Feature 9 · Reliability AI · Well B-12

AI Rod Failure Prediction

Representative SRP rod-string failure model combining load, impact, rod floating, speed, fluid condition and operating history.

Closed-loop AI workflow

Sensor Data
Well Digital Twin
AI Detection
Root Cause
5Failure Prediction
6CSS + SRP Simulation
7Recommendation

Rod failure risk

Well B-12 · SRP rod string

AI-calculated from current representative well and SRP telemetry.

MODEL STATUS

HIGH

72%

HIGH failure probability

7-day predicted rod-string failure likelihood
Model confidence: 91%

BEFORE OPTIMIZATION

72%

Synthetic / model-based estimate

AFTER OPTIMIZATION

31%

Model / simulation estimate only

7 DAYS

72%

Immediate operating exposure

14 DAYS

79%

Fatigue accumulation forecast

30 DAYS

86%

Planning horizon

Rod stress

87%

Peak rod load

92%

Impact loading

78%

Stroke / SPM

2.55 m / 5.8

VFD

43 Hz

Crude viscosity

690 cP

Risk contributors

Relative share of modeled risk exposure

High rod stress

Rod-string stress is above the representative operating envelope.

25%

Peak rod load

Peak polished-rod load is approaching the modeled design limit.

21%

Impact loading

Dynamic load reversals are increasing fatigue accumulation.

18%

High SPM

Current speed profile is above the recommended SRP setting.

15%

High viscosity + reservoir cooling

Cooling and viscosity increase lifting resistance.

12%

Operating history

Recent high-load runtime elevates cumulative fatigue exposure.

9%

Failure-risk outlook

Current SRP operating forecast across the maintenance planning window.

7 DAYS14 DAYS30 DAYS0%25%50%75%100%

AI recommendations

Actions ranked to lower fatigue and rod-float exposure.

  1. 1

    Reduce SPM from 5.8 to 4.9 and constrain VFD to 41 Hz to lower modeled impact loading.

  2. 2

    Verify pump fillage on the next dynamometer card; investigate fluid pound if fillage remains low.

  3. 3

    Schedule rod-string visual and connection inspection within the next 48 hours.

Open SRP Load Monitor

Explainable prediction

The risk model is elevated because polished-rod load, impact loading and rod-floating likelihood are simultaneously high. Declining temperature and higher viscosity amplify the load response, while the recent operating history indicates sustained fatigue exposure. All outputs are representative demo data for SIH prototype presentation.

OIL-TWIN AI · Baghewala Heavy Oil Operations · DEMO MODE · REPRESENTATIVE DATA
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