Which One Is More Important in Chemical Flooding for Enhanced Court Heavy Oil Recovery, Lowering Interfacial Tension or Reducing Water Mobility?
Bibliographic record
Abstract
A total of 33 sandpack flood tests were carried out to investigate the effects of interfacial tension (IFT) and water-phase viscosity on enhanced heavy oil recovery by chemical flooding. The amount of oil recovered by alkaline-only flooding increased sharply with the NaOH concentration in the range of 0.3−0.5 wt %. The oil recovery only varied slightly with the changing alkaline concentration outside the range. The coexistence of the surfactant and NaOH reduced the IFT between the oil and aqueous phase to an ultra-low level. However, the amount of oil recovered by alkaline/surfactant flooding only increased slightly with an increasing NaOH concentration up to a threshold value of 0.5 wt %. Beyond this threshold value, the recovery efficiency stopped increasing with the alkaline concentration and its value was lower than that of the alkaline-only displacing process. The addition of a polymer improved the tertiary oil recovery by increasing the viscosity of the water phase, although it also increased the IFT slightly. The combination of alkaline with polymer was more effective than polymer only upon enhancing the tertiary oil recovery. Comparing the results of tertiary oil recovery shows that the tertiary oil recovery of Court oil is correlated better with water-phase viscosity than IFT; i.e., increasing the viscosity of the water phase is more effective than lowering IFT in improving the tertiary oil recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".