Selection of Optimal Solvent Type for High Temperature Solvent Applications in Heavy-Oil and Bitumen Recovery
Bibliographic record
Abstract
Abstract The selection of 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 qulity 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 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 mixture formation is quantified by the diffusion rate. These two parameters need to be quantitatively and qualitatively determined to select the suitable solvent for heavy-oil recovery also supported by static experiments that measure solvent diffusion (and oil recovery) from a rock saturated with heavy-oil and exposed to solvent diffusion at static conditions. This paper focuses on these tests and uses three oil samples with a wide range of viscosities (250-153, 000 cp), and three liquid solvents with different carbon numbers varying between C7 and C13. The methodologies applied for diffusion rate measurement were optical applying image analysis under UV light (for processed -mineral- oil) and CT scanning (for heavy-oil obtained from fields). Next, viscosity and asphaltene precipitation measurements were conducted after mixing the crude oil and solvents to quantify the mixing quality. Then, core experiments 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. Based on 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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".