Flexible risers are being deployed in more and more demanding applications in terms of water depth, remote locations, temperature, pressure and corrosive fluids. Focus has been put on long term riser integrity in general, and on fatigue performance in particular, as knowledge of pipe behavior and properties has been advanced over the last decade. In this context, accurate and consistent estimation of riser global and local response to external loading is essential. A methodology has been developed to efficiently calculate irregular wave stress time histories of tensile armour wires for flexible risers. The stress time histories are calculated directly from the global loads which are usually generated by using commercially available well proven global analysis tools. The methodology elevates the dynamic analysis of flexible risers from the conventional regular-wave approach to irregular-wave time-domain approach. This in turn allows a better assessment of the fatigue performance and provides a better fit-for-service assessment or an opportunity to reduce design conservatism. This methodology also allows for consistent stochastic fatigue evaluations to be performed in time domain simulations using the well established stochastic analysis approach. All flexible riser non-linear hysteretic effects are included and phase shift between tension and curvature is also fully accounted for. The key ingredient lies in the generation of transfer functions of all stress components using a validated local analysis (LA) tool based on finite element method. This is done because direct use of the LA tool for long time domain simulations is very computationally intensive and impractical. The stress transfer functions allow direct mapping of the tension and curvature readings to individual stress components, which are combined in a phase consistent manner to obtain the total stress-time histories. This methodology should also work well for other systems having complicated cross sections such as dynamic umbilicals and integrated production bundle, etc. Accuracy of the proposed methodology should be equivalent to that of using the LA tool directly provided that the stress transfer functions are constructed appropriately. In comparison with the traditional regular-wave methodology, this irregular wave approach has been shown to provide a significant fatigue-life improvement for the flexible riser tensile-wire in a deep water West Africa application.
Four sensors were installed on the Snorre A TLP (Tension Leg Platform) on 16th April 2014 and retrieved on 10th May 2014, to document motions of the vessel, top tensioned riser (TTR) and flexible jumper connecting the TTR (Top Tensioned Riser) with the topside piping. The data recorded represents 3828 data sets. Associated significant wave height and peak period is synchronous data extracted from the Miros wave measurement radar and stored in the environmental data base. The SmartMotion riser sensors are certified for service in the Wellbay. The sensors are modelled into the OrcaFlex (1) “calibration” analysis model in order to simulate the motion responses in the same format as recorded offshore (accelerations and rates of rotation), and to carry out verification of the OrcaFlex model by comparing both raw data and filtered/integrated derivatives. This work provides a basis for life extension of the Jumpers and provides valuable feedback to design and analysis of TLP and Spar Jumpers between TTRs and topside Headers.
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.
There are more than 3500 dynamic unbonded flexible risers in operation worldwide. In addition, there is a considerable number and kilometres of flexible flowlines and jumpers on the seabed. The average riser age is more than 10 years and a number of risers are soon reaching their original design service life of 15-25 years. There is a need to learn more about the time driven degradation processes in flexible risers in order to ensure continued safe operation, and to meet the increasing demand for service life extension. By investigating a large range of damaged and intact unbonded flexible risers from West Africa, UK, The Netherlands and Norway, 4Subsea is continuously improving the understanding on how flexible risers and flowlines in operation degrade over time. As the variability of the degradation and failure development is significant, a high number of observations is needed to establish consistent trends and basis for reliable analysis and assessments. Trends of emerging challenges are observed through these investigations, however most of all it is recognized that the complexity of the annulus environment, corrosion processes and polymer degradation is high and deserves continuous attention. 4Subsea has investigated some 60 000 m of flexible risers, flowlines, jumpers, cables and umbilicals from 13 fields and 6 operators. Examination of used flexible pipes by 4Subsea and others have to a large degree validated the design assumptions and conclusions, however there are exceptions. Variability in degradation mechanisms and their development is found to be significant. Some weaknesses are identified and improvements are implemented in the recent updates of API specifications and recommended practices. The referenced investigations are performed for several operators, and it is seen that sharing information will give the operators considerable benefits. Cooperation initiatives overcoming confidentiality issues are progressing and will give improvements to reliability and safety for flexible pipes. Combined, significant improvements in basis for safety-, reliability- and life time analysis for the operated assets are experienced. In broad terms the experiences show that polymer ageing issues, and in particular challenges related to high temperature operation, need to be prioritized when investigating the possibility for life extension. Current methods for degradation modelling are uncertain. Tensile armour wire fatigue analysis needs to take into account dynamics from calibrated models, response measurements and advanced stress predictions as well as correct annulus environment to be representative for the real exploitable life of the structures. Pitting corrosion and loss of material due to corrosion are key factors to take into account in fatigue modelling. Serious corrosion issues are correlated with oxygen access as typically experienced in connection with large external sheath damages near the water surface. A successful life extension for a particular flexible pipe requires a thorough process starting with an assessment of current status of the flexible pipe with basis in original design data, operational data records, inspection, test and monitoring data. When the assessment of the current status concludes that the complete system is well suited for further safe operation, a prognostic assessment is performed based on best-available information on future service conditions. As the lifetime of a flexible riser or pipeline may be several decades, the premise for assessing safe operation may have to be re-established, incorporating new methods, knowledge and differing industry experiences. Experiences from investigations of more than 75 used risers and umbilicals provide a unique basis for focusing on life time assessment issues, as well as target areas of inspection, monitoring and research. This also involves various compensating measures such as external sheath repair, improvement of annulus vent flow, online condition monitoring or special inspection campaigns.
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.
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