MARINE GROWTH COLONISATION PROCESS IN ...

Please cite this paper as: Schoefs F., " Boukinda Mbadinga, M.L., Schoefs F., Quiniou V., Birades M., “Marine Growth Colonisation Process in Guinea Gulf: data analysis”, Journal of Offshore Mechanics and Arctic Engineering (Publication of the American Society of Mechanical Engineers), Published May 2007, Vol. 129, Issue 2, pp. 97-106, doi:10.1115/1.2355518” MARINE GROWTH COLONISATION PROCESS IN GUINEA GULF: DATA ANALYSIS Morgan L. BOUKINDA M. and Franck SCHOEFS Mechanics and Genie civil Institute Nantes University 2 rue de la Houssiniere, BP 92208 44322 Nantes – France Ph.: +33 (0) 6.67.42.79.07 ; +33 (0) 6.89.79.41.81 ; +33 (0) 2.51.12.55.22 [email protected] ; [email protected] Valerie QUINIOU-RAMUS TOTAL S.A. - DGEP/TDO/TEC/GEO Tour Coupole, 2 Place de Coupole La Défense 6 - Cedex 45 92078 PARIS LA DÉFENSE – France Ph.: +33 (0) 1.47.44.44.83 [email protected]

Michel BIRADES and Raymond GARRETTA TOTAL S.A. - DGEP/TDO/TEC/STR CSTJF - Avenue Larribau 64018 Pau Cedex – France Ph.: +33 (0) 5.59.83.69.25 [email protected] [email protected]

ABSTRACT Colonisation process of marine growth increasingly arouses the interest of the oil industry because engineering design or reassessment of platforms requires forecasting of biological fouling specific to the area where they are located. Numerous publications related to marine growth on installations of North Sea [1-3], Gulf of Mexico [4] or oil sites in other parts of the world [5-7] tried to answer the need to supply more quantitative data on biological fouling. Unfortunately, there is a lack of qualitative and quantitative published data in the Gulf of Guinea, where biological fouling has a significant impact on cost of structural inspection and maintenance because of the importance of colonisation due to favourable environmental parameters (warmer temperature of seawater, intense action of the waves generating a mixing of the nutritive elements, strong light…). This paper is dedicated to bridge that gap and describe ecosystem dynamics of marine growth in the Gulf of Guinea. We describe patterns of development for dominant types of bio-fouling in the region. Analyses of evolution and related kinetics parameters are carried out to give main trends of colonisation process. Predictive models of marine growth development and their statistical parameters are proposed for reassessment and engineering design.

Key words: Gulf of Guinea, Marine growth development, predicting modelling, engineering design,

1.0 INTRODUCTION When assessing the loading on offshore structures, main difficulties are to model the environmental solicitations (wave action, wind and currents….) and to take into account biological fouling specific to the installation area into load

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computation. Process of colonisation of marine growth remains particularly difficult to quantify because of the biodiversity and the influence of seasonal conditions. For successfully solving these problems it is necessary to conduct modelling of biological fouling and to make long-term forecasts. Basic research into marine growth phenomenon, in particular study of community development dynamics of fouling organisms, vertical distributions and quantitative parameters of predicting models is thus necessary. In this paper, we focus on the Gulf of Guinea. In this region where biological fouling is significant because of favourable environmental parameters, there is a paucity of qualitative and quantitative published data characterising the ecosystem dynamics of bio-fouling communities. This paper endeavours to bridge this gap and presents the marine growth assessment on four sites, where the Exploration & Production branch of the Oil Company Total operates several shallow water platforms. Colonisation process of marine growth shows significant differences in the thickness and the nature of dominant species between the various regions considered in the Gulf of Guinea. The nature of the colonisation depends on geographical locations of the exploitation sites, the water depth, the environmental parameters (waves, water currents, nutrients availability, and physico-chemical parameters of seawater…); furthermore, the date of the platforms installation in relation with the date of release of spores and larvae will affect the fouling nature and growth rate. At least twenty varieties of marine growth have been identified on Gulf of Guinea structures and the patterns of development for dominant types of fouling organisms are described in Section 2. For engineering design or reassessment of platforms, we require long-term forecasts of biological fouling specific to the installation area. To provide quantitative parameters easily exploitable by engineers, two stages of analysis are considered. The first one, described in Section 3, consists in analysing the development of marine growth according to the date of installation of the structures. This approach allows specifying temporal families of evolution associated to the different sites and defining a predicting model of evolution (linear or not linear). The second stage described in Section 4 consists in proposing quantitative predicting models and their statistical parameters (average evolution with time, standard deviation, coefficient of variation….). This approach will allow defining the phases of increase and evolution of the bio-colonisation according to the depth. The paper concludes with a discussion of the current situation and possible ways ahead.

