Visualization of Chemical Heavy Oil EOR Displacement Mechanisms in a 2D System
Bibliographic record
Abstract
This study aims to develop a visual understanding of the macro-displacement mechanisms associated with heavy oil recovery by water and chemical flooding in a 2D system. The sweep efficiency improvements by water, surfactant, polymer, and surfactant-polymer (SP) were evaluated in a Hele-Shaw cell with no local pore-level trapping of fluids. The results demonstrated that displacement performance is highly correlated to the mobility ratio between the fluids. Surfactant and water reached similar oil recovery values at similar mobility ratios; however, they exhibited different flow patterns in the 2D system—reductions in IFT can lead to the formation of emulsions and alter flow pathways, but in the absence of porous media these do not lead to significant improvements in oil recovery. Polymer flooding displayed a more stable front and a higher reduction in viscous fingering. Oil recovery by SP was achieved mostly by polymer rather than due to the effect of the surfactant. The surfactant in the SP slug washed out residual oil in the swept zone without increasing the swept area. This shows the impact of the surfactant on reducing the oil saturation in water-swept zones, but the overall oil recovery was still controlled by the injection of polymer. This study provides insight into the fluid flow behavior in diverging flow paths, as opposed to linear core floods that have limited pathways. The visualization of bulk liquid interactions between different types of injection fluids and oil in the Hele-Shaw cell might assist in the screening process for new chemicals and aid in testing the production process.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".