Based on global sensitivity analysis (GSA), this paper proposes a new risk assessment method for an offshore structure design. This method quantifies all the significances among random variables and their parameters at first. And by comparing the degree of importance, all minor factors would be negligible. Then, the global uncertainty analysis work would be simplified. Global uncertainty analysis (GUA) is an effective way to study the complexity and randomness of natural events. Since field measured data and statistical results often have inevitable errors and uncertainties which lead to inaccurate prediction and analysis, the risk in the design stage of offshore structures caused by uncertainties in environmental loads, sea level, and marine corrosion must be taken into account. In this paper, the multivariate compound extreme value distribution model (MCEVD) is applied to predict the extreme sea state of wave, current, and wind. The maximum structural stress and deformation of a Jacket platform are analyzed and compared with different design standards. The calculation result sufficiently demonstrates the new risk assessment method’s rationality and security.
In August 2005, Hurricane Katrina and Rita attacked the coastal areas of the USA and the Gulf of Mexico. Including causing serious damage in New Orleans, it also caused a great loss to petroleum industry in the Gulf of Mexico. According to the incomplete statistic, Katrina and Rita resulted in 116 destroyed and damaged platforms and more than eight oil refineries were forced to cease production. And just in March 11, 2011, a huge earthquake of magnitude 9.0 occurred besides the northeast coast of Japan. The powerful quake sparked the tsunami wave of ten meters in Japan which led to an unexpected nuclear leakage accident. This accident became a death blow to the Japanese economy. Previous studies concluded that a tendency toward the growth of intensity and losses of each disaster is also appearing [
In addition, as the law of nature is not clear enough, the design process for offshore structure usually could not predict the real extreme sea state accurately as a result of many kinds of uncertainties. However, because of the great variety of uncertainties, it’s not practicable to take all of them into account at the same time. Some uncertainties may have a very small influence on offshore structure design, and some may be hard to calculate by a lack of statistical data. This kind of uncertainty requires a heavy calculating works and the result may not be credible to people. That is why global sensitivity analysis (GSA) is essential for structure design. GSA can quantify the significances among random variables and their parameters. By comparing the degrees of importance, all minor factors would be negligible. In this way, GUA can be totally simplified and the heavy workloads reduced.
Besides the uncertainty factors, all those natural disasters also indicate that the current design criteria have definite defects and deficiencies. The early API practice (1995) requires that the design load for fixed platform is 100 yr. return period wave height combined with associated wind and current. According to API, the meaning of “associated” is somewhat ambiguous. Wave, wind, and current are regarded as independent factors, and the combined effect of environment factors is neglected, which lacks scientific principles. Fortunately the API_RP_2A_WSD revised in 2000 rectified this requirement. According to the new edition of API, environment loads, with the exception of earthquake load, should be combined in a manner consistent with the probability of their simultaneous occurrence for the loading condition being considered. Although API (2000) has taken the combination of the environment loads into account, it does not figure out how to use the joint probability theory to analyze, and the effect of different uncertainties in design loads is not considered too. This may result in a hidden peril to the offshore structure design in the future.
In this paper, a new risk assessment method for structural design, which combines GUA with GSA, is proposed. The GSA is made in order to measure the influence of different factors and simplify the GUA. Then the combined effect of environmental loads, sea level, and marine corrosion uncertainties in the structural design stage is studied. At last, different design methods are compared.
The main objective of GSA focuses on the influence of failure probability and structural response on random variables and their parameters in the reliability model. It can compare horizontally the significance among random variables and their parameters.
For an output variable
Global sensitivity analysis is very helpful in dealing with engineering problems. And it could play a guiding role in the area of environmental impact prediction, simulation and assessment, and improve accuracy and reliability of prediction. Based on MCEVD, the global uncertainty analysis (GUA) and global sensitivity analysis (GSA) as main tools can be used for input sea environments with uncertainties and corresponding sensitivity of structure responses. After the simplification of GSA, the structural response calculation works could be greatly reduced; see Figure
Application of GUA and GSA to structural response.
