Microseismic case study: Investigating the natural fractures and faults of the Muskwa and Evie shale play in northeastern British Columbia
Bibliographic record
Abstract
In this case study the natural fracture and faulting characteristics of the Muskwa and Evie shale play in Northeastern British Columbia are discussed. The Muskwa is an ideal formation for studying the acoustic effects of hydraulic fracture stimulation as these shales are known to be generally very brittle, producing lengthy linear fractures. The Evie shales have a different characteristic than the Muskwa shales, consisting of variably calcareous siliceous shales (McPhail, 2008). Both of these shales were studied during microseismic monitoring while hydraulic fracture treatments were performed. The recording geometry for this study was a near-surface array operated during hydraulic fracturing for nine horizontal wells, eight of them within the Muskwa Formation and the ninth in the Evie Formation. Two different completion techniques were used, affecting how the rock fractured: perforation and plug and ball and sliding sleeve. The pointset has a dominant 70° fracture azimuth. The focal mechanisms and b-values support this trend result. The Muskwa stimulations differ from the Evie stimulations in that the Evie produces shorter, complex fractures with lower b-values and lower stimulated rock volume (SRV). The b-value calculated for the H well is 2.97 while it is less than 1 for the A well. This indicates that the fractures in the Muskwa are hydraulically induced while the fractures in the Evie are due to fault reactivation. The SRV calculated for the H well in the Muskwa was 5.5 times more than the SRV for the A well located in the Evie.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".