Structural Controls on Stress and Microseismic Response - A Horn River Basin Case Study
Bibliographic record
Abstract
Abstract This study demonstrates that geologic structure can significantly alter stresses within the completion zone and change the characteristics of microseismicity. Completions and microseismic data are used to examine the effects of geologic structure on an executed completions program. The microseismic was recorded on a 98-station near-surface array for approximately 200 stages on 7 wells. Two prominent faults are identified using microseismic data. The differences between microseismic event populations from hydraulic fracture stimulation and fault reactivation are assessed in terms of event source mechanisms and event parameters. We examine changes in calculated bottomhole instantaneous shut-in pressure (ISIP) gradient near faults and the associated change in microseismic behavior. Stress regime, maximum horizontal stress (SHmax) directions and stress magnitudes along faults are examined with a stress inversion approach using source mechanism information. Results show that hydraulic fractures, particularly in the Muskwa shales, have high b-values and tend to be characterized by dip-slip failures. Events relating to fault reactivation are characterized by strike-slip events and have low b-values. The orientations of SHmax derived from stress inversion for fault populations align with regional SHmax estimates from regional borehole breakout measurements. Stages intersected by prominent faults show significant increases in measured ISIP, which are more pronounced in the Evie completion zone.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".