Selective comparison of the oil pipeline sections based upon datasets of multiple in-line inspections [1] showed that there is a significant group of sections with 3d position changed again and again after repairs. At the same time, increasing volume of in-line inspections makes it impossible to analyze a spatial position of each pipeline section over time. It provokes adapting methods of multidimensional data analysis for automating detection of significant deviations in a spatial position of the pipeline. First phase of data preparation algorithm includes checking the uniqueness headers of dataset, lack of duplicates and gaps, lack of special characters, unprintable characters and extra spaces. The second phase includes checking misses, as well as significant and rapid changes in trends. Method of detecting significant deviations in a spatial position of the oil pipeline consists of four main steps: evaluating correlation coefficients of datasets, selecting the grouping method [2], analyzing intra-group statistics and assigning compensating activities for each group of pipeline sections.