Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines

2020 ◽  
Vol 134 ◽  
pp. 178-188 ◽  
Author(s):  
Yan Cui ◽  
Noor Quddus ◽  
Chad V. Mashuga
Keyword(s):  
Risk Assessment ◽  
Game Theory ◽  
Bayesian Network ◽  
Oil And Gas ◽  
Assessment Model ◽  
Third Party ◽  
Risk Assessment Model ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines

Establishment of Risk Assessment Model for Ship Collision using Applied Bayesian Network -Collision Accidents between Fishing vessels and Cargo Vessels -

2015 ◽  
Author(s):  
Takeshi Shinoda ◽  
Koji Uru
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Benefit Analysis ◽  
Fishing Vessels ◽  
Formal Safety Assessment ◽  
Trial Cost

In this study, a risk assessment model for ship collisions is proposed according to the guidelines for Formal Safety Assessment (FSA) approved by IMO in 2002. The analysis is applied to ship collisions between fishing and cargo vessels owing to their high frequency and enormous damage. Bayesian network theory for risk analysis has been applied to reveal a causal relationship on human factors. A trial evaluation of Risk Control Options (RCOs) for collisions is attempted through the calculation of the dominance index. Finally, a trial cost benefit analysis for RCOs is considered through Gross Cost of Averting Fatality (GCAF) in FSA.

Risk Management at TransCanada Pipelines

1998 ◽  
Author(s):  
Kevin Cicansky ◽  
Glenn Yuen
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
High Pressure ◽  
Natural Gas ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Gas Pipelines ◽  
Natural Gas Pipelines

This Paper presents the method TransCanada PipeLines uses to assess the integrity risks with respect to operating its high pressure natural gas pipelines. TransCanada PipeLines’ experiences, results and successes gained through the implementation of its risk program, TRPRAM (TransCanada Pipelines Risk Assessment Model) are highlighted.

scholarly journals A Review of Data-Driven Oil and Gas Pipeline Pitting Corrosion Growth Models Applicable for Prognostic and Health Management

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Roohollah Heidary ◽  
Steven A. Gabriel ◽  
Mohammad Modarres ◽  
Katrina M. Groth ◽  
Nader Vahdati
Keyword(s):  
Stochastic Process ◽  
Pitting Corrosion ◽  
Oil And Gas ◽  
Health Management ◽  
Assessment Model ◽  
Data Driven ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines ◽  
Process Based Models ◽  
Prognostic And Health Management

Pitting corrosion is a primary and most severe failure mechanism of oil and gas pipelines. To implement a prognostic and health management (PHM) for oil and gas pipelines corroded by internal pitting, an appropriate degradation model is required. An appropriate and highly reliable pitting corrosion degradation assessment model should consider, in addition to epistemic uncertainty, the temporal aspects, the spatial heterogeneity, and inspection errors. It should also take into account the two well-known characteristics of pitting corrosion growing behavior: depth and time dependency of pit growth rate. Analysis of these different levels of uncertainties in the amount of corrosion damage over time should be performed for continuous and failure-free operation of the pipelines. This paper reviews some of the leading probabilistic data-driven prediction models for PHM analysis for oil and gas pipelines corroded by internal pitting. These models categorized as random variable-based and stochastic process-based models are reviewed and the appropriateness of each category is discussed. Since stochastic process-based models are more versatile to predict the behavior of internal pitting corrosion in oil and gas pipelines, the capabilities of the two popular stochastic process-based models, Markov process-based and gamma process-based, are discussed in more detail.

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