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Title: A practical approach in pipeline corrosion modelling: Part 2 – Short-term integrity forecasting
Downloadable: Yes 
Catalog No.: 2162s
Date of Publication: Jun 1 2009 12:00AM
Price: $25.00 US
Authors: Dr Érika S M Nicoletti*, Ricardo Dias de Souza, and Dr Sérgio da Cunha Barros
Abstract: THE PIPELINE industry is continuously being required to meet the expectations of its many stakeholders, driven by the market’s rising energy demands, and the requirements for increased profitability, operational safety, and environmentally-friendly procedures. Consequently, more-sophisticated fitness-for-purpose analyses are required in order to achieve maintenance cost reductions while keeping or improving the system’s overall reliability. In such a complex context, limit-state approaches are best fitted to achieving successful outcomes for those wide-ranging but conflicting expectations. Indeed, cutting-edge pipeline defect-assessment codes have embraced this philosophy, but none have included clear and concise guidance on the subjects of forecasting corrosion growth and estimating in-line inspection (ILI) tool measurement error.

The current work has been undertaken aiming to provide a set of guidelines on modelling and analysis procedures for corrosion metal-loss growth on ageing pipelines, using as its input corrosion-monitoring and inspection data. In the preliminary stage, ILI results and electrical-resistance probe (ERP) readings from several oil pipelines were evaluated in order to define the typical variances in pipeline corrosion. This investigative work gave rise to the development of a predictable relationship between the growth rate and its standard deviation, and a short-term forecasting model has been developed based on the premise of a steady metal-loss rate coefficient of variation. In this paper, the mathematical framework for this is detailed based on different configurations of the input data: single and multiple ILI, with or without the addition of ERP results. Additionally, two case studies are given which illustrate the model’s application and results. The model is easily implemented using commercially-available mathematical spreadsheets, and the entire procedure demands little skilled work. The results are highly reproducible, with their overall quality relying mostly on the consistency of the input data.


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