Littérature scientifique sur le sujet « Metocean characterization »

Real time vessel simulation has become an integral part of design of navigation channels, harbor geometries, and marine terminals. Generalized guidance for channel width, bend radii, and turning basin dimensions is documented in numerous sources (e.g. PIANC, ASCE) based on typical environmental parameters of current magnitude, wind speed, and wave height. Common to all the guidance is to confirm and finalize geometry and operability based on vessel simulation studies. A real time vessel simulator incorporates various data to represent the response of a vessel to helm controls of the pilot such as water depth, currents, waves, winds, drag, rudder force, and tug boat power. A key component of the simulation is that the computations occur in real time such that the pilot does not notice any lag due to computer processing. As such the fidelity of environmental input to the simulation has often been limited to avoid congestion. Furthermore, vessel simulation software was designed for quick modification in training simulations with simple parameters to apply the conditions uniformly over the model domain. As a result, the implementation of metocean conditions in real time simulation is often truncated based on a simple characterization of sea state and currents to a snap shot of time, represented by a static current field representing peak and ebb or flood tide or by simple representative vectors. However, there are aspects of assessing channel design which benefit from simulating the changing of metocean conditions simultaneously with vessel maneuvering. With improving processing power of simulators, it is possible to incorporate time-varying numerical modeling results directly with fine resolution. This paper presents applications of coastal engineering tools and techniques for real time vessel simulation in conjunction with high resolution coastal hydrodynamic modeling for waterway design.

The main objective of this work is the characterization of the wave/wind induced oscillations on the power performance of the wind turbine operating on a WindFloat floating system. To assess the potential impact on the wind turbine power performance induced by these oscillations, the nacelle movements of the WindFloat wind turbine were monitored using accelerometer sensors synchronized with : 1) metocean data measured with a buoy; 2) wind turbine power data installed in the WindFloat floating system; and 3) wind speed data gathered from a nacelle-mounted LiDAR. Based on this data, a clustering analysis approach is proposed. No meaningful relationship between the ocean parameters and the nacelle movements or the wind power production could be established. The obtained results suggest that the dynamic adaptation of the drive train (mainly due to wind turbine torque control) to a fast oscillating (primary energy) moving force is the source of the largest oscillations in the nacelle of the WindFloat wind turbine. Nevertheless, results suggest that the wind/wave induced oscillations and their impact on the power performance of the WindFloat wind turbine is low considering its nominal capacity. Outcomes of this work were extremely relevant to demonstrate the stability of the WindFloat system, and, consequently, also important for the development of the floating wind offshore industry (and other technologies).

ABSTRACT This paper presents two methodologies to provide short-term and medium-term forecast of oil spill trajectories at local and regional scales. For short-term predictions (within 48 hours), a high-resolution operational oil spill forecast system is developed in Belfast Lough (Northern Ireland). Hydrodynamics are based on a Delft3D model which uses daily boundary conditions and meteorological forcing obtained from Copernicus Marine Environment Monitoring Service (CMEMS) and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions in the Lough using the oil spill numerical model TESEO. The system is integrated in a user-friendly web application that allows end users to launch the oil spill model both in case of pollution threat and for training purposes. For mid-term predictions (15–60 days), a stochastic methodology to provide probabilistic oil spill forecasts is presented and applied to the Bay of Biscay (Northern Spain). The method encompasses the following steps: 1) Classification of representative atmospheric patterns using principal component analysis and the k-means technique; 2) Setup of an autoregressive logistic model taking into account seasonality, covariates, long-term trends and autoregressive terms. In the case of an accident, we sample the evolution of the metocean conditions using the autoregressive model, which provides us with possible evolution patterns for these conditions during the forecasting period. These results are used to force the oil spill transport model TESEO allowing the characterization of trajectories in probabilistic terms. Drifting buoys released in Belfast Lough and observations reported during the Prestige accident have been used to validate the operational system and the medium-term forecasting methodologies.

