Hydraulic Fracture Propagation: Combined Analysis of Low-Frequency DAS and Microseismicity
Bibliographic record
Abstract
Summary Distributed Acoustic Sensing (DAS) is an emerging technology in hydraulic fracture monitoring that enables continuous, real-time measurements along the entire length of a fibre optic cable. DAS benefits from being supported by other diagnostic tools such as microseismicity to analyze fracture propagation beyond the fibre. In this study, we analyze low-frequency DAS strain rate fronts and associated microseismicity of a treatment of 38 stages, with their corresponding pumping curves, to obtain information on the characteristics of hydraulic fracture geometry and propagation. Low-frequency DAS and microseismicity reveal different aspects of hydraulic fracture propagation. The low-frequency DAS response only records strain rate changes near the observation well, whereas the microseismicity is spatially distributed over a much larger volume. Furthermore, low-frequency DAS and microseismicity record at different sampling frequencies, which in turn makes them sensitive to phenomena occurring on different time scales. Temporal analysis of both low-frequency DAS plots and microseismic growth patterns reveals distinct propagation regimes, highlighting the intricate nature of fracture dynamics over time and across different stages.
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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 | 0.001 |
| 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".