Clay-Asphaltene Interaction During Hybrid Solvent-Steam Injection Into Bitumen Reservoirs
Bibliographic record
Abstract
Abstract The goal of solvent-steam-flooding is enhancing bitumen recovery by the simultaneous development of miscibility and reduction of oil viscosity. Though this strategy reduces greenhouse gas emissions, solvents are expensive. Additionally, bitumen recovery performance is affected by oil/solvent/clay/asphaltene interactions on pore-scale. The solvent dose and type must be optimized to maximize recovery, while minimizing environmental impacts and operational costs. To investigate the performance of solvent-steam processes, six core flooding experiments were conducted on a Canadian bitumen sample with 8.8°API and 54,000 cP. Propane-steam flooding was tested and compared to steam-flooding. The effect of reservoir clays is studied by repeating experiments without clay addition. Three propane flowrates were tested to examine the impact of solvent dosages. After the experiments, asphaltene, clay, viscosity, and water content in produced oil were measured. Propane-steam flooding increased recovery factors, accelerated production, and had higher quality oil than steam-flooding. The lowest propane flowrate (1:9 vol/vol) improved oil recovery by 23%, indicating that higher solvent concentration may not be needed. This work reveals that bitumen microscopic displacement efficiency is enhanced by up to 88% with the addition of solvent to steam flooding. It is proposed that pore-scale interactions, solvent flowrate, and clays also highly influence produced oil quality and oil recovery rates.
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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 | 0.000 |
| 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) | 0.001 | 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".