Experiments and Analysis of Multiscale Viscous Fingering during Forced Imbibition
Bibliographic record
Abstract
Abstract Immiscible displacement of one fluid by another in porous media has practical applications where viscous oil is produced by water injection. A greater understanding of the flow patterns that evolve during such unstable displacements yields insights to improve predictive capability and increase oil recovery. Immiscible multiphase displacement exhibits a wide range of behaviors depending on the relative magnitude of viscous, capillary, and gravity forces. Using flow visualization images from forced imbibition experiments carried out in etched-silicon micromodels, we show that the conventional Darcy-type modeling of fluid flux is not predictive under unstable, immiscible, imbibition conditions at the scale of interest. When a less viscous fluid displaces a more viscous fluid at low capillary numbers, the displacement patterns show fractal, self-similar characteristics arising from viscous instabilities as well as local capillary control of interface movement. We show that such complex displacement patterns are modeled using statistical theories. We derive a scaling model to describe quantitatively the functional forms for saturation, fractional flow, and capillary dispersion profiles using the self-similarity characteristics inherent in the displacement patterns. For the specific range of flow rates (Nc~10−7) and oil to water viscosity ratios (M~8-400) considered in our experiments, both capillary and viscous forces are important and the displacement shows features similar to the invasion-percolation regime in the presence of a viscous gradient. Results show that functional relations of the scaling model are in considerable agreement with our experimental data. Keywords: imbibition, nonequilibrium effects, viscous instabilities, multiphase flow
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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 | 0.000 | 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".