining advantages. Data on the physical properties (viscosity and density) and the composition of both the partitioned phases are needed to design and optimize solvent-based processes in reservoir engineering calculations. Phase partitioning experiments conducted at the Alberta Research Council Laboratories along with the experimental data are presented in this paper. Introduction Both gravity-based and cyclic processes for heavy oil recovery may involve the use of solvent which dissolves in heavy oil or bitumen at relatively high solvent/oil ratios. Earlier works at ARC, and recent work in the Thermal Gravity Strategic area, have shown that at high loadings the oil/solvent mixture partitions into a solvent- rich oil phase and a heavy-ends-rich oil phase. The two liquid oleic phases have significantly different densities and viscosities. The phase partitioning may have significant impact on the performance of a solvent-based heavy oil recovery process. In particular, VAPEX and other gravity-based processes involving solvents may have their performance enhanced if the low-viscosity phase carries the bulk of the oil production, and the heavy-ends-rich phase contains most of the asphaltene and is essentially immobile.
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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 | 0.003 | 0.001 |
| 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".