Heavy oil recovery using ASP flooding: A pore‐level experimental study in fractured five‐spot micromodels
Bibliographic record
Abstract
Abstract Although alkaline‐surfactant‐polymer (ASP) flooding has proven efficient for heavy oil recovery, the displacement mechanisms and efficiency of this process should be discussed further in fractured porous media. In this study, several ASP flooding tests were conducted in fractured glass‐etched micromodels with a typical waterflood geometrical configuration, i.e. five‐spot injection‐production pattern. The ASP flooding tests were conducted at constant injection flow rates but different fracture geometrical characteristics. The ASP solutions consisted of five polymers, two surfactants, and three alkaline types. It was found that using synthetic polymers, especially hydrolyzed polyacrylamide with high molecular mass, as well as cationic surfactant increases the ultimate recovery. The location of the injection well with respect to the fracture system plays a significant role in the ASP flooding performance, i.e. an increase in the angle associated with the longitudinal extension of fractures with respect to the main flow direction resulted in enhanced oil recovery and also postponed the wetting phase breakthrough time. Mechanistic study of this displacement process revealed that dispersive and diffusive behaviour of the ASP front enhanced the fluid transport from fracture to matrix and increased the microscopic displacement efficiency. Emulsification and coalescence mechanisms were responsible for ASP frontal advancement. Residual oil in the invaded region, which was observed in the form of discontinuous oil ganglia dispersed in the invaded pore bodies or in the form of pendular bridges formed around some of the solid particles, was mobilized in the form of oil wads through the droplets of the displacing phase.
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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 | 0.001 | 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".