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.
Operation of underground pipelines contributes to bending stresses in its walls. The situation is significantly aggravated at plots with changing geological conditions: freezable swamps, landslide slopes and permafrost. Therefore, in order to ensure trouble-free operation, changes in the pipeline spatial position shall be analyzed. A spatial position of every section of a pipeline is characterized with a bending radius and a turn angle and is set up at a design stage. Monitoring changes in an oil pipeline spatial position bases upon regular in-line inspections, strength calculations and comparative analysis.
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In: S. Belim et al. (eds.): OPTA-SCL 2018, Omsk, Russia, published at http://ceur-ws.org
In-line inspections provide information about high-altitude situation, bending radius
and turn angle of every section of an oil pipeline. A fragment of comparative analysis
of bending radius and turn angles over 3 years is represented in Table 1.
The in-line inspection database consists of more than 700 000 records for every
survey [
Comparative analysis of bending radii based upon in-line inspections showed that there is a significant group of repaired sections with stable decreasing bend radii (see Fig. 2a, sections No 100950, 96600, reparation works were made in 2015 year and Fig. 2b, sections No 95200, 141480, reparation works were made in 2014 year). Apparently, it depends on the quality of the repairs and soil conditions.
2013 2014 2016 2017 100950 96600 100920 33650 2013 2014 2016 2017 12570 102660 95200
141480 2015 Year 2015
Year
Analysis based on the in-line inspection data has shifted from the purpose of finding defects that had to be repaired to monitoring of the pipeline's condition. Thus, the purpose of this work is automated identification of pipeline sections with deteriorating spatial position, despite of compensating activities. 3
Previously the in-line inspection results was observed right after delivery and then
archived. But today these archives are used in different types of analysis years after
the actual inspections have taken place. Cluster analysis allows to categorize and to
visualize large amount of data that are specific to the oil and gas industry. Paper [
Patent [
At the first stage we do focus on dataset formation (see Fig. 3).
1. Dataset formation 2. Dataset clustering 3. Inside analisys • Selecting nonnormative radiuses • Data preprocessing: merging datasets, missing values imputation, estimating correlation dependencies • Choosing clusterization technique • Defining a cluster number • Choosing a distance metric • Visualising clusters • Calculating cluster statistics • Defining compensating activities
The data preprocessing algorithm checks unique headers; absence of duplicates and
omissions; presence of special characters, unprintable characters, extra spaces. If
missing values are scattered across the entire dataset, record deleting can destroy an
appreciable fraction of the data. Therefore, at the first step for each thirty-kilometer
site of a pipeline, we delete records if missed measurements exceed 20% [
Distances between cluster objects are calculated according to the following formula: (1) i - is a counter, i = 1, N; N - is the number of TCs; v and p are parameters of the distance metric. The selection of v and p is based on the following criteria:
- if necessary for lowering the impact of large individual differences, v = p = 1 (the Manhattan distance);
- if necessary, increase or decrease the weight of a dimension for which corresponding objects vary, v = p = 2 (the Euclidean distance) or v = 2, p = 1 (the squared Euclidean distance).
Clustering of a composite set of bending radii with preliminary determination of a
number of clusters is realized in the language R using functions kmeans, aggregate
and clusplot from the cluster() library [
Raw datasets show a significant number of missed measurements (Table 2).
Correlation analysis of time-separated measurements showed that the smallest correlation coefficient for datasets bounded by non-normative bending radii is 0.53, and for complete datasets is 0.14. It happened due to different types of in-line inspections equipment, a significant number of repairs, and deterioration of soil bearing capacity.
As a clustering result, we obtained two sets of pipeline sections for each site of the
oil pipeline. Visualizing clusters (see Fig. 4) we used principal components and
determined the abscissa and ordinate axis as dimensionless values of the first and second
principal components [
Fragment analysis of appointment of compensatory actions to pipeline sites depending on the cluster is presented in table 3.
Section 14130 15610 119170 121510 138400 148580 157510
Cluster’ statistics trends in bending radii over time show that the pipeline sections are predominantly distributed across clusters as follows: a negative trend and a neutral trend. 6
To process in-line inspection’s data, we applied cluster analysis. It allowed grouping pipeline sections into two sets: requiring compensating activities and monitoring. It significantly simplified our analysis task and made it possible to identify in relationship between the laying conditions and the spatial position of the pipeline. Novelty of the proposed approach consists of: - developed method of automated allocation a pipeline sites requiring compensatory activities; - revealing the trend and detecting significant deviations in the values of controlled parameters, affecting strength, reliability and service life of a pipeline.