Effect of control domains of fractures and caves on reactive transport in porous media
Bibliographic record
Abstract
It is often highly difficult to predict the main flow path of reactive fluids in porous media with complexly distributed fractures and/or caves. Based on Darcy's law, this research defines the control domain of fractures/caves that represents the interference range of fractures/caves. This control domain can be used to identify the connectivity possibility between fractures/caves and predict the flow paths of reactive fluids at the optimal flow rate. Furthermore, after simplifying the geometry of fractures/caves, a threshold geometric aspect ratio of 20 is set to distinguish fractures and caves, concerning the flow mechanisms, by clarifying the ranges of control domains. Moreover, the control domain theory is combined with the two-scale continuum model for acidizing carbonate rocks to estimate the flow paths of acid in fractured-vuggy carbonate rocks at an optimal flow rate, thereby validating the accuracy of the reactive fluid main flow path estimation based on the control domain theory. The primary criterion to determine the reactive transport in porous media with complex fracture/cave distribution is the overlap degrees of control domains of adjacent isobaric bodies along their width and length directions, while the directions of isobaric bodies offer supplementary material. If the control domains of two isobaric bodies overlap with each other perpendicular to the flow direction, these isobaric bodies have higher odds of connected fluid flow.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".