Correctly predicting the structural behavior of the Pioneering Spirit is vital for ensuring the structural integrity of the ship and the cargo. A detailed finite element model is used to predict the structural behavior of the Pioneering Spirit. The finite element method is based on fundamental principles in solid mechanics. However, when using finite element models considerable differences between the predicted and observed behavior of a structure can occur, even when best industry practices are used to create such models. Because of these differences there is a need to validate the detailed finite element model of the Pioneering Spirit. Finite element model updating is a method that can validate finite element models. In this method the discrepancy between the measured behavior and the observed behavior is minimized by modifying model assumptions and parameters. Currently a number of sensors is installed on the Pioneering Spirit, which can be used to find the measured behavior. Whether or not the measured behavior is detailed enough to be used in the validation of the finite element model is the subject of this research. To investigate this a simplified finite element model of the Pioneering Spirit was created using beam elements, this model provides the predicted behavior. Then sensitivity-based finite element model updating was implemented and applied to the beam model. Simulated measurements were used to show that the beam model can be updated using the current sensor setup. When actual measurements were used to update the beam model it was found that the beam model does not correlate with the measured behavior, making it impossible to update the beam model in a meaningful way. The detailed model does correlate with measured behavior. By assuming that the method will work similarly for the detailed model as it did for the beam model, it can be concluded that the detailed model can be validated using the current sensor setup for a static case. For a dynamic case this is not possible.

Over 450 oil platforms need to be removed from the North Sea in the next decade. Each of these platforms is custom designed for installation and service lifetime, but not for decommissioning. Allseas' Pioneering Spirit is the largest heavy lift vessel in the world, capable of lifting complete topsides in a single-lift operation. The vessel’s lift system places up to eight lift points against the bottom of the platform and uses hydraulic cylinders to transfer the weight from the substructure to the vessel in a matter of seconds. But even with this vessel, removing an oil platform is technically challenging; the structural integrity needs to remain guaranteed, while minimizing the lift preparation cost as the structure will be scrapped. The goal of this thesis is to define a general approach that uses cost optimization for the platforms’ lift preparation scope. The costs of the platform’s lift preparation scope consists mainly of two things; installation of lift points and reinforcement of the structure to avoid exceedance of their design resistance. In order to minimize the cost, the challenge is to find a lift configuration which minimizes the combined cost of the number, type and location of the lift points and the amount of reinforcement. A solution should not exceed constraints such as the allowable load per lift point, the number of lift points and the relative height difference between lift points. A lift configuration is statically undetermined, and the relative height difference between lift points is used to get different load distributions for the same configuration. In this way, forces are redistributed to strong points in the structure. Detailed finite elements models are used to prove the structural integrity of a topsides during decommissioning. The number of degrees-of-freedom of such models is far too large for them to be used in an optimization algorithm. Model order reduction is used to condense the mass and stiffness matrix to the possible lift points, while keeping internal forces of interesting elements related to these points to check the structural integrity of a solution. The reduction allows for hundreds of lift configurations to be solved per minute. The minimal costs for a lift configuration in the defined domain is found with the genetic algorithm. The optimization can include an excess amount of lift locations of different possible types. The location and load distribution over the lift points are used to define the cost function. The algorithm makes a selection that suits the constraints. The cost function gives the sum of the cost of the number and type of lift points and the number and type of structural failures. This thesis presents a systematic approach for finding the optimal lift configuration for any offshore structure. The method is tested on a case study topsides by comparing the standard configuration with the optimized configuration. It is shown that by using the proposed method, the areas that need structural reinforcements are reduced from ten to zero and the amount of lift points is reduced from eight to seven, leading to a cost reduction of 27% on the offshore preparation scope. The algorithm shows promising results for this topsides removal project. It is possible to include a new finite element model into the algorithm, which together with a generic cost function, makes sure the algorithm is conveniently applicable to other topsides removal projects.