Using Percentiles and Distributions to Evaluate Drilling Performance
Percentiles locate observations within a defined population. Metric direction, analytical unit, weighting, cohort, spread, and sample size determine their meaning.
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Practical articles about drilling data architecture, statistics, engineering models, real-time systems, directional work, and applied AI.
Percentiles locate observations within a defined population. Metric direction, analytical unit, weighting, cohort, spread, and sample size determine their meaning.
Read article →Averages can hide repeatability, multimodal behavior, and rare operational delays. Distributions reveal what is typical, variable, and worth investigating.
Read article →Time preserves operational sequence; depth preserves location. Converting between them changes eligibility, weighting, aggregation, and resolution.
Read article →Choose sampling rate from the shortest behavior that must remain visible. Acquisition, analysis, aggregation, and display can operate at different rates.
Read article →Rig state gives raw channels operational meaning and routes each interval to the correct KPI, model, event detector, or alert. Classification errors propagate everywhere.
Read article →Reliable real-time analytics starts with channel identity, units, clocks, quality, wellbore and BHA context, lineage, and recovery from delayed data.
Read article →Time and depth answer different drilling questions. Reliable alignment requires explicit grain, eligibility, direction, tolerance, survey authority, and provenance.
Read article →Drilling data needs time, depth, wellbore, rig-state, equipment, unit, and provenance context. Without that structure, valid measurements can still produce the wrong engineering answer.
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