2.0 MARINE GROWTH IN GULF OF GUINEA When a structure is immersed in seawater, it is rapidly covered by an unavoidable fouling. Its growth is a complex phenomenon and a lot remains misunderstood. Structures which are partially built afloat or transported to the

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exploitation site may have the larvae of marine organisms attached before they arrive at their offshore locations [2]. Then geographical location, distance from shore, waves, water currents, nutrients availability, physico-chemical parameters of seawater, presence of protection systems, human activity and the date of the platform installation in relation with the date of release of spores and larvae will affect the fouling nature and growth rate [8]. Recent measurements show that marine growth thickness may be considerable (superior to 150 mm) depending on geographical site location. They may have a significant impact on logistics and cost of structural inspection and maintenance operations. In this section, we describe the major types of marine growth present in the four regions of the Gulf of Guinea located in Figure 1. Data which are considered in this paper concern more than 150 jacket platforms between 1 and 35 years old. Measurements are made on horizontal chords, which are the most critical structural members for fatigue analysis. 2.1 Main fouling organisms in Gulf of Guinea In the Gulf of Guinea, there are numerous types of marine growth that we can also find on offshore structures in other parts of the world. Indeed, some species which colonise structures are not influenced by geographical location but simply by physico-chemical factors such as temperature and salinity. The more representative hard fouling organisms are corals and barnacles. The growth of corals is favoured by ideal environmental conditions to their development (warmer temperature of seawater, intense action of waves and profusion of nutrients…). Colonisation of corals is particularly important between the surface and about fifty meters in depth but the covering can also be significant in deeper water. Barnacles are one of the frequent biological fouling organisms that we can find on oil sites located in North Sea, Gulf of Mexico, Japan coasts…. Their proliferation is not limited by the geographical factors. When we consider soft fouling organisms, seaweeds (brown and green), hydroids and bryozoans are major species. They are present in all water depths and colonize all maritime regions. Their geographic distribution is not influenced by the temperature and the rate of salinity. They can also be present in all types of water depths. The others species present on offshore structures are sponges, tubeworms, anemones, oysters and sea urchin. They are in small proportion and colonise offshore structures in a marginal way. 2.2 Patterns of development for dominant types of marine growth in Gulf of Guinea Spatial distribution of marine growth is based on information obtained during inspections on Jacket structures at different depth levels. The figures 2, 3, 4 and 5 show likely distribution with depth of fouling organisms on typical structures located in the four selected regions after 5 and 10 years. We use a very classical classification of marine growth in two categories, soft and hard, which is very useful when computing hydrodynamic coefficients. But

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sometimes competition between species of marine growth generates multi-layer colonisation process. Measurement of thickness and estimation of percentage of cover for each species becomes, in this case, more complex. To overcome this difficulty and make easy exploitation of data by engineers, Det Norske Veritas [9] proposed a model of characterisation of biofouling. It presents the possibility of homogenising the hydrodynamic coefficients in specific group of biofouling. Species are classified into 5 homogenous groups whose contracted names are KELK, FILG, OSOF, MUSS and OHAR. Their signification and the most important marine growth types in each group are stated in Table 1. This classification seems accurate and completes enough for a reliability-based load computing study. Keeping in mind this work for feature developments, we choose here to present all the species to give a more precise description of colonisation. 2.2.1 Characterisation of marine growth in Region "A" During the first months of platforms installation, surface of structures are covered by multicellular species (mosses) and calcareous depot. After two years, macro-fouling organisms such as barnacles (in the young state), seaweeds, hydroids, bryozoans and sponges start developing. Tubeworms, oysters and corals appear after four years on some structures. Then the development of biological fouling follows a complex process of colonisation. Figure 2 gives a general idea of typical colonisation encountered in region "A" after five and ten years. After five years, Barnacles, seaweeds and hydroids are the most representative species with depth. Oysters develop from surface to middle water. Sponges and bryozoans cover surface of structural elements in shallow water. After 10 years, barnacles, seaweeds and hydroids are always dominant. Sponges disappeared and some oysters are present in shallow water only. Extension of bryozoans is more important in depth except in shallow water where they are absent. We find some corals too. The latest information obtained in region "A", for structures implanted since twelve years, shows that the main fouling organisms present are: barnacles, corals, algae and some hydroids and bryozoans. 2.2.2 Characterisation of marine growth in Region "B" The first information shows that after two years of installation of platforms, barnacles, hydroids, seaweeds and some anemones, oysters and sponges colonised in various ways inspected offshore structures. Four years after platforms installation, there is an emergence of corals. Then colonisation process evolves as typical colonisation illustrated on figure 3. After five years, barnacles, seaweeds and hydroids are the main occupants, colonising the whole water depth and structure and co-existing with mixed colonies of oysters and anemones. Between surface and middle water, there are beginnings of corals colonisation. Sponges and bryozoans are in small proportion in shallow water only. After ten years, the extent of corals increases. From surface to middle water, cover percentage of barnacles and corals is quasiequivalent. We note that tubeworms develop from the middle level to deep water and the extent of seaweeds and bryozoans decrease with depth. For some structures, oysters and sponges are present in shallow water. Whereas some

