Predictive Maintenance
Fix it before it breaks. Days of advance warning, by equipment class.
WorkSync ML models continuously analyze SCADA telemetry across every monitored asset, comparing current behavior against learned degradation signatures for rod pumps, ESPs, gas compressors, and gas-lift wells. When a pattern match exceeds confidence thresholds, the system generates a predictive alert ahead of the expected failure window, scores it by production at risk, and ranks it into the daily work plan.
The problem
Reactive maintenance costs more. Scheduled maintenance wastes more.
Most oil and gas operations run on a reactive model: equipment fails, someone notices hours or days later, a work order is created, parts are sourced, and a crew is dispatched. During that entire window, production is deferred.
Scheduled maintenance helps but is inherently wasteful, replacing components on a calendar regardless of actual condition. You either replace too early, wasting the remaining useful life, or too late, after damage has already begun.
The gap between those two approaches is where predictive maintenance lives: using real-time data patterns to identify the exact window when intervention is needed, and routing a crew there before the failure instead of after it.
Four equipment classes
Each class has its own failure signatures. Each gets its own models.
Rod Pump
Signatures
Pump fillage degradation, gas interference, parted rod, fluid pound
ML detects
Per-well dynamometer ML on card shape, load, and fillage trends
Equipment deep-diveESP
Signatures
Bearing wear, motor temperature drift, intake gas locking
ML detects
Intake pressure trends, motor temperature, vibration signature changes
Equipment deep-diveGas Compressor
Signatures
Valve, ring, and packing wear before unplanned downtime
ML detects
Discharge temperature creep, suction pressure change, runtime pattern shifts
Equipment deep-diveGas Lift
Signatures
Instability, slugging, valve loading
ML detects
Injection and production pressure interplay against the learned envelope
Equipment deep-diveIn practice
From signature to scheduled intervention.
An illustrative scenario: a rod-pump well on a Tuesday morning. The model detects a subtle increase in peak polished rod load combined with a slight decrease in pump fillage, a pattern consistent with traveling valve wear.
The system generates a predictive alert with an estimated time to failure, the production at risk, and a recommended action: pull rods, replace the traveling valve. The task is scored and inserted into the next available crew work plan.
The crew arrives Wednesday with the right parts and equipment, and the intervention runs at scheduled cost. Without the early warning, the well fails Thursday night, sits undetected until the morning report, and waits for an emergency rig, days of deferred production plus the emergency premium instead of a planned few hours.
Continue the cluster
Prediction is the input. The ranked plan is the product.
The work it triggers
When a signature flags
The ML layer
What runs underneath
Frequently asked
What maintenance foremen ask.
What is predictive maintenance in oil and gas?
Predictive maintenance uses real-time data patterns to identify the window when equipment intervention is needed, instead of waiting for a failure (reactive) or replacing components on a calendar regardless of condition (scheduled). ML models compare current behavior against learned degradation signatures per well and per equipment class, and flag developing problems days before the failure window.
What equipment classes does WorkSync cover?
Four primary classes, each with its own signature library: rod pumps (fillage degradation, gas interference, parted rod, fluid pound), electric submersible pumps (bearing wear, motor temperature drift, gas locking), gas compressors (valve, ring, and packing wear via discharge temperature and vibration trends), and gas-lift wells (instability, slugging, valve loading). Chemical injection drift is tracked as a supporting signal across classes.
How far in advance does the warning arrive?
Degradation signatures typically change days before the failure crosses a fixed SCADA threshold, which is what turns an emergency callout into a scheduled intervention. The lead time varies by equipment class and signal density, and the alert always carries the estimated failure window alongside the recommended action.
What happens when a predictive alert fires?
The alert is scored by production at risk and intervention cost, then ranked into the next available crew work plan through the Work Engine. The crew arrives with the right parts before the failure, instead of after it. Detection without dispatch is just another dashboard; the ranking and routing are what move the number.
Does this require new sensors?
No. The models run on the SCADA telemetry, pump-off controller data, and production accounting signal most operators already collect. If a specific well later earns additional instrumentation on its own ROI math, add it then; the deployment does not gate on a hardware refresh.
See the at-risk equipment on your own wells.
4-week pilot on the stack you already own. Pick one field. Day 14 we show the at-risk equipment list. Day 28 you decide, under the Impact Guarantee: we charge when your number moves.