Pattern recognition techniques implementation on data from In-Line Inspection (ILI)

Abstract

Onshore pipeline failure caused by corrosion represents about 16% of the overall number of incidents during the period from 2004 to 2011 according to databases such as CONCAWE and PHMSA. In-Line Inspection (ILI) is one of the available inspection techniques used to determine overall pipeline status, highlighted because it establishes a clear perspective of inner and outer condition of the pipe against the failure modes and wall thickness. Furthermore, it supports measures to prevent risk based on standards such as ASMEB31G or API579-1/ASME FFS-1. However, this approximation could represent a conservative assessment of the pipeline status, taking into account the uncertainty associated with ILI inspection tools such as MFL and UT. Several researches have been conducted to analyze available inspection techniques attempting to reduce noise generated by their inspection tools, and determine procedures in order to establish correct metal loss detection, excelling pattern recognition analysis and reliability concepts. Therefore this work seeks to transform a set of data obtained from two ILI runs, into useful information to support decision making in risk analysis based on pattern recognition techniques and reliability concepts, in order to obtain base failure frequencies for prior analysis from individual and grouped flaws. Moreover, growth corrosion and remaining life models supported on the standards mentioned above were evaluated using a pressure failure criteria. As a result it was obtained that the failure probability of the grouped flaws increases 10% in comparison with the corresponding flaws evaluated individually.

A condition-based dynamic segmentation of large systems using a Changepoints algorithm: A corroding pipeline case

“…Empirical approaches usually cover standards or best practices of Oil & Gas companies; see for instance [13,14]. Numerical approaches usually deal with Finite Elements simulations, and probabilistic approaches evaluate plastic collapse, yielding, or leak failure criteria based on safety margins or limit state functions (see [15][16][17][18]). In either case, these analyses aim to support decisions for integrity management and pipeline risk analysis.…”

“…The defects measuring tool was a Magnetic Flux Leakage (MFL). Based on information reported in Amaya-Gómez et al [15] about the inspection vendor, it can be assumed a circumferential uncertainty of 5 during the inspection. The measurement uncertainties of the defect depth, length, and width are given by…”

“…This work evaluates the pipeline reliability only under a burst failure criterion, bearing in mind that no degradation process has been included so far; nevertheless, other release modes may occur like a small or large leak or even a fullbore rupture. If a probabilistic [15,44] or stochastic [9,53] degradation process is implemented, these limit state functions can be assessed together. Let g 1 , g 2 , and g 3 denote limit state functions for a leak, burst, and rupture at an individual corrosion defect, respectively: g 1 = 0.8t d max , g 2 = P b P, g 3 = P rp P,…”

Modeling of pipeline corrosion degradation mechanism with a Lévy Process based on ILI (In-Line) inspections

“…The jumps are non-negative and happen infinitely often in any finite time interval, properties that makes these processes suitable to model progressive degradation. In fact in most cases, corrosion defects can be modeled rather accurately using a gamma distribution [5,17,20,34,35]. In what follows, the basic properties of these processes are briefly described below.…”

“…It follows a progressive degradation process in which the condition of the structure decreases continuously with time. Existing approaches to evaluate corrosion-based degradation include: (i) phenomenological descriptions [3,4], (ii) random variable adjustments [5,6], (iii) stochastic processes [7,8], (iv) simulation processes [9,10], (v) empirical approaches [11,12], and (v) deterministic approaches [13]. Considering any of these degradation processes, the main challenge of a corrosion assessment lies in predict the condition of the pipeline between scheduled inspections to prevent any possible failure.…”