Fleet-cohort drivers
Across similar stages in your fleet, which inputs most correlate with the outcome? Spearman rank correlation — correlation, not causation. Use this to generate hypotheses, then test with Sensitivity sweep or Design Copilot.
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versionmust staydownhole-fleet-cohort-preset-v1. - Only change values inside the
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"8000", not8000. wellIdsmust be an array of strings, e.g.["BAK-01", "BAK-02"].- Dates must be ISO —
"2024-06-01". Booleans aretrueorfalse(no quotes).
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Drop wells with fewer matching stages.
Drop stages with screen-out rate ≥ this fraction.
Cohort N=24. Top 3 drivers of eur (Spearman ρ — correlation, not causation):
• clusterSpacing: ρ=-0.71 (n=24) — lower clusterSpacing correlates with worse eur.
• fluidIntensity: ρ=-0.41 (n=24) — lower fluidIntensity correlates with worse eur.
• stageLength: ρ=-0.23 (n=24) — lower stageLength correlates with worse eur.
Method: Spearman rank correlation with two-sided t-approx p-value. Not a causal model — pair with a single-factor sweep before changing a job plan.
Holds other factors fixed (ridge β), tracks the strongest driver across vintage years, and pools per-basin ρ via DerSimonian-Laird. Hypothesis generators, not causal claims.
Ridge partial coefficients · top 4 factors
n=24 · R²=0.81 · λ=0.1Vintage drift · clusterSpacing vs eur
±2 yr rolling window, ρ vs yearCross-basin meta-cohort · clusterSpacing
k=3 · I²=18%| Basin | ρ | n | var(z) |
|---|---|---|---|
| Bakken | -0.71 | 24 | 0.048 |
| Eagle Ford | -0.93 | 8 | 0.200 |
| Permian | -0.81 | 8 | 0.200 |
- clusterSpacing — ρ=-0.705 · p=0.000 · n=24
- fluidIntensity — ρ=-0.410 · p=0.035 · n=24
- stageLength — ρ=-0.234 · p=0.258 · n=24
The memory entry is a project-memory record (index + body file). The check mark confirms this route is the one described by that entry — sidebar item, Spearman driver finder, and route path all match.