Measuring Hydraulic Fracture Growth in Naturally Fractured Rock
Bibliographic record
Abstract
Abstract A field experiment was carried out to measure hydraulic fracture growth in naturally fractured rock. Hydraulic fracture interactions with pre-existing natural fractures, shear zones, veins, and adjacent hydraulic fractures were measured and mapped during the project. Tiltmeter and microseismic arrays were installed to test the performance of these monitoring methods in determining the fracture geometry, which was eventually revealed by the mine-through mapping. The physically mapped fractures were oriented approximately horizontally, perpendicular to the minimum stress direction. They crossed natural fractures and shear zones, but were offset by some shear zones, most often oriented with an approximate 45° dip. The analysis of the tiltmeter data correctly predicted fractures to be horizontal. Microseismic monitoring, although a proven method for imaging hydraulic fractures, did not resolve the fracture orientation or size for conditions at the E48 Northparkes site because of a lack of recorded micro-seismic events. The hydraulic fractures grew through solid rock, along natural fractures and stepped along inclined shear zones. Proppant was distributed throughout the fractures, including in the offset portions. Initial modeling indicates higher treatment pressure and slower extension rate for a stepped 2D hydraulic fracture compared to a straight fracture.
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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 | 0.000 |
| 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".