Heavy Oil (Bitumen) and Alkane Solvent Partitioning (HASP) Process
Bibliographic record
Abstract
Abstract Both gravity-based solvent and cyclic solvent processes for recovery of heavy oil or bitumen may involve relatively high solvent/oil ratios. It has been experimentally observed that at high solvent loadings, the oil/solvent mixture partitions into a solvent-rich oil phase and a heavy-ends-rich oil phase. The partitioning may have significant beneficial impact on the performance of a solvent-based process such as VAPEX, or other gravity based processes if the low-viscosity phase carries the bulk of the oil production, and the asphalt-rich phase is mostly asphaltene and essentially immobile. 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. The project objective was to analyze the laboratory test data on multiple phases partitioning of a selected oil/solvent system. The experiments, conducted with propane and Athabasca UTF bitumen at different solvent loading, have shown the oil/solvent mixture partitions into a solvent-rich oil phase and a heavy-ends-rich (mostly asphaltenes) oil phase with significantly higher densities and viscosities. CMG-WinProp software was used to model the phase partitioning. It was possible to get a good match between the test data on the liquid phase separations and some properties such as densities, but not viscosities.
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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 | 0.000 | 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) | 0.002 | 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 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".