Numerical modeling of low-frequency distributed acoustic sensing signals for mixed-mode reactivation
Bibliographic record
Abstract
ABSTRACT In recent years, the development of distributed acoustic sensing (DAS) technology has enabled direct monitoring of subsurface strain during hydraulic fracturing (HF) operations. Most low-frequency (<1 Hz) DAS (LFDAS) signals exhibit strain or strain-rate patterns that are characteristic of propagating tensile hydraulic fractures. However, mixed-mode fault reactivation, consisting of shear slip (mode II) with tensile opening (mode I), can occur in cases where propagating hydraulic fractures intersect preexisting natural fractures or faults. In this study, we show an anomalous LFDAS signal that was observed during an HF operation, with characteristics that differ from typical signals from tensile hydraulic fractures. Anomalous characteristics include the onset of an asymmetric compression-extension doublet after pumping was terminated. The location of the signals, coupled with the image log and seismic data, suggests that mixed-mode reactivation occurred on a preexisting fault. We use a simplified numerical model based on the displacement discontinuity method (DDM) to simulate the anomalous LFDAS signal. Results find that the first-order characteristics of the anomalous signal can be approximated as an initial tensile fault opening (mode I) followed by a shear slip (mode II) on a fault. Therefore, we demostrate the approach of using DDM to investigate mixed-mode fault reactivation during HF operations.
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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".