Fracture characterization in Sc-CO2 quasi-static and shock fracturing: laminated shale and PMMA visualization experiments
Bibliographic record
Abstract
ABSTRACT: The loading method of Sc-CO2 significantly impacts the fracturing effectiveness in shale oil and gas reservoirs. This study conducted true triaxial fracturing and visualization experiments on laminated shale and PMMA under quasi-static and shock fracturing conditions, using CT scanning and high-speed photography. Results show that quasi-static fracturing forms a complex but narrow, bedding-controlled fracture network with weak cross-layer propagation. In contrast, shock fracturing creates wider fractures, overcoming bedding and stress constraints. PMMA tests revealed that quasi-static fracturing occurs through slow erosion, while shock fracturing happens in milliseconds, driven by impact loading and fluid migration, leading to surface stress concentration, spalling, or fragmentation. Sc-CO2 shock fracturing produces 7.28% higher fracture concentration, 8.96% higher fractal dimension, 5.20% higher complexity, 5.40 and 3.18 times greater width and volume than quasi-static fracturing, forming wider, more complex networks. These findings provide a theoretical foundation for the further development of Sc-CO2 shock fracturing technology.
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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 | 0.000 | 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) | 0.002 | 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".