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08503 Validation of a Predictive Model for Microbiologically Influenced Corrosion

Picture for 08503 Validation of a Predictive Model for Microbiologically Influenced Corrosion
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Product Number: 51300-08503-SG
ISBN: 08503 2008 CP
Author: Reeta Sooknah, Sankara Papavinasam, and Winston Revie
Publication Date: 2008
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Corrosion is one of the main threats to the integrity of oil and gas pipelines. It can occur both on the inside wall and outside surface of the pipelines. The control of corrosion is an ongoing challenge in pipeline operations. Predicting and assuring pipeline integrity and serviceability entail the use of sensors and monitoring tools as well as predictive models. Our aim is to develop an integrated internal pitting corrosion model, which incorporates the aspect of microbiologically influenced corrosion (MIC) in addition to non-MIC pitting corrosion. This paper presents the two components of this integrated model designed to predict internal corrosion in oil and gas pipelines. The first module, a pitting-corrosion model predicts when localized corrosion conditions (not related to MIC) will result in pipeline failures. The second module predicts the susceptibility of microbiologically influenced corrosion (MIC) inside pipelines. An evaluation of the MIC model based on four case histories of pipeline failures indicates that the occurrence of MIC can be predicted.
Corrosion is one of the main threats to the integrity of oil and gas pipelines. It can occur both on the inside wall and outside surface of the pipelines. The control of corrosion is an ongoing challenge in pipeline operations. Predicting and assuring pipeline integrity and serviceability entail the use of sensors and monitoring tools as well as predictive models. Our aim is to develop an integrated internal pitting corrosion model, which incorporates the aspect of microbiologically influenced corrosion (MIC) in addition to non-MIC pitting corrosion. This paper presents the two components of this integrated model designed to predict internal corrosion in oil and gas pipelines. The first module, a pitting-corrosion model predicts when localized corrosion conditions (not related to MIC) will result in pipeline failures. The second module predicts the susceptibility of microbiologically influenced corrosion (MIC) inside pipelines. An evaluation of the MIC model based on four case histories of pipeline failures indicates that the occurrence of MIC can be predicted.
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