A Model for Surface Diffusion of Adsorbed Gas in Nanopores of Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Surface diffusion plays a key role in gas mass transfer due to the majority of adsorbed gas with abundant nanopores of organic matter in shale gas reservoirs. Surface diffusion simulation is very complex as a result of high reservoir pressure, surface heterogeneity and non-isothermal desorption in shale gas reservoirs. In this paper, a new model of surface diffusion for adsorbed gas in shale gas reservoirs is established, which is based on a Hwang model derived under low pressure condition and considers the effect of adsorbed gas coverage under high pressure. Additionally, this new model considers the effects of surface heterogeneity, isosteric sorption heat and non-isothermal gas desorption. Results show that: (1) the surface diffusion coefficient increases with pressure and temperature, while it decreases with activation energy and gas molecular weight; (2) contributions of viscous flow, Knudsen diffusion and surface diffusion to the total gas mass transfer are varying during the development of shale gas reservoirs, which are mainly controlled by nanopore-scale and pressure; (3) in micropores (pore radius < 2nm), the contribution of surface diffusion to the gas mass transfer is dominant, up to 92.95%; in macropores (pore radius > 50nm), the contribution is less than 4.39%, which is negligible; in mesopores (2nm < pore radius < 50nm), the contribution is between micropores and macropores.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".