Solvent Selection Criteria and Optimal Application Conditions for Heavy-Oil/Bitumen Recovery at Elevated Temperatures: A Review and Comparative Analysis
Bibliographic record
Abstract
Sole thermal or solvent methods for heavy-oil recovery are not effective enough to deliver cost efficient processes. Hybrid applications of those two techniques have been proposed to take advantage of each and a wide range of investigations have been recently performed focusing on extreme conditions such as bitumen containing sands and carbonates, deep reservoirs, and oil-wet fractured carbonates. What is critically important in these applications is to determine the best performing solvent for a particular application and optimal application conditions for a given solvent at high temperature conditions. In this study, the results from various reported works on the hybrid applications of thermal (mainly steam) and solvent methods were complied, analyzed, and compared. Attention was given to a comparative analysis of steam-over-solvent injection in fractured reservoirs (SOS-FR) method. Steam/solvent methods show a promising outcome in general, while specific modifications must be taken into account for different application situations. These were discussed and specified, especially from proper solvent type and optimal application conditions for alternate injection of steam and solvent in different reservoir types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".