Three-dimensional fracture space characterization and conductivity evolution analysis of induced un-propped fractures in shale gas reservoirs
Bibliographic record
Abstract
Huge numbers of induced unpropped (IU) fractures are generated near propped fractures during hydraulic fracturing in shale gas reservoirs. But it is still unclear how their fracture space and conductivity evolve under in-situ conditions. This paper prepares three types of samples, namely, manually split vertical/parallel to beddings (MSV, MSP) and parallel natural fractures (NFP), to represent the varied IU fractures as well as their surface morphology. Laser scan and reconstruction demonstrate that the initial fracture spaces of MSVs and MSPs are limited as the asperities of newly created surfaces are well-matched, and the NFPs have bigger space due to inhomogeneous geological corrosion. Surface slippage and consequent asperity mismatch increase the fracture width by several times, and the increase is proportional to surface roughness. Under stressful conditions, the slipped MSVs retain the smallest residual space and conductivity due to the newly sharp asperities. Controlled by the bedding structures and clay mineral hydrations, the conductivity of MSPs decreases most after treated with a fracturing fluid. The NFPs remain the highest conductivity, benefitting from their dispersive, gentle, and strong asperities. The results reveal the diverse evolution trends of IU fractures and can provide reliable parameters for fracturing design, post-fracturing evaluation, and productivity forecasting.
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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) | 0.001 | 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".