Diffusion and Dispersion Dominated Solvent Injection Processes in Oil Saturated Porous Media: 3-D Visualization Experiments Using Laser Technology
Bibliographic record
Abstract
Abstract A technique to visualize miscible displacement in porous media is introduced in this paper. After saturating the model made of different sized glass beads with oil, solvent was introduced to mix and displace it. The refractive indices of saturated and injected fluids were made the same by mixing them with lower and higher indices of refraction. This method, called refractive index matching, made the model transparent. Fluorescent dyes that were only visible with excitation of laser were dissolved in the solvent. A laser sheet scanned the model while synchronous pictures were taken by two high speed cameras from two sides of the model. Two groups of models were worked on: (1) Solvent diffusion under purely static conditions, and (2) injection/production through a pair of horizontal wells (VAPEX type). From the diffusion experiment, it was found that the solvent fronts were always higher close the surface of the model. It was more obvious as the model size became larger. The total diffusion time was proportional to the length of the side of the models. In the VAPEX scaled model experiment, a dome shaped solvent chamber was observed. The size of the solvent chamber decreased as distance of the injection port increased due to pressure decrease. The solvent had to spread along the top of the model before starting to displace the oil down. It is hoped that the visual observation will help in understanding the processes in the reservoir to optimize the design of solvent injection for oil recovery.
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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".