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Please fill in your manuscript title. Flexible Riser Fatigue Counter Developed from Field Measurements and Machine Learning Techniques

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Christoffer Nilsen-Aas 4Subsea AS Jan Muren 4Subsea AS Håvard Skjerve 4Subsea AS Jacob Qvist 4Subsea AS Rasmus Engebretsen 4Subsea AS Helio Alves 4Subsea AS Melqui Santos 4Subsea AS Sandro Pereira Shell Brazil Leury Pereira Shell Brazil

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Abstract 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.

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Introduction Life extension of unbonded flexible risers is a challenge for many operators due to the uncertainties associated with the key parameters driving the deterioration; how the riser was designed, manufactured and installed, and how it is operated, which non-predicted events with impact to the short, medium and long-term integrity have occurred. In addition, the industry knowledge and experiences on corrosion, polymer ageing and fatigue improve and give new and more precise life time assessment methods. Fatigue service life of flexible risers is calculated in the design phase using models based on field specific environmental data derived for the area and loads for the required design conditions as per project specifications and international standards. Hence, the use of real measured data from the riser motions and from environmental data sources can result in significant reduction in uncertainties, improving the life extension analysis for fatigue and other failure drivers. These data, when combined with high-end engineering assessments and considerations on data reliability, will give better insight and contribute to continued operations with acceptable risk for the risers. A methodology is refined by using live environmental data collected from different sources combined with riser response measurements. These measurements come from sensors installed at carefully selected positions on the FPSO and risers. The information can be used to improve fatigue calculation models of all types of dynamic risers, bringing more confidence to the life extension process and enabling a cost-effective decision-making process to mitigate risk.

Figure 1 FPSO Fluminense offloading crude at Bijupira & Salema Field (photo MODEC)

A case example is presented for the risers suspended from the FPSO Fluminense producing from the Bijupirá & the Salema fields, operated by Shell in Brazil. The ongoing work illustrates how real environmental data and response monitoring are used to reduce uncertainties and improve the life extension process concerning fatigue life of flexible risers. This work has been conducted using EU Copernicus Marine Service Information and is part of a research program with Universidade Federal do Rio de Janeiro (UFRJ). Outline methodology The improved armour wire fatigue methodology developed in this project uses accurate motion response sensors directly installed on the riser and interfacing FPSO structures. The response data is combined with measured environmental data to build more realistic fatigue models. The objective is to develop a model that represent the real-life responses of the FPSO in various swell and wind driven sea states defining with high accuracy the long-term characteristics of the riser system.

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Typically for tail end production and life extension projects of older assets, the access to high quality environmental data may be a challenge. Hence, efficient use of existing environmental data gives valuable improvements to the understanding of environmental loadings. Wind, wave and surface current data from different sources are assessed and verified for quality and potential weaknesses under varying conditions. Actual operational data such as topside and subsea pressures and temperatures from the production information (PI) system are also utilized instead of design limits to further increase accuracy of fatigue development. Integration of the operator’s process instrument network and riser status databases enables use of actual exposure conditions.

Figure 2 Integration of motion sensors, subsea & topside process data and various sources of environmental data for processing and presentation to onshore and offshore stakeholders by live dashboards

Measurement data, component data and new insight from the improved analytical models are visualized by live contextualized dashboards. The main objective of the dashboards is to provide up to date information in an intuitive and effective manner to involved engineers and managers. Easy access to both input and output enhance collaboration between offshore operations and onshore support, strongly improving the decision process for safe and extended service life of all infrastructure from reservoir to deck. As the expertise in the operators, manufacturers and contractors’ organizations are widely spread, it is vital that all contributors solving challenges, assessing risk or optimizing operation have access to the same relevant, updated and consistent data. Environmental data For the case discussed in this paper, site measurements of wave or current was not available. A system combining data from a wave radar at a nearby facility, wave height measurement by satellites, and “now cast” (a short-term forecast modelling the present wind, wave and current) from a calibrated southern Atlantic wave, wind, current model is applied. The two latter sources are generated using EU Copernicus Marine Service Information [[1.]]. The Copernicus model uses the full regional and local observation grid, also along the coast of Brazil, and is maintained and run by Météo-France. The observations from the 5-month assessment of wave data for the Fluminense FPSO position are that the Miros radar, [2.], data average significant wave height (Hs) is 12% lower than the Copernicus “now cast” data. At the same time, the maximum 3hr Hs from the Copernicus “now cast” is 6% lower than the wave radar measurements. The latter deviation could likely be attributed to the fact that the wave radar is a point measurement, while the Copernicus model is a grid model with more smoothening or averaging of the wave fields. The general trend is that the satellite measurements support the average level of the Hs predicted by the Copernicus model, and we consider the 12% shift to be a site or equipment related effect. The satellite measurements are known to be part of the background data for the Copernicus model and inevitable these two data sources will to a large degree be correlated. See [3.]. In addition to data on significant wave height, the prediction models developed are utilizing the full data set of “now cast” predictions in the Copernicus model. The data covers swell, and local wind driven wave components with heights, periods and

