Bitumen Recovery by the Steam-Over-Solvent Injection in Fractured Reservoirs (SOS-FR) Method: An Experimental Study on Grosmont Carbonates
Bibliographic record
Abstract
We adapted the previously introduced SOS-FR (Steam-Over-Solvent Injection in Fractured Reservoirs) method and conducted 12 experiments using preserved core samples from the Grosmont formation. The method applied in this study is based on soaking rather than continuous injection. The samples were immersed in hot water (90 °C), first to mimic low-temperature presteaming to condition the reservoir for solvent injection. This was followed by a solvent soaking period under varying conditions (duration, solvent type, etc.). Heptane and the distillate obtained from a heavy oil upgrading facility were used as solvents. Finally, the core samples were soaked in hot water again. Oil recoveries varied between 40% and 90% OOIP, with a mean value of 68%. Asphaltene precipitation, as a percentage of original oil in place (OOIP), was measured between 6.5 wt % and 33 wt %. The oil recovery and asphaltene precipitation depended on the solvent type, the solvent exposure duration, the position of matrix rock (horizontal or vertical), and the duration and number of solvent/hot water cycles. Most importantly, the last phase (hot water immersion) yielded substantial recovery of solvent diffused into matrix oil by applying a temperature value close to the boiling point of the solvent. The solvent retrieval was extremely fast and varied between 62% and 82% of the solvent diffused into the core during solvent exposure. Experimental observations look promising for further applications, as indicated by the high recovery values. The important aspects are that the solvent from readily available distillates used for transportation of heavy oil are very responsive and the temperature requirement for final hot water injection applied to retrieve solvent was <100 °C. Solvent retrieval was extremely quick and reasonably high, which is more likely to make the process economical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".