Fracture Permeability of Gas Shale: Effects of Roughness, Fracture Offset, Proppant, and Effective Stress
Bibliographic record
Abstract
Abstract Domestic gas shale production is made economic through new completion practices which include horizontal wells and multiple hydraulic fractures. The performance of these fractures is improved through the injection of proppant. Success has largely been based on empiricism through field experiments. We attempt to remove some uncertainty in this empiricism through a series of laboratory controlled experiments. We have measured the permeability of fractured rock as a function of effective stress, proppant, proppant distribution and fracture offset. Our findings indicate that fracture offset is as effective as propping a fracture; both increase initial permeabilities more than 1000 fold over initial fracture values. However, the pressure dependence of the propped fracture is stronger, i.e. the permeability is reduced more per increment of pressure than the offset fractures. Neither obeys the simple cubic pressure dependence law proposed by Walsh. A simple monolayer of proppant is as effective as a fairway distribution of proppant in enhancing permeability. Initial fracture permeability is dependent on surface roughness, quantified as root mean square asperity heights. Pressure dependence of permeability of these fractured surfaces does obey the Walsh permeability models. SEM observations of surfaces and proppant suggest a new approach to proppant design.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".