Adsorption behavior modeling of confined hydrocarbons in shale heterogeneous nanopores by the potential theory
Bibliographic record
Abstract
Abstract In this article, a new method is proposed to quantificationally evaluate the effect of pore heterogeneity on the adsorption behavior of fluids in nanopores. First, the assumptions of furrowed, sinusoidal, and ravine pore surfaces are proposed to represent the heterogeneous nanopores in shale. Under the assumptions, a multicomponent potential theory of adsorption (MPTA) is coupled with Peng–Robinson equation of state (PR EOS) to model the adsorption behavior of hydrocarbons in nanopores. And, the geometrical and chemical heterogeneities in shale nanopores are, respectively, simulated by a spatial alteration and an amplitude deformation on potential energy. The fluid–fluid interaction is modeled by PR EOS, and the fluid‐pore wall surface interaction is simulated by a Steel 10‐4‐3 model for slit‐like nanopores and by a modified Lennard–Jones (LJ) 12‐6 model for cylindrical ones. Thereafter, the results of our theory are compared against the experimental data of shale rocks to validate its accuracy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".