A review on steam-solvent processes for enhanced heavy oil/bitumen recovery
Bibliographic record
Abstract
Abstract Steam injection is widely used for heavy oil and bitumen recovery. The advantage of this process is its high recovery factor and its high oil production rate. However, the high production rate is associated with excessive energy consumption, carbon dioxide generation, and expensive post-production water treatment. Some of these disadvantages are overcome or reduced by the addition of solvent mixtures to steam. The steam-solvent processes are complex oil displacement methods involving simultaneous heat, mass, and fluid transport. These processes are not clearly understood despite their apparent importance to the oil industry. Systematic studies are essential in the design, analysis, and evaluation of the steam-solvent processes as well as in mathematical simulation. These studies provide valuable insights for petroleum engineers to improve the oil recovery efficiency when applied in a reservoir. Results of these processes are scattered in many publications over more than 40 years and are not readily available for most petroleum engineers. The purpose of the paper is to present a review of current knowledge and available data, and to delineate the steam-solvent processes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 0.002 |
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".