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Ovelia, Hanna
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The Optimization Of Failure Risk Estimation On The Uniform Corrosion Rate With A Non-Linear Function Hartoyo, Fernanda; Fatriansyah, Jaka Fajar; Mas'ud, Imam Abdillah; Digita, Farhan Rama; Ovelia, Hanna; Asral, D. Rizal
Journal of Materials Exploration and Findings Vol. 1, No. 1
Publisher : UI Scholars Hub

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Abstract

Failures in the oil and gas pipeline system are conditions that must be avoided and anticipated because the losses due to the failures can occur at a very high level. Internal corrosion is one of the significant causes of the failures in pipeline systems. In addition, this type of corrosion is due to the high content of carbon dioxide and other corrosive substances in crude oil and natural gas. Therefore, an optimal inspection scheduling system is required to prevent the possibility of pipeline failures due to corrosion and to avoid any overspending on the budget due to excessive inspection scheduling. Risk-based testing (RBI) is one of the best methods to define a test planning system by using an optimal risk assessment. In this article, a Monte Carlo random number generator is applied by using a huge number of random iterations to approximate the actual risk value of a pipeline system with a limited sample at the scene. The nonlinear corrosion rate function is used for comparison with the commonly used linear corrosion rate function based on ASTM G-16 95. Once a risk value is estimated, the value is monitored based on an assessment of the risk matrix for each corrosion rate function by using the RBI method. The results show that the nonlinear corrosion rate function provides a more accurate approach to estimating the actual risk value and ultimately leads to an optimal inspection planning system.
Pipeline Risk Analysis Optimization with Monte Carlo Method Using Gamma Distribution Digita, Farhan Rama; Fatriansyah, Jaka Fajar; Ridzuan, Abdul Rahim; Ovelia, Hanna; Mas'ud, Imam Abdillah; Tihara, Irma Hartia; Linuwih, Baiq Diffa Pakarti
Journal of Materials Exploration and Findings Vol. 2, No. 3
Publisher : UI Scholars Hub

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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.
Co-Authors Asral, D. Rizal Digita, Farhan Rama Fatriansyah, Jaka Fajar Hartoyo, Fernanda Linuwih, Baiq Diffa Pakarti Mas'ud, Imam Abdillah Ridzuan, Abdul Rahim Tihara, Irma Hartia