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sea urchins appear in shallow water, anemones disappear. The latest information obtained in region "B", for platforms installed since fourteen years, shows that the main fouling organisms present are corals, barnacles and seaweeds. There are equally some species as sponges, hydroids, sea urchin, tubeworms and oysters but they are in very weak proportion and spread on structures in a marginal way. 2.2.3 Characterization of marine growth in Region "C" Figure 4 presents typical marine growth colonisation for fixed structures in region "C". During the first five years, barnacles and hydroids are dominant species. Corals, sponges and seaweeds are present but in weak proportion. Some oysters develop too. We observe a strong decrease of barnacles whereas colonies of corals take width quickly with time. After ten years, surface of structures are quasi covered by corals but we can find some barnacles too. For structures near the coast for example, corals may represent around eighty percent of organisms and reach high thickness (about 150mm). Sponges and seaweeds are less important and colonise structures in a marginal way. Some oysters and hydroids are present in shallow water. 2.2.4 Characterization of marine growth in Region "D" Two years after platforms installation, hard fouling which colonise offshore structures are barnacles and some oysters. For soft fouling, we observe sponges, seaweeds, hydroids and bryozoans. This biological fouling is present until approximately four years. Illustration proposed in figure 5 gives the typical colonisation obtained in region "D" after five and ten years. Barnacles and sponges are the most represented species on offshore structures. Corals are present too but their surface coverage is less than barnacles. Hydroids, bryozoans and seaweeds colonise the first twenty meters. There are some oysters in shallow water only. The latest information obtained in region "D" after ten years of platforms installation show that barnacles are dominant on the structures. Proportion of corals and sponges decrease and some anemones appear. Hydroids, Bryozoans, seaweeds spread on shallow water but their percentage of cover are less than the other fouling organisms. All biological fouling presented above are in direct competition for space, food, light, and in most case each established community appears at distinct depth zones. Some types of fouling are found to grow not only on clean surface but also on other types of fouling [2]. The process of colonisation of marine growth does not follow a general mode of evolution with time and space. It can diverge from a region to another one, from a site or a structure to the other one.

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3.0 MARINE GROWTH DEVELOPMENT MODELLING Analysis conducted here is based on specific characterisation given in literature [1, 2, and 3] for marine growth study (hard and soft fouling). To analyse and make the most of marine growth data (thickness and percentage of cover), two approaches are considered: - The first consists to analyse marine growth development versus structure age at inspection date. From this approach, we can relate biofouling process with water depth and time. - The second consists to analyse marine growth evolution from date of installation of structures (civil year). From this approach we have historical marine growth evolution and we can emphasize a relation (if it exists) between climatic events and bio-colonisation process. The aim is here to point out these global climatic as well as local physico-chemical effects; a work on correlations between velocity and level of marine growth, physico-chemical data and climatic events is included in an actual work. 3.1 Profiles of biological fouling versus structure age at given inspection date The method for quantitative analyses of marine growth has been standardised in the offshore petroleum sector according to Marine Technology Directorate [10, 11]. The average thicknesses of hard and soft organisms are calculated separately. The homogenised thickness of hard marine growth ( t h ) is calculated as below: n

∑ C i × hi th =

i= 1

100

(1)

where n is the number of groups of marine growth, C i is the percentage of cover, h i is the average height (both corresponding to group number i). Soft organisms pose many problems in calculation of reliability. Their physical characteristics are taken into account with difficulty in usual modelling. A possible way to overcome this difficulty is to calculate a compressed thickness value which is an estimation of the average thickness when all organisms are flattened on the structures. The homogenised compressed thickness of soft fouling ( t s ) is calculated as follow: n

∑ C i × hi × CTF ts =

i= 1

100

(2)

Where Compressed Thickness Factor (CTF) is the ratio of compressed thickness to extended length (or size) of representative organisms within the group [8]. It is determined under moderate and extreme conditions. A total of 15 different CTFs, from 0.08 to 1, have been obtained to describe the different types and forms of soft growth. A CTF of 1 indicates that the organisms are incompressible. Although this information is useful for the exploitation of the data, it

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turns out that they do not provide any indication from a hydrodynamic viewpoint, like apparent roughness. The estimation of the error of measurement, for two sites with single colonisation of marine growth as corals only (Region C) on offshore structures for the first site and a mixture of barnacles and oysters for the second one (Region D), can be used for estimating the relative error following (3). Unfortunately, estimation of error in measurements is hard due to the lack of investigation in this way. It can be shown to be higher than 0.15 mm and generally less than 50 mm.

Δth = th

Δth

⎛ Δ hi

∑ thi i = ∑ ⎜⎜

⎝ hi

+

ΔCi ⎞⎟ Ci ⎟⎠

(3)