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direction, as well as wind and current speeds and directions. In total 22 parameters describing the environment acting on the FPSO with risers and mooring lines, are available for every 3-hour period. These parameter sets are available for a historic look back of 2 years, as well as daily updates. Figure 3 shows the comparison of Copernicus “now cast”, the Miros wave radar observations and the Copernicus satellite measurements for May 2018. Compared to no field measurements of the wave condition, these data provide significant value to riser fatigue predictions.

Figure 3 Wave data, total sea, from Copernicus "now cast" model, with Miros wave radar measurements and satellite measurements utilized in the FPSO motion and fatigue analysis improvements and predictions data (example Feb – June 2018)

Operational process data Similarly, for the process data needed to assess the accumulated riser armour wire fatigue damage, online access directly to the operator’s process information server is arranged. The operator has assessed criticality and sensitivity of all data on the operator’s network and advised that data needed for subsea infrastructure life time extension may be shared. Hence data on pressure and temperature at the subsea manifolds and topside process facilities relevant for assessment of ageing, corrosion and fatigue is directly accessible in suitable format for forecasting and input to analysis through live contextualized dashboards. In relation to fatigue calculations, the temperature data is needed for pipe bending stiffness estimates for realistic calculation of riser global response, as well as in corrosion assessment, ageing analytics and monitoring of Corrected Inherent Viscosity (CIV) development in the polyamide pressure sheath dashboards. More important to the fatigue predictions of dynamic flexible risers is the bore pressure. The tensile wire frictional stress is highly dependent on the bore pressure which is the dominating parameter influencing the cross section contact pressure. Higher bore pressure means higher stress ranges and accelerated fatigue development. As time series of bore pressure at the measurement point closest to the risers are available, frequency statistics may be derived for input to traditional fatigue analysis, as well as hour to hour data utilized for the online fatigue counter. See Figure 4 for an example for pressure in a Water Injection (WI) riser over a 4-year period.

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Figure 4 Topside pressure and temperature for a water injection riser, all historic data is available (example – 2012 data)

233.5

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Motion monitoring Two motion sensors were installed on a WI riser suspended from the bow turret; one sensor was positioned near the riser hang-off at the turret and the other sensor was installed directly on the riser outer sheath just below the lower edge of the bend stiffener. In addition, one motion sensor was installed in the accommodation/office superstructure to record the FPSO response. The latter sensor package was online for the whole campaign, transferring encrypted and live measurement data to a cloud-based storage and processing service. The sensor is only doing outgoing calls from inside the firewall on the offshore facility enabling a plug-and-play installation, using data capacity when available. This means that connection with the sensor can be established without any configuration to the onboard network or interference with central control room (CCR) safety and automation system (SAS). The data is immediately available to the project team and other stakeholders in contextualized dashboards. All the motion sensors used measured motions in 6 degrees of freedom (x, y, z, rx, ry, rz), at 10Hz continuously, [4.] For the autonomous EX sensors installed on the riser and hang-off structure, no cabling was required which simplify installation and operation of the sensors. At 10Hz, the sensor operation time is up to 1 year with local storage of all data. The data transmission is made through non-contact reader when desired, or at the end of the campaign.

