Experimental and Numerical Investigation of Dynamic Gas Adsorption/Desorption–Diffusion Process in Shale
Bibliographic record
Abstract
Shale gas is produced by gas transport under constant reservoir temperature and down hole pressure conditions. Therefore, it is of great importance to study the dynamic gas adsorption/desorption-diffusion process in shale, under isothermal and constant production pressure conditions. Accordingly, a new experimental method and apparatus has been designed and tested for studying shale gas transport behavior. The essence of the method includes accurately measuring the gas going into or coming out of a shale sample with respect to time. The accuracy and sensitivity of the method are confirmed by conducting experiments with methane and helium, and comparing the outcomes from adsorption isotherm obtained using the traditional constant-volume method. With this newly designed method, a two-stage transport process was observed by comparing the dynamic gas transport of N 2 and CH 4 . Free gas transports first due to the pressure gradient, which is followed by the desorption and transportation of the adsorbed gas. Besides, tests under five pressures were conducted. It is found that for the same differential pressure, higher external pressure could accelerate the process while decrease the amount of transported gas. Finally, the dynamic adsorption–diffusion (DAD) mathematical model is presented to analyze the gas transport mechanisms in shale depicting the adsorption/desorption–diffusion process under isothermal and constant external pressure. By calculating the production rate for free gas and adsorbed gas, the two stages of the transport process can be identified. This study provides a straightforward method to experimentally determine the dynamic gas adsorption/desorption–diffusion process in shale, which is a relatively simple but information–rich technique for the assessment of shale gas targets.
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".