Numerical and Experimental Analysis of Diffusion and Sorption Kinetics Effects in Marcellus Shale Gas Transport
Bibliographic record
Abstract
Abstract Nano-scale pores and a dual storage mechanism shared between free and adsorbed gas make the transport behavior in shale gas reservoirs very different from conventional macropore reservoirs. This work explores a straightforward model for the gas transport behavior in shale nanopores, which couples sorption, diffusion, and sorbed-phase surface diffusion phenomena. The model combines two governing equations for free and sorbed gas phase transport processes in nanopores, respectively: a diffusion-based equation for free gas phase transport, and a surface-diffusion equation for the sorbed phase. Mass transfer between the two phases is quantified by kinetic models of sorption. The two governing equations are solved simultaneously using finite element methods (FEM). Model performance is successfully validated by closely matching density propagation profiles of a gas transport experiment obtained by quantitative X-ray computerized tomography (CT) imaging for a Marcellus shale sample. Transport-related parameters estimated from history matching are shown to be consistent with literature data.
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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) | 0.000 | 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".