Multiscale Pore-Structure Characterization by Combining Image Analysis and Mercury Porosimetry
Bibliographic record
Abstract
Abstract This article introduces a multiscale pore structure characterization method using a combination of mercury porosimetry and image analysis. The method was used to determine the distribution of pore volume by pore size and to estimate the pore-to-throat size aspect ratio. The key idea of the method is that pore size distribution obeys a fractal scaling law over a range of pore size. On this basis, scattering intensity data computed from the measured two-point correlation function and those measured from mercury porosimetry are extrapolated in the size range 0.01 μm < r < 1000 μm, using the known fractal scaling law. A set of siltstone samples taken from Daqing Oilfield was analyzed through this method. Distribution of pore volume by pore size over the entire range of pore length scales was determined. The results demonstrated significant similarities in the pore structure of all samples. The image analysis results were in qualitative agreement with the results of mercury intrusion/extrusion tests. The results were also compared with some other samples (including siltstone, sandstone, and dolomite) that had been analyzed using similar methods. It is shown that the surface fractal dimension obtained by analysis of MIP data is consistent with the value obtained by image analysis for different samples with different porosity and permeability. Novel information on the pore-to-throat aspect ratio is obtained by comparing the complete pore volume distribution (PVD) to the MIP data.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".