Permeability and Methane Adsorption Capacity through Nanopore Modeling: A Case Study of Shale in the Sichuan Basin
Bibliographic record
Abstract
Shale pores are characterized by a wide size distribution, limited throat connectivity, and complex morphology. An accurate and comprehensive quantitative and qualitative description of shale pore structure is a fundamental requirement for evaluating shale reservoir capacity and developing shale gas exploration and development plans. In this paper, shale pores were characterized by the N 2 adsorption/desorption, low-pressure CO 2 adsorption, mercury injection capillary pressure (MICP), focused ion beam-scanning electron microscopy (FIB-SEM), and three-dimensional reconstruction methods. The results of N 2 adsorption/desorption and CO 2 adsorption indicate that the micropores (<2 nm in diameter) are extremely developed and play a dominant role in the specific surface area of shale, while the mesopores (2–50 nm) and macropores (>50 nm) contribute more to the pore volume of shale. The FIB-SEM and three-dimensional reconstruction visualizes that most mesopores and macropores are detected in organic matter but are poorly interconnected. Nanopore modeling of supercritical CH 4 adsorption results indicates that micropores and fine mesopores play a dominant role in the adsorption capacity of shale. Permeability simulations at the nanoscale further clarify that increased tortuosity significantly reduces permeability. This study provides insights to deeply understand gas adsorption and permeability from the nanoscale perspective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".