01
Critical rod-float signal
Baghewala Field · B-12 CSS + SRP monitoring
AI FAILURE DETECTION
The model detects the heavy-oil failure pattern: reservoir cooling drives viscosity up, loading rises, and SRP performance starts to deteriorate.
Closed-loop AI workflow
Sensor Data
Well Digital Twin
3AI Detection
4Root Cause
5Failure Prediction
6CSS + SRP Simulation
7Recommendation
64°C
Reservoir temperature · D12
690 cP
Modeled viscosity after cooling
91%
Detection confidence
CSS cooling → SRP risk trace
Representative B-12 telemetry across a CSS production cycle.
AI model verdict · Well B-12
Rod-float pattern detected
91% confidence
Cooling after CSS has increased modeled viscosity. At 10 SPM, the rod string is operating above the representative safe loading envelope.
Reservoir cooling88%
Viscosity increase84%
SPM above recommended band91%
Detected well-event queue
Representative events prioritized by risk, production exposure and model confidence.
| Event | Signal | Observed / limit | Confidence | Severity | State |
|---|---|---|---|---|---|
| ANM-B12-01 Well B-12 · SRP | Rod-float risk | 82% / 60% | 91% | critical | Open |
| ANM-B12-02 Well B-12 · Reservoir | Thermal decline | 64 °C / 68 °C | 88% | high | Open |
| ANM-B17-01 Well B-17 · SRP | Impact loading | 54% / 50% | 82% | warning | Watching |