Upstream Oil & Gas Operator
OCI-based asset performance management reducing unplanned downtime by 31%.
All case studiesAn upstream oil and gas operator managing 200+ wells and 3 central processing facilities across the Middle East engaged Cydez Technologies to deploy an asset performance management (APM) platform on Oracle Cloud Infrastructure. The operator produced 85,000 barrels of oil equivalent per day, with annual revenue exceeding USD 2 billion — making unplanned downtime extraordinarily costly.
The operator experienced an average of 14 unplanned shutdown events per quarter, each costing between USD 800K and USD 2M in lost production, emergency maintenance, and deferred production. Equipment health monitoring relied on manual operator rounds every 4 hours and periodic vibration analysis performed by third-party contractors quarterly. There was no predictive capability — maintenance was either calendar-based (resulting in unnecessary maintenance) or reactive (resulting in costly failures).
Cydez deployed the APM platform on OCI, integrating SCADA and process historian data from all wells and processing facilities via OPC-UA and MQTT protocols. ML models trained on 5 years of equipment failure data and continuous sensor streams predicted bearing wear, compressor degradation, heat exchanger fouling, and valve failures 7-14 days before occurrence with 89% accuracy. A real-time asset health dashboard provided operations managers with risk-ranked equipment views, and automated work order generation in Oracle ERP triggered preventive action before failures occurred.
- APM platform on OCI for 200+ wells
- SCADA and historian integration via OPC-UA/MQTT
- ML predictive maintenance models
- Real-time asset health dashboards
- Automated work order generation in Oracle ERP
- Equipment failure root cause analysis
An upstream oil and gas operator managing 200+ wells and 3 processing facilities in the Middle East experienced an average of 14 unplanned shutdown events per quarter, each costing USD 800K-2M in lost production. Equipment health monitoring relied on manual rounds and periodic vibration analysis. There was no predictive capability — maintenance was either calendar-based or reactive.
Cydez deployed an asset performance management platform on OCI, integrating SCADA and historian data from all wells and processing facilities. ML models trained on 5 years of equipment failure data predicted bearing wear, compressor degradation, and valve failures 7-14 days before occurrence. A real-time asset health dashboard provided operations managers with risk-ranked equipment views, and automated work order generation in Oracle ERP triggered preventive action before failures occurred.
How we delivered this project
Discovery
Conducted 10-week assessment across all 3 processing facilities and representative well sites. Cataloged 2,400 critical equipment items with failure history. Mapped SCADA tag lists and historian data structures. Analysed 5 years of maintenance records and failure incident reports.
Design
Designed the OCI platform architecture with real-time data ingestion, ML model serving, and dashboard layers. Created ML model specifications for 6 equipment categories (compressors, pumps, heat exchangers, valves, generators, turbines). Designed the asset health scoring framework and work order integration with Oracle ERP.
Development
Built OCI data ingestion pipelines for SCADA and historian data via OPC-UA and MQTT. Trained ML models on OCI Data Science using 5 years of failure data. Developed the real-time asset health dashboard on React with OAC for historical analytics. Built automated work order generation flows in Oracle ERP via OIC.
Launch
Deployed facility-by-facility over 12 weeks. Each facility required SCADA connectivity testing, model calibration against local equipment characteristics, and operator training. Conducted 90-day shadow monitoring where predictions were validated against actual outcomes before enabling automated work orders.
What we built
Predictive Maintenance
ML models predicting equipment failure 7-14 days before occurrence with 89% accuracy. Coverage: compressors, pumps, heat exchangers, valves, generators, and turbines. Continuous learning from new failure data.
Asset Health Scoring
Real-time health scores for 2,400 critical equipment items based on sensor data, maintenance history, and operating conditions. Risk-ranked views enabling maintenance teams to prioritise proactively.
SCADA Integration
Real-time data ingestion from SCADA systems across 200+ wells and 3 processing facilities via OPC-UA and MQTT. Sub-minute data freshness for critical equipment sensors.
Automated Work Orders
When ML models detect impending failure, work orders are automatically generated in Oracle ERP with equipment details, predicted failure mode, recommended action, and required spare parts.
Root Cause Analysis
Post-failure analysis tools correlating equipment sensor data, maintenance history, operating conditions, and environmental factors. Pattern identification across similar equipment for fleet-wide learning.
Executive Dashboards
Portfolio-level asset health visibility for management. Production uptime tracking, maintenance cost trending, and predictive vs. reactive maintenance ratio. ROI tracking against baseline failure rates.
Measurable outcomes
- 31% reduction in unplanned downtime within 12 months
- Predictive alerts issued 7-14 days before equipment failure with 89% accuracy
- Annual maintenance cost reduced by USD 4.2M through condition-based scheduling
- Mean time to repair improved by 45% through proactive parts staging
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