Well-Log-Based Velocity and Density Models for the Montney Unconventional Resource Play in Northeast British Columbia, Canada, Applicable to Induced Seismicity Monitoring and Research
Bibliographic record
Abstract
Abstract We present detailed velocity and density models for the Montney unconventional resource play in northeast British Columbia, Canada. The new models are specifically essential for robust hypocenter determination in the areas undergoing multistage hydraulic-fracturing operations and for detailed analysis of induced seismicity processes in the region. For the upper 4 km of the sedimentary structure, we review hundreds of well logs and select sonic and density logs from 19 locations to build the representative models. For depths below 4 km, we extend our models using data from the southern Alberta refraction experiment (Clowes et al., 2002). We provide one set of models for the entire Montney play along with two separated sets for the southern and northern areas. Specifically, the models for the southern and northern Montney play are based on logs located in and around the Kiskatinaw Seismic Monitoring and Mitigation Area and the North Peace Ground Motion Monitoring area, respectively. To demonstrate the usefulness of our detailed velocity model, we compare the hypocenter location of earthquakes that occurred within the Montney play as determined with our model and the simple two-layered model (CN01) routinely used by Natural Resources Canada. Locations obtained by our velocity model cluster more tightly with the majority of events having root mean square residual of <0.2 s compared with that of <0.4 s when the CN01 model is used. Cross sections of seismicity versus depth across the area also show significant improvements in the determination of focal depths. Our model results in a reasonable median focal depth of ∼2 km for events in this area, which is consistent with the completion depths of hydraulic-fracturing operations. In comparison, most solutions determined with the CN01 model have fixed focal depths (0 km) due to the lack of depth resolution.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".