Technical resources
Practical articles about drilling data architecture, statistics, engineering models, real-time systems, directional work, and applied AI.
A drop in standpipe pressure is one of the classic signatures of a drillstring washout, but mud-pump degradation can produce much of the same behavior. This article explains how flow-out trends, pressure-to-flow relationships, modeled hydraulic pressure, time history, rig state, and sensor validation can be combined to distinguish the two failure modes more reliably.
Read article →Wellbore tortuosity is increasingly used to evaluate directional performance and BHA quality, but the calculated value depends partly on how the trajectory was surveyed. This article examines how survey spacing changes the geometric features visible to a tortuosity calculation, why resampling does not automatically solve the problem, and what engineers should normalize before comparing wells or BHAs.
Read article →Rate of penetration is one of the most visible drilling-performance metrics, but high ROP does not necessarily mean the drilling process is operating efficiently. This article examines how MSE, torque behavior, drilling dysfunctions, formation context, hole cleaning, bit condition, and wellbore quality change the interpretation of ROP—and why the best operating point cannot be determined from drilling speed alone.
Read article →An unexpected rig signal can mean that an instrument is wrong or that the drilling process itself has changed. This article examines why simple threshold alarms are insufficient, how physical relationships between drilling measurements provide analytical redundancy, and how uncertainty and operational context can be used to distinguish questionable data from meaningful changes in the drilling process.
Read article →A drilling-performance comparison should show distributions, denominators, comparable cohorts, and uncertainty before it ranks wells, runs, rigs, or connection phases.
Read article →A practical guide to joining time-based drilling streams with measured-depth records while preserving rig state, tolerance, survey provenance, duplicates, and data-as-of context.
Read article →Drilling data looks like ordinary sensor data until time, measured depth, surveys, rig states, BHAs, sidetracks, and late-arriving records have to agree. This article explains the data model required to keep those contexts intact.
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