Solvent-Based and Solvent-Assisted Recovery Processes: State of the Art
Bibliographic record
Abstract
Summary Steam injection is a widely used oil-recovery method that has been commercially successful in many types of heavy-oil reservoirs, including the oil sands of Alberta, Canada. Steam is very effective in delivering heat that is the key to heavy-oil mobilization. In the distant past in California, and also recently in Alberta, solvents were/are being used as additives to steam for additional viscosity reduction. The current applications are in field projects involving steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS). The past and present projects using solvents alone or in combination with steam are reviewed and evaluated, including enhanced solvent SAGD (ES-SAGD) and liquid addition to steam for enhancing recovery (LASER). The use of solvent in other processes, such as effective solvent extraction incorporating electromagnetic heating (ESEIEH) and after cold-heavy-oil production with sand (CHOPS), are also reviewed. The theories behind the use of solvents with steam are outlined. These postulate additional heavy-oil/bitumen mobilization; oil mobilization ahead of the steam front; and oil mobilization by solvent dispersion caused by frontal instability. The plausibility of the different approaches and solvent availability and economics are also 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".