AI-Assisted Production Advisory Models for Well-Flowline Failure Prediction

The patent applies preliminary action by using physics-based models to simulate and predict equipment failures before they actually occur. The system performs virtual experiments and calculates failure probabilities in advance, allowing operators to take preventive measures before the failure happens, rather than waiting for pattern recognition to detect an already-occurred failure.

The patent introduces physics-based models as an intermediary between raw sensor data and failure detection. These models serve as a mediator that simulates equipment behavior, compares simulated results with actual sensor data, and identifies deviations that indicate impending failures, enabling predictive rather than reactive failure detection.

The patent applies feedback by using actual sensor data from the equipment to validate and calibrate the physics-based models. The system continuously compares simulated results with measured data, uses the discrepancies to adjust and refine model parameters, and iteratively improves model accuracy through this closed-loop feedback process.

The patent applies parameter changes by systematically varying and calibrating multiple physics-based parameters (such as thermal conductivity, heat capacity, boundary conditions) to match actual equipment behavior. The system adjusts these parameters based on calibration data to optimize the model's predictive accuracy for specific equipment configurations.