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A Comprehensive Empirical Evaluation of Probabilistic Corrosion Growth Rate Models

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Traditional Corrosion Growth Rate (CGR) models used in the integrity assessment of corroded pipelines are deterministic. A common Magnetic Flux Leakage (MFL) inline inspection (ILI) tool performance specification on general corrosion anomaly depth is +/- 10% Wall Thickeness (WT) at 80% confidence which corresponds to a standard deviation of 7.81% WT.  Probabilistic Corrosion Growth Rate (PCGR) models incorporate these large measurement uncertainties and provide more realistic reliability assessments

Product Number: 51323-18883-SG
Author: Gabriel Langlois-Rahme, Mahmoud Ibrahim, Miaad Safari, Yanping Li, Chike Okoloekwe
Publication Date: 2023
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$20.00

Probabilistic Corrosion Growth Rate (PCGR) models are typically studied on one or two Inline Inspections (ILIs) of a small sample size and use evaluation metrics that are operator specific. This comprehensive empirical assessment aims to expand the evaluation of PCGR models by examining anomalies that had distinct Box-to-Box (B2B) matches in four successive ILIs, resulting in 57,678 individual external corrosion anomalies from twelve pipelines of differing products, vintages, coatings, material specifications, and operating environments. This paper evaluates several PCGR models which are a combination of well-known industry models and simulation-based regression techniques. These models were evaluated using a historical train-test split on each studied anomaly set, where three prior ILIs were used to simulate the depth of each anomaly projected to the year of the fourth ILI. The simulated depths were compared to the measured depths of the final ILI. Statistical metrics and graphical techniques were used to measure a model’s predictive ability to effectively simulate the mean depth and uncertainty. The models presented in this paper are starting points for oil and gas transmission pipeline operators with ILI data to model corrosion growth with time. This work provides empirical guidance on how to use multiple corrosion ILI B2B matches to predict corrosion depth growth.

Probabilistic Corrosion Growth Rate (PCGR) models are typically studied on one or two Inline Inspections (ILIs) of a small sample size and use evaluation metrics that are operator specific. This comprehensive empirical assessment aims to expand the evaluation of PCGR models by examining anomalies that had distinct Box-to-Box (B2B) matches in four successive ILIs, resulting in 57,678 individual external corrosion anomalies from twelve pipelines of differing products, vintages, coatings, material specifications, and operating environments. This paper evaluates several PCGR models which are a combination of well-known industry models and simulation-based regression techniques. These models were evaluated using a historical train-test split on each studied anomaly set, where three prior ILIs were used to simulate the depth of each anomaly projected to the year of the fourth ILI. The simulated depths were compared to the measured depths of the final ILI. Statistical metrics and graphical techniques were used to measure a model’s predictive ability to effectively simulate the mean depth and uncertainty. The models presented in this paper are starting points for oil and gas transmission pipeline operators with ILI data to model corrosion growth with time. This work provides empirical guidance on how to use multiple corrosion ILI B2B matches to predict corrosion depth growth.

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Estimating Corrosion Rate Risk Distributions using Machine Learning and Geospatial Analytics

Product Number: 51320-14640-SG
Author: Joseph Mazzella, Thomas Hayden, Haralampos Tsaprailis, Len Krissa
Publication Date: 2020
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Estimating corrosion growth rates for underground pipelines is a challenging problem. There are confounding variables with complex interaction effects that may result in unexpected outcomes. For instance, the relationship between soil conditions and AC interference is highly non-linear and challenging to model. This work expands upon prior work using a suite of machine learning tools to estimate corrosion rates. However, instead of estimating a single corrosion growth rate for a single girth weld address (GWA), this work estimates a distribution of potential corrosion growth rates. Modeling distributions provide a more effective risk-measurement framework, especially concerning high volatility or areas of severe tail risk. 

This work relies heavily on machine learning and geospatial tools - particularly artificial neural networks and gradient boosted trees to estimate the corrosion rates and non-linear processes. Building upon prior work using data from a North American Operator, the models in this paper use additional variables from recent research in AC interference and microbiologically influenced corrosion to construct a higher accuracy and distribution-based model of pipeline corrosion risk. 
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50 mV As a Screening Criteria for Shorted Casings – Case Study

Product Number: 51323-18899-SG
Author: Keith Parker, Len Krissa, Kris Hoisington
Publication Date: 2023
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While performing cathodic protection surveys, carrier pipe and casing potential readings are typically recorded at the same test station location near the end of a casing. Comparing these potentials should reveal a difference between the cathodically protected pipe versus an unprotected and electrically isolated casing. The difference in potentials is one of available tests to determine whether a casing may be electrically shorted to the carrier pipe. The pipe-to-electrolyte and casing-to-electrolyte potential comparison is usually the initial “screening” method.
Picture for Validation of Corrosion Growth Rate Models
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51317--9504-Validation of Corrosion Growth Rate Models

Product Number: 51317--9504-SG
ISBN: 9504 2017 CP
Author: Yan Ping Li
Publication Date: 2017
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Validation results of feature level and joint level CGR based on feature matching and signal matching. These results enable pipeline operators to establish defect repair schedules and re-inspection intervals with increased confidence.
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