•  
  •  
 

Journal of Materials Exploration and Findings

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.

References

Agustar, A, Iskandar, I & Putra, W N 2022, ‘The Comparison of Pipe Thickness Selection Method Using Full Flange Rating and non-Full Flange Rating of Cryogenic Services in an LNG Plant Construction’, Journal of Materials Exploration and Findings (JMEF), 1(2), pp.23–34.

Alviansyah, M, Hartoyo, F, Nurullia, Z N & Kurniawan, A 2022, ‘Dynamic RBI with Central Difference Method Approach in Calculation of Uniform Corrosion Rate: A Casestudy on Gas Pipelines’, Journal of Materials Exploration and Findings (JMEF), 1(2), pp.51–61.

Cai, Y K, Zhao, Y, Ma, X B, Zhou, K & Wang, H 2018, ‘Long-term prediction of atmospheric corrosion loss in various field environments’, Corrosion, 74(6), pp.669-682.

Chalid, M, Fikri, A I, Satrio, H H, Joshua, M & Fatriansyah, J F 2017, ‘An investigation of the melting temperature effect on the rate of solidification in polymer using a modified phase field model’, International Journal of Technology, 7, pp.1321-1328.

Desjardins, G 2003, ‘Reliability Approach to Optimized Pipeline Integrity Planning Based On Probabilistic Assessment of Corrosion Rates and Future Corrosion Severity’, in Pipeline Rehabilitation & Maintenance Conference. Berlin: Desjardins Integrity.

Dhaneswara, D, Marito, H S, Fatriansyah, J F, Sofyan, N, Adhika, D R & Suhariadi, I 2022, ‘Spherical SBA-16 particles synthesized from rice husk ash and corn cob ash for efficient organic dye adsorbent’, Journal of Cleaner Production, 357, p.131974.

Dhaneswara, D, Suharno, B, Ariobimo, R D S, Sambodo, D B & Fatriansyah, J F 2018a, 'Effect of coating layer of sand casting mold in thin-walled ductile iron casting: reducing the skin effect formation', International Journal of Metalcasting, 12, pp.362-369.

Dhaneswara, D, Agustina, A S, Adhy, P D, Delayori, F & Fatriansyah, J F 2018b, ‘The Effect of Pluronic 123 Surfactant concentration on The N2 Adsorption Capacity of Mesoporous Silica SBA-15: Dubinin-Astakhov Adsorption Isotherm Analysis’, Journal of Physics: Conference Series, 1011(1), p.012017.

Hartoyo, F, Fatriansyah, J F, Mas'ud, I A, Digita, F R, Ovelia, H & Asral, D R 2022, 'The optimization of failure risk estimation on the uniform corrosion rate with a non-linear function', Journal of Materials Exploration and Findings (JMEF), 1(1), p.3.

Hartoyo, F, Irianti, G P, Fatriansyah, J F, Ovelia, H, Mas'ud, I A, Digita, F R, Fauzi, A & Anis, M 2023, ‘Weibull distribution optimization for piping risk calculation due to uniform corrosion using Monte Carlo method’, Materials Today: Proceedings, 80, pp.1650-1655.

Nešić, S 2007, ‘Key issues related to modelling of internal corrosion of oil and gas pipelines – A review’, Corrosion Science, 49(12), pp. 4308–4338.

Ossai, C I 2012, ‘Advances in Asset Management Techniques: An Overview of Corrosion Mechanisms and Mitigation Strategies for Oil and Gas Pipelines’, ISRN Corrosion, 2012, pp. 1–10.

Papavinasam, S 2014, Corrosion Control in the Oil and Gas Industry. Elsevier.

Thodi, P, Khan, F & Haddara, M 2009, ‘The selection of corrosion prior distributions for risk based integrity modeling’, Stochastic Environmental Research and Risk Assessment, 23(6), pp. 793–809.

Tiggor, T P & Riastuti, R 2022, ‘Risk Management of Carbon Steel Piping in Sweet Environment Multiphase Fluid Production’, Journal of Materials Exploration and Findings (JMEF), 1(2), pp. 35–50.

Wang, W, Liang, K, Wang, C & Wang, Q 2014, ‘Comparative analysis of failure probability for ethylene cracking furnace tube using Monte Carlo and API RBI technology’, Engineering Failure Analysis, 45, pp.278-282.

Younsi, K, Chebouba, A, Zemmour, N & Smati, A 2013, ‘Pipeline integrity assessment using probabilistic transformation method and corrosion growth modeling through gamma distribution’, Oil and Gas Facilities, 2(2), pp.51-60.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.