New insights into methane storage through coal pore opening and closure mechanisms during transient supercritical CO2 fracturing
Bibliographic record
Abstract
This study focuses on the often-overlooked closed pores in coal, which play a crucial role in isolating and storing significant amounts of methane, thereby directly impacting the efficiency of methane extraction. Using low-temperature nitrogen adsorption (LP-N2A) and small-angle x-ray scattering (SAXS) combined with multifractal theory, we examined the dynamics of pore opening and closure during supercritical CO2 (SC-CO2) fracturing at various pressures. Initially, chemical dissolution and the extraction of small organic molecules increased the surface area and volume of open pores. Stress-induced pore opening reduced closed pore volume, potentially increasing methane release. Enhanced fractal dimensions indicated greater pore heterogeneity. As fracturing progressed, pore interconnectivity improved, facilitating methane migration. Matrix contraction slightly expanded closed pores, increasing closed porosity. Fractal parameter decreases reflected changes in pore-scale correlation and reduced density. The isolation effect of closed pores delayed stress transmission, leading to asynchronous responses between total and open pores. Later, larger open pores collapsed, fragmenting the coal and increasing pore volume and surface area, while new closed pores raised closed porosity. These findings offer insights into how pore structure evolution during fracturing regulates methane at the micropore level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".