Comprehensive Review about Methane Adsorption in Shale Nanoporous Media
Bibliographic record
Abstract
Shale/tight gas plays an increasingly important role to meet the growing global energy demand and reduce carbon emissions. Unlike conventional reservoirs, shale formations are subject to rock heterogeneity and have pore size distributions ranging from sub-1 nm to a few micrometers. Thanks to the large number of nanosized pores, adsorbed methane capacity plays a dominant role in total shale gas-in-place. Methane adsorption behaviors can vary drastically in micropores and mesopores, and rock surface type may also greatly affect its adsorption. In this review, we provide a systematic discussion on measurements of shale rock properties including rock compositions and pore structures such as specific surface area (SSA) and pore size distribution (PSD), which are important parameters for methane adsorption in shale nanoporous media. We also provide in-depth discussions on experimental measurements on methane (excess) adsorption in shale nanoporous media, methane adsorption behavior characterization based on molecular simulations, and various excess-adsorption-to-absolute-adsorption conversion methods. We pay particular attention to the assumptions and working mechanisms proposed in various interpretation methods which are embedded in pore structures (SSA and PSD) and absolute adsorption characterizations. In the end, we summarize the key challenges in the methane adsorption characterization in shale media.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".