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The ageing of offshore infrastructure presents a constant and growing challenge for operators. Ageing is characterised by deterioration, change in operational conditions or accidental damages which, in the severe operational environment offshore, can be significant with serious consequences for installation integrity if not managed adequately and efficiently. An oil field consisting of twelve well head platforms, a living quarter platform (XQ), a flare platform (XFP) and a processing platform (XPA) are the focus of this paper, providing an overview of the integrity assessment process. In order to ensure technical and operational integrity of these ageing facilities, the fitness for service of these offshore structures needs to be maintained. Assessments of the structural integrity of thirteen identified platforms under existing conditions were undertaken as these platforms are either nearing the end of their design life or have exceeded more than 50% of their design life. Information on history, characteristic data, condition data and inspection results were collected to assess the current state and to predict the future state of the facility for possible life extension. The information included but was not limited to as built data, brown fields modifications, additional risers and clamp-on conductors and incorporation of subsea and topside inspection findings. In-service integrity assessments, pushover analyses, corrosion control and cathodic protection assessments and weight control reports were completed to evaluate the integrity of these facilities for requalification to 2019 and life extension to 2030. The analytical models and calculations were updated based on the most recent inspection results and weight control reports. A requalification and life extension report was prepared for each platform to outline the performance criteria acceptance to achieve requalification until 2019 and life extension until 2030. This paper documents the methodology to assess the platform structural integrity in order to evaluate platform integrity for the remaining and extended design life. An overview of various aspects of ageing related to these offshore facilities, representing risk to the integrity, the required procedures and re assessment criteria for deciding on life extension of these facilities is presented. This paper also provides an overall view of the structural requirements, justifications and calibrations of the original design for the life extension to maintain the safety level by means of maintenance and inspection programs balancing the ageing mechanisms and improving the reliability of assessment results.

One key aspect when developing a robust health management system for turbines is the development of accurate and robust fault classifiers. The paper illustrates the application of a hybrid Stochastic-NeuroFuzzy-Inference System to fault diagnostics and prognostics for turbine performance. The random fluctuations of turbine performance parameters in different varying operating conditions are modeled using a multivariate stochastic model. The fault diagnostic and prognostic are computed using a stochastic flow-path analysis model of the turbine. At any time, the fault risk condition is approached as a conditional reliability problem based on the measurement of parameter deviations from the normal operating condition. The paper illustrates the application of the proposed system to a typical aircraft turbofan engine for in-flight engine performance diagnostic and prognostic. 1.0Turbine Performance Modeling Figure 1 shows a sketch of a typical turbofan engine including the performance parameters considered herein for fault diagnostic and prognostic. Figures 2 and 3 show pressure variations as a function of the high-pressure shaft speed stationary conditions versus highly transient operating conditions, respectively. It is obvious from these figures that although for slow varying conditions the pressure closely follows a nonlinear relationship with shaft speed, for highly transient operating conditions the pressure deviates from this nonlinear path due to highly transient conditions and significant changes in the inlet conditions, namely inlet pressure, temperature and mass flow. This means using deviations from a fitted polynomial regression line for diagnostics, as commonly used in engine health monitoring application based on ground-test data, is not suited to in-flight conditions. In fact the large stochastic variability projected on the pressure-speed plane in Figure 3 is only apparent. This large variability is mostly due the transient variations induced by the pilot maneuvers. A key aspect for getting realistic predictions for in-flight operating conditions is to separate the true statistical variabilities (random part) from the functional variabilities introduced by engine transient behavior. For fast transient conditions the functional dependence between turbine performance parameters becomes complex and highly nonlinear. If these transient functional dependencies between multiple parameters are ignored then the statistical variability is overestimated and the computed fault risks are unreliable, being highly overestimated as shown in Figure 3.As physics-based, analytical GPA models only cater for quasi-stationary engine operation, an alternative scheme capable of including the highly transient in-flight conditions has been developed. This lead to the formation of a stochastic diagnostic-prognostic model based on parameter statistical deviations from an adaptive networkbased fuzzy inference system GPA model. This model was developed by calibrating the analytical

This chapter presents two nondeterministic applications where hybrid architectures have been employed to provide enhanced health-management reasoning for the detection, diagnosis, and prognosis of faults (that are typically defined by some loss of system functionality).The first application illustrates a prognostic health management (PHM) system capable of predicting faults of air or ground vehicle engines under highly transient in-operation conditions. The system's predictions also include the associated confidence or risk levels. To adequately address the complex problem of probabilistic in-operation diagnostics and prognostics, a hybrid stochastic-neuro-fuzzy inference system was developed that is a combination of stochastic parametric and nonparametric modeling techniques. This hybrid nondeterministic inference system, named StoFIS, is an integration of multivariate stochastic space-time process models with adaptive network-based fuzzy inference system models using clustering techniques [1]. StoFIS provides a hierarchical data-fusion modeling to maximize the extracted information used for diagnostic-prognostic reasoning. StoFIS is used to quantify the fault risks of an engine system at any given time and project their risk evolution in the future for risk-based prognostics.

With many of the offshore platforms around the globe well past their design life, developing targeted and cost-effective approaches to reassess and manage the life extension of these facilities is critical for operators. Although standards such as API RP 2SIM and ISO 19901-9 provide an excellent framework for management of the structural integrity of individual platforms, operators that manage a significant number of facilities need to develop strategies for overseeing the life extension of a fleet of aging multidiscipline assets, with the objective of maximizing return while maintaining an acceptable level of risk. The paper presents a systematic risk matrix based approach to provide a predictive assessment of the residual lives of the offshore facilities using the available design and condition data, re-assessment results based on asset specific or grouped approaches, and existing inspection results and strategies. Weighting of the influence of each parameter, adapted for different asset classes to capture state-of-the-art approaches within each discipline or system, is used to predicted residual life. This method has the ability to handle sparse data and incorporate recent or planned modifications. The increased likelihood of failure with time due to damage or degradation, as well as other threats such as obsolesce, is captured through time dependent factors to provide an estimate of the residual life. The method used provides a flexible assessment of the health and residual life estimates for assets from a sub-system through to a full field perspective based on the existing risk tolerance and management strategies of the operator. This provides operators with a valuable tool to assist in optimizing the life cycle costs for the field. If the overall risk profile is not acceptable, then high level what-if analyses can be performed and incorporated into the risk model to review likelihood or consequence reducing measures as the facilities age. This may include additional assessments (e.g. platform specific ultimate strength or fitness-for-service assessments of major equipment), changes to fabric maintenance or risk based inspection plans, load reductions, upgrading of instrumentation and control systems, implementation of strengthening, modification or repair programs, or decommissioning. Advisian has successfully applied this approach for both offshore and onshore assets. Unlike most life extension programs which are typically limited to a single discipline, this method provides a flexible multidisciplinary approach with the ability to incorporate findings covering topside structures, pipelines, piping, rotating and static equipment, electrical and instrumentation for a whole of field assessment.