Automated mapping of hydraulic fractures using bedding-plane slip events
Bibliographic record
Abstract
During hydraulic fracturing, focal mechanisms of microseismic events often exhibit a near-horizontal nodal plane. Previous studies have pointed out that this type of focal mechanism is consistent with stick-slip behavior on relatively weak bedding planes, adjacent to expanding vertical tensile fractures. Based on this model, we have developed an automated method to map hydraulic-fracture segments, in which each segment is bracketed by a pair of bedding-plane-slip events. Clustering analysis is applied first, in order to associate events with the corresponding treatment stage in a robust manner. Next, the clustered catalogue is filtered to remove out-of-zone events, as well as those that lack sub-horizontal nodal planes. The last step in the procedure builds a discrete fracture network model using a graphical approach. This method is applied to microseismic observations from a hydraulic-fracture monitoring program in the Kaybob-Duvernay region of Alberta, Canada. Calculated fracture lengths exhibit an apparent power-law distribution, while inferred fracture azimuths are oblique to the regional SHmax direction. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Start Time: 3:55 PM Location: 217B Presentation Type: Oral
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".