This paper takes a Jacket platform with 30 m design depth of water in the South China Sea as an example, and analyzes the global sensitivity of structural stress. In the actual calculation, to clarify the issue, eliminate the influence of structural forms (e.g., stress concentration in tubular joint and green water occurrence) and make the result have general significance, the Jacket is just simplified to a certain extent (see Figure
The Jacket platform model.
Based on the finite element model of Ansys, stress sensitivity of Jacket platform is analyzed considering the change of each input variable (wave height, wind velocity, current velocity, section thickness, and sea level, resp.) The sensitivity vector is
Stress Sensitivity of Jacket.
According to the GSA result, it can be concluded that the sensitivities of different factors vary greatly. Wave height is the most sensitive factor to the Max. stress of platforms, section thickness, and sea level second, current third and wind at last. As the wind sensitivity is very low and current is usually regarded as a fixed value, the GUA of wind and current will not be taken into account. In addition, since sea level’s sensitivity is negative, it is not recommended to be used in the calculation for maximum stress.
Ocean environmental loads concerning offshore platform design mainly include wave, wind, current, ice, and earthquake. As for the most fixed platforms, wave height is the dominant factor. This paper mainly takes the uncertainty of wave height into account.
Climate change around the world due to greenhouse effects has made a deep influence on human society. The duration of the most extreme events, such as tropical cyclones, is also increasing [
Mori et al. [
Spatially averaged mean, top 10 and EI of Hs (NP: Northern Pacific, NA: Northern Atlantic, JP: Pacific Ocean near Japan).
According to the wave height data in the South China Sea from 1979 to 1987, the future wave height can be predicted as in Table
Wave height data in the South China Sea from 1979 to 1987.
Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
Wave height (m) | 9.57 | 13.08 | 10.89 | 11.33 | 11.25 | 13.53 | 11.33 | 11.25 | 10.23 | 13.97 |
| ||||||||||
Number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
Wave height (m) | 11.48 | 10.91 | 13.86 | 13.42 | 11.57 | 11.52 | 12.98 | 12.1 | 11.55 | 11.06 |
Sampling with different methods, the result may have a great difference. Some common methods are as follows. Maximum method: take the maximum value as a sample within a long time return period; POT method: Peak Over Threshold method: this method would set a threshold value; all the data greater than this value would be taken.
Considering the maximum method and POT method, the sampling result by different method has been made as in Table
Sampling result by different methods.
Max. (m) | 12.8 | 10.3 | 12.3 | 10.3 | 12.7 | 7.4 | 12.6 | 13.2 | 11.8 | 10.5 |
POT (m) | 13.2 | 12.8 | 12.7 | 12.6 | 12.3 | 12.2 | 11.8 | 11.0 | 10.5 | 10.3 |
As the methodology and involved probabilistic models are selected and the corresponding sample is established, the different distribution models, such as PIII distribution General Extreme Value distribution (GEV) and MCEVD, can be fitted to the data samples. The prediction result for wave height can be seen in Table
Prediction result for wave height by different probabilistic models.
Distribution models | Wave height with 100 yr return period (m) |
---|---|
PIII | 8.63 |
GEV | 8.83 |
MCEVD | 6.92 |
Because of the greenhouse effect, global warming is raising the temperature by 0.013°C per year which leads to sea level rise at last. According to the latest data, most glaciers around the world suffer a substantial retreat within the coming 50 years in the enhanced greenhouse climate [
Based on previous studies, the sea level rising velocity is confirmed as 2.0 mm/yr [
Based on the statistical data of storm surge in the South China Sea from 1979 to 1987 (see Table
Statistical data of storm surge in the South China Sea (1979–1987).
