Condition monitoring of subsea pipelines considering stress observation and structural deterioration

The increasing demand by the world for energy has prompted the development of offshore oil and gas pipelines as the mode of transportation for hydrocarbons. The maintenance of these structures has also gained much attention for research and development with novel methodologies that can increase the efficiency of integrity management. This paper presents a probabilistic methodology for monitoring the condition of offshore pipelines and predicting the reliability when consideration is given to structure deterioration. Hydrodynamic simulations are carried out for an offshore pipeline to obtain the time history data from which the stress ranges are computed using a rainflow counting algorithm. To model the fatigue damage growth, a Bayesian Network (BN) is established based on a probabilistic solution of Paris’ law. Corrosion effects are also incorporated into the network providing a more realistic prediction of the degradation process. To demonstrate the application of the proposed methodology, a case study of a Steel Catenary Riser (SCR) subjected to fatigue cracks and corrosion degradation is studied. This method provided the growth rate of a crack during its lifetime during which the safety of operation can be assessed and efficient maintenance plans can be scheduled by the asset managers. The proposed method can also be applied by the designer to optimize the design of pipelines for specific environments.

[1]  T Breton,et al.  Identification of failure type in corroded pipelines: a bayesian probabilistic approach. , 2010, Journal of hazardous materials.

[2]  Preben Terndrup Pedersen,et al.  Bayesian Networks as a Decision Support Tool in Marine Applications , 2001 .

[3]  Vikram Garaniya,et al.  Developing a quantitative risk-based methodology for maintenance scheduling using Bayesian Network , 2016 .

[4]  Ne Dowling,et al.  Fatigue Failure Predictions for Complicated Stress-Strain Histories , 1971 .

[5]  Faisal Khan,et al.  Domino effect analysis of dust explosions using Bayesian networks , 2016 .

[6]  Vikram Garaniya,et al.  Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring , 2017 .

[7]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[8]  John Dalsgaard Sørensen,et al.  Bayesian Networks as a Decision Tool for O&M of Offshore Wind Turbines , 2010 .

[9]  M. Matsuichi,et al.  Fatigue of metals subjected to varying stress , 1968 .

[10]  P. Davis,et al.  Subsea pipeline infrastructure monitoring: A framework for technology review and selection , 2015 .

[11]  Faisal Khan,et al.  Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network , 2013 .

[12]  Tapan K. Sen.  Probability of Fatigue Failure in Steel Catenary Risers in Deep Water , 2006 .

[13]  Jing Lin,et al.  Improved FTA Methodology and Application to Subsea Pipeline Reliability Design , 2014, PloS one.

[14]  Jhareswar Maiti,et al.  Modeling risk based maintenance using fuzzy analytic network process , 2012, Expert Syst. Appl..

[15]  Hongwei Zhu,et al.  Quantitative risk analysis on leakage failure of submarine oil and gas pipelines using Bayesian network , 2016 .

[16]  Vikram Garaniya,et al.  Pitting Degradation Modeling of Ocean Steel Structures Using Bayesian Network , 2017 .

[17]  Faisal Khan,et al.  Risk-Based Assessment of Offshore Crude Oil Pipelines and Condition Monitoring Systems , 2016 .

[18]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[19]  Shan Huang,et al.  Dynamic response of steel catenary riser using a seabed interaction under random loads , 2013 .

[20]  Benoît Iung,et al.  Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas , 2012, Eng. Appl. Artif. Intell..

[21]  Paolo Trucco,et al.  A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation , 2008, Reliab. Eng. Syst. Saf..

[22]  Daniel Straub,et al.  Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks , 2009 .

[23]  Prasanta Kumar Dey,et al.  Risk-based model aids selection of pipeline inspection, maintenance strategies , 2001 .

[24]  Vikram Garaniya,et al.  Risk-based maintenance of offshore managed pressure drilling (MPD) operation , 2017 .

[25]  David Barber,et al.  Bayesian reasoning and machine learning , 2012 .

[26]  Vikram Garaniya,et al.  Risk analysis of deepwater drilling operations using Bayesian network , 2015 .

[27]  Vikram Garaniya,et al.  Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network , 2016 .

[28]  John U. Okoli,et al.  On the Fatigue of Steel Catenary Risers , 2016 .