=Paper=
{{Paper
|id=Vol-2098/paper37
|storemode=property
|title=Automated Detection of Significant Deviations in a
Spatial Position of Oil Pipelines
|pdfUrl=https://ceur-ws.org/Vol-2098/paper37.pdf
|volume=Vol-2098
|authors=Alla Yu. Vladova
}}
==Automated Detection of Significant Deviations in a
Spatial Position of Oil Pipelines==
https://ceur-ws.org/Vol-2098/paper37.pdf
Automated Detection of Significant Deviations in a
Spatial Position of Oil Pipelines
Alla Yu. Vladova
V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia
avladova@ipu.ru
Abstract. 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 meth-
ods 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 dupli-
cates and gaps, lack of special characters, unprintable characters and extra spac-
es. The second phase includes checking misses, as well as significant and rapid
changes in trends. Method of detecting significant deviations in a spatial posi-
tion of the oil pipeline consists of four main steps: evaluating correlation coeffi-
cients of datasets, selecting the grouping method [2], analyzing intra-group sta-
tistics and assigning compensating activities for each group of pipeline sections.
Keywords: Multidimensional dataset · Pipeline sections · Compensating activi-
ties · Monitoring · Repair · R-programming · Inline inspections
1 Introduction
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 com-
parative analysis.
Copyright © by the paper’s authors. Copying permitted for private and academic purposes.
In: S. Belim et al. (eds.): OPTA-SCL 2018, Omsk, Russia, published at http://ceur-ws.org
432 A. Yu. Vladova
2 Data Source
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.
Table 1. Changes in angles and radii in 2013-2016 years.
Section A2013, ° R2013, m A2014,° R2014, m A2015,° R2015, m A2016,° R2016, m
12570 0 393 2 382 1 388 0 386
33650 187 444 187 423 190 433 186 460
92050 343 547 337 527 347 568 340 518
92060 342 554 336 525 346 571 340 518
95200 349 519 344 566 350 497 348 527
96600 177 502 175 516 179 510 176 528
100920 350 462 349 482 350 495 349 466
102650 176 548 176 504 174 538 177 517
102660 176 548 176 509 174 540 176 525
102670 355 477 350 514 352 519 349 520
102840 354 538 2 494 0 534 357 509
104400 186 397 184 410 187 409 185 406
The in-line inspection database consists of more than 700 000 records for every sur-
vey [3]. Fig. 1 illustrates the ratio of stressed sections with non-normative bending
radius, to whole amount of stressed sections at some sites of the oil pipeline.
22414
20000 17 713
15 179
15000
Sections
10000
5 516
4023 3114
5000 1866 1147
321 165 264 175
0
27-30 32-34 36-38 40-41
A part of the pipeline
Stressed sections, exceeding yield strength Stressed sections
Nonnormative radiuses Sections
Fig. 1. Comparative analysis of sections within the pipeline sites.
Automated Detection of Significant Deviations in a Spatial Position of Oil Pipelines 433
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). Ap-
parently, it depends on the quality of the repairs and soil conditions.
Normalized radius
0,87
0,67
2013 2014 2015 2016 2017
Year
100950 96600 100920 33650
a)
0,8
Normalized radius
0,78
0,76
0,74
0,72
0,7
2013 2014 2015 2016 2017
Year
12570 102660 95200 141480
b)
Fig. 2. Changing overtime: а) bend radius; b) turn angles.
Analysis based on the in-line inspection data has shifted from the purpose of find-
ing 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 Cluster Analysis in Oil and Gas Industry
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 [4]
suggests diagnosing gas leaks with the sound produced by broken pipeline. Sound
434 A. Yu. Vladova
analysis is carried out using Fast Fourier transform with subsequent clustering on
mind spectrum. Paper [5] uses fuzzy clustering algorithm to classify types of defects
of underground pipeline bases upon the in-line inspections data. [6] offers a grouping
algorithm of distributed data, analyzes data of independent monitoring systems. The
paper [7] shows dimensionality reduction of a pipeline route thermal field-analyzing
task based on clustering thermowells.
Patent [8] builds a model of geological environment at drilling process, clustering
volumetric and qualitative parameters of the reservoir to optimize trajectory and char-
acteristics of drilling. Patent [9] performs clustering rock formations at the site of
well to define their differences, to identify heterogeneity, to offer visual indication of
best collectors and to provide best potential for commercial exploitation of specific
wells. Patent [10] proposes a method of evolutionary search with clustering of signs
of limiting states of constructions of complex objects, their defects and damages lead-
ing to pre-emergency situations.
4 Clustering Spatial Position of Pipeline Sections
At the first stage we do focus on dataset formation (see Fig. 3).
