Steam and Solvent Coinjection Test: Fluid Property Characterization
Bibliographic record
Abstract
Abstract As part of a study of steam and solvent co-injection processes for MacKay River reservoir, a number of fluid properties of MacKay River bitumen-solvent mixtures were measured for three candidate diluents (referred to as Solvents 1, 2 and 3) at conditions relevant to the co-injection process under consideration. They include viscosities of bitumen-solvent mixtures for Solvent 1 and 2, densities of bitumen-Solvent 2 mixtures of different solvent content and liquid-vapor phase behaviour of the bitumen-Solvent 2-water mixture at a given mixing ratio. The results showed that the effectiveness of solvent-dilution on oil viscosity became less prominent as temperature increased. The differences among the diluents in lowering the viscosity of MacKay River bitumen were insignificant. At elevated temperatures and above the solvent saturation pressures, increasing solvent loading in the bitumen-Solvent 2 mixture from 10.7 vol% to 19.5 vol% reduced the density of the mixture by only 3 - 5%. At 1750 kPag, the bitumen-Solvent 2-water fluid mixture containing 5.8wt% of solvent and 58wt% of water remained as two liquid phases at temperature up to 165 °C. The mixture formed a three-phase system (one vapor phase and two liquid phases) at 195 °C, with only 6.8wt% of the solvent dissolved into the oil. Small quantities of C10+ components from the bitumen were also detected in vapor phase. Mixture viscosities were calculated and fitted to a linear-logarithmic mixing rule. A good match with the measured viscosity was achieved at all temperatures below the bubble point of the mixture. K-values of the solvent pseudocomponents were determined using HYSYS and fitted to the correlation to estimate K-value as a function of temperature and pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".