Correlation Evaluation and Schematic Analysis of Influencing Factors Affecting Pore and Fracture Connectivity on the Microscale and Their Application Discussion in Coal Reservoir Based on X-ray CT Data
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The connectivity of the pore/fracture system is the key to CO 2 injection and CH 4 production, which is of great significance in analyzing the correlation and weight of the influencing factors affecting the connectivity on the microscale. First, the 3D reconstruction of the coal reservoir is realized. Second, the characteristic parameters of pore/fracture structures are analyzed. Next, the characteristics of absolute permeability are analyzed, and then the correlation and weight analysis of the influencing factors are realized. Finally, the schematic analysis and application discussion of the influencing factors are carried out. The results show that porosity is the key factor restricting fluid migration. The heterogeneity of the reservoir can be characterized by the volume changes of the pore/fracture, organic matter, and mineral. The interconnected pores/fractures are mainly distributed in sheets and bands. The coordination number ranges from 1 to 15. The Ferret diameter is 0–10 μm. The tortuosity is 2.27111, 1.9034, 3.98522, and 3.51516, respectively, and the Euler characteristic number is 0.931868, 0.974719, 0.921144, and 0.897697, respectively. The permeability of the SH and YW samples is higher than that of the RL and PY samples. The single weight of the influencing factor is as follows: coordination number > Ferret diameter > Euler characteristic number > porosity > tortuosity. The analysis area of the comprehensive evaluation score of the influencing factors and the permeability value can be divided into three grades. There is a positive correlation among the coordination number, the quantity equilibrium of pores and throats, and the connectivity. The shape factor gradually increases with the increase of the Ferret diameter. The reservoir permeability is indirectly characterized by the coordination number, Ferret diameter, tortuosity, Euler characteristic number, and shape factor. This study can provide new ideas for clarifying the correlation degree and weight value of the characteristic parameters and can enrich the development of 3D digital core and CO 2 -ECBM technology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".