Effectiveness of dip-in DAS observations for low-frequency strain and microseismic analysis: The CanDiD experiment
Bibliographic record
Abstract
The Canadian Dip-in-DAS (CanDiD) project involved wireline deployment of an optical fiber in a lateral well to record distributed acoustic sensing (DAS) observations. The project took place in January 2021 during hydraulic- fracturing operations at a multi-well pad. The program was carried out by the University of Calgary as part of the Microseismic Industry Consortium, in partnership with an oil and gas operator and several wireline and DAS service providers. The DAS recordings from zipper-frac completions in 6 horizontal wells show typical signatures of low-frequency strain signals associated with fracture-driven interactions (FDI’s or “frac hits”). These signals enabled fracture azimuth to be determined, which indicate a systematic variation in azimuth with depth in the reservoir zone. This variation is interpreted to represent a depth- dependent rotation in the maximum horizontal stress direction. A few atypical low-frequency signals are best explained by shear slip along horizontal planes of weakness. Using a machine-learning based approach, microseismic events were detected and processed, although it was not possible to obtain process hypocenters from a single fiber. In the same frequency band as the microseismic events, numerous coherent noise events with symmetrical linear moveout were observed in close proximity to the FDIs. The results of this investigation show the utility of dip-in DAS deployments to provide insights about fracture geometry and stress orientations.
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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 | 0.001 | 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)
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".