E3S Web of Conf.
Volume 225, 2021II International Conference “Corrosion in the Oil & Gas Industry” 2020
|Number of page(s)||4|
|Published online||05 January 2021|
Using of artificial neural networks to assess the residual resource of trunk pipelines
1 Saint Petersburg Mining University, Department of Transportation and storage oil and gas, 2, 21st Line, St Petersburg 199106, Russia
2 Saint Petersburg Mining University, Department of Development and exploitation of oil and gas fields, 2, 21st Line, St Petersburg 199106, Russia
* Corresponding author: firstname.lastname@example.org
A large number of oil and gas pipelines in the Russian Federation have been in operation for over 20 years. For these pipelines, the issue of assessing the residual resource is relevant. Today, much attention is paid to the problem of long-term durability of pipelines. Trunk pipelines are under the influence of cyclic loads and influences arising during operation. The acting stresses in the pipe wall do not exceed the allowable ones, however, they cause micro-damage to the metal structure. When assessing the cyclic fatigue of a metal, the main criterion is the relative damage to the metal. The use of non-destructive testing methods (ultrasonic and magnetic), as well as the establishment of a relationship between the number of cycles and diagnostic parameters, will improve the accuracy of the residual life assessment. When analyzing several diagnostic parameters, the question of data interconnection becomes relevant. Since establishing an empirical or semi-empirical relationship between ultrasonic and magnetic properties is a complex task, artificial neural networks (ANNs) can be used to solve this problem. The use of ANN in the diagnostics of trunk pipelines will increase the accuracy of the assessment and eliminate the subjectivity of data interpretation.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.