For engineering quasi-static or fatigue calculations hard fouling are of higher concern than soft fouling and results for hard fouling are only presented below. Profiles are plotted at a given age of structures, here 14 years old for the illustration, at the inspection date. Profiles established in each region are illustrated on figures 6, 7, 8 and 9 respectively for region A to D. Figure 6 represents the variation of average thickness with depth for four structures in region "A". Fourteen years after their installation, the average thickness of biofouling at surface varies from 25 to 40 mm. We observe a great scattering in depth with an increase and a decrease of thickness of hard fouling depending on the platform. Profiles for five platforms in region "B" are illustrated in figure 7. Average thickness varies from 40 to 90 mm in the wave action area. In depth, we observe a continuous diminution of average thickness. Profiles in region "B" are representative of the marine development: generally, marine growth thickness is more important in surface than in depth as we have favourable environmental conditions. Mean thickness is more important than in region "A". For region "C" profiles are plotted in figure 8. There is an important dispersion of mean thickness in surface from 60 to 120 mm and a great scattering with depth. This result is remarkable because the four structures are in the same zone and depth. It is in region "C" that we have the most important marine growth thicknesses (above 200 mm). Profiles for seven platforms in region "D" are presented in figure 9. We observe a strong dispersion in surface. Depending on the structure, mean thickness varies from 1.5 to 100 mm. This result may be explained by influence of external environmental factors or platforms design and exploitation; incertitude on measurements, which is less than 10 mm, cannot explain alone this scatter. Evolution of profiles with depth is very heterogeneous too. Specialised studies about this point are presently being carried out and results will be published in the future. Increase of marine growth is influenced by environmental conditions and river regime when structures are placed near river mouth. It is generally translated by more important thicknesses in surface (favourable conditions) than in depth. If the level of physico-chemical parameters varies (important detritus amount, water very diluted, and weak penetration of luminosity in surface due to river sediments) it can result in strong variations in surface or tidal zone.

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This may be the case for some structures as Ptf-A2, Ptf-C3, and Ptf-D2. We note on these ones that mean thickness is less important near the wave action area than in depth. Contrary to what is observed in North Sea [12], the nature of marine growth and smaller wave actions lead to suggest that there is no cleaning effect of storm waves. To improve our understanding of colonisation process of marine growth, it is important to identify major events such as severe winters, or inter-annual climatic variability, which might be the cause of decrease of marine growth. In view to emphasize them, we study, in the following, biofouling variations from date of installation.

3.2 Marine growth evolutions with time, from date of installation (civil years) For more convincing results, we suggest here to plot curves of evolution versus civil year, and even months for one depth in the wave action area (5, 8, 10 meters depending on the site); here, some species of marine growth are particularly affected by waves, currents and physico-chemical parameters and play a dominant role for the loading computation on the structure. This representation can highlight correlation between variations of marine growth and climatic events. We can analyse if particularly severe environmental or rare climatic events (storm of strong intensity, exceptional floods of rivers) are at the source of a natural "cleaning" of marine growth. If such correlations appear, a more detailed analysis should be investigated at the considered site; it must then consider the typology of marine growth (percentage of cover, species ...). To get a general overview of the evolution of marine growth with time, data concerning structures with the same date of installation have been averaged. This approach allows on one hand to specify families of temporal evolution according to sites and on the other hand to define simple predicting model such as linear or multi-linear ones. Models parameters are then computed from the complete data base. 3.2.1 Marine growth evolutions in Region "A" Two distinct studies were carried out for this site taking into account the depth of water of the fields of exploitation. The first study concerns platforms in water depth lower than 60 meters. The second study relates to the fields in water depth greater than 100 meters. It has been shown that for this site, depth has no effect and results are similar. Then in the following all data are treated together. Three studies have been led at 10, 20, 30 meters of depth, as beyond there are not enough data. The results at 10 meters are only illustrated here, on figure 10. At this depth, the curves grow with time except the ones obtained for the platforms fixed in 1985 where we note a decrease after 1993. At 20 and 30 meters of depth (not illustrated here) we also have an increase in the data of marine growth except for platforms installed in 1984 and 1986 where we observe a decrease respectively from 1988 and 1993. . We can assume linear or multi-linear predicting models on all the water depth (see section 4).

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3.2.2 Marine growth evolutions in Region "B" The fields of exploitation studied being mainly in high depths (between 86 and 103 meters) at a quite similar distance to the coasts, platforms were gathered within a single database. Four studies have been led at 8, 20, 40 and 60 meters in depth. Beyond there are not enough data. Only results obtained at 8 meters in depth are illustrated on figure 11. At 8 and 20 meters of depth, we can distinguish two stages of evolutions: a first period of increase before 1991 and a second period of decrease after 1991. Below 20 meters of depth, we have a continuous increase with time. On the first twenty meters, a nonlinear (multi-linear) predicting model is thus more adapted for marine growth modelling. Beyond a linear model is more suitable. There is a need in further study to analyse the correlation between climatic and physicochemical parameters before/after 1991 caused by local or global effects and this multi-linear trend. 3.2.3 Marine growth evolutions in Region "C" All the exploitation fields are within water depths lower than 60 meters. Three distinct studies were carried out according to the distance of the location of platforms from the coasts. The first study concerns platforms near coast, "C3". For this study, marine growth evolution has been led at 5, 15 and 25 meters in depth. The second study concerns platforms positioned between the closest fields and furthest away from the coasts,"C2". For this one, marine growth evolution has been led at the same depths as the previous study. The last study concerns platforms located furthest away from the coasts,"C1". Marine growth evolution has been led at 6, 20 and 40 meters in depth. Beyond these different depths, they are not enough data. The results obtained with 5 meters of depth, for the site of the exploitation located in intermediate position ("C2"), are illustrated on figure 12. For platforms situated in region "C2", curves of evolution are mainly increasing with time (from 1972 to 2004) and depth. For platforms located near coast ("C3") we can note at 5 and 15 m in depth a first increasing phase (between 1963 and 1984) and a second phase (between 1985 and 2001) where we can assume constant evolution with time. At 25 m, curves of evolution increase with time. A linear model of evolution, for all the depth of water, is more adapted for the modelling of the marine growth for platforms situated in regions "C2" and "C1". For platforms near coast ("C3"), a nonlinear (multi-linear) predicting model is more adapted for marine growth modelling. 3.2.4 Marine growth evolutions in Region "D" For this study, the exploitation fields lying mainly by low water depths (lower than 25 meters); platforms were gathered within a single study. Three curves of evolution are given at 5, 10 and 15 meters of depth. Beyond there are not enough data. The results obtained at 5 meters are illustrated on figure 13. At this depth, the curves have chaotic evolutions but we can emphasise three stages of evolution, especially for structures more than 5 years old in 1987: a first on with an increase up to 1987, a second one, between 1987 and 1996, where the thicknesses of biofouling are