Typhoon number | Surge (m) |
---|---|
7909 | 0.46 |
7910 | 1.09 |
7915 | 0.21 |
7919 | 0.61 |
8001 | 0.18 |
8002 | 0.48 |
8003 | 0.26 |
8004 | 0.41 |
8101 | 0.93 |
8102 | 0.15 |
8209 | 0.52 |
8211 | 0.87 |
8219 | 0.29 |
8305 | 0.27 |
8310 | 0.58 |
8402 | 0.5 |
8403 | 0.15 |
8406 | 0.66 |
8407 | 0.19 |
8409 | 0.77 |
8504 | 0.19 |
8506 | 0.32 |
8508 | 0.24 |
8519 | 0.28 |
8607 | 0.35 |
8615 | 0.49 |
8617 | 0.39 |
8700 | 0.18 |
8701 | 0.26 |
8704 | 0.36 |
8705 | 0.17 |
8707 | 0.43 |
8711 | 0.69 |
Because tide has its own law of motion, the periodical change is fluctuated by some other factors such as geographical reasons [
Calculated spring tide from different inputs.
Input | output | ||
---|---|---|---|
|
|
|
Maximum sea level (cm) |
1* | 2* | 3* | |
4.08 | 127.54 | 41.60 | 156.12 |
2.08 | 123.47 | 41.53 | 151.11 |
−14.92 | 127.05 | 40.14 | 139.62 |
4.08 | 126.41 | 41.39 | 155.32 |
20.8 | 125.22 | 41.25 | 152.45 |
14.92 | 124.69 | 41.39 | 146.41 |
2.08 | 124.44 | 41.22 | 151.64 |
Note: 1*: annual mean sea level.
2*: amplitude of constituent
3*: amplitude of constituent
For offshore structures, Jacket part is inevitably exposed to air and sea water at the same time, which may lead to a serious marine corrosion. And as known to all, the structural resistance would definitely be reduced by the loss of section thickness. See Figure
(a) Cross-section of a bar under axial stress showing corrosion loss, (b) cross-section of a plate under bending stress showing effect of corrosion, and (c) cross-section of a plate subject to pitting from one side,
According to various coupon tests and observations, the material loss due to corrosion can be seen as a function of time [
Corrosion loss by time.
Time (yr) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
Loss (mm) | 0.18 | 0.30 | 0.36 | 0.42 | 0.48 | 0.54 | 0.59 | 0.65 |
| ||||||||
Time (yr) | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
Loss (mm) | 0.71 | 0.77 | 0.83 | 0.88 | 0.94 | 1.00 | 1.06 | 1.12 |
| ||||||||
Time (yr) | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
Loss (mm) | 1.17 | 1.23 | 1.29 | 1.35 | 1.41 | 1.47 | 1.52 | 1.58 |
Corrosion model.
The velocity of water may influence the marine corrosion rate. The widely accepted work in this area is made by LaQue. Figure
Effect of different water velocities on corrosion loss.
Time (day) | 80 | 100 | 120 | 160 | 200 | 240 | 280 | 320 |
---|---|---|---|---|---|---|---|---|
0.35 m/s | 45 |
60 |
70 |
85 |
110 |
125 |
150 |
170 |
0.45 m/s | 60 |
80 |
100 |
125 |
150 |
175 |
200 |
220 |
Difference | 15 |
20 |
30 |
40 |
40 |
50 |
50 |
50 |
Effect of water velocity on corrosion loss [
The effect of pollution can be obtained simply from considering the influence of environmental pollution. In fact, many events can lead to a serious water pollution such as oil spill or agricultural and industrial wastewater discharge [
Comparison by polluted and unpolluted sites.
Time (yr) | 0.5 | 1 | 1.5 | 2 |
---|---|---|---|---|
Unpolluted site | 0.10 mm | 0.15 mm | 0.26 mm | 0.34 mm |
Polluted site | 0.10 mm | 0.15 mm | 0.32 mm | 0.42 mm |
Corrosion loss period of exposure curves for two similar sites, with site B found to have a high nutrient level [
This example applied 143 typhoons of the Dapeng Bay and selects the wind velocity, wave height, and current velocity of each process as samples. Based on the diagnose tests and Kolmogorov-Smirnov inspection, the sample points and the curves of general extreme value (GEV) distribution fit well. The location parameter
Parameters of variables.
