Characterization of Methane Excess and Absolute Adsorption in Various Clay Nanopores from Molecular Simulation
Bibliographic record
Abstract
In this work, we use grand canonical Monte Carlo (GCMC) simulation to study methane adsorption in various clay nanopores and analyze different approaches to characterize the absolute adsorption. As an important constituent of shale, clay minerals can have significant amount of nanopores, which greatly contribute to the gas-in-place in shale. In previous works, absolute adsorption is often calculated from the excess adsorption and bulk liquid phase density of absorbate. We find that methane adsorbed phase density keeps increasing with pressure up to 80 MPa. Even with updated adsorbed phase density from GCMC, there is a significant error in absolute adsorption calculation. Thus, we propose to use the excess adsorption and adsorbed phase volume to calculate absolute adsorption and reduce the discrepancy to less than 3% at high pressure conditions. We also find that the supercritical Dubinin-Radushkevich (SDR) fitting method which is commonly used in experiments to convert the excess adsorption to absolute adsorption may not have a solid physical foundation for methane adsorption. The methane excess and absolute adsorptions per specific surface area are similar for different clay minerals in line with previous experimental data. In mesopores, the excess and absolute adsorptions per specific surface area become insensitive to pore size. Our work should provide important fundamental understandings and insights into accurate estimation of gas-in-place in shale reservoirs.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".