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more or less constant such as the third one beginning after 1996. Other results obtained at 10 and 15 meters of depths show two stages: an increase of marine growth thickness before 1996 and a constant value after 1996. A nonlinear (multi-linear here) model is thus more adapted to propose predicting models for the growth of biological fouling in the region D.

4.0 PREDICTING MODELS OF MARINE GROWTH EVOLUTION IN GUINEA GULF The objective is here to supply predicting models of marine growth evolution easily exploitable in engineering. The analysis consists to evaluate, from the results obtained by the first approach, parameters of predicting models of marine growth for every considered site. In order to have the finest and most detailed approach (without aggregation of the data), we compute the parameters for each platform: for each site, during the first stage, these parameters are the coefficient (a) and the ordinate at the beginning (b) of the linear model. It allows computing the mean value and the standard deviation for each parameter. The computation of (b), not fixed at 0, leads to analyse the goodness of fit with the opportunity to use a linear model: in fact the initial thickness should be very small. If (b) is very fair, the linear approximation fits well the data. If (b) is not negligible, the kinetics of initial growth should be modelled with more data and another model.

4.1 Region A Section 3 of the paper suggests a set of linear or multi-linear models for each site and at several depths d. On site “A” a linear model has been proposed with the form:

t

h

(d, t ) = a d t + b d

(4)

Where t is the time in years (platform age) and parameters at depth d, ad and bd, are deduced from regression analysis. Their mean value and standard deviation, are given in table 2, with their statistics. The variations of the parameters are rather significant: that is explained by the fact that data is missing at certain depths of water. However, results are satisfactory because the slope decreases from 4 mm/year at depth 10 m to 2 mm/year at depth 50 m: it can be understood by effects such as the access to light and food near the surface. It is noticed that the coefficient of variation of the mean slope (a) strongly decreases with the depth from 75% (10 m) to 50 % (50 m), which is coherent with the fact that at these depth, the surface phenomena with strong dispersion (temperature, turbidity...) are less significant. Coefficient (b), largely less than 10 mm, is very fair and lower than the error on measurements. It shows that the choice of a linear approximation cutting axis at point O is relevant. In this case, couple of statistics (m; σ) for (a) are

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respectively (4; 3), (3; 2), (2; 1) at depth 10, 30 and 50 meters. A complete study of profiles with depth and time, based on figures such as Figure 7, leads to the following model of profile with depth z: - Linear between surface and 30 m: t

(1) h

(z,t) = t

h

(10,t) +

t

h

(10,t) − t

h

(30,t)

22

(z − 10)

- Uniform between 30 m and bottom: t (h2) (z, t ) = t h (50, t )

(5) (6)

4.2 Region B Section 3 concludes that for this site and for the first twenty meters of depth, a multi-linear not-continuous predicting model is better adapted to marine growth modelling (figure 14). Note that the model should have a discontinuity around 1991. Beyond, a linear model is more suitable. Parameters (a) and (b) of the linear model (4) for the first stage and (c) for the uniform model of the second stage are given in table 3. Let us first consider the first stage. Values of growth rate for platforms implanted after 1991 are introduced in this data base. Near the surface, initial values of growth rate (a) are four times higher than in region “A”. Variations of parameters show a great dispersion of the data especially for (b) but its mean value remains very fair, less than 2; it is equal to 0 when only one value for all platforms is available on the first stage. As for region “A”, the slope decreases with depth, from 16 mm/year at depth 8 m to 1 mm/year at depth 60 m. It is noticed that the coefficient of variation of the slope decreases with the depth too, from 75 % (8 m) to 50 % (40 m) but is higher at depth 60 m (100 %). The same conclusions as for region “A” can be deduced. Let us consider now the second stage with single parameter (c), defined only for the first 20 meters depth. Its mean value as its coefficient is decreasing with depth 8 to 20 meters respectively from 73 to 50 mm and from 64 % to 46 %. Coefficient (b), largely less than 2 mm, is very fair and shows that the choice of a linear approximation cutting axis at point O is relevant. In this case, couple of statistics (m ; σ) for (a) are similar to those obtained upon. A complete study of profiles with depth and time, based on figures such as Figure 8, leads to the following model of profile with depth z: - Linear between surface and 40 m: t (h1) ( z, t ) = t h (8, t ) + - Uniform between 40 m and bottom: t (h2) ( z, t ) =

t

h

t

h

(8, t ) − t

( 40, t ) + t

h

12 h

(20, t )

( z − 8) (7)

(60, t )

(8) 2 In Eq. (7), depth 20 meters is preferred than 40 meters because it leads to more conservative results. In Eq. (8)

due to the lack of data and great uncertainty, the mean value of measurements at 40 and 60 meters is selected.

