Dynamics of Viscous Fingering in Porous Media in the Presence of In Situ Formed Precipitates and Their Subsequent Deposition
Bibliographic record
Abstract
Abstract Understanding the dynamics of miscible viscous fingering is of paramount importance in a wide range of applications, from groundwater hydrology to enhanced oil recovery. In this work, we investigate the effect of precipitation and deposition of an initially dissolved component and the subsequent porosity and permeability variations on miscible viscous fingering in porous media. The commonly used stream function‐vorticity formulation does not allow for nonsolenoidality of the velocity vector, which is the case when the porosity field varies with time. This is why we develop a velocity‐vorticity formulation, making no further simplifications on the flow field. For numerical simulations, the governing equations are solved using the hybridization of pseudospectral and compact finite difference methods, and the time stepping is carried out through high‐order semi‐implicit schemes. Using our methodology, we tackle the problem of precipitation of an initially dissolved component through an infinitely fast and reversible reaction. It is found that the deposition of the formed precipitates in porous media results in more severe interfacial instabilities, the effect of which is attenuated as the viscosity ratio between the displacing and resident fluids increases. In addition, our results show that the formed precipitates are mostly concentrated around the interface where the two fluids actively mix. We also characterize the alterations of the local and global permeability fields, and interestingly, we find that the ultimate value of the overall permeability of the porous media scales linearly with the log‐viscosity ratio at any fixed deposition rate. It is worth mentioning that, without loss of generality, the developed methodology and analysis in this study are applicable to other forms of precipitation reactions (e.g., finite rate and irreversible) in porous media.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".