Flow instabilities of time‐dependent injection schemes in immiscible displacements
Bibliographic record
Abstract
Abstract Flow displacements in homogeneous porous media can result in instabilities at the interface between the fluids. Such instabilities may dramatically affect the overall efficiency of the displacement process and often need to be controlled. Flows that involve time‐dependent injection schemes are analyzed to determine their effects on the growth of instabilities and the nonlinear development of finger structures in immiscible displacements. Predictions for monotonic and cyclic schemes in radial displacements are presented and compared with their constant injection counterpart. Moreover a controlled injection scheme that allows minimizing the instabilities is proposed. A hybrid model accounting for the discontinuities across the interface is implemented, and the problem is solved numerically. The effects of different parameters including the phase shift, amplitude, and period as well as the role of the mobility ratio and surface tension are discussed. A set of injection policies are observed to lead to the strongest attenuation of instabilities. Moreover, optimal phase shifts for cyclic displacements can result in the strongest enhancement or attenuation of the instability, depending on whether the flow involves extraction. A novel approach, referred to as the controlled injection scheme, has also been proposed and analyzed. In this scheme the flow is continuously adjusted in response to the growth rate of the instabilities resulting in a better capability of suppressing the development and growth of fingers.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".