Study on the Adsorption, Diffusion and Permeation Selectivity of Shale Gas in Organics
Bibliographic record
Abstract
As kerogen is the main organic component in shale, the adsorption capacity, diffusion and permeability of the gas in kerogen plays an important role in shale gas production. Based on the molecular model of type II kerogen, an organic nanoporous structure was established. The Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) methods were used to study the adsorption and diffusion capacity of mixed gas systems with different mole ratios of CO2 and CH4 in the foregoing nanoporous structure, and gas adsorption, isosteric heats of adsorption and self-diffusion coefficient were obtained. The selective permeation of gas components in the organic pores was further studied. The results show that CO2 and CH4 present physical adsorption in the organic nanopores. The adsorption capacity of CO2 is larger than that of CH4 in organic pores, but the self-diffusion coefficient of CH4 in mixed gas is larger than that of CO2. Moreover, the self-diffusion coefficient in the horizontal direction is larger than that in the vertical direction. The mixed gas pressure and mole ratio have limited effects on the isosteric heat and the self-diffusion of CH4 and CO2 adsorption. Regarding the analysis of mixed gas selective permeation, it is concluded that the adsorption selectivity of CO2 is larger than that of CH4 in the organic nanopores. The larger the CO2/CH4 mole ratio, the greater the adsorption and permeation selectivity of mixed gas in shale. The permeation process is mainly controlled by adsorption rather than diffusion. These results are expected to reveal the adsorption and diffusion mechanism of gas in shale organics, which has a great significance for further research.
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 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 | 0.000 | 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) | 0.000 | 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".