Top of the Line Corrosion (TLC) is a serious concern for the oil and gas industry and has been the cause of numerous pipeline failures. Many research projects have been developed in order to better understand the mechanisms and to develop accurate predictive tools for TLC. The corrosion mechanisms implemented in most of the available TLC prediction models are mostly based on laboratory based experimental data. Therefore, it is essential to validate the model’s capabilities by using field data.

A new approach in comparing model predictions with field data is proposed in this work. Information collected from a sweet field having experienced TLC issues was analyzed processed and then used as an input for the TLC predictive model to simulate the evolution of temperature, pressure, water condensation rates (WCR) and TLC rate along the pipeline. The simulation results were then compared with in-line inspection (ILI) data. Challenges encountered in the analysis of the information about the field conditions (inaccuracy and variability of production data) as well as the ILI data are discussed and a coherent methodology for comparison with simulation results is proposed.

Subject
Mechanistic models, Piping, Modeling, Predicted corrosion rate, Top of the line corrosion, Pipelines, Thickness loss, Corrosion attacks, Experimental data, Penetration, Corrosion modeling, Wall thickness, Corrosion data
Keywords:
Top of the line corrosion modeling, field data, magnetic flux leakage (MFL) data
© 2012 Association for Materials Protection and Performance (AMPP). All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise) without the prior written permission of AMPP. Positions and opinions advanced in this work are those of the author(s) and not necessarily those of AMPP. Responsibility for the content of the work lies solely with the author(s).
2012
Association for Materials Protection and Performance (AMPP)
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