Microseismic-Derived Correlations to Production in the Horn River Basin
Bibliographic record
Abstract
Abstract Production from two multilateral pads in the Horn River Basin is compared to microseismic-derived parameters. Microseismic was recorded on a near-surface array in both cases. The number of events recorded on each well tends to have a positive correlation to that well's initial production, while the magnitude of those events does not tend to be a good indicator of production in all zones. Fracture models created from located microseismic events also tend to correlate well to production: modeled fracture area, fracture volume and stimulated reservoir volume all show positive correlations. The method in which the rock fractures can also be an indicator of initial production. Wells with higher percentages of dip-slip type rock failures, which can be associated with hydraulic fractures, tend to have higher initial production. In contrast, wells with a larger proportion of strike-slip events, which are typical of fault reactivations in this zone, tend to have diminished production compared to neighboring wells with fewer reactivation events. By understanding what microseismic parameters positively and negatively impact initial production, operators can optimize well production. This can be done in real-time or in installments during lengthy completions programs through the identification of failure type, fracture geometry, and the relative number of events being recorded.
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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 | 0.001 | 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".