Selection of Optimal Solvent Type for High-Temperature Solvent Applications in Heavy-Oil and Bitumen Recovery
Bibliographic record
Abstract
The selection of the most suitable solvent for an efficient heavy-oil recovery process is a critical task. Low carbon number solvents yield faster diffusion, but the mixing quality may not be high. Also, high carbon number solvents yield a better quality mixing (much less asphaltene precipitation), but the mixing process is rather slow. Hence, the understanding of solvent selection criteria for solvent-aided recovery processes has established two main aspects of oil–solvent (liquid–liquid) interaction: (1) oil–solvent mixture quality and (2) rate of mixture formation. Oil–solvent mixture quality is determined by two parameters: (1) viscosity and (2) asphaltene precipitation. The rate of mixing is quantified by the diffusion rate. Both mixture quality and mixing rate need to be quantitatively and qualitatively determined to select the suitable solvent for heavy-oil recovery. In addition to this, experiments that measure the solvent diffusion rate (and oil recovery) into a rock sample saturated with heavy oil at static conditions are needed to support the observations obtained from the liquid–liquid interaction of solvent and oil. This paper focuses on these tests and uses three oil samples with a wide range of viscosities (250–476 000 cP) and three liquid solvents with different carbon numbers varying between C 7 and C 13 . Core experiments at different temperatures were performed on Berea sandstone samples using the same solvent–heavy oil pairs to obtain the optimum carbon size (solvent type)–heavy oil combination that yields the highest recovery factor and the least asphaltene precipitation. On the basis of the fluid–fluid (solvent–heavy oil) interaction experiments and heavy-oil-saturated rock–solvent interaction tests, the optimal solvent type was determined considering the fastest diffusion and best mixing quality for different oil–solvent combinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".