4.3 Region C Section 3 concludes that for this region, a linear model of evolution, on all the depth of water, is well adapted for the modelling of the marine growth for platforms situated between the closest fields and furthest away from the coasts and platforms located furthest away from the coasts. For platforms near coast (sub-region C3), a multi-linear predicting

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model is thus more adapted for marine growth modelling. Parameters (a) of the linear model (4) for the first stage and (c) for the uniform model of the second stage are given in table 4. Note that value of (b) is not presented because it equals 0 when only one measurement is available in the first stage for all platforms. Parameters (a) and (b), for structures located furthest away from the coasts (sub-regions C1 and C2), are given in table 4. Initial slope (a) is between values obtained for region “A” and “B” but closer to those of region “A”. Variations of this parameter are smaller and decreases with depth from 7 mm/year at depth 6 m to 3 mm/year at depth 40 m in sub-region C1. In C2 and C3, this value is greater (10mm/year at depth 5 m in sub-region C3): moreover it seems that it decreases with the distance to the coast whatever the depth. The coefficient of variation of the slope (a) is fair in comparison to region “A” and “B” (30 % near the surface) and takes a value similar sites with depth (43% at depth 40 m) in region C1; results are similar in sub-regions C2 and C3. Values for (b) are very fair, less than the error on measurement (see section 2), and confirm the fact that the choice of linear fitting cutting axis at point O is relevant for this site. In this case, couple of statistics (m; σ) for (a) are respectively (7; 1), (6; 2), (3; 1) at depth 6, 20 and 40 meters for C1 and (10; 4), (9; 3), (8; 2) at depth 5, 15, 25 meters for C2. They are very close to those obtained upon and identical for the site C3. A complete study of profiles with depth and time, based on figures such as Figure 8, leads to the following model of profile with depth z: - Linear between surface and 20 m for sub-region C1 and 15 meters for others:

t

(1) h

t

(z, t ) = t h (6 or 5, t ) +

h

(6 or 5, t ) − t

h

(20 or 15, t )

14 or 10 - Uniform between 20 m (for sub-region C1 and 15 meters for others) and bottom:

t (h2) (z, t ) = t h (40 or 25, t )

(z − 6 or 5)

(9)

(10)

4.4 Region D Section 3 suggests a multi-linear model with two or three stages depending on the depth, for prediction of the biological fouling in the region D (figure 15). Parameters (a), (b) and (c) obtained for the two or three stages, are given in table 5. Initial slope (a) is close to those obtained for region C. Its coefficient of variation is very high whatever the depth: 63 % near the surface and 100 % in the bottom. Values for (b) are very fair, less than 5 mm, and confirm the fact that the choice of linear fitting cutting axis at point O is relevant for this site. In this case, couple of statistics (m; σ) for (a) are similar to those obtained upon. A complete study of profiles with depth and time, based on figures such as Figure 9, leads to the following model of profile with depth z: - Uniform between surface and 10 meters: t (h1) ( z, t ) = - Linear between 10 m and bottom:

t

h

(5, t ) + t 2

h

(10, t )

(11)

t h (15, t ) − t h (10, t ) t (h2) ( z, t ) = t h (10, t ) + ( z − 10) 5

12

(12)

In Eq. (11) due to the lack of data and great uncertainty, the mean value of measurements at 40 and 60 meters is selected. Note that contrary to what it was observed in region A, B, C, bellow 10 meters, marine growth increases with depth; this phenomenon must be still investigated.

6. CONCLUSION Marine growth modelling is of first importance for reassessment of existing structures or design of new ones. This paper aims to give the main trends of the colonisation typology and process in Gulf of Guinea. First, a qualitative analysis allows pointing the main species present with depth for four regions and at two dates (5 and 10 years). These main species are identified and the complexity of colonisation processes appears clearly: influence of depth reflects the role of several factors as temperature and access to light and influence of time illustrates the competition of species at a given depth. This complexity is the source of the scatter observed in evolution models especially for sites B and D. Both hard and soft marine growths are observed but this paper focuses mainly on hard marine growth. In the third and fourth part, the marine growth modelling was carried out according to analysis of thickness evolution with time in two ways: - in the third part, profile of thickness with depth are plotted for two given periods after installation of platforms, in order to distinguish the general evolution of the process of bio-colonisation with time and depth. - starting from this first analysis, section 4 suggests to identify models parameters by taking account the date and not the time after installation, especially for regions “B” and “D”. It allows pointing out external factors acting at given dates which affect radically the evolution of marine growth. Main statistics of parameters for multilinear-models (average and standard deviation of the slope for example) are provided. In regions “A” and “C”, linear models are proposed whatever the depth except in one case (at surface in subregion C3, very close to the coast). We also note on these sites that the mean value and the coefficient of variation of the slope strongly decrease with depth, which is coherent with the fact that the surface phenomena with strong dispersion (temperature, turbidity...) are less significant in depth. In regions “B” and “D”, we assume multi-linear models. These sites show the importance of a study of correlation between the oceanic data and the evolution of the biological fouling. They also suggest the role of the competitiveness of the species. Indeed, contrary to regions “A” and “C” where one specie remains dominant during the life of the structure, the mode of colonisation of the biological fouling in region “B” and “D” is characterized by a first stage of colonisation during the first years, where barnacles are dominant, a mixed stage, in region “D”, where barnacles and corals colonise the surface of the structure and a stage of re-colonisation where corals prevail on the surface of the structural elements.

