Keitaro, Bintang (2026) Pengembangan Dan Implementasi Software Risk Based Inspection Pada Sistem Perpipaan Pipa Penyalur API 5L X52 Segmen Rawajati PT.XYZ. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sistem perpipaan pada industri minyak dan gas bumi memiliki peran penting dalam menjamin kelancaran transportasi fluida, sehingga kegagalan pipa dapat menimbulkan dampak serius terhadap keselamatan, lingkungan, dan keandalan operasi. Penelitian ini bertujuan untuk melakukan perhitungan manual penilaian risiko pada pipa penyalur gas API 5L X52 segmen Rawajati PT. XYZ menggunakan metode Risk Based Inspection (RBI) berdasarkan standar API RP 580 dan API RP 581, mengintegrasikan hasil perhitungan manual RBI dengan classical machine learning untuk prediksi kategori risiko, serta mengembangkan perangkat lunak RBI yang mampu melakukan perhitungan risiko secara otomatis. Mekanisme degradasi yang dianalisis meliputi thinning dan cracking. Penilaian risiko dilakukan melalui perhitungan Probability of Failure (PoF) dan Consequence of Failure (CoF) yang selanjutnya dipetakan ke dalam risk matrix. Hasil analisis menunjukkan bahwa mekanisme thinning menjadi kontributor utama peningkatan PoF, dengan laju penipisan sebesar 0,04324 mm/tahun dan remaining life sebesar 3,44 tahun pada RBI Date 2025. Klasifikasi risiko berada pada kategori 3B dan meningkat menjadi 4B pada Plan Date 2035 apabila tidak dilakukan inspeksi tambahan. Integrasi hasil RBI dengan model neural network menghasilkan akurasi prediksi kategori risiko sebesar 90% dan konsisten dengan perhitungan manual. Perangkat lunak yang dikembangkan mampu menghitung PoF, CoF, dan remaining life secara otomatis serta memberikan rekomendasi kategori risiko, sehingga berpotensi menjadi decision support system yang efektif dalam pengelolaan integritas pipa penyalur gas.
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Pipeline systems in the oil and gas industry play a critical role in ensuring reliable fluid transportation; therefore, pipeline failures may lead to severe consequences for safety, the environment, and operational reliability. This study aims to perform a manual risk assessment of an API 5L X52 gas transmission pipeline in the Rawajati segment of PT. XYZ using the Risk Based Inspection (RBI) methodology in accordance with API RP 580 and API RP 581, to integrate the manual RBI results with classical machine learning for risk category prediction, and to develop an RBI software capable of automated risk calculations. The degradation mechanisms considered in this study are thinning and cracking. Risk assessment was conducted through the calculation of Probability of Failure (PoF) and Consequence of Failure (CoF), which were subsequently mapped into a risk matrix. The results indicate that thinning is the dominant contributor to PoF escalation, with a corrosion rate of 0.04324 mm/year and a remaining life of 3.44 years at the 2025 RBI date. The risk classification was identified as category 3B and increased to category 4B at the 2035 plan date in the absence of additional inspections. Integration of RBI results with a Neural Network achieved a prediction accuracy of 90%, demonstrating strong consistency with manual RBI calculations. The developed software enables automated PoF, CoF, and remaining life calculations and provides risk category recommendations, indicating its potential as an effective decision support system for gas pipeline integrity management.
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