Influence of solvent-to-oil mass ratios on high-pressure asphaltene precipitation
Bibliographic record
Abstract
Asphaltene precipitation during solvent-based enhanced oil recovery under reservoir conditions impedes production efficiency and degrades oil quality. This study aims to optimize solvent-to-oil mass ratios to mitigate asphaltene issues and enhance oil upgrading. Experiments using a high-pressure cell simulating reservoir environments were conducted with solvent-to-oil ratios of 3:1, 5:1, 7:1, and 9:1 at temperatures of 120 °C and 250 °C. Increasing the ratio from 3:1 to 7:1 significantly enhanced in-situ asphaltene precipitation, but further increase to 9:1 offered minimal additional benefit, indicating a plateau. Concurrently, the asphaltene content in upgraded oil decreased with higher ratios, stabilizing beyond 7:1. At 250 °C, a substantial reduction in total asphaltene content—averaging a 5.8 wt% decrease-was observed. The discovery of the plateau at 7:1 and the reduced asphaltene precipitation at elevated temperatures provides a novel perspective on balancing solvent use with operational efficiency. These findings contribute to cost-effective and environmentally sustainable practices in enhanced oil recovery operations. These findings identify an optimal solvent-to-oil ratio and temperature under reservoir conditions for maximizing asphaltene precipitation and minimizing asphaltene content in upgraded oil. Optimizing these parameters is crucial for effective asphaltene management and improving the efficiency of solvent-based enhanced oil recovery processes.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".