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Models of thickness profile are then suggested for the four regions. If they are self-consistent for regions “A” and “C”, they must be taken with care for region “B” and “D” for the same reasons as before. They are mainly non linear with a first range between surface and 10 or 20 meters and a second one between 20 meters and bottom. Analysis of correlation between marine growth trends and climatic and/or physico-chemical sea parameters is presently carried out and results will be published in the near future to complement the present publication.

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Figure 1. Studied regions in Gulf of Guinea

Figure 2. Distributions of fouling organisms with time on typical structures established in region "A"

Figure 3. Distributions of fouling organisms on typical structures established in region "B".

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Figure 4. Distributions of fouling organisms on typical structures established in region "C".

Figure 5. Distributions of fouling organisms on typical structures established in region "D".

Age of structures at the time of inspection : 14 years 0

25

50

75

100

Depth(m)

0

125

150

Mean thickness (mm)

-20

Ptf-A1

-40

Ptf-A2 Ptf-A3

-60

Ptf-A4

-80 -100

Figure 6. Profiles of hard fouling with depth at inspection date given, region "A".

16

Age of structures at the time of inspection : 14 years 0

25

50

75

100

125

150

0

Mean thickness (mm)

Depth (m)

-20

Ptf-B1

-40

Ptf-B2

-60

Ptf-B3

-80

Ptf-B4

-100

Ptf-B5

-120

Figure 7. Profiles of hard fouling with depth at inspection date given, region "B".

Age of structures at the time of inspection : 14 years 0

25

50

75

100

125

150

0

Mean thickness (mm)

Depth (m)

-10 -20

Ptf-C1

-30

Ptf-C2

-40

Ptf-C3

-50 -60

Figure 8. Profiles of hard fouling with depth at inspection date given, region "C".

Age of structures at the time of inspection : 14 years 0

25

50

75

100

Depth (m)

0

125

150

Mean thickness (mm)

-5

Ptf-D1

-10

Ptf-D2

-15

Ptf-D3

-20

Ptf-D4 Ptf-D5

-25

Ptf-D6

-30

Ptf-D7

-35

Ptf-D8

Figure 9. Profiles of hard fouling with depth at inspection date given, region "D".

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Figure 10. Curves of evolution of hard fouling according to the year of the platforms installation (region A)

Figure 11. Curves of evolution of hard fouling according to the year of the platforms installation (region B)

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Figure 12. Curves of evolution of hard fouling according to the year of the platforms installation (region C)

Figure 13. Curves of evolution of hard fouling according to the year of the platforms installation (region D)

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180 160 Mean thickness (mm)

140 120 8m

100

20 m 40 m

80

60 m

60 40 20 0 0

5

10

15

20

25

Platforms age (years)

Figure 14. Predictive models of the evolutions of hard fouling according to the depth (region B)

100 90

Mean thickness (mm)

80 70 60

5m

50

10 m 15 m

40 30 20 10 0 0

5

10

15

20

25

30

Platforms age (years)

Figure 15. Predictive models of the evolutions of hard fouling according to the depth (region C)

Groups Type of fouling Code KELK Long flapping weed kelp, wrack FILG Fine filamentous growth sea weeds, hydroids, filamentous bryozoans OSOF Other soft growth sea anemones, soft corals, sponges, ascidians, soft tubeworms, starfish MUSS Mussels and other shells mussels, saddle oysters OHAR Other hard growth barnacles, calcareous tubeworms, hard bryozoans, corals Table 1. Summary groups of marine growth 1984 – 1998 a

b 10 m (nb = 11) m=4 σ=3 m=2 σ=3 30 m (nb = 12) m=3 σ=2 m=1 σ=3 50 m (nb = 5) m=2 σ=1 m=0 σ=1 Table 2. Parameter of the predictive models in region A (m: mean; σ: standard deviation; nb: number of platforms)

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1980 – 1990 (nb = 26) 1991 – 2002 (nb = 18) a c m = 16 σ = 12 m = 73 σ = 47 1980 – 1990 (nb = 26) 1991 – 2002 (nb = 26) 20 m a c m=9 σ=7 m = 50 σ = 23 1980 – 2002 (nb = 19) 40 m a b m=2 σ=1 m=2 σ=2 1980 – 2002 (nb = 19) 60 m a b m=1 σ=1 m = 0.3 σ = 1 Table 3. Parameter of the predictive models in region B (m: mean; σ: standard deviation; nb: number of platforms) 8m

