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Research Papers: Structures and Safety Reliability

Safety of Pipelines Subjected to Deterioration Processes Modeled Through Dynamic Bayesian Networks

[+] Author and Article Information
O. G. Palencia

Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, No. 1,
Lisboa 1049-001, Portugal
e-mail: oscar.palencia@centec.tecnico.ulisboa.pt

A. P. Teixeira

Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa
Av. Rovisco Pais, No. 1,
Lisboa 1049-001, Portugal
e-mail: teixeira@centec.tecnico.ulisboa.pt

C. Guedes Soares

Fellow ASME
Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, No. 1,
Lisboa 1049-001, Portugal;
Subsea Technology Laboratory,
Ocean Engineering Department,
COPPE,
Federal University of Rio de Janeiro,
Rio de Janeiro 21941-972, Brazil
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received July 22, 2017; final manuscript received June 16, 2018; published online August 13, 2018. Assoc. Editor: Celso P. Pesce.

J. Offshore Mech. Arct. Eng 141(1), 011602 (Aug 13, 2018) (11 pages) Paper No: OMAE-17-1124; doi: 10.1115/1.4040573 History: Received July 22, 2017; Revised June 16, 2018

This paper studies the application of Dynamic Bayesian Networks (DBNs) for modeling degradation processes in oil and gas pipelines. A DBN tool consisting of a matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modeling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Different sets of simulated corrosion measurements are used to increase the accuracy of the model predictions. Several parametric studies are conducted to investigate how changes in the observed corrosion (depth and length) and in the frequency of inspections affect the pipe reliability.

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Figures

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Fig. 1

Pipeline with a corrosion defect

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Fig. 3

Unrolling of a DBN

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Fig. 4

Dynamic Bayesian network model for time-dependent reliability analysis of corroded pipes

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Fig. 2

Example of a DBN model

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Fig. 5

Simulated corrosion depth values

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Fig. 9

Limit state function mean values

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Fig. 10

Limit state function and standard deviation values

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Fig. 11

Reliability index and remaining thickness of the pipe

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Fig. 8

Reliability index with and without evidences

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Fig. 12

Reliability index using different sets of simulated evidences

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Fig. 13

Corrosion defect depth

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Fig. 14

Reliability index using different burst pressure prediction methods

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Fig. 6

Dynamic Bayesian Network tool convergence analysis and computational time (in seconds)

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Fig. 7

Reliability index, comparison with Teixeira et al. [2] (DBN model with random variables discretized in 250 bins)

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Fig. 18

Corrosion depth with decreasing pressure

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Fig. 15

Reliability index using different intervals between inspections

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Fig. 16

Reliability index with decreasing pressure

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Fig. 17

Corrosion depth rate with decreasing pressure

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