Figure 5 EX autonomous sensors measuring 6 degrees of freedom (DOF) motions at 10Hz at a selected riser and guide tube /turret hang-off. No cables used, only ropes for dropped object protection during installation

The monitoring program at FPSO Fluminense was performed from February to July 2018. All data were being processed and analytics developed in parallel with adapting dashboards to the needs of the integrated monitoring project. Selected preliminary results are discussed in this paper. The motion monitoring data may be used for improved structural, mooring and riser analysis, as well as development of machine learning and analytics for armour wire fatigue prediction. The initial focus was to assess environmental data in relation to FPSO motions, in particular the roll motions. These results show a consistent correlation between the environmental data (mainly wave and wind data) and the FPSO motion response. The response measurements and environmental data are used to calibrate and enhance the global analysis model. The enhanced model enables investigation of the correlation between fatigue damage, operational conditions and measured motions. Assessing hot spot behavior under fatigue with high confidence permits the prediction of fatigue behavior of the risers during the extended life. The review of the different data sources for the wave conditions concluded that the Copernicus ‘now-cast’ data was typically within 10% of the other sources; Miros radar on nearby vessel and satellite measurements. As the Copernicus model gives info on wind, surface current, and the two most important components of the wave train, swell and local wind driven seas, it was concluded to apply these data for the analytics. Swell is important for the total wave conditions in Brazilian waters, and is important for roll motion, especially for free weather vaning FPSOs. Some seastates are totally dominated by swell, while in a few cases the local wind generated seas dominate. In between, the magnitude of these wave components typically varies in the range 30-70% each. An example showing total Hs, with contributions from swell and local wind driven seas, along with measured standard deviation of vessel roll and vessel heading relative to wave direction, are shown in Figure 6.

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Figure 6 Wave conditions (height & direction) and measured roll motion at FPSO Fluminense in May 2018

For free weather vane FPSOs, with the vessel turning freely around the bow turret, the vessel heading relative to the incoming waves will be of significant importance for the FPSO motions, in particular the roll motions. In sea states dominated by swell, it is important to get good insight in the heading of the FPSO relative to the incoming swell. During seastates with high wind, the FPSO will align with the wind direction, and may expose the vessel to beam seas swell, possibly giving higher roll motions. As vessel roll is a key factor for the riser bending near the hang-off where the bend stiffener is located, a good representation of the wind, current, swell and vessel heading is needed in models to perform reliable fatigue analysis. When high quality field measurement data is available at a good resolution in time, better understanding of the dynamics and model improvement is achievable within a reasonable time. However, for periods without access to environmental and motion response data, one must have good analytical models for both vessel response and for global and local riser analysis. Machine Learning A key activity in this project has been to develop a heading model for the FPSO, predicting the weather vaning response from input of wind, waves and surface current. This is a complex analysis if performed from only vessel particulars by time domain analysis; a testament of several publications on the topic, see [5.]. Our preferred approach has been to use field measurement data and machine learning to establish a FPSO heading prediction model. The more data obtained, the model improves, however there are a few challenges. So far, the model is trained on averages from 3hr seastates, so for transient conditions where the environment changes significantly, inaccuracies are inevitable. The model is trained as more field measurement data is available, and significant improvements are seen from the initial models to the current model. Several different machine learning approaches have been tested to find the best possible approach for our dataset of 4 months with 3hr statistical data, giving about 600 samples, after 3hr periods with nearly no motions removed. The Machine Learning processes used for developing the heading model was also used for the prediction of FPSO and riser motion responses, as shown in Figure 7. The ambition was to obtain a sufficiently accurate model enabling reliable prediction of dynamic responses in time periods where no response measurements are available.

