Micro-PIV: A New Technology for Pore Scale Flow Characterization in Micromodels
Bibliographic record
Abstract
Abstract Etched micromodels have often been used to visualize complex fluid flow and displacement mechanisms at the pore scale. Studies have been reported of two and three phase processes such as waterflooding and water-alternating-gas (WAG) injection. Many of these models have been etched in glass although the use of etched silicon in their manufacture allows for the much more precise replication of two-dimensional (2D) network structures. A feature of previous micromodel studies is that they have almost exclusively been confined to making qualitative observations; e.g. descriptions of pore-scale displacement events, the effects of wettability, the formation and collapse of films etc. However, quantitative information relating for example to the network velocity field at the pore scale has never been reported and this issue is addressed in this paper for the first time. Particle Imaging Velocimetry (PIV) is a well-known technique used in large-scale fluid mechanics experiments. It has recently been adapted to study microfluidics problems. As a result, the velocity field can now be measured at any location in pore-scale micromodels where the channels have widths of typical rock pores (10s – 100s μm). For the first time, this new technology, known as micro-PIV (μ-PIV), has been used to study the flow of Newtonian and non-Newtonian fluids in model pore channels with sizes typical of natural sandstone. In this study, we apply μ-PIV to the flow of water, and non-Newtonian fluids in straight micro-capillaries. The μ-PIV experimental velocity profiles match the analytical solution for water, and the finite element calculations for non-Newtonian fluids, thus validating the technique. μ-PIV was then applied to the flow in a Berea sandstone replica micromodel. The potential of this technique is highlighted for use in several other applications of complex flows 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".