Recent Advances in the Use of Temporary Optical Fiber Deployment for Downhole Hydraulic Fracture Monitoring
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Bibliographic record
Abstract
Distributed acoustic sensing (DAS) has transformed hydraulic fracture monitoring in recent years. We report two of the first projects in Canada, called the Canadian Dip-in DAS (CanDiD) projects, in which a temporary optical fiber was deployed to monitor hydraulic fracturing operations. The main goal of CanDiD is to evaluate the effectiveness of a retrievable optical fiber for frac monitoring based on the analysis of both microseismic and low-frequency DAS signals. The DAS recordings from zipper-frac completions in horizontal wells show clear signatures of crosswell strain associated with fracture-driven interactions (FDIs). These signals enable fracture azimuth to be determined, indicative of the maximum horizontal stress (SH max ) direction. Using a machine learning–based approach, microseismic events were detected and processed, although it was challenging to obtain process hypocenters from a single fiber. Numerous coherent noise events, which we interpret as high-frequency waves that propagate along the wireline due to fiber slip, initiate in close proximity to the FDIs. During another hydraulic fracturing program in western Canada, low-frequency DAS signals from the CanDiD-2 project provide evidence for fracture initiation, reactivation, and termination. The results of these investigations demonstrate the utility of temporary DAS deployments to provide insights about fracture geometry and stress orientations.
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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 | 0.001 |
| 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) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it