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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 growth rate for underground pipelines is a non-linear multivariate problem. There are many potential confounding variables such as soil parameters cathodic protection AC/DC interference seasonal / climate conditions and proximity to unique geographic features such as wetlands or polluted environments. The work presented provides an approach for estimating underground corrosion growth rates using a dataset of observations from a North American pipeline operator. Extensive geospatial data is utilized that has been obtained from public and private sources and extrapolated using Inverse distance weighted (IDW) interpolation. This work presents a model using IDW to estimate parameters involving soil interference geography and climate factors for any location in North America.Using this data this work then presents several different machine learning approaches including Generalized Linear Models eXtreme Boosted Trees and Neural Networks. All three provide an accurate estimation for corrosion growth rates for an underground asset at any latitude and longitude pair in North America. Each method comes with potential benefits and pitfalls specifically; trade-offs between model accuracy and transparency. This work presents a framework for comparing geo-spatial and machine learning estimates. Findings and a framework are provided for owners to assess how to think machine learning on their own assets.
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Product Number: 51320-14640-SG
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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. 

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