ML Anomaly Detection
Per-well models that learn what “normal” looks like — and catch the subtle issues fixed thresholds miss.
The Problem: Fixed Thresholds Don't Work
Traditional SCADA systems define “normal” using static high/low thresholds set by an engineer. The same threshold applies regardless of season, well history, or operational context. This approach worked when operators had 50 wells. At scale, it creates more noise than signal.
One Threshold for Every Well
A fixed low-pressure alarm at 150 PSI makes no sense when some wells normally run at 160 and others at 400. The result: constant false positives on some wells and missed failures on others.
Blind to Seasonal and Operational Changes
After a workover, a well behaves differently for weeks. During winter, surface temperatures shift readings. Fixed thresholds cannot account for context that changes over time.
Only Catches Threshold Breaches
A well gradually declining 2% per day will never breach a fixed threshold until production has dropped significantly. By then, the damage is done and revenue is already lost.
Alarm Fatigue
When 40% of alarms are false positives, operators stop trusting the system. Real problems get buried in noise and critical issues are missed.
How WorkSync Solves It
WorkSync trains an individual ML model for every well. Each model learns that well's unique operating signature — its seasonal patterns, decline curve, response to workovers, and normal variability under different conditions.
Per-Well Baselines
Instead of a universal threshold, each well has its own learned definition of normal. A reading that is perfectly fine on Well A may be a clear anomaly on Well B.
Context-Aware Detection
Models account for time of day, ambient temperature, recent maintenance, and production trends. A pressure drop after a rod change is expected — the same drop without context is a real alarm.
Subtle Pattern Recognition
ML detects gradual degradation, cyclical anomalies, and multi-parameter correlations that no fixed threshold can capture. A slow 1% daily decline across three parameters simultaneously is invisible to SCADA but clear to the model.
80%+ False Alarm Reduction
By understanding what is truly abnormal for each specific well, the system eliminates the noise that drives alarm fatigue — letting operators focus on real problems.
How It Works in Practice
Scenario: An operator runs 800 wells across western Oklahoma. Their SCADA system generates 120+ alarms per day. Field crews investigate an average of 45 — and find real problems on only 25.
After deploying WorkSync anomaly detection, per-well models train on 12 months of historical data. Within two weeks, the system reduces daily alerts to 30 — with 28 confirmed as real issues requiring action.
Operators no longer waste half their day chasing ghosts. The alerts they do receive come with context: what changed, how it compares to the well's history, and what the economic impact is if left unaddressed.