Enhanced Reservoir Characterization Using Hydraulic Fracture Microseismicity
Bibliographic record
Abstract
Abstract Microseismic measurements were integrated with seismic reservoir characterization and injection data to investigate variability in the hydraulic fracture response between three horizontal wells in the Montney shale in NE British Columbia, Canada. When wells were close enough, hydraulic fractures were found to interact with pre-existing faults, which acted as a barrier to fracture growth, and resulted in relatively large-magnitude microseismicity. Pre-existing faults were identified by edge detection/ant tracking algorithms applied to seismic reflection data, as well as from advanced analysis of the microseismicity, including microseismic deformation levels, magnitude-frequency characteristics and composite failure mechanism analysis. In cases where the wells were far from pre-existing faults simple, planar hydraulic fractures were observed, although there was a tendency to grow towards regions of low Poisson’s ratio based on amplitude versus offset inversion of the seismic reflection data. The tendency for the hydraulic fractures to be asymmetric and grow preferentially towards the low Poisson’s ratio region is attributed to material property changes and associated lower stresses in these regions. Insight from the enhanced reservoir characterization with integrated microseismic and treatment data is being used for better well placement, improved completion designs and increased production.
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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 | 0.001 | 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) | 0.001 | 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".