Recent Advances in the Use of Temporary Optical Fiber Deployment for Downhole Hydraulic Fracture Monitoring
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".