Deepwater FPSO topside systems are increasingly vulnerable to hydrocarbon leaks due to aging infrastructure, aggressive process conditions, and complex degradation mechanisms. Traditional inspection and integrity management approaches—often calendar-based and sequential—struggle to keep pace with these challenges, resulting in elevated leak frequency, inefficient resource use, and higher operational risk. The need for an advanced, data-driven integrity optimization methodology has become critical for ensuring the reliability and safety of high-production deepwater assets.This study introduces a Hybrid Integrity-Driven Optimization Model that integrates Dynamic Risk-Based Inspection (RBI), Advanced NDT decision algorithms, and Failure Mode Assessment (FMA) into a unified predictive framework. The model employs a mathematically formulated risk-ranking engine that updates dynamically based on incoming inspection data, degradation mechanism characterization, and optimized selection of inspection technologies. The approach is designed to convert fragmented integrity workflows into a coherent system of predictive intelligence.The hybrid model was developed using a combination of probabilistic risk equations, mechanism-informed weighting factors, and algorithmic NDT selection logic. It was validated against real-world inspection workflows collected from deepwater FPSO topside systems, including corrosion monitoring results, NDT datasets, anomaly registers, and inspection campaign reports. The study assessed the model’s performance in identifying emerging high-risk circuits, predicting potential leak locations, and optimizing inspection scheduling relative to traditional methods.
The findings reveal that the proposed framework exhibits superior predictive capabilities, accurately identifying high-risk piping segments before the onset of functional failure. The integration of FMA improved degradation mode representation, while the smart NDT selection algorithm enabled more efficient allocation of inspection resources. A strong correlation was observed between predicted high-risk circuits and historical leak events, underscoring the reliability of the model’s risk-ranking outputs. Overall, the hybrid model significantly enhanced leak detection efficiency, reduced unnecessary inspection scope, and increased confidence in planning condition-driven inspection intervals. The Hybrid Integrity-Driven Optimization Model represents a substantial advancement in offshore integrity management, offering a robust method for reducing leak frequency, minimizing unplanned downtime, and improving overall asset reliability. By transforming inspection data into actionable predictive intelligence, the model provides a scalable roadmap for proactive integrity management and establishes a new benchmark for safety and performance in deepwater FPSO operations.
Keywords: advanced NDT optimization, deepwater FPSO integrity, failure mode assessment (FMA), hydrocarbon leak prevention, risk-based inspection (RBI)