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Figure 7 Data flow in the Machine Learning process used for training and validating the models for FPSO heading, roll and riser response

The Machine Learning model training process starts with input data from the period with available response measurements from the field. The input data is taken from the Copernicus “now cast” after approving these as sufficiently accurate descriptions for the waves, wind and current conditions (magnitude and directions) at the FPSO location. Further, the model is given a selection of FPSO and riser response measurements in form of a “training dataset”. A selection of various ML-models is trained. After training, the models are validated by running input validation data sets, not previously seen by the models. The predicted response, or vessel heading is then checked against measured data. The table below shows the validation results for the five most promising ML algorithms checked. Two methods are shown to give 90% (or more) predictions with an error less than 16% of the measured max value. 19% percent translates to one standard deviation of the measurements. Sensitivies are also run with shorter and longer training period, and as expected more training gives better models. In the current work, the ML algorithm was trained based on 4 months of measurement data which proved to be on the lower end in order to obtain reliable models. The measurement data set is far too small for other methods like e.g. neural networks. Table 1 Validation results for FPSO motion prediction with five different ML models

Deviation from measurement max

Extra randomized tree Random forest Decision tree Gradient Boosting

K nearest neighboors

8% 76% 72% 68% 68% 60%

16% 91% 90% 88% 89% 84%

24% 98% 96% 96% 96% 94%

Unlike time domain simulation models, as often developed in OrcaFlex, [6.], the ML models do not get any information about physics, masses, loads or stiffnesses when predicting motion responses. In parallell with the training of ML models, a state of the art OrcaFlex time simulation model for FPSO and risers was calibrated to give improved results compared to measurements. However, the ML models proved to be on the same level or better in the predictions when both models were fed with similar input from the environmental conditions. The OrcaFlex model had accurate as-built descriptions of FPSO, mooring and risers.

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In Figure 8, the measured FPSO turret dynamic inclination, 3-hour standard deviation is shown along with predictions performed for training and validation datasets. The average response predictions fit very well with measurements, and the accuracies are good. 100% of the predictions are within 25% of the measurements, while 80% of predictions are within 10% of the measurements. Used for a long-term process like armour wire fatigue, these results are well within accuracy requirements and significantly better than time domain simulations without (and with) access to field measurements. Compared with the original design analysis, the accuracies of ML predictions based on real environmental data and models trained on measured field data, enables a large step forward towards reliable dynamics in flexible riser fatigue.

Figure 8 Measured guide tube inclination (combined roll & pitch), 3-hr standard deviations against ML training and validation data

Fatigue Counter The development towards a reliable online ‘fatigue counter’ for the flexible riser armour wires started with validating the available environmental data for the FPSO site. Obviously, a site with a comprehensive measurement program for waves (swell and wind driven), current (velocity, direction and depth profile) and wind (velocity and direction) with good time resolution would be preferable, however in this project a robust and accurate alternative was found. This approach will be relevant for the majority of brown field assets, however newer installations in deeper waters will likely have more on-site measurements to support integrity management analytics. Combining good quality response measurements and environmental data, enables a ML model that predicts global vessel dynamic responses with high accuracies. As studies of key drivers for riser fatigue on Fluminense have identified vessel dynamic responses to be the dominating factor giving dynamic stress in armour wires, reliable prediction of FPSO motions has been the key focus. Bending in the top of the risers hung off in the bow turret is dominated by vessel roll with a small contribution from vessel pitch and direct wave actions on the riser pipe near the sea surface. Dynamic tension variations near the top end fitting is dominated by vertical accelerations at the connection point in the turret, also a driver originating in FPSO dynamics. Reliable global dynamic responses are a key element in unbonded flexible riser armour wire fatigue predictions. Two other key inputs are vital; the axial and circumferential local stresses in the wire hotspots, along with the appropriate SN-curve describing the fatigue resilience of the wires exposed to a representative annulus environment. The local stress variations may be derived from global pipe bending, twist and tension variations by means of direct strain measurements on the wires on site, experimental full-scale testing in a lab, or most commonly by 3D finite element analysis or dedicated local analysis tools like BFLEX, [7.]. The latter is in this project used to give the global riser response to local dynamic stress in the wires. The local dynamic stress in the wires is dependent on bore pressure in the pipe, and therefore needs to be based on input from information on operational data as described above. Local stress concentration due to end fitting geometry and possible corrosion or pitting also needs to be accounted for in the prediction of dynamic stress ranges. The SN curves depends on wire material type and details and the annulus environment, and are found by dedicated lab-test programs, e.g. in the Corrosion Fatigue JIP going for several years at the Sintef Ocean lab,[8.] and [9.]. The annulus environment may be derived from separate studies, supported by annulus gas sampling taken from the topside end fitting annulus vent system.

