Location: Alberta, Canada
How Machine Learning Reduced Alarm Overload and Improved Operator Focus at a SAGD Facility
A SAGD facility used machine learning and predictive analytics to distinguish important process alarms from repetitive and low-value notifications helping operators focus on conditions requiring immediate attention.
Result at a Glance
Project Overview
The Challenge
The facility’s Distributed Control System continuously generated large volumes of operational alarms.
During a severe alarm event on 11 December 2020, more than 770 alarms were triggered in a single day. Many were repetitive, irrelevant or provided limited operational value.
Key Challenges
- High alarm volumes during abnormal events
- Repetitive and low-value notifications
- Difficulty distinguishing critical and nuisance alarms
- Limited intelligent filtering and prioritization
- Increased risk of operator fatigue
- Potential delays in responding to important conditions
- Increased risk of production disruption
Before Alarm Intelligence
DCS Alarm Data → High-Volume Notifications → Manual Prioritization → Operator Overload
Operators had to assess large alarm volumes without intelligent filtering or prioritization during alarm floods.
The Solution
Drishya AI collaborated with the Indian Institute of Management Bangalore to develop a data-driven alarm-intelligence solution.
Three years of historical alarm data were analysed using machine learning, deep neural networks and predictive analytics.
The Solution Included
- Analysis of three years of historical alarm data
- Machine-learning pattern detection
- Deep-neural-network modelling
- Predictive alarm analytics
- Classification of chattering, nuisance and stale alarms
- Real-time alarm filtering and prioritization
- Integration with the facility’s existing DCS
How It Works
The solution analyses alarm behaviour, identifies recurring patterns, classifies low-value alarms and prioritizes notifications requiring operator attention.
| Before | After |
|---|---|
| Unfiltered alarm volumes | Intelligent alarm filtering |
| Manual prioritization | Machine-learning classification |
| Repetitive notifications | Reduced nuisance alarms |
| Alarm overload | Sharper operator focus |
| Reactive assessment | Data-driven prioritization |
| Potentially delayed responses | Faster operational decisions |
Business Impact
40% Reduction in Alarm Floods
Machine learning and predictive analytics reduced alarm floods by 40% without requiring replacement of the facility’s existing control system.
The Results
- Achieved a 40% reduction in alarm floods
- Improved real-time filtering and prioritization
- Enabled operators to focus on relevant process conditions
- Reduced exposure to repetitive notifications
- Improved operator efficiency
- Supported faster operational decision-making
- Integrated with the existing control environment without disrupting operations
Key Takeaway
Machine learning and predictive analytics reduced alarm floods by 40%—helping operators focus on higher-value alarms and respond faster without replacing the existing control system.
