A Modified Dent-Fractal Mathematical Model to Investigate the Water Vapor Adsorption on Nanopore Structure Heterogeneity from the Longmaxi Shale, Sichuan Basin, China
Bibliographic record
Abstract
The study of water vapor adsorption (WVA) isotherms on shales is crucial to comprehend the adsorption–desorption behavior and deposit mechanism of water in shale pore systems. To systematically investigate the relationship between fractal dimension and WVA in shale reservoirs, a new Dent-fractal (DF) model was developed and the ability of different adsorption models to match with WVA experimental data was evaluated. The role of shale pore structure heterogeneity controlling the amount of WVA is also discussed. The results indicate that WVA on shale involves the monolayer–multilayer adsorption and capillary condensation. On the one hand, the GAB and Dent and DF models were found to be the best models for fitting and predicting WVA isotherms in Longmaxi shale. On the other hand, the DLP and DS models had the worst fitting qualities for WVA adsorption data. The pore structure of micropores has a more significant effect on WVA adsorption than that of meso-macropores. The larger surface and pore volume of micropores can provide more adsorption sites and space, which is favorable for WVA. In low-Rh conditions, the higher surface area, the pore surface complexity rises, resulting in higher water vapor monolayer adsorption. Under high-Rh conditions, for shale reservoirs with a highly heterogeneous pore structure, the relationship between the heterogeneous pore structure of the shale and the amount of water adsorbed in multiple layers is not obvious due to the formation of clusters of water molecules.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".