Forming the toolset for development of a system to control quality of operation of underground pipelines by oil and gas enterprises with the use of neural networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.161484

Keywords:

underground pipelines, oil and gas enterprises, surface defect, polarization potential, corrosion fatigue, neural network, metal service life

Abstract

A set of defining parameters for modeling stages of a surface defect propagation in the outer surface of a metal pipeline taking into consideration fatigue strength has been formed.

For a section of a pipeline with a surface defect, it was proposed to use an algorithm of forecasting polarization potentials using means of neural networks. A procedure of functioning of the testing set was elaborated for estimating efficiency of neural networks. The procedure includes appropriate training methods.

According to the results of analysis of interconnected deformation and corrosion processes, elements of a methodology of formation of information support for forecasting service life of a linear part of underground metal pipelines taking into consideration corrosion fatigue have been developed.

Known results of estimation of service life of underground metal pipelines assumed linear nature of corrosion rate. Relevant information was presented in international and national standards. Recent experimental studies have shown that it is advisable to take into consideration nonlinear nature of corrosion rate in the outer surface of underground metal pipelines (BMP).

A BMP section was inspected with the aid of a polarization potential meter together with a contactless current meter and principles of using neural networks for processing experimental results were formulated. An example of actual BMP was considered and analyzed for metal of a pipe of 17G1S grade steel with a corrosion defect in its outer surface. This analysis has resulted in estimation of metal service life and revealed nonlinearity characterized by magnitude of d=1.136.

A control method and procedures for estimating polarization potentials with the help of neural networks were proposed. They make it possible to describe the process of corrosion defect propagation into the depth of the pipe wall physically sound and mathematically more correct in contrast to the standard procedures.

The information presented is important for improving methods of control of underground metal pipelines operated by oil and gas enterprises, in particular, methods of correct measurement and evaluation of polarization potentials and anode currents in insulation defects taking into consideration nonlinearity of informative parameters

Author Biographies

Vitalii Lozovan, Karpenko Physico-Mechanical Institute of the NAS of Ukraine Naukova str., 5, Lviv, Ukraine, 79601

Postgraduate student

Department of Electrophysical Methods of Non-Destructive Testing

Ruslan Skrynkovskyy, Lviv University of Business and Law Kulparkіvska str., 99, Lviv, Ukraine, 79021

PhD, Associate Professor

Department of Business Economy and Information Technology

Volodymyr Yuzevych, Karpenko Physico-Mechanical Institute of the NAS of Ukraine Naukova str., 5, Lviv, Ukraine, 79601

Doctor of Physical and Mathematical Sciences, Professor

Department of Electrophysical Methods of Non-Destructive Testing

Mykhailo Yasinskyi, Ukrainian Academy of Printing Pid Holoskom str., 19, Lviv, Ukraine, 79020

PhD, Associate Professor

Department of Engineering Mechanics

Grzegorz Pawlowski, Zaklad Handlowo-Uslugowy BHP Kostrzynska str., 17, Gorzyca, Poland, 69-113

PhD, Company Owner

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Published

2019-04-02

How to Cite

Lozovan, V., Skrynkovskyy, R., Yuzevych, V., Yasinskyi, M., & Pawlowski, G. (2019). Forming the toolset for development of a system to control quality of operation of underground pipelines by oil and gas enterprises with the use of neural networks. Eastern-European Journal of Enterprise Technologies, 2(5 (98), 41–48. https://doi.org/10.15587/1729-4061.2019.161484

Issue

Vol. 2 No. 5 (98) (2019): Applied physics

Section

Applied physics

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Copyright (c) 2019 Vitalii Lozovan, Ruslan Skrynkovskyy, Volodymyr Yuzevych, Mykhailo Yasinskyi, Grzegorz Pawlowski

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