Fracturing in coals with different fluids: an experimental comparison between water, liquid CO2, and supercritical CO2
Bibliographic record
Abstract
Abstract The present work conducted laboratory experiments of fracturing in fat coals, anthracites, and mudstones. Three different fluids were selected as the fracturing fluids, including water, liquid CO2 (L-CO2), and supercritical CO2 (Sc-CO2). The resulting fracture morphologies and fracture apertures of the coal specimens were investigated using 3D morphological scanning, and the permeabilities of the samples were measured before and after fracturing. The experimental results showed that the breakdown pressures of Sc-CO2 fracturing were the lowest among the three fracturing fluids, and the average single fracture apertures of the ScCO2-induced cracks were the smallest amongst the three fracturing fluids. In addition, the number of cracks and the roughness coefficients induced by Sc-CO2 fracturing were larger than those caused by water and liquid CO2. The viscosity of the fracturing fluid and the capillary effect are key factors that affect the crack propagation path and fracture surface topography. The results suggest that Sc-CO2 has the largest diffusion length, and thus is capable of permeating the coal matrix through small pores and causing more extensive fractures. Additionally, the effective hydraulic apertures of coal specimens produced by Sc-CO2 fracturing were wider than those induced by water and liquid CO2. The experimental results indicate that Sc-CO2 fracturing has huge potential to enhance coalbed methane recovery.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".