Mooring Integrity and Machine Learning

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Abstract Maintaining the expected position is critical to the overall safe operation of a floating oil platform. Mooring systems are critical to the integrity of the platform. Relying on instrumentation for monitoring the mooring line tensions represents multi-faceted challenges. Therefore, alternative methods have been introduced across the industry to reduce the costs and complexities of maintaining these systems. The paper discusses implementation of the Position Response Learning System (PRLS), a novel concept for addressing the integrity of mooring systems. PRLS is based on emerging data technologies and particularly machine learning that bridges the gap between a variety of global position measurements of the oil-platform and the mooring integrity paradigm. Machine learning enables learning from large amount of data without explicit programming. The PRLS concept does not require expensive line-tension measurement systems, but rather the global motion systems enhanced with the metocean monitoring. The global motion systems that include DGPS and MRU are typically already installed on oil platforms. In addition to measured data, PRLS can utilize a plethora of other data sources, including numerical simulations, model test data, and most importantly, the real-time and archived field data other than the line tensions. When the data are coupled with machine learning methods, they provide reliable, robust, and cost-effective solutions to address the integrity of the mooring system in real time of the oil platform. The article illustrates how the PRLS can identify a mooring line failure and even indicate which of the mooring line fails. Preliminary results based on the simulated data show that the accuracy of such predictions is better than 98%. The PRLS runs in the background independent of other integrity monitoring systems. It requires retraining periodically with new field data to improve the prediction robustness and accuracy. PRLS may be deployed on all types of floating platforms under a relatively moderate capital expense, and with very low operational costs when compared to high capital and operational expenses of a subsea mooring line monitoring system.