Estimation of Internal Pit Depth Growth and Reliability of Aged Oil and Gas Pipelines— A Monte Carlo Simulation Approach

CORROSION ◽  
10.5006/1543 ◽  
2015 ◽  
Vol 71 (8) ◽  
pp. 977-991 ◽  
Author(s):  
Chinedu I. Ossai ◽  
Brian Boswell ◽  
Ian J. Davies
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Oil And Gas ◽  
Simulation Approach ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines ◽  
Pit Depth

Time Dependent Reliability Analysis for Oil and Gas Pipelines: A Bayesian Spectral Analysis-Based Deterioration Model

2020 ◽  
Author(s):  
Ngandu Balekelayi ◽  
Solomon Tesfamariam
Keyword(s):  
Growth Rate ◽  
Spectral Analysis ◽  
Reliability Analysis ◽  
Growth Model ◽  
Oil And Gas ◽  
Time Dependent ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines ◽  
Pit Depth ◽  
Over Time

Abstract Oil and gas pipelines are essential infrastructures that sustain the economy of modern society. They are designed for continuous and reliable operations over their service lives. Once installed, however, their reliability is affected by several threats among which external corrosion plays a significant role. Corrosion-based pit depth growth reduces the wall thickness over time that consequently affect the mechanical strength and the hydraulic performance of the pipeline. Pipeline utility managers rely on the corrosion growth rate models to plan their maintenance, rehabilitation and/or replacement. Existing pipeline deterioration models are mostly based on the power law function that relates the pit depth with the exposure time and rarely include the soil factors that can have effect on the corrosion growth rate. Moreover, the way these factors affect the corrosion rate is complex and cannot be captured with simple linear relationship. This paper uses data found in the literature to build a nonlinear pit depth growth model based on Bayesian spectral analysis regression technique. All continuous covariates are allowed to have smooth nonlinear spectral representations of their effect function on the pit depth growth. The discrete (i.e. categorical) factors are modeled using the ordinary least squared algorithm. The final semiparametric model allows to capture all pit depth measurements, even those difficult to be modeled using high degree polynomials. The stochastic nature of the pit depth growth is captured through the Bayesian approach. A time dependent reliability analysis using subset simulation is carried out to evaluate the changes occurring in the probability of failure of the pipe over time and allow for a better planning and management of these important infrastructure. The model is applied on a bare pipe directly exposed to the soil environment over time. The Bayesian pit depth growth model is accurate enough to allow the computation of the time dependent reliability of pipelines considering both the mechanical and hydraulic reliabilities.

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