Abstract
Missing values, outliers, out-of-order timestamps, unit inconsistencies and baseline drift degrade online monitoring data on drilling-fluid properties in deep wells. We validate an evaluation–marking–remediation–validation workflow on a multi-source database of about 500,000 business records and on 150,000 instrument and replay records that are registered separately by source and are not summed, from five wells. Six metrics describe data quality: missing rate, out-of-order rate, outlier rate, unit consistency, baseline drift and noise. Missing values are treated in tiers: short gaps are interpolated, medium gaps are interpolated as last-observation-carried-forward with a mask, and long gaps invalidate the window. Offline filling uses forward filling, linear interpolation or random forest prediction. MAD/Hampel detection with operating-condition matching separates sensor artefacts, operational transients and genuine anomalies. Signal enhancement combines robust denoising, multi-scale fusion and a trend-confidence threshold. On the same replay dataset, the missing rate fell from 6.8% to 0.7%, the outlier rate decreased from 3.6% to 0.8%, the baseline-drift index fell from 1.47 to 0.61, the noise level decreased by 40.8%, and the usable-record ratio rose from 77.0% to 98.0%. On a fixed judgement set of 55 genuine events and 164 negative exposure intervals, the operator that retains operations and multi-metric co-movement recovered 55 of 55 genuine events at a negative-interval false alarm rate of 25.61%, against 0% recall for the 3-sigma rule and 54.55% recall for Hampel alone; this comparison covers simplified cleaning operators and is not equivalent to the complete remediation chain. Warning models trained on the remediated data reached an F1 of 93.0–94.0% and lead times of 1.8–2.8 h on the replay test dataset. The raw data replay and the field application are two different data sources and are not compared as an effect of remediation.