Pore Network and Morphological Characterization of Pore-Level Structures
Bibliographic record
Abstract
Abstract Due to the computational simplicity and time efficiency, pore network and morphological techniques are two practical approaches for characterization of pore-scale microstructures. The methods are quasi-static and exploit pore space spatial statistics to simulate pore invasions. Here, both procedures are evaluated applying the workflows to pore-level micro-scale subdomains of Sandstone, Carbonate and Shale formations. A statistical approach is also utilized to improve the accuracy of Shale characterization by spatial restoration of fragmentary parts of organic matter. Post-processing results include relative permeability and capillary pressure curves, absolute permeability, formation factor, and thermal connectivity. The results appear to suggest that the accuracy of pore network modeling in the characterization of subdomains of micro-CT images is compromised by the presence of limited number of network elements, ignoring the resistance of pore elements, multi-scale structures, and tight/weak connections represented by an inadequate number of voxels. Pore network extraction negatively affects the accuracy of petrophysical predictions and ignores solid matrix and its thermal and electrical properties. The pore morphological approach accurately reproduces the fluid occupancies, efficiently deals with a variety of rock configurations and resolutions, and preserves connectivity and details of original images having more geometrical features than the pore network modeling. However, it predicts limited step-wised data points and realizations sourcing from its voxel-based nature. In addition, direct simulations confirm that stochastic conditional reconstruction of organic matter inside shale sub-volumes remarkably boosts the pore space connectivity and improves the accuracy of predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".