C1

1972 – 2004 a

6 m (nb = 10) 20 m (nb = 10) 40 m (nb = 10) C2

m= 7 m=7 m=3

b σ= 2 σ=2 σ=1

m = -6 m = -5 m=2 1970 – 2002

a 5 m (nb = 10) 15 m (nb = 10) 25 m (nb = 9) C3

σ = 18 σ = 18 σ=7

m= 9 m=8 m=7

b σ= 3 σ=3 σ=3

m = 13 m=8 m=6

σ = 13 σ = 12 σ = 14

1963 – 2001 1963 – 1984 (nb = 10) 1985 – 2001 (nb = 16) 5m a c m = 10 σ= 5 m = 159 σ = 72 1963 – 1984 (nb = 12) 1985 – 2001 (nb = 15) 15 m a c m = 10 σ=4 m = 156 σ = 53 1963 – 2001 (nb = 11) a b 25 m m=7 σ=5 m=1 σ=4 Table 4. Parameter of the predictive models in region C1, C2 and C3 (m: mean; σ: standard deviation; nb: number of platforms)

1976 – 1987 (nb = 21) 1988 – 1996 (nb = 22) 1997 – 2003 (nb = 31) a b c1 c2 m= 8 σ= 5 m=2 σ=6 m = 49 σ = 32 m = 43 σ = 37 1976 – 1996 (nb = 30) 1997 – 2003 (nb = 22) 10 m a b c m=4 σ=3 m=2 σ = 10 m = 52 σ = 34 1976 – 1996 (nb = 19) 1997 – 2003 (nb = 13) 15 m a b c m=3 σ=3 m = 0.2 σ=6 m = 74 σ = 44 Table 5. Parameter of the predictive models in region D (m: mean; σ: standard deviation; nb: number of platforms) 5m

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7. REFERENCES [1] Picken, G.B., 1985, "Review of marine fouling organisms in the North Sea on offshore structures", Discussion Forum and Exhibition on Offshore Engineering with Elastomers, Plastics and Rubber Inst., London, vol. 5, pp. 5.15.10. [2] Wolfram, J., Jusoh, I., and al Sell, D., 1993, "Uncertainty in the estimation of the fluid loading due to the effects of marine growth", Proc. 12th O.M.A.E, Glasgow, vol. II, pp. 219-228. [3] Theophanatos, A., 1988, "Marine growth and hydrodynamic loading of offshore structures", PhD thesis, University of Strathclyde – UK. [4] Heideman, J.C., 1981, Biological and engineering parameters for macro-fouling growth on platforms offshore Louisiana, In: Oceans'81: Conf. Rec. vol. 1 pp. 550-557. Boston, Mass. [5] Picken, G.B., 1983, "Fouling below 100 meters on the European Continental Shelf", progress in Underwater Technology, Proceedings of the subsea Challenge Conference, paper C14, paper C14. [6] Sankalpa, M., 1991, "Marine growth on offshore structures in Indian offshore waters and removal strategy", First International Offshore and Polar Engineering Conference, I.S.O.P.E, P.o. Box 1107 Golder U.S.A., pp. 143-147. [7] Gorin, A.N., 1975, "The fouling of hydrotechnical constructions in the ports of the North-Western part of Japan Sea", In: Fouling in the Japan and Okhotsk Seas, G.M. Kushnareva (Ed.) pp.14-20. Vladivostok: Far East Sci. Centre Acad. Sci. USSR. [8]

Compère, C., Segonzac, M., 2002, n°R.INT.DITI/GO/MM/99-16, pp. 67.

"Marine

growths

on

offshore

structures",

internal

report

[9] Johannessen, K.I., 1987, "Marine Growth Data Bank - Final Report Summary "Veritas Offshore Technology and Services A/S.VERITEC Report n° 87 - 3219, pp. 35. [10] Aberdeen University Marine studies Ltd., 1986, "Appraisal of marine growth on offshore installations", Study URP72/56.

[11] Aberdeen University Marine studies Ltd., 1992, "Appraisal of marine growth on offshore installations", Study URP92/102. [12] Faber, M.H, 2000, Risk based inspection – the framework. In International workshop on reliability and risk based inspection planning, ETH, Zurich, Suisse. [13] Schoefs, F., Boukinda, 2004, "Modeling of marine growth effect on offshore structures loading using kinematics field of water particle", Proc. 14th I.S.O.P.E, 23-28 May 2004, Toulon France, pp. 419-426. [14] Boukinda M., Quiniou -Ramus V.., Schoefs F., Birades M., Lahaille A., Garretta R., 2005, "Marine growth colonisation process and profile in guinea gulf: from inspection data to load computing", Ocean Engineering Symposium, Proc. of 24th int. conf. on Offshore Mechanics and Arctic Engineering, (O.M.A.E'05), Halkidiki. [15] Schoefs, F., 2002, "Sensitivity and uncertainty studies for the modelling of marine growth effect on offshore structures loading", Proc.21th O.M.A.E, Oslo, Norway.

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