A Prediction Model for External Corrosion Rate for Buried Pipelines in Clay Soil

• José Faría González Centro de Estudios de Corrosión, Facultad de Ingeniería, Universidad del Zulia, Estado Zulia, Maracaibo 4001, Venezuela
• Lisseth Ocando Centro de Estudios de Corrosión, Facultad de Ingeniería, Universidad del Zulia, Estado Zulia, Maracaibo 4001, Venezuela https://orcid.org/0009-0002-7452-2605

DOI: https://doi.org/10.22209/rt.v46a16

Keywords: classification tree, external corrosion, neural networks, pipelines, prediction models

Abstract

Many studies have shown the need in the Venezuelan oil industry to implement viable alternatives in the field of pipeline integrity management. Thus, the aim of this work is to propose a prediction model for the external corrosion rate of buried transmission pipelines in a crude oil production field with clay soils, located in the west of Zulia State in Venezuela. After the collection, revision and classification of soil and operating parameter data in the field, a definition of input and output variables was carried out, used to generate 2 models, one regression type and the other classification type. For the neural network model, a low regression fit (R²) of 6.62 % and an RMSE of 2.13 were obtained, indicators of low model efficiency due to the restrictions of the data provided and sample size. On the contrary, for the decision tree classification model, an accuracy of 98.14 % was obtained, when classifying the corrosion rate in severity ranges. This classification tree model will serve as a starting point for subsequent research to delve deeper into the area.

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