Interpretation of low-frequency distributed acoustic sensing data based on geomechanical models
Bibliographic record
Abstract
ABSTRACT Distributed acoustic sensing (DAS) is a technology that enables continuous, real-time measurements along the entire length of a fiber-optic cable. The low-frequency band of DAS can be used to analyze hydraulic fracture geometry and growth. In this study, the low-frequency strain waterfall plots with their corresponding pumping curves were analyzed to obtain information on fracture azimuth, propagation speed, number of fractures created in each stage, and restimulation of preexisting fractures. We also use a simple geomechanical model to predict fracture growth rates while accounting for changes in treatment parameters. As expected, the hydraulic fractures principally propagate perpendicular to the treated well, that is, parallel to the direction of maximum horizontal stress. During many stages, multiple frac hits are visible, indicating that multiple parallel fractures are created and/or reopened. Secondary fractures deviate toward the heel of the well, likely due to the cumulative stress shadow caused by previous and current stages. The presence of heart-shaped tips reveals that some stress and/or material barrier is overcome by the hydraulic fracture. The lobes of the heart are best explained by the shear stresses at 45° angles from the fracture tip instead of the tensile stresses directly ahead of the tip. Antennas ahead of the fracture hits indicate the reopening of preexisting fractures. Tails in the waterfall plots provide information on the continued opening, closing, and interaction of the hydraulic fractures within the fracture domain and stage domain corridors. The analysis of the low-frequency DAS plots thus provides in-depth insights into the rock deformation and rock-fluid interaction processes occurring close to the observation well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".