Use of Solvents With Steam - State-of-the-Art and Limitations
Bibliographic record
Abstract
Abstract Steam injection is a widely used oil recovery method that has been commercially successful in many types of heavy oil reservoirs, including oil sands of Alberta. Steam is very effective in delivering heat that is the key to heavy oil mobilization. In the distant past, and also recently, solvents are being used as additives to steam for additional viscosity reduction. This was done previously in California heavy oil reservoirs also. The current applications are in SAGD (Steam-Assisted Gravity Drainage) and CSS (Cyclic Steam Stimulation) field projects. The past and present projects using solvents are reviewed, and evaluated viz ES-SAGD (Enhanced solvent SAGD) and LASER (Liquid Addition to Steam for Enhancing Recovery). The theories behind the use of solvents with steam are outlined. These postulate (1) additional heavy oil mobilization; (2) oil mobilization ahead of the steam front, and (3) oil mobilization by solvent dispersion due to frontal instability. The plausibility of the different approaches is discussed. Recent theoretical work is described that compares thermal and solvent diffusion, showing that the time scales of the two processes are quite different casting doubt on the effectiveness of the use of solvents with steam. The numerical and analytical solutions have been compared for effect of cold solvent, hot solvent, steam only, and co-injection of solvent and steam on bitumen mobilization. The outcome of this study can be used for better understanding of mechanisms and theories behind co-injection of solvent with steam.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".