Pipeline Risk Analysis Optimization with Monte Carlo Method Using Gamma Distribution

Authors

Farhan Rama Digita, University of IndonesiaFollow
Jaka Fajar Fatriansyah, University of IndonesiaFollow
Abdul Rahim Ridzuan, Universiti Technologi MARAFollow
Hanna Ovelia, University of IndonesiaFollow
Imam Abdillah Mas'ud, University of IndonesiaFollow
Irma Hartia Tihara, University of IndonesiaFollow
Baiq Diffa Pakarti Linuwih, University of IndonesiaFollow

Abstract

The inspection process of piping components in the oil and gas industry is one of the most crucial things, given the high risk posed by pipeline system failures, which have a huge impact on losses, both from environmental and financial aspects. Risk-based inspection with the Monte Carlo method is one of the efforts that can be made to minimize failures in piping systems, by involving data distribution to calculate the probability of component failure. Although the normal distribution is commonly used for generating random variables, its use in corrosion rate calculation can lead to overestimation due to negative corrosion rate values. Overestimation can result in inaccurate data and higher risk values, which can cause increased inspection costs. Therefore, the use of gamma distribution as a random variable generator can be a solution to reduce the bias level and increase the accuracy of the normal distribution analysis results. The gamma distribution is proven to prevent overestimation, so it can avoid inspection cost losses because the resulting risk value is lower than the normal distribution.

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Recommended Citation

Digita, Farhan Rama; Fatriansyah, Jaka Fajar; Ridzuan, Abdul Rahim; Ovelia, Hanna; Mas'ud, Imam Abdillah; Tihara, Irma Hartia; and Linuwih, Baiq Diffa Pakarti (2023) "Pipeline Risk Analysis Optimization with Monte Carlo Method Using Gamma Distribution," Journal of Materials Exploration and Findings (JMEF): Vol. 2: Iss. 3, Article 5.
DOI: 10.7454/jmef.v2i3.1041
Available at: https://scholarhub.ui.ac.id/jmef/vol2/iss3/5