A mathematical determination of the pore size distribution and fractal dimension of a porous sample using spontaneous imbibition dynamics theory
Bibliographic record
Abstract
Wallace sandstone has been extensively used by the construction industry for a long time in Nova Scotia. Apart from oxide analysis and a few strength-related parameter data found on some websites, petrophysical data regarding pore-sized distribution and fractal dimension are lacking. In the petroleum engineering literature, the spontaneous imbibition dynamics mechanism has been modeled where imbibition time has been linked to imbibition rise. One of the models links imbibition time to imbibition rise through a group of parameters that integrate the fractal dimension and sediment tortuosity. Based on the assumption of a bundle of parallel capillary tubes model found in the petrophysical literature, we have used the spontaneous imbibition model to derive an equation that links fractal dimension to porosity and permeability. Using literature source data on Wallace sandstone core samples, we have calculated the fractal dimension and pore size distribution using our equation. Results show that this sandstone has a significant level of heterogeneity. Calculation using another literature source data shows that our equation calculates fractal dimensions that are closer to those reported for the capillary pressure method. Although the assumption of bundle of parallel capillary tubes leads to deviations in calculated fractal dimensions using literature source data with experimentally determined porosity, permeability and fractal dimensions, our equation calculates meaningful values of fractal dimensions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".