Abstract

Erosion threatens the integrity of pipeline system through wall thinning. This effect is worsened when corrosion and erosion interact. Therefore, it is necessary to estimate the ranges of possible erosion rates under a set of operating conditions rate for the safe operation of pipeline. However, data uncertainty (including missing data) challenges the erosion rate estimation. This study shows how to use and erosion probabilistic model to (1) gather the most useful data depending on threat mechanism and already collected information about pipeline conditions, (2) help pipeline operators use probabilistic results to inspect for threats in the most useful locations (number of inspection sites is also considered), (3) help pipeline operators optimize inspection time intervals and (4) help pipeline operators tailor erosion mitigation strategies to specific pipeline and operating conditions.

Introduction

Erosion is one of the major threats of the pipeline integrity1 when it's transporting liquid hydrocarbon products with solid particles. The erosion process decreases the effective wall thickness and therefore reduces the capacity of the pipeline to contain the pressured product. This can induce serious consequences including property, health and safety, environment, and business costs.2 To help maintain the integrity of the pipeline and minimize the erosion threat, there is a need for an effective tool to estimate the erosion rates for pipelines in different operation conditions.3

The traditional guidelines in the industry related to corrosion management include ISO(1) 13703, which suggested a maximum velocity of 5 m/s for single phase liquid transportation line.4 For the two-phase flow lines erosion calculation, API(2) RP 14E was widely used, with the equation below:5

Where ve Is the erosional velocity (ft/s), ρ Is the is the fluid mixture density at flowing conditions (lb/ft3), and C is an empirical constant (equation) which can range from 100 to 125 for solid free two-phase flow (a lower C is suggested when solids are present but the guidance to define this value is not available).

Keywords:
artificial intelligence, machine learning, upstream oil & gas, information, network model, responsibility, midstream oil & gas, retrieval system, pipeline, rights
Subjects:
Pipelines, Flowlines and Risers, Storage Reservoir Engineering, Information Management and Systems, Natural gas storage, Artificial intelligence
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