It is known that the mooring system response of floating production units subjected to environmental loads is nonlinear. Even though wave elevations can be assumed as Gaussian processes for short-term periods, corresponding line tension responses generally are not, due to second-order slow-drift floater motions and intrinsic nonlinearities of the system. In this work, short-term extreme responses are estimated based on two different approaches. In the first one, a number of probability distributions are fitted to the tension time histories’ peaks samples and classic order statistics is applied to determine the most probable extreme line tension corresponding to a short-time period (3-h) in order to identify the one with best performance. The effect of correlation between consecutive peaks in the extremes estimation is investigated through the one-step Markov chain condition by using a Nataf transformation-based model. In the second approach, a more robust and recently developed method named average conditional exceedance rate (ACER) is investigated, where dependencies between maxima can be easily taken into account. Additionally, effects of major parameters in dynamic analyses, such as simulation length and discretization level of the wave spectrum, are evaluated. All time-series-based extreme estimates are compared with the estimates directly obtained from a sample of epochal maxima (Gumbel method). Numerical examples cover two study cases for mooring lines belonging to Floating Production Storage and Offloading (FPSO) units installed offshore Brazil. It is shown that the consideration of dependence between peaks leads to lower extreme estimates and that both approaches return accurate results.

Mooring systems are responsible to limit the offsets of oil and gas floating production systems due to environmental actions. Therefore, they are extremely important for the overall safety of the floating unit and of the risers system connected to it. Mooring lines are subjected to pre-tension and environmental loads effects and must be designed to comply with the ultimate, accidental and fatigue limit state criteria. In general, floating units mooring lines are composed by a top chain segment, a rope segment, and a bottom chain segment. Most of the accounted failures in mooring lines are related to fatigue damage in the chain links of the top or bottom segments. Material degradation due to corrosion effects plays an important role in this failure mechanism. Engineering practice usually recommends that a constant corrosion rate model is to be adopted in the design. Periodic inspections are to be carried out in the mooring lines in order to assess the corrosion effects and other issues during the operational life of the floating system. However, corrosion is a complex phenomenon, which behavior is difficult to predict. Adopting a deterministic corrosion rate can lead to non-realistic results, compromising the system safety and the inspection planning. Therefore, to consider the problem uncertainties and comply with standards guidance, this work proposes a time-dependent fatigue reliability-based method to update the inspection planning using the results of previous inspections. The method is applied in a case study of a corroded chain segment of a mooring line, assuming a continuously-updated corrosion rate model and the S-N curve fatigue approach. Results show that the proposed method, based on solid safety assumptions, is a feasible and more reasonable way to define inspection dates, avoiding the mooring system to operate in unacceptable levels of risk.

It is known that the mooring system of floating platforms responds non-linearly to environmental loads. Even though the wave-frequency excitation can be assumed as a Gaussian process, the line tension generally is not a Gaussian process due to the second-order slow-drift floater motions and the nonlinearities of the system itself. Distinct short-term time-domain analyses with the same wave spectrum excitation, i.e., distinct realizations of the response process, lead to a set of distinct values for the simulated individual maximum observed line tensions. Therefore, the ideal practice for estimating extreme tension values should be to perform a sufficiently large number of independent simulations along with an extreme statistical analysis considering the sample of the maximum line tension identified in each simulation. However, this process can be very time-consuming and cumbersome for everyday design applications. In this paper, the short-term line tension is assumed to be a non-Gaussian ergodic process. The extreme tension is then estimated based on the peaks sample of just a single simulated tension time-history. A number of known probability distributions are fitted to the peaks of the time series and classic order statistics theory is applied to determine the most probable extreme line tension corresponding to a specified short-time period (3-h) in order to identify the one with best performance. The proposed probability distribution models for the tension peaks are the 3-parameter Weibull distribution, the Weibull distribution fitted to the tail of the data (Weibull-tail) and the Shifted Generalized Lognormal Distribution (SGLD). The estimated extreme values are also prone to uncertainties due to time-domain simulation details. The effects of the major parameters in the dynamic analysis, such as simulation length and discretization level of the wave spectrum, are therefore investigated using several simulated mooring line tension time-histories. Furthermore, the effect of correlation between consecutive line tension peaks in the extreme values estimation is investigated through a Nataf transformation-based model for joint probability distribution for the peaks and the one step Markov chain condition. It is shown that this latter consideration leads to extreme value estimates that are invariably smaller than those obtained by standard order statistics. These estimates are also shown to be closer to the extreme estimates directly obtained from a sample of largest values taken from several distinct numerical simulations. Numerical examples cover two study cases for mooring lines belonging to FPSO (Floating, Production, Storage and Offloading) units to be installed in Brazilian waters.