Wind speed and significant wave height are the most relevant metocean variables that support a wide range of engineering and economic activities. Their characterization through remote sensing estimations is required to compensate for the shortage of in situ observations. This study demonstrates the value of satellite altimetry to identify typical spatial patterns of wind speed and significant wave height in the northeastern region of the United States. Data from five altimetry satellite missions were evaluated against the available in situ observations with a 10 km sampling radius and a 30 min time window. An objective analysis of the collective altimeter dataset was performed to create aggregated composite maps of the wind speed and significant wave height. This asynchronous compositing of multi-mission altimeter data is introduced to compile a sufficient sampling of overpasses over the area of interest. The results of this approach allow for quantifying spatial patterns for the wind speed and significant wave height in the summer and winter seasons. The quality of altimeter estimations was assessed regarding the distance from the coast and the topography. It was found that while the altimeter data are highly accurate for the two variables, bias increases near the coast. The average minimum and maximum wind speed values detected in buoy stations less than 40 km from the coast were not matched by the aggregated altimeter time series. The method exposes the spatial and time gaps to be filled using data from future missions. The challenges of the objective analysis near the coast, especially in semi-enclosed areas, and the implications of the altimeter estimations due to the land contamination are explained. The results indicate that the combination of altimetry data from multiple satellite missions provides a significant complementary information resource for nearshore and coastal wind and wave regime estimations.

Offshore engineering projects require the management of a huge amount of heterogeneous georeferenced data - among others metocean, geophysical, geotechnical, and environmental, which need a Data Model, data visualization and data analytics features on a common geographic basis. A Digital Data Platform (DDP) has been developed on a GIS ambient with the aim to speed up the engineering design process (i.e. minimization of routine operations), and also prevent misalignment of the data originating from different sources from Owner to Suppliers and any potential loss of information. The proposed GIS architecture is composed by two main components: i) the Data Model geodatabase, and ii) the GIS-Model Toolbar add-in. The proposed development represents a step forward on the definition of a common specification and dictionary for offshore project execution overcoming the current bottlenecking and inefficiency on the design phases between the project owner and the engineering contractor. The paper illustrates “what” and “how”, and in particular: i) the geodatabase and Data Model framework, ii) the required parameters to be organized and stored for offshore engineering design, and iii) the widgets implementation (i.e. GIS-based tools). Its application on a case study project with practical examples is presented.

Les opérations de maintenance de l’éolien en mer sont sensibles aux incertitudes des prévision météo-océaniques. Les modèles de prévision numérique sont limités par leur coût de calcul pour l’estimation des incertitudes, ce qui pousse au développement de méthodes basées sur l’apprentissage profond. L’importance des mesures in-situ en mer est mis en exergue par les résultats de cette thèse. Une méthode basée sur le clustering non supervisé de données de modèle numérique est proposée pour la définition d’un réseau de capteurs optimal pour la reconstruction de la ressource en vent. Des méthodes d’apprentissage profond sont proposées pour la prévision météo-océaniques probabiliste. Nous montrons leur intérêt pour assimiler un grand nombre de données d’entrée. Une hypothèse de postérieur Gaussien et une approche générative utilisant les flots normalisants sont comparées. Ceux-ci permettent de relâcher les hypothèses sur la distribution postérieure, maintenant une capacité d’échantillonnage et de calcul exact de la vraisemblance. Un cas d’étude réaliste est construit sur une zone représentative pour l’éolien en mer en France. Pour la prévision jointe du vent et des vagues, les propriétés non-Gaussiennes des flots normalisants se sont montrées bénéfiques à la calibration de la prévision. Un cadre d’évaluation représentatif des opérations en mer est proposé incluant la génération de scénarios et mesurant l’impact économique et le risque lié à la prise de décision. Nous montrons qu’il est crucial de prendre en compte le risque dans la sélection et l’évaluation des modèles de prévision
Offshore wind energy maintenance operations are highly sensitive to forecast uncertainty. Numerical weather prediction are limited by their computational cost for the uncertainty estimation and the update frequency, which is an argument for the development of data-driven methods. The importance of offshore measurements is highlighted by the results. A method for designing an optimal sensors network is proposed using unsupervised clustering. This method has been used by the French weather service to define future networks of floating LIDAR for offshore wind. Deep learning models for the joint probabilistic forecasting of metocean parameters are proposed. Their relevance for assimilating a large amount of input data is demonstrated. A Gaussian posterior and a generative approach using normalizing flows are compared. It is shown that the use of normalizing flows can relax any assumption on the shape of the forecast probability density while maintaining sampling and likelihood computation capabilities. A real case study dataset is built on a relevant area for offshore wind. The probabilistic models are adapted for joint wind and wave forecasting, for which the non-Gaussian properties of the normalizing flows is beneficial for forecast reliability. An evaluation framework dedicated to offshore operations is proposed, including the generation of probabilistic scenarios and the measure of decision-making economic impact. It is shown that the search for an economic optimum in the probabilistic decision-making leads to higher risk during operations, and this should be taken into account for forecast selection and evaluation

