Numerical Simulation of Heavy Oil (Bitumen) and Alkane Solvent Partitioning (HASP) Process
Bibliographic record
Abstract
Abstract Phase partitioning experiments conducted at Alberta Research Council (ARC) have shown that Athabasca UTF bitumen and propane mixture partitions into a solvent-rich light oil phase and a heavy-ends-rich oil phase. The partitioning may have beneficial impact on the performance of a gravity based process such as VAPEX and other solvent based processes if the low-viscosity phases carries the bulk of the oil production, and the heavy phase left in the reservoir is mostly asphaltene and essentially immobile. It is considered essential to treat the bitumen as a multi-component fluid with asphaltene as one of the components in order to determine the fluid properties of the upgraded oil in the propane based recovery process. Bitumen was treated both as a single component and as a multi-component fluid in the numerical simulations of the propane based recovery process. Fluid properties such as gas-liquid K values for components representing the bitumen (saturates, aromatics, polars and asphaltenes), as determined through CMG-WinProp earlier, was used in the numerical simulations with CMG-STARS. The challenges involved in representing bitumen as a multi-component fluid in the simulation of the recovery process is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".