Effects of depth in external and internal corrosion defects on failure pressure predictions of oil and gas pipelines using finite element models

Colindres, S. Capula; Méndez, Gerardo Terán; Velázquez, J. C.; Cabrera-Sierra, R.; Angeles-Herrera, D.

doi:10.1177/1369433220924790

Cited by 14 publications

(3 citation statements)

References 44 publications

(63 reference statements)

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“…Hence, XFEM is suitable for problems with interior boundaries, discontinuities, or singularities because of the need of remeshing and high mesh densities [30]. In predicting the failure pressure of corroded pipelines using a stress-based von Mises criterion, the entire surface of the model coincides with the edge of the FEA [2,9,31,32]. In such cases, FEM is adequate [30].…”

Section: Finite Element Methods (Fem) For Pipeline Failure Pressure P...mentioning

confidence: 99%

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An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

2021

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Conventional pipeline corrosion assessment methods for failure pressure prediction do not account for interacting defects subjected to internal pressure and axial compressive stress. In any case, the failure pressure predictions are conservative. As such, numerical methods are required. This paper proposes an alternative to the computationally expensive numerical methods, specifically an empirical equation based on Finite Element Analysis (FEA). FEA was conducted to generate training data for an ANN after validating the method against full scale burst test results from past research. An ANN with four inputs and one output was developed. The equation was developed based on the weights and biases of an ANN model trained with failure pressure from the FEA of a high toughness pipeline for various defect spacings, defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials, with a R2 value of 0.99. Extensive parametric study was subsequently conducted to determine the effects of defect spacing, defect length, defect depth, and axial compressive stress on the failure pressure of the pipe. The results of the empirical equation are comparable to the results from numerical methods for the pipes and loadings considered in this study.

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Section: Finite Element Methods (Fem) For Pipeline Failure Pressure P...mentioning

confidence: 99%

“…As such, it is not trivial to develop the empirical equation, as large data will be required to analyze this nonlinear relationship. As such, this nonlinearity can be analyzed using FEM simulation and the data could be used to validate the equation [32].…”

Section: Finite Element Methods (Fem) For Pipeline Failure Pressure P...mentioning

confidence: 99%

An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

2021

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“…Therefore, the value is classified according to Table 1. The Table enlists the characteristic dimensions of different typical pipes failures, according to [2,29,30]. Also, the location and code given by our algorithm in the identification process is provided.…”

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confidence: 99%

Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

Sepulveda-Valdez,

Sergiyenko,

Tyrsa

et al. 2024

Mathematics

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This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions.

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A Bayesian network‐based susceptibility assessment model for oil and gas pipelines suffering under‐deposit corrosion

Dao,

Adumene,

Sajid

et al. 2024

Can J Chem Eng

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Oil and gas pipelines are exposed to harsh operating conditions that facilitate their susceptibility to complex corrosion mechanisms. This affects their integrity and results in failure with associated consequences. Capturing these complex corrosion phenomena requires a robust approach. This study proposes the application of a dynamic probabilistic model to capture the key influential factors that contribute to the complex under‐deposit corrosion (UDC) mechanism in oil and gas pipelines. The Bayesian network model assesses the pipeline's susceptibility (degradation rate) to the UDC, capturing parametric dependencies. The predicted corrosion rates are input data for the corrosion propagation prediction. Three semi‐empirical corrosion propagation models are used for a comparative assessment to establish the degree of susceptibility given the prevalent influential factors and model parameters. The proposed approach is tested on an offshore pipeline, and the degree of impact of the key influential parameters is predicted. The result shows a percentage increase in the degradation rate by 18.7%, 33.2%, 35.8%, and 63.4%, respectively, for the various interaction scenarios. The present approach offers an adaptive and robust technique that would provide an early warning guide on the rate of pipeline degradation to aid integrity management for offshore assets suffering from deposit corrosion.

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Effects of depth in external and internal corrosion defects on failure pressure predictions of oil and gas pipelines using finite element models

Cited by 14 publications

References 44 publications

An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

A Bayesian network‐based susceptibility assessment model for oil and gas pipelines suffering under‐deposit corrosion

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