Inspection of deepwater risers for flaws or pits using ILI tools can be challenging. Some lines are designed as “non-piggable”, and it is not unusual for an inspection to be incomplete because of physical constraints. As with any measurement, there will be a degree of error. While deterministic conclusions cannot be reached based on such incomplete data sets, probabilistic methods can be used effectively to make judgments about fitness for service. Commonly, different sections along a riser or flowline experience different fatigue spectra and extreme loads. Applying the loads from the sections with the highest loading to all flaws/pits can be too conservative. It is useful to employ statistical methods to assess the probability that a large defect occurs in a region with critical loads. These methods are especially useful when ILI data are incomplete or when estimates of damage must be made based on lines in similar corrosion environments. Properties and parameters other than inspection findings have an element of uncertainty. Fracture toughness, yield stress, and fatigue crack growth rates will be known in terms of mean and standard deviation. Soil properties may be known in terms of upper and lower bound. Likewise, there will be a range of uncertainty about service history and chemical environment. In such cases where fitness for service is based on the interaction of multiple random variables, Monte Carlo methods are appropriate for determining if the probability of failure is sufficiently low to tolerate. In the case of deepwater risers and flowlines where failure could result in loss of containment of hydrocarbons, permissible failure rates are on the order of 1E−5 to 1E−6 per year. This paper examines a riser and a flowline case study. For each case, a fitness for service analysis is conducted using a Monte Carlo simulation to evaluate the probability of failure based on incomplete ILI data and statistical characterization of other pertinent parameters. The results are compared against the conclusions of deterministic analysis.
Using of Pro/E Integrated Mechanical Structure Analysis Module, the structural analysis of scraper and brush on picking unit under a certain load of stress and deformation and optimized analysis of some primary parts were performed. The size and distribution of stress field were calculated, which could provide references for the physical prototyped and reliability design for product design. Moreover, it optimized the size of scraper and brush on picking unit that has some practical significance for structural optimization design. So the performance of structure of the designed model can be evaluated,researched and optimized in a real working environment.
In checking the fitness of fatigue critical welded structure, the stress concentration at the weld due to the weld geometry needs to be considered. Where fatigue is assessed using crack growth methodology, two approaches are commonly used. In the offshore industry in regions where BS 7910 [1] is followed, the effect of weld geometry is assessed using the Mk factor approach. The Mk factor directly magnifies the stress intensity. Mk factor solutions are available for T-butt weld joints from the British Standard BS7910. Alternatively, API579 [2] offers stress intensity solutions that can account for the stress profile through the wall thickness of the pipe. In using this method, the engineer will use an FEA program to find the stress profile for use as an input for the stress intensity factor computation. Since the goal is the assessment of crack growth, the stress profile must represent the cyclic changes in stress. Further, a histogram of such profiles is required. While the Mk factor approach of BS7910 offers the easier path by supplying factors for pre-solved geometries, the API approach offers an opportunity to refine the solution by conducting relatively simple linear FEA of the un-cracked component. This study compares the two approaches using an example taken from offshore riser fatigue analysis.
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