This thesis encompasses a set of different subjects related to metocean variables but studied from different perspectives. The metocean variables are mainly significant wave heights and wind velocities and, to a lesser extent, wave periods. The extreme value theory is used to probabilistically characterized the metocean variables by means of the generalized extreme value distribution (GEV). The effect of seasonality is included by considering monthly maxima and using harmonic and subharmonic functions (i.e., time dependency in the GEV model is incorporated). Although Mexican information was not available to this study, the studies are considered applicable to Mexican coasts in the Gulf of Mexico and the Pacific, since available public information from U.S. buoys located in the Atlantic and Pacific oceans relatively close to the Mexican coasts is employed. For the Pacific region, the GEV model accounting for seasonality is applied to data from a buoy (this is reported in an article in the appendix and summarized as a book chapter in the compendium of publications) and comparisons are carried out versus analogous results for buoys in the Gulf of Mexico obtained in a previous study (included also in the appendix). In other part of the thesis (another book chapter in the compendium), but also for the buoy in the Pacific Ocean, a study is carried out to assess the impact of including or excluding an atypical wave height in the seasonality and in future projections (i.e., wave heights associated with given return periods), since an atypically large significant wave heigh was observed for the considered buoy. One more study (an article in the compendium) introduces the wind velocity as a Metocean variable to be characterized with the time-dependent GEV model from data of a buoy in the Gulf of Mexico. This wind velocity is not for monthly maxima, but for the recorded wind velocity which simultaneously occurred with the maximum significant wave heights. This allowed to propose a simplified approach to determined concurrent significant wave heights and associated wind velocities for given return periods, while accounting for seasonality and quantitatively establishing the uncertainty in the correlated metocean variables in question. This proposal can be potentially used for design and engineering purposes, if the metocean are considered as hazards which imposed demands on coastal (and structural) engineering systems. Additionally, the effect of varying the considered time window for the extreme projections is explored. In a final study (also an article in the compendium), an introduction to the reliability of coastal (and also structural) engineering systems is presented; a breakwater is used as case-study. The coastal structure is subjected to the action of wave heights with different wave periods, for which the joint Longuet-Higgins distribution is used, and the overtopping probability of failure is computed by using classical and revisited reliability approaches. Future studies could combine the characterization of metocean variables as time-dependent GEV models and the used reliability approaches to further investigate the reliability of coastal and offshore systems.
Esta tesis abarca diferentes temas relacionados con variables meteoceanográficas (metocean) pero estudiadas desde diversas perspectivas. Estas variables son principalmente el oleaje significativo y la velocidad de viento, y en menor medida el período de oleaje. Se emplea la teoría de valores extremos para caracterizar probabilísticamente las variables meteoceanográficas mediante el uso de la distribución de extremos generalizada (GEV, por sus siglas en inglés), incluyendo el efecto de la estacionalidad al considerar máximos valores mensuales, así como funciones armónicas y subarmónicas, lo que significa que el modelo GEV es función del tiempo. Aunque no se contó con información mexicana para el presente trabajo, se considera que lo desarrollado aquí puede aplicarse a las costas mexicanas, ya que se usaron datos de boyas estadounidenses situadas en los océanos Atlántico y Pacífico y relativamente cercanas a costas mexicanas. Para la región del Pacífico se aplica el modelo GEV a una boya (esto se describe en un artículo en el apéndice y resumido como capítulo de libro en el compendio de publicaciones) y los resultados se comparan con resultados análogos de un estudio previo, pero para boyas localizadas en el Golfo de México (dicho estudio también está contenido en el apéndice). En otra parte de la tesis, pero también para la boya del Pacífico (otro capítulo de libro en el compendio), mediante un estudio se estima el impacto de incluir o excluir un dato atípico de la altura de oleaje en la estacionalidad y proyecciones a futuro (i.e., las alturas de oleaje asociadas a periodos de retorno dados), ya que se observó una ola atípicamente alta para la boya considerada. Un estudio más (un artículo del compendio) incorpora a las velocidades de viento como variable meteoceanográfica para también caracterizarla como un modelo GEV que depende del tiempo, con datos de una boya situada en el Golfo de México. Estas velocidades de viento no corresponden a las máximas reportadas en cada mes, sino a aquellas que ocurrieron simultáneamente con las máximas alturas significativas generadas por oleaje. Esto conllevó a proponer un método simplificado para determinar alturas de oleaje significativo concurrentes con los vientos asociados a la misma boya y tiempo y para un periodo de retorno dado, y al mismo tiempo incorporando efectos de estacionalidad y estableciendo de manera cuantitativa la incertidumbre para las variables correlacionadas mencionadas. Esta propuesta es potencialmente útil para propósitos de diseño e ingenieriles, si las variables meteoceanográficas se consideran como peligros que imponen demandas a sistemas de ingeniería costeros (y estructurales). Adicionalmente, se explora el efecto de utilizar diferentes ventanas de tiempo en las proyecciones de valores extremos. En un estudio final (también un artículo del compendio) se presenta una introducción a la confiabilidad de sistemas de ingeniería costera (y también estructural), usando un rompeolas como caso de estudio. La estructura costera se somete a la acción de oleaje con diferentes periodos, mediante el uso de la distribución de Longuet-Higgins, y se calculan las probabilidades de falla por rebase aplicando métodos de confiabilidad clásicos, y otros métodos consultados en retrospectiva y reconsiderados prospectivamente. Estudios futuros podrían combinar el uso de modelos GEV como función del tiempo para caracterizar variables meteoceanográficas con el uso de métodos de confiabilidad, para investigar más a fondo la confiabilidad de sistemas costeros y costa afuera.

