Immiscible Viscous Fingering: The Simulation of Tertiary Polymer Displacements of Viscous Oils in 2D Slab Floods
Bibliographic record
Abstract
Immiscible viscous fingering in porous media occurs when a high viscosity fluid is displaced by an immiscible low viscosity fluid. This paper extends a recent development in the modelling of immiscible viscous fingering to directly simulate experimental floods where the viscosity of the aqueous displacing fluid was increased (by the addition of aqueous polymer) after a period of low viscosity water injection. This is referred to as tertiary polymer flooding, and the objective of this process is to increase the displacement of oil from the system. Experimental results from the literature showed the very surprising observation that the tertiary injection of a modest polymer viscosity could give astonishingly high incremental oil recoveries (IR) of ≥100% even for viscous oils of 7000 mPa.s. This work seeks to both explain and predict these results using recent modelling developments. For the 4 cases (µo/µw of 474 to 7000) simulated in this paper, finger patterns are in line with those observed using X-ray imaging of the sandstone slab floods. In particular, the formation of an oil bank on tertiary polymer injection is very well reproduced and the incremental oil response and water cut drops induced by the polymer are very well predicted. The simulations strongly support our earlier claim that this increase in incremental oil displacement cannot be explained solely by a viscous “extended Buckley-Leverett” (BL) linear displacement effect; referred to in the literature simply as “mobility control”. This large response is the combination of this effect (BL) along with a viscous crossflow (VX) mechanism, with the latter VX effect being the major contributor to the recovery mechanism.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".