Current pipeline inspection tools allow a detail description of the pipeline physical condition throughout the system length. Such tools provide a large number of samples usually contained in extensive data files.

Pipeline engineers build up models based on pipeline physical data among other information. Diameter, wall thickness, maximum operating pressure, segment length and level profiles are key factors for a proper pipeline modeling.

Mistakes in the data selection during the configuration stage may lead to systematic errors in the model's estimations.

Large profile data sets are impractical and require significant computing power. The challenge lies in selecting the best representative points from the large profile while controlling the accuracy of the new data set.

This paper describes an algorithm used for downsizing the initial profiles while adjusting the final set to parameters such as: maximum error, minimum distance between data points, maximum number of points in the final data set.


It is assumed that a simulator performs hydraulic calculations for those points configured in the elevation profile (dominant points). Of course, the simulator may also perform calculations on any intermediate point by assuming level interpolation or some other technique.

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