Improving the diagnostics of underground pipelines at oil­and­gas enterprises based on determining hydrogen exponent (PH) of the soil media applying neural networks

Larysa Yuzevych,Vitalii Lozovan,Ruslan Skrynkovskyy,Mykhailo Yasinskyi,Volodymyr Yuzevych,Ihor Ogirko,Grzegorz Pawlowski

doi:10.15587/1729-4061.2019.174488

Abstract

A set of key parameters and information flows has been formed to simulate stages of probing the outside surface of underground metal pipelines (UMP) taking into account pH of the soil contacting with the pipe metal. Specimens of 17G1S steel placed in acid, alkaline and neutral media were examined using a polarization potential meter in a complex with a contactless current meter. Principles of application of neural networks (NN) in processing experimental results were formulated. A database has been developed. It meets the initial conditions for controlling the soil pH at the boundary with the metal under real conditions. Elements of the optimization approach for assessing pH of a coated UMP in the soil medium were proposed. The approach is based on the multiplicative qualimetric criterion of quality for the UMP section taking into account two groups of coefficients. The first group of coefficients refers to the internal coefficients and characterizes the metal pipeline and the second group refers to the external medium (i.e., soil electrolyte). Elements of the optimization approach for assessing pH of the coated pipeline in the soil medium were proposed. An NN was presented for the pipeline-coating system, which: 1) is capable of solving the problem of cluster analysis and image classification; 2) makes it possible to process data without their prior spectral transformation operating with discrete counts of information signals. The proposed NN type allows it to dynamically expand its own knowledge base of possible types of defects in controlled objects (pipelines) in the process of operation. With the help of the NN, soil pH was assessed for an UMP of 17G1S steel for three situations. The above information is important for improving the methods for controlling oil-and-gas enterprise UMPs, in particular, the methods for a correct assessment of anode current density in metal defects taking into account nonlinear character of informative parameters.

Highlights

The main types of failure of underground metal pipelines (UMP) of oil-and-gas enterprises include [1, 2]: 1) mechanical failure of structural material; 2) corrosion as an arbitrary process of metal failure caused by electrochemical, chemical, biochemical interaction with the medium
A concept of ensuring reliable and safe longterm operation of oil-and-gas enterprise UMPs taking into account hydrogen exponent of the soil contacting with the pipe metal is important
The results of analysis of [22, 23] show that due to application of neural networks (NN), it is possible to analyze the information obtained in diagnosing a coated pipeline section with the help of CCM and PPM instruments and predict service life of a UMP with detected defects taking into account value of the pipe surface pH and the effect of corrosion fatigue in the metal in conditions of electrochemical corrosion

Summary

Introduction

The main types of failure of underground metal pipelines (UMP) of oil-and-gas enterprises include [1, 2]: 1) mechanical failure of structural material; 2) corrosion as an arbitrary process of metal failure caused by electrochemical, chemical, biochemical interaction with the medium. External factors of electrochemical corrosion of metal (of oil-and-gas enterprise UMP) include [1]: 1) acidity (alkalinity) of the soil that: a) is characterized by activity of hydrogen ions, composition and concentration of solutions; b) is determined by hydrogen exponent (pH) of the soil media; 2) temperature, pressure; 3) electrolyte flow rate; 4) contact with other metals, etc. A concept of ensuring reliable and safe longterm operation of oil-and-gas enterprise UMPs taking into account hydrogen exponent (pH) of the soil contacting with the pipe metal is important. The proposed concept is based on assessment of a real service life of underground pipelines of oil-and-gas enterprises This assessment combines exploitation experience (past damage statistics) and early prognostication of future damages using modern methods and means, in particular appropriate instruments such as PPM and CCM. It is clear from the aforesaid that the issue of diagnosing the of oil-and-gas enterprises UMPs operated in soil media based on current realities with application of neural networks is relevant and requires additional studies

Literature review and problem statement

The aim and objectives of the study

Findings

Conclusions