AI Data QA
Eliminate false alarms before they start — clean data in, clean decisions out.
The Problem: Bad Data Creates Expensive Ghosts
The single biggest source of wasted field time in exception-based operations is chasing ghosts — alarms generated not by real equipment problems but by bad data. Sensor drift, stuck values, communication gaps, and outlier spikes generate false exceptions that look identical to real ones.
An operator investigating a false alarm spends the same time driving, the same fuel, and faces the same road risk as one investigating a real problem. When 30-40% of alarms are data artifacts, the waste compounds across every crew, every day.
Sensor Drift
Pressure transducers lose calibration over months. A sensor reading 10 PSI high generates false alarms on every poll cycle until someone physically recalibrates it.
Stuck Values
A frozen sensor reports the same value indefinitely. Without detection, the system believes the well is operating normally while actual conditions may have changed drastically.
Communication Gaps
Cellular and radio links drop during storms, equipment resets, or carrier outages. Missing data points can trigger false exceptions or mask real problems.
Outlier Spikes
Electrical noise, lightning, or RTU restarts can produce extreme one-off readings. A single spike to 9999 PSI triggers every alarm in the system if not filtered.
How WorkSync Solves It
WorkSync Data QA operates as the first layer in the intelligence pipeline — cleaning and validating every data point before it reaches anomaly detection or economic scoring. The principle is simple: if the input data is wrong, every downstream decision built on it will also be wrong.
Drift Detection
ML models compare sensor trends against expected physics and neighboring wells to identify gradual calibration drift before it causes false alarms.
Stuck Value Identification
Statistical analysis flags sensors reporting identical values beyond expected precision for longer than normal. Alerts are suppressed until the sensor is confirmed operational.
Gap Interpolation
When communication drops, the system estimates missing values using recent history and correlated parameters, maintaining anomaly detection continuity without generating false positives.
Outlier Filtering
Physics-based bounds and statistical outlier detection remove impossible readings before they enter the detection pipeline. No more 9999 PSI alarms.
How It Works in Practice
Scenario: An operator's SCADA system monitors 500 wells and generates an average of 85 daily exceptions. After investigation, crews confirm that only 50 are real problems — a 41% false positive rate.
WorkSync Data QA is deployed as the first processing layer. Within one week, the system identifies 12 sensors with calibration drift, 8 with stuck-value patterns, and flags recurring communication gaps on 23 RTUs. Exceptions from known bad data sources are suppressed with a data quality tag.
Daily actionable alerts drop to 55 — with a 91% confirmed-real rate. Crews recover an average of 2.5 hours per day previously spent investigating data artifacts. That time is redirected to real problems that recover production and revenue.