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For the current project, normalized fatigue damage is calculated for the relevant range of global FPSO dynamics using the calibrated OrcaFlex and BFLEX models, as shown in Figure 9. Similar curves are made for all risers, at selected hot spots along the riser length and circumference, for actual SN-curves and operational pressures. For each of these, parametric curves are fitted to give a quick and on average accurate wire fatigue damage directly from measured or ML predicted FPSO motion response.

Figure 9 Normalized fatigue damage for each riser for given bore pressure, SN-curve and hot spot (examples)

When combining quality weather data and observations, field measurements of FPSO, turret and riser motions, normalized fatigue damage curves derived from validated models, the live “fatigue counter” can be established. The fatigue counter will automatically account for bore pressures different from planned operational pressure, as well as environmental conditions as experienced on the field. Dependant upon conservatism built into the original design basis and analysis methods in the design phase, a possible life extension may be achieved and documented. By giving planned changes to operational conditions or possible changes in annulus environments in the time to come, future scenarios may be efficiently exploited, by assuming that wave, wind and current in the coming years, in average, will be similar to the recently passed years. All stakeholders may get increased insight based on the same basis data, ML models and validated analytics.

Figure 10 Fatigue counter block diagram

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Conclusions Fatigue assessment of flexible risers as performed during the design phase may often show to be conservative when compared to the real field experiences. Life extension analyses can benefit from the use of updated information from environmental data systems, response measurements from in situ sensors and operations information systems, as less conservatism is expected when using real data, however sometimes the experienced environment and operations are more demanding than accounted for in the design. The key point is reducing uncertainties, and assumptions to achieve a more reliable estimate for remaining life. Results obtained from FPSO Fluminense measurement campaign confirm that a reliable correlation can be established between environmental information and FPSO response from motion sensors, followed by significant improvements in the analysis models. The same methodology may be used for improved structural, mooring and riser analysis, as well as development of machine learning and analytics for armour wire fatigue prediction On this basis, a “live” fatigue counter is developed and presented in an online dashboard so that relevant stakeholders are provided with the necessary information to support extended operation of the investigated components, confirming risk are acceptable. Acknowledgements We acknowledge the great opportunity offered by Shell Brazil to work on this project, the excellent collaboration with offshore operations personnel both at Shell and MODEC. We highly appreciate the data and assistance from PETROBRAS/CENPES (Centro de Pesquisas Leopoldo Américo Miguez de Mello) - Risers and Pipeline and Ocean Engineering groups. The project is organized through COPPE (Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia) / UFRJ, and we appreciate the good cooperation. Environmental model- and satellite-data from the EU marine research program Copernicus is highly appreciated.

References

[1.] http://marine.copernicus.eu/ [2.] Ø. Grønlie; “Wave Radars – A comparison of concepts and techniques”, Hydro International, Vol 8, No. 5, June

2004. Republished with permission from Hydro International. [3.] Rio Oil & Gas 2018, Fatigue Assessment for Flexible Riser Life Extension based on Integration of Environmental

Data and Riser Response Measurements, J.Muren, C.Nilsen-Aas, H.Skjerve, H.Alves, E. Berentsen, M.Santos /4Subsea, L.Pereira, F.Duncan /Shell Brazil

[4.] https://www.4subsea.com/wp-content/uploads/2018/04/Data-Sheet-SMS-Gyro_SIM_I.4.18.pdf [5.] Heading Instability Analysis of FPSOs, 2015, Razieh Zangeneh, Krish P. Thiagarajan Department of Mechanical

Engineering, University of Maine Orono, ME, USA [6.] https://www.orcina.com/SoftwareProducts/OrcaFlex/ [7.] https://www.sintef.no/globalassets/sintef-ocean/pdf/bflexfactsheet_2018_aug.pdf [8.] Environmental effects on fatigue strength of armour wire for flexible risers, Berge S, Langhelle N K and Eggen T

G (2008), OMAE2008-57132 [9.] Surface Characterisation and Fatigue Strength of Corroded Armour Wire, Stig Berge (NTNU), T. Wang & N.

Langhelle (Marintek), OMAE2014-24140