This paper describes a live fatigue prediction methodology comprising measured motion response, maritime environment and process data for a Floating Production Storage and Offloading vessel (FPSO) moored in 700m water depth offshore Brazil. The measured data is utilized to improve traditional time domain dynamic analysis models, along with Machine Learning (ML) techniques. The resul of this is significant reduction in uncertainties, enabling live riser fatigue predictions and providing a basis for life extension and improved accuracy of riser and vessel response analysis. The methodology consists of using a combination of autonomous and online motion response sensors directly installed on the riser and interfacing FPSO structures. The measured environmental data, FPSO and riser response data are utilized in a ML environment to build more realistic riser response and fatigue prediction models. As FPSO heading is important for vessel dynamics, especially roll, and the vessel dynamics are a key factor in the riser dynamics at this field, the first focus was directed towards predicting vessel heading relative to swell. The heading model developed by ML showed good agreement and was used as a key tool in a traditional fatigue analysis using OrcaFlex & BFLEX. This analysis was based on historical sea states from the last two years (from EU's Copernicus Marine Environment Monitoring Service). The results show that the fatigue analysis from the design phase is conservative and life time extension is achievable. As the fully instrumented measurement campaign ended after 4 months, the work focused on utilizing all the captured data to give improved insight and develop both traditional simulation and ML-models. For future fatigue predictions based on the developed "fatigue counter", the ambition is to maintain good accuracy with less instrumentation. In the present phase, FPSO and riser response data from a 4-month campaign have been used to establish a ‘correlation’ between riser behavior, environmental data and FPSO heading and motion. Calibration of a traditional numerical model is performed using measurement data along with a direct ‘waves to fatigue’ prediction based on modern ML techniques. This illustrates enabling technologies based on combination of data streams from multiple data sources and superior data accessibility. The correlations established between different field data allow the development of a "live" riser fatigue model presenting results in online dashboards as an integrated part of the riser Integrity Management (IM) system. All relevant stakeholders are provided with necessary information to ensure safe and extended operation of critical elements of the FPSO. The paper illustrates the power and applicability of modern numerical techniques, made possible by combining data from 6 different streaming data sources, ranging from satellites to clamp-on motion sensors.

This paper describes developments in the fatigue counter methodology and how digitalization is used to deliver valuable online technical service. The fatigue counter methodology consists of using a combination of autonomous and online motion response sensors directly installed on the riser and interfacing Floating Production Storage and Offloading vessel (FPSO) structures. The measured environmental data, FPSO and riser response data are utilized in a machine learning (ML) environment to build more realistic riser response and fatigue prediction models. The results of this is significant reduction in uncertainties, enabling live riser fatigue predictions and providing a basis for life extension and improved accuracy of riser and vessel response analysis. As there is a need to run some risers at higher pressures, the optimum time periods for such high-pressure service can be found without compromising the flexible riser service life. A recent field case is presented whereby the fatigue counter ingest, process and present data on a modern digital infrastructure. The full service was setup based on available onboard sensors including a 6 degree of freedom (DOF) vessel motion response unit (MRU), temperature and pressure transmitters and forecast model for weather. Input and output data are shared through well documented and safe application program interfaces (APIs) between the operating company and the fatigue counter 3rd party service. The operating company receives live updates of accumulated fatigue damage and remaining service life. This enables the operating company to build contextualized and high value dashboards presented on their organizational front end. The paper illustrates the power and applicability of combining modern numerical methods with digital techniques, made possible by streaming input and output data on safe and well documented APIs.