Predictive Maintenance
Predictive maintenance uses real-time sensor data and machine learning models to predict when equipment will fail - before it actually does. This allows operators to schedule repairs proactively, avoiding costly unplanned shutdowns and extending equipment life.
Maintenance Strategies Compared
Reactive
Fix it after it breaks.
- • Highest downtime and cost
- • Emergency mobilisation
- • Safety risk from sudden failure
Preventive
Fix it on a fixed schedule.
- • Reduces surprise failures
- • May replace healthy equipment
- • Higher maintenance costs
Predictive
Fix it when data says it is needed.
- • Optimal timing for repairs
- • Lowest total cost of ownership
- • Maximises equipment run life
How Predictive Maintenance Works
Collect historical and real-time data
Gather vibration, temperature, pressure, current, and operational history from sensors and SCADA systems over months or years.
Train a machine learning model
Use labelled failure data (past failures with their sensor signatures) to train a model that learns the patterns preceding a failure.
Deploy the model on live data
The trained model continuously analyses incoming sensor data and generates a health score or "remaining useful life" (RUL) estimate for each piece of equipment.
Alert and act
When the model detects early signs of degradation, it alerts the maintenance team with enough lead time to plan a repair - days or weeks before failure.
Use Case: Predicting ESP Failure
An operator deploys an ML model trained on 3 years of ESP failure data from 200 wells. The model monitors motor temperature, vibration, intake pressure, and current draw. It detects a gradual increase in vibration amplitude and a slow rise in motor temperature on Well-B12 that matches the pattern of a bearing failure. The model predicts failure within 14 days with 85% confidence. The maintenance team schedules a workover for the following week, replacing the pump before it fails. The planned workover costs $300,000 and takes 3 days. An unplanned emergency workover would have cost $600,000+ and resulted in 2 weeks of deferred production worth $1.5 million.
Common ML Techniques Used
Anomaly Detection
Identifies when sensor readings deviate from normal operating behaviour. Useful when labelled failure data is scarce.
Classification Models
Categorises equipment into states: healthy, degrading, or failing. Random Forest and Gradient Boosting are popular choices.
Time-Series Forecasting
Predicts future sensor values to estimate remaining useful life. LSTM neural networks are commonly used for sequential data.
Survival Analysis
Estimates the probability of failure over time given current conditions. Cox proportional hazards and Weibull models are common.