Abstract Ocean renewable energy has a central role to play in decarbonizing the global energy system. The emergence of new technologies such as floating wind farms will significantly increase offshore wind deployment by providing access to large areas of the seabed that are not suitable for fixed bottom turbines. Operations and Maintenance (O&M) is estimated to contribute 50% to an offshore wind farm’s total operational cost. The ability to improve the efficiency of O&M activities will enable offshore wind to compete with traditional fossil-based and onshore-renewable generation methods. To achieve this, an accurate characterization of the metocean environment is a mechanism of reducing delays and costs across the entire project lifecycle. One of the most significant costs associated with offshore operations is accessing a site with vessels. Site access is determined using vessels constraints in the maximum allowable meteorological and ocean (metocean) conditions and is defined as weather window analysis. However, industry guidelines and standards rely on historical data and do not consider the impact of climate change on the marine climate and the associated vessel operability. This requires the use of climate projection data. The opportunity to use an existing industry metric such as weather windows will tailor the climate projection data to the end-users needs. This paper’s findings suggest that climate change will alter the metocean environment and vessel operability for the case study location investigated. The findings demonstrate the value of site-specific assessment of the future wave climate to inform operational decision making. The main conclusion is that longer-term planning will require the offshore wind sector to consider the impact of climate change on O&M activities.

