Abstract

Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it may remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stresses, and the electrolyte chemistry beneath the disbonded coating. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In addition, data uncertainties make the prediction of SCC even more challenging. The complex interactions of various seemingly unrelated parameters and varying mechanisms has been addressed using Bayesian network models. Two Bayesian network models have been created to predict both high pH and near neutral pH crack growth rates. This publication presents a new SCC model that combine the previous high pH and near-neutral pH SCC models.

Introduction

Environmentally Assisted Cracking (EAC) of gas transmission lines constitute about 2.6% of the total number of significant incidents recorded in the U.S. Pipeline and Hazardous Materials Administration (PHMSA) database [1]. For the hydrocarbon liquid pipelines, the EAC-related incidents constitute about 1%. Although Stress Corrosion Cracking (SCC) incidents are a relatively small percentage of significant incidents, it is important to predict the location and rate of growth of SCC because of the potential for catastrophic consequences from the growth of undetected cracks. There are essentially two forms of SCC of pipelines: High-pH SCC and Near-neutral pH SCC. Of the EAC incidents reported from 2004 to 2019 in the PHMSA database, 11 were near-neutral pH SCC, 2 were HIGH PH SCC, 18 did not report the type of SCC, and 5 were other forms of EAC. SCC can be longitudinal in the form of crack colonies due to internal pressures or circumferential in cases where there is a longitudinal stress, either from girth weld residual stresses or other external bending forces. SCC of pipelines has been studied extensively, as indicated by a selection of papers [2-22]. The SCC Direct Assessment procedure [23] describes the collection of information necessary for the likelihood of SCC at a given location.

This content is only available via PDF.
You can access this article if you purchase or spend a download.