Production Optimization
Upstream Operator — CIS
Well Stock Optimization via Virtual Metering and Predictive Maintenance
Deployed two integrated ML tools across a large ESP well fleet — restoring data reliability and eliminating unplanned downtime — without new wells or capital investment.
84%
ESP failure prediction accuracy
+1.6%
Average oil rate per well across the portfolio
<1.8%
Average virtual flow meter error
The Situation
A mature upstream operator with a large ESP well fleet faced two compounding problems that were eroding production performance and driving up maintenance costs.
First, production data was unreliable. Without accurate per-well flow measurement, regime decisions were based on estimates — creating a gap between planned and actual production that widened over time.
Second, ESP failures were reactive. Pump failures were detected after the fact, triggering unplanned workovers, lost production days, and emergency repair cycles that were difficult to plan or budget for.
What Was Not Working
No accurate per-well flow measurement — production accounting based on estimates
ESP failures detected reactively — no early warning system in place
Well regimes not optimized due to data quality gaps upstream
Maintenance planned on schedule, not on actual equipment condition
The Approach
Tool 01
Virtual Flow Meter
A hybrid physical-ML model was built and deployed to calculate per-well oil and fluid production rates in real time — without additional hardware. The physical model computes derived parameters (pump discharge pressure, motor cooling efficiency, fluid dynamics) which feed as engineered features into the machine learning layer.
Model inputs
- →69 technological parameters per well
- →Well construction and ESP passport data
- →Historical failure and downtime records
- →Integration with OSISoft PI data infrastructure
<1.8%
Average error across full well stock
86%
Of wells below 5% error threshold
72%
Of wells below 1% error threshold
Tool 02
Predictive ESP Failure Detection
A hybrid model combining physics-based computation with statistical machine learning was trained on the operator's full failure history to predict both the occurrence and type of failure before it happens — enabling planned maintenance instead of emergency workovers.
Training data
- →233 documented failures across 4 failure types
- →Physics-based features: motor cooling efficiency, pump discharge pressure, TMS data recovery
- →Integral (time-aggregated) signal features to capture degradation trends
- →Outputs: failure flag, failure type classification, equipment health index
84%
Overall failure prediction accuracy
4 types
Failure classes identified and classified
Verified Results
+1.6%
Average oil production rate per well
Across the full well stock, achieved through accurate flow measurement enabling better regime management.
84%
ESP failure prediction accuracy
Failures predicted before occurrence, enabling planned maintenance scheduling and elimination of emergency workovers.
<1.8%
Average virtual flow meter error
72% of wells achieved sub-1% measurement error. Full integration with existing enterprise data platforms.
Integration
Both systems were fully integrated with the operator's existing data infrastructure (OSISoft PI) and deployed within the client's IT environment. No additional field hardware was required. The anomaly detection and health monitoring interface consolidated all platform data into a single operational view accessible to production teams.
Similar challenges in your well stock?
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