Abstract This paper presents a method to generate maps of offshore wind power at turbine hub height from spaceborne Synthetic Aperture Radar (SAR) data. Two techniques based on machine learning are presented. The first can be trained with metocean buoys and the second one, more precise, requires on-site profiling Lidars. If Lidars are not available, SAR surface winds at 10m are improved with machine learning. They are then extrapolated at 40m with a classical power law, and then at higher altitudes with an atmospheric numerical model. If profiling Lidars are available, parameters from the numerical model are added as input to the machine learning algorithm and the training is performed directly at turbine hub height with the Lidar data. Once the wind at turbine hub height is obtained, the wind power is then calculated using a Weibull distribution. The resulting maps are compared with the outputs of the numerical model. The maps based on SAR data provide a much higher level of detail and a better estimation of the coastal gradient, which is important to optimize wind farm siting and estimate the potential energy production. The accuracy of the wind power is found to be in the range ±5% compared to the Lidars.

Abstract Environmental contours are often applied in probabilistic structural reliability analysis to identify extreme environmental conditions that may give rise to extreme loads and responses. It represents an approximate method for performing long-term extreme response analyses in cases where full long-term analyses are not feasible due to computationally heavy and time-demanding response calculations. There are various methods for deriving environmental contours given a set of metocean data. These relate to different approaches for modelling the joint behaviour of the metocean variables, i.e., a joint distribution function fitted to the data, but also different ways of establishing the environmental contour given a joint distribution for the environmental variables. In light of this, a benchmark exercise was announced at OMAE 2019 [1], asking for contributions from different practitioners involved with environmental contours. Various bivariate datasets are provided and two exercises are specified for which different solutions are elicited. The first part of the exercise concerns the estimation of the actual contours, whereas the second part relates to the uncertainty characterization of the contours in light of sampling variability. This paper is a response to this announcement and provides one contribution to these benchmark exercises; environmental contours based on a direct sampling approach as well as contours based on the IFORM approach will be presented. Both sets of contours are based on the same models for the joint distribution of the environmental variables, i.e., a conditional model where the joint distribution is modelled as a product of a marginal model for one variable and a conditional model for the other. Both the joint modelling of the environmental variables and the different approaches to estimate environmental contours are described in this paper and the results for the provided datasets are shown.

This paper describes a novel approach to the characterisation of the winds associated with squall events. Squalls are short duration events during which wind speed increases rapidly and is often characterised by a substantial change in direction. Squalls are an important input for the design of offshore structures, particularly weather vaning facilities such as turret moored FPSO’s for which squalls may induce substantial mooring loads. This paper presents the development of a synthetic squall database covering 63 years and combining direct high resolution wind measurements together with weather balloon observations of the lower atmosphere and historical daily maximum wind gusts. The squall database was a key input into a mooring Response Based Analysis (RBA) of a turret moored FPSO located offshore North Western Australia. Details of the synthetic squall database development are presented including: regional squall regime characterization, direct measurements, identification of peak squall events from long term measured datasets, characterisation based on weather balloon observations, generation of representative synthetic time histories of squall events. The squall database, coupled with a conventional hindcast metocean database including associated wave and currents parameters, allowed characterization of the weather vaning performance of the FPSO and identification of design loads for mooring design purposes.

Fatigue assessments are a central component of subsea well system analysis and are essential in understanding overall well system performance. In recent years there has been an increased focus on understanding fatigue in well systems. This focus has taken on many forms — a broad industry JIP, technical studies by individual operators, development of a variety of technologies for subsea instrumentation and monitoring and case studies implementing this technology. An overarching objective of all these efforts is to clarify some of the complexity that is inherent to well system fatigue. As with any analysis, well system fatigue analysis benefits from appropriately defined inputs. These inputs affect both sides of the analysis equation — loading considerations and resistance considerations. A majority of the efforts to date have examined loading considerations. Inputs such as metocean criteria and soil properties can have a dramatic impact on the analysis results. Accurate characterization of these inputs is desired, though often difficult to achieve. As a result, large safety factors are often employed to provide a margin against this uncertainty. Often overlooked, however, is the resistance half of the assessment, particularly the accurate characterization of fatigue hotspots such as welds and connectors. Conservative analysis of hotspots is frequently employed as these components are not amenable to inspection, however the conservative assumptions can result in non-realistic assessment of fatigue performance. This paper presents some additional considerations for minimizing the uncertainty in well system fatigue analysis, through thorough materials characterization and design analysis, focusing on connector hotspots. The information presented is representative of actual considerations utilized for ExxonMobil projects. Fatigue performance is improved through finite element characterization of the connector design and “hand picking” component match-ups to minimize the resulting stress amplification. Attention to the total life fatigue properties, including experimental assessment of the material used, is needed to provide an accurate representation of fatigue performance. Finally, in the absence of this information, mitigation of fatigue risks can be obtained by displacing fatigue hotspots.

