Analisis Resiko Pada Gas Slug Catcher Menggunakan Metode Risk-Based Inspection (RBI) Dengan Integrasi Bayesian Network (BN)

Christy, Shamanta Basilea (2025) Analisis Resiko Pada Gas Slug Catcher Menggunakan Metode Risk-Based Inspection (RBI) Dengan Integrasi Bayesian Network (BN). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Industri minyak dan gas merupakan salah satu sektor utama dalam penyediaan energi global, di mana proses eksplorasi gas seringkali menghasilkan fluida produksi yang terdiri dari campuran gas, kondensat, dan cairan lainnya. Salah satu tantangan dalam sistem pengaliran fluida tersebut adalah fenomena slugging, yaitu akumulasi cairan dalam pipa yang dapat menimbulkan gangguan operasional, tekanan mendadak, serta menurunkan efisiensi sistem. Untuk mengatasi hal tersebut, digunakan gas slug catcher, yaitu peralatan yang berfungsi untuk memisahkan fluida menjadi fase gas dan cairan sebelum dialirkan ke tahap proses berikutnya. Penelitian ini bertujuan untuk menganalisis risiko kegagalan pada gas slug catcher menggunakan metode Risk-Based Inspection (RBI) yang diintegrasikan dengan pendekatan Bayesian Network (BN). Analisis RBI dilakukan dengan menghitung damage factor, generic failure frequency, dan management system factor untuk memperoleh nilai probability of failure, serta menghitung consequence of failure. Selanjutnya, model Bayesian Network dikembangkan dengan mengidentifikasi variabel risiko utama, menentukan state dan conditional probability table, lalu melakukan simulasi inference untuk berbagai skenario kondisi lapangan. Hasil analisis RiskBased Inspection (RBI) menunjukkan bahwa area Manhole pada Gas Slug Catcher memiliki tingkat kerusakan tertinggi, dengan damage factor (DF) sebesar 77,37. Sebagai perbandingan, komponen lain seperti Nozzle 1, Nozzle 5, dan Nozzle 16 menunjukkan nilai DF yang jauh lebih rendah, yakni hanya 0,1, sehingga menghasilkan PoF sebesar 3,06 × 10⁴ events/year atau sekitar 30.600 kejadian per tahun, mengacu pada nilai generic failure frequency (GFF) sebesar 3,06 × 10⁵ dan FMS mendekati 1. Sementara itu, pendekatan berbasis Bayesian Network (BN) memberikan nilai probabilitas kegagalan dalam bentuk persentase. Pada skenario normal, ketika seluruh kondisi peralatan dianggap berada dalam keadaan ideal, hasil simulasi menunjukkan bahwa probabilitas PoF High sebesar 24%. Artinya, dari seluruh kemungkinan kondisi sistem, 24 dari setiap 100 skenario memprediksi bahwa risiko kegagalan berada dalam kategori tinggi, sedangkan sisanya berada dalam kategori rendah. Ketika kondisi memburuk probabilitas PoF High meningkat menjadi 56%, atau lebih dari setengah kemungkinan skenario menunjukkan adanya risiko kegagalan tinggi. Peningkatan ini menggambarkan bagaimana model BN menangkap interaksi antar variabel penyebab kerusakan, serta memperlihatkan sensitivitas sistem terhadap perubahan kondisi. Namun, keterbatasan yang dihadapi dalam pemodelan BN meliputi keterbatasan jumlah data historis, penggunaan asumsi dan expert judgment dalam pembuatan CPT, serta belum dilakukannya pemisahan model untuk masingmasing komponen secara spesifik.
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The oil and gas industry is one of the primary sectors in global energy supply, where gas exploration processes often produce mixed-phase fluids consisting of gas, condensate, and other liquids. One of the key challenges in fluid transmission systems is the slugging phenomenon— an accumulation of liquid in pipelines that can lead to operational disruptions, sudden pressure surges, and decreased system efficiency. To mitigate this issue, a gas slug catcher is used to separate the fluid into gas and liquid phases before entering the next processing stage. This study aims to analyze the failure risk of a gas slug catcher using the Risk-Based Inspection (RBI) method integrated with a Bayesian Network (BN) approach. The RBI analysis involves calculating the damage factor (DF), generic failure frequency (GFF), and management system factor (FMS) to determine the probability of failure (PoF), as well as calculating the consequence of failure (CoF) based on fluid characteristics and potential impacts. The Bayesian Network model is developed by identifying key risk variables, defining their states, building the conditional probability table (CPT), and performing inference simulations under various field conditions. The RBI analysis results show that the Manhole area of the Gas Slug Catcher has the highest damage factor (DF) of 77.37. This results in an estimated PoF of 2.37 × 10⁷ events/year, indicating a statistical likelihood of approximately 23 million failure events per year in absolute probabilistic terms. While this figure does not reflect actual occurrences, it represents a very high level of risk, making the Manhole area a top priority for inspection planning and risk mitigation. In contrast, other components such as Nozzle 1, Nozzle 5, and Nozzle 16 have significantly lower DF values of 0.1, corresponding to a PoF of 3.06 × 10⁴ events/year (around 30,600 events), based on a GFF of 3.06 × 10⁵ and an FMS near 1— indicating relatively low and acceptable risk levels. Meanwhile, the Bayesian Network approach provides failure probability estimates in a more practical percentage format. Under normal operating conditions—where parameters such as wall thickness, corrosion rate, pressure, and temperature remain within safe limits—the simulation yields a PoF High of 24%, meaning that 24 out of every 100 scenarios predict a high-risk failure condition. As operational conditions deteriorate, the PoF High increases to 56%, with more than half of the simulated scenarios indicating high failure risk. This increase demonstrates the BN model’s ability to capture interactions among risk variables and highlights the system's sensitivity to condition changes. The integration of RBI and BN provides a more realistic probabilistic insight and supports more dynamic inspection decision-making. However, the Bayesian Network modeling faces limitations, including restricted availability of historical data, reliance on assumptions and expert judgment in CPT construction, and the absence of component-level separation in the model. Despite these constraints, the integrated approach shows strong potential for enhancing risk management and optimizing inspection strategies for gas slug catcher equipment.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Gas Slug Catcher, Risk-Based Inspection, Bayesian Network,
Subjects:	Q Science > QC Physics > QC271 Temperature measurements Q Science > QC Physics > QC320 Heat transfer Q Science > QC Physics > QC765 Magnetic materials T Technology > TN Mining engineering. Metallurgy > TN880.5 Natural gas pipelines
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Material & Metallurgical Engineering > 28201-(S1) Undergraduate Thesis
Depositing User:	Shamanta Basilea Christy
Date Deposited:	05 Aug 2025 10:38
Last Modified:	05 Aug 2025 10:38
URI:	http://repository.its.ac.id/id/eprint/127638

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