Shape parameter |
Scale parameter |
Location parameter |
Dn | Dn (0.05) | |
---|---|---|---|---|---|
|
0.1655 | 0.9630 | 2.1907 | 0.0604 | 0.1123 |
|
−0.6084 | 8.2255 | 34.4579 | 0.1512 | 0.2941 |
|
−0.3314 | 0.4525 | 1.3346 | 0.0918 | 0.2941 |
According to the diagnose figure, the confidence level of the sample all falls between 95% confidential interval. In order to avoid repetition, only the diagnose figure of wave height (
Diagnostic checks of
The theory of Compound Extreme Value Distribution (CEVD) was first proposed by Liu and Ma in 1980 [
When the dimension
When the trivariate nested logistic model can be involved into (
Trivariate layer structure (
Comparing different design load standards, this paper gives three definitions about the design extreme loads, takes a Jacket platform with 30 m design depth of water in the South China Sea as an example, and analyzes the maximum structural stress and deformation with different design standards. The result is showed in Table API design method: 100 yr. return period wave height combined with associated wind and current. The 100 yr. return period wave height is usually calculated by fitting Pearson-III curves. GUA design method: owing to MCEVD model, this method considers the correlation among the factors and takes the simultaneous wave, wind, and current as design criteria. And based on GUA, 100 year sea state is defined by taking account of the influence of wave change and other uncertainty factors (marine corrosion and sea level), so it can give the real “100 year” sea state. GSA and GUA design method: By GSA, the global uncertainty analysis is simplified. Only wave height and marine corrosion are regarded as main uncertainty factors. Other uncertainties are negligible. This method is also based on MCEVD model.
Comparison of calculated results by different definitions.
Wave (m) | Wind (m/s) | Current (m/s) | Sea level (m) | Section |
Max. DOF (m) | Max. stress (Pa) | Joint return period (yr) | |
---|---|---|---|---|---|---|---|---|
API | 8.63 | 39.90 | 1.24 | 0 | 25.4 | 0.3088 |
|
150 |
GUA | 8.72 | 45.86 | 1.50 | 3 | 22.9 | 0.3507 |
|
180 |
GUA and GSA | 8.72 | 45.86 | 1.50 | 0 | 22.9 | 0.3516 |
|
185 |
DOF: degree of freedom, representing the displacement of structure.
Stress and deformation of Jacket.
MCEVD considers both the frequency and the correlation among the sea environment events. In the 100 yr return period extreme sea state predicted by MCEVD, both wind and current speed are greater than API method, and, on the other hand, wave height (6.92 m) is relatively lower. However, after global uncertainty analysis, wave height, wind and current velocity are all greater than API method. So it indicates that the extreme design loads by traditional API method are underestimated. The global uncertainty analysis is quite necessary. In addition, after the simplification of GSA, GSA and GUA method could reduce the total calculation works and also make a relatively accurate result.
Faced with the increasing trend of natural disasters, this paper proposed a new method for risk assessment of offshore structures. The following results sufficiently demonstrate proposed method’s rationality and security. In the 100 yr return period extreme sea state predicted by MCEVD, both wind and current speed are greater than API method, and, on the other hand, wave height is relatively lower. However, considering the influence by the uncertainty of wave, the wave height becomes higher than API method and the joint return period reaches 180 yr. So it indicates that the extreme design loads given by API traditional method is underestimated. And the analysis result of a Jacket platform also shows that both Max. DOF and stress are greater than API method. The difference between the results of analysis with GUA and GUA and GSA methods is very small. That proves that it is not necessary to take other minor uncertainty factors into account in platform design and GSA is very helpful in simplifying the GUA. The Max. DOF and Stress of GUA and GSA method is a little greater than GUA method. That’s because the sensitivity of the rise of sea level is negative (sea-level rise may lead to the occurrence of green water on the platform deck, but this paper does not consider this effect). For the analysis of extreme loads for the Jacket part, it is not recommended to calculate sea rise. Due to the lack of statistical data, many other uncertainty factors, such as ice or earthquake, are not considered, which may require a deeper research.
This work is supported by the National Natural Foundation of China (no. 51010009) and 973 Program (2011CB013704).