Abstract The Liza Phase 1 development project features the Liza Destiny Floating Production, Storage, and Offloading (FPSO) vessel, moored 190 km offshore Guyana in 1,743 m (5,719 ft) of water, and four subsea drill centers supporting 17 wells. Not only was this a Greenfield development that required an integrated team to prepare for Operations; it was also located in a New Frontier that required development of logistics and marine infrastructure to support multi drillship and FPSO operations in challenging metocean and tidal conditions. In addition, early operations and production testing was further complicated by the COVID-19 pandemic and safety protocols put in place to keep the workforce safe. Three aspects of achieving First Oil are discussed, highlighting challenges and lessons learned: Managing subsea completions, well cleanup, and flow assurance while drilling was ongoing Enabling accurate data collection from Multi-Phase Flow Meters (MPFMs) and downhole pressure gauges, which was critical to developing foundational understanding of well performance for reservoir characterization and management Establishing an integrated asset team and workflows to ensure life cycle value capture by managing complex marine operations, commissioning, and surveillance while meeting stringent COVID-19 protocols Lessons learned from Destiny Operations will be incorporated into future projects, including a robust digital strategy centered on a fiber ring to shore, which will enable high-speed communications for future FPSOs, and an onshore integrated operations control center for improvement of long-term operations. Early understanding of reservoir connectivity and performance from data collection will continue to inform the reservoir management strategy so as to maximize asset value for the country of Guyana.

Abstract Atlantic Shores Offshore Wind is developing one of the largest offshore wind energy projects along the U.S. East Coast. Given the large lease area covering 741 km2 and with minimal pre-existing geophysical, geotechnical, environmental, and marine archaeological data availability when the lease was awarded, significant front-end efforts were required to complete project design and regulatory site characterization. Collection of the information needed to progress the Construction Operations Plan and develop a project's detailed design parameters would typically take up to 4 years to finalize. This long duration is exacerbated by the misaligned timing of geophysical information needed early for permitting purposes compared to detailed geotechnical information acquired later, when project design essentials are better defined. This timing issue was managed through innovative phasing and integration of geoscience efforts in the first few years of the development. Coordinated acquisition of geohazards, geophysics, marine archaeology, geotechnics, and benthic habitat data, designed to cover the range of project variables within the project design envelope, optimized the survey campaign and resulted in a future-proof site characterization baseline. This case study highlights various technological, operational and strategic innovations implemented in the following areas: fisheries management and simultaneous vessel operations (SIMOPS), survey line planning, environmental and benthic planning, geotechnical tools and techniques, phased ground model development, data quality assurance and control, offshore operations oversight, data management and regulatory strategies. Refinement to survey plans, including orientation, sequencing, clustering, and multi-purposing data acquisition, delivered multiple efficiencies as the project matured. The team achieved geoscience data quality objectives and reduced survey durations by carefully considering commercial fishing intensity, metocean conditions, geological features, and survey line design or layout. Close coordination with multiple technical package teams was necessary to understand and anticipate evolving engineering data needs and minimize duplicate data gathering. This integrated approach enabled the project to accelerate the identification and interpretation efforts needed to answer critical questions for geotechnical ground modeling, archaeological paleolandscape modeling, geologic history determination, and benthic habitat mapping in ways that are unique and innovative to the offshore wind industry. The unprecedented use of new data displays and innovative mapping tools allowed various project development and engineering design experts from across the global project team to easily access the wealth of geoscientific information developed without the need for specialty software or extensive training. The approach also realized valuable benefits in the areas of offshore safety, achieving critical milestones, and supporting Atlantic Shores Offshore Wind goals of environmental stewardship, being a good neighbor and leading with science.