Fractal Analysis of the Pore Structure of Marine Shale in the Sichuan Basin
Bibliographic record
Abstract
The fractal characteristics of shale significantly influence pore distribution patterns and surface roughness, thus impacting reservoir properties such as permeability, porosity, and diffusion coefficients. However, existing studies have not fully characterized these fractal features, particularly in ultralow-pressure regions. This study comprehensively investigated the fractal characteristics of shale by integrating low-pressure N 2 adsorption, CO 2 adsorption, mercury intrusion, focused ion beam-scanning electron microscopy (FIB-SEM), and three-dimensional reconstruction techniques. The Sierpinski fractal model was employed to determine fractal dimensions in the ultralow-pressure interval based on the N 2 isotherms and adsorption mechanism. By extracting pores from images obtained using the FIB-SEM technique, the fractal dimensions ( D 3 ) of the three-dimensional digital cores were calculated based on the box-counting model. Additionally, investigating the correlation between porosity and D 3 values revealed a direct logarithmic relationship between porosity and D 3 values. This correlation suggests that the fractal dimensions in shale can extend to other physical parameters associated with porosity. A machine learning algorithm was applied to predict the fractal dimensions of shale from the Sichuan Basin. These findings not only deepen the understanding of shale reservoir properties but also offer a solid theoretical foundation for shale gas development and enhanced 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 | 0.002 | 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 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".