FRACTAL ASPECTS OF MISCIBLE DISPLACEMENT IN ROUGH FRACTURES: AN EXPERIMENTAL APPROACH
Bibliographic record
Abstract
Experiments were performed to study the effect of fracture surface roughness on fluid distribution during miscible displacement. The transparent replicas of single fractures obtained from seven different rocks were prepared and the surface roughness of each sample was described by fractal dimensions using the variogram, power spectral, and triangular prism (TP) techniques. Then, the effect of flow rate and viscosity on the geometry of the displacement front during miscible radial injection was investigated experimentally. The fractal dimensions of the fronts were obtained using box counting fractal analysis at different time lapses. The fractal values of invasion front varied from lithology to lithology, due to different surface roughnesses controlled by the lithology of the rocks. Although fluctuations of fractal values were observed during the growth of the front, fractal dimensions typically yielded an increasing trend. Fractal dimension became more stable with increasing flow rate and developed modestly with increasing viscosity. Finally, relationships between the fractal dimensions of displacement fronts and fracture surfaces were quantitatively analyzed and correlated in order to improve the prediction of fluid distribution within a single fracture during miscible displacement. Overall, correlations were observed between the surface characteristics and front fractal dimension values with some exceptions. In summary, to determine the probable distribution of miscible fluid and development of the front, all parameters except power spectral density (PSD) fractal dimension can be applied in the case of high viscosity ratios. In the case of low injection rates, TP could be applicable. No fractal behavior was present at extreme injection and low viscosity ratios, thus no correlation can be determined for the miscible displacement.
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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 | 0.001 | 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 | 0.001 |
| 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".