1. Dataset formation 2. Dataset clustering 3. Inside analisys
• Selecting • Choosing • Calculating cluster
nonnormative clusterization statistics
radiuses technique • Defining
• Data preprocessing: • Defining a cluster compensating
merging datasets, number activities
missing values • Choosing a distance
imputation, estimating metric
correlation • Visualising clusters
dependencies
Fig. 3. Stages of clustering analysis of bend radiuses.
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% [2]. At the
second step, we impute missing values with row-means. The data preprocessing algo-
rithm in terms of R language uses functions manyNAs, is.na и na.aggregate from
libraries DMwR и zoo.
Distances between cluster objects are calculated according to the following formu-
la:
Automated Detection of Significant Deviations in a Spatial Position of Oil Pipelines 435
𝑃(𝑥, 𝑥 ∗ ) = (∑𝑁 ∗ 𝑣 1/𝑝
𝑖=1 |𝑥𝑖 − 𝑥𝑖 | ) ), (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 corre-
sponding 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 [11].
5 Results
Raw datasets show a significant number of missed measurements (Table 2).
Table 2. Fragment of a dataset with multiple missing measurements.
Section 2012 2013 2014 2015 2016
142980 NA 1470 NA 1427 1380
144080 NA NA 1826 2061 2295
144470 NA 1489 1580 1608 1521
Correlation analysis of time-separated measurements showed that the smallest cor-
relation 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 de-
termined the abscissa and ordinate axis as dimensionless values of the first and second
principal components [11].
436 A. Yu. Vladova
Fig. 4. Sections, distributed in two clusters.
Fragment analysis of appointment of compensatory actions to pipeline sites depend-
ing on the cluster is presented in table 3.
Table 3. Summary analysis of selected oil parts
Part Cluster Section A sample of the bend radius Compensatory
trend, changing overtime actions
23-24 1 14130 Repair
2 15610 Monitoring
27-29 1 119170 Repair
2 121510 Monitoring
32-34 1 138400 Repair
2 148580 Monitoring
36-38 1 157510 Repair
2 173480 Monitoring
Automated Detection of Significant Deviations in a Spatial Position of Oil Pipelines 437
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 Conclusions
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 relation-
ship 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.
References
1. Vanaei, H. R., Eslami, A., Egbewande, A.: A review on pipeline corrosion, in-line inspec-
tion (ILI), and corrosion growth rate models. International Journal of Pressure Vessels and
Piping, 149, 43-54 (2017)
2. Kabakoff, R.: R in Action., Manning Publications (2011)
3. Surikov, V.I., Mogilner, L.Yu., Vladova, A.Yu., Tambovtsev, A.V., Provorov A.V.: Crea-
tion, introduction and support of archive for electronic copies and digitized data of trunk
oil pipeline route. Science & Technologies: oil and oil products pipeline transportation
4(20), 52-60 (2015)
4. Shibata, A., Konishi, M., Abe, Y., Hasegawa, R., Watanabe, M., Kamijo, H.: Neuro based
classification of gas leakage sounds in pipeline. In: Proceedings of the IEEE International
Conference on Networking, Sensing and Control, 298-302 (2009)
5. Ziashahabi, M., Sadjedi, H., Khezripour, H.: Automatic segmentation and classification of
pipeline images using mathematic morphology and fuzzy k-means algorithm. In: Machine
Vision and Image Processing (MVIP), IEEE, pp. 1-5 (2010)
6. Naldi, M.C., Campello, R.J.G.B.: Evolutionary k-means for distributed datasets. In: Brazil-
ian Symposium on Neural Networks. vol. 127, 30-42 (2014)
7. Vladova, A.Yu.: Algorithmic support of information system for geotechnical monitoring
of hydrocarbon transportation in permafrost conditions. Information technologie. 23(3),
205-212 (2017)
8. Gzara, Kais B.M., Dzhain, V.: Determination of characteristics of bed components on site
of works performance. RU 2574329 C1, 4 (2016)
9. Suares-Rivera, R., Khandverger, D.A., Soudergren, T.L.: Method and apparatus for multi-
dimensional data analysis to identify rock heterogeneity. RU 2474846 C2, 4 (2013)
10. Bekarevich, A.A., Budadin, O.N., Morozova, T.Yu, Toporov, V.I. Method for adaptive
forecasting of residual operating life of complex objects, and device for its implementa-
tion. RU 2533321 C1, 32 (2014)
11. Pison, G., Struyf, A., Rousseeuw, P.J.: Displaying a clustering with CLUSPLOT. Compu-
tational Statistics & Data Analysis 30(4), 381-392 (1999)