A Critical Analysis of the Relationship Between Statistical- and Fractal-Fracture-Network Characteristics and Effective Fracture-Network Permeability
Bibliographic record
Abstract
Summary Estimation of effective fracture-network permeability (EFNP) is an essential part of modeling transport processes in naturally fractured reservoirs. A practical way of doing this is to use correlations that consider the statistical and physical characteristics of the networks. Thus, selection of the proper parameters to be characterized and/or measured that are highly correlative to the network permeability is critical. In this study, we analyzed fractal-based correlations previously developed by Jafari and Babadagli (2011a, 2011b) to clarify the physical relationship among network properties and the correlation parameters. It was shown that the connectivity index is a more-powerful parameter to rely on in permeability estimation, especially at percolation ranges far from the threshold. Also, it was of high interest to inspect the effect of physical parameters of a fracture network on different fractal dimensions as well as their positive/negative correlation with permeability to make a distinction between the mathematical and physical contributions of variables. We explained the earlier observation of Jafari and Babadagli (2009) regarding the method to determine fractal dimensions and their observed differences, which were found to be related to the computational scheme. That is why the box-counting fractal dimension gives the highest correlation compared with other fractal dimensions, especially the sandbox fractal dimension. The conditions of a strong correlation among different fractal dimensions and the scale-dependency of correlations in natural and synthetic patterns were also addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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".