In this paper, a detailed procedure to study the mooring line conditional strength reliability of a semi-submersible platform in a 100-year sea state is presented. A fully coupled analysis is conducted to calculate the mooring line tension of a deepwater semi-submersible floating system operated in the 100-year wave condition in South China Sea. 3-h extreme mooring line tensions are estimated by the average conditional exceedance rate (ACER) method from the data obtained by 10 and 20 min fully coupled dynamic simulations, and the results are validated by the global maximum method. A kriging metamodel is trained to predict the 3-h mooring line extreme tension taking into account the effect of random hydrodynamic drag coefficients. The hydrodynamic sampling points are generated by Latin hypercube sampling technique. A reliability analysis is carried out by Monte Carlo simulation considering the random hydrodynamic drag coefficients and mooring line breaking strength.

This paper aims at assessing the reliability of pipelines with local corrosion defects subjected to external pressure. Several collapse strength models are calibrated and then used to formulate the reliability problem of corroded pipelines. Model uncertainty factors are derived for the various collapse strength models based on available experimental results to better predict the effect of local corrosion defects on the reduction of the collapse strength of pipelines. The model uncertainty factor is defined as function of the depth of the local corrosion defect and calibrates the overconservative predictions of collapse strength models that deal with the effect of corrosion defects by considering a uniform reduction of the pipe thickness. The collapse strength models together with the corresponding model uncertainty factors are then used to formulate the reliability problem of pipelines with local corrosion defects subjected to external pressure. Parametric and sensitivity analyses are performed for different levels of corrosion damages to identify the influence of the various parameters on the collapse probability of corroded pipelines under external pressure. Finally, an approach is suggested to calibrate a design code formulation that is conservative when the minimum pipe thickness is used to represent a local corrosion defect. The approach consists of identifying an equivalent depth of the corrosion defect, corresponding to an intermediate thickness of the corroded pipeline larger than the minimum thickness, that adjusts the design code to match the safety levels of the collapse strength model calibrated to the experimental results.

This paper studies the application of Dynamic Bayesian Networks (DBNs) for modeling degradation processes in oil and gas pipelines. A DBN tool consisting of a matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modeling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Different sets of simulated corrosion measurements are used to increase the accuracy of the model predictions. Several parametric studies are conducted to investigate how changes in the observed corrosion (depth and length) and in the frequency of inspections affect the pipe reliability.

Operations and maintenance activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing operations and maintenance actions in industrial systems. Generalized stochastic Petri nets (GSPNs) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of operations and maintenance activities of an offshore wind turbine. The merits of GSPN in modeling complex and multicomponent systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays, and costs are included in the model. The weather windows for accessing the wind turbine are also modeled. Corrective maintenance (CM) based on replacements and age-dependent preventive maintenance (PM) with imperfect repair are modeled and compared in terms of the wind turbine's performance (e.g., availability and loss production) and of the operations and maintenance costs.