Main Article Content
Abstract
Effective oil and gas pipeline management requires a data-driven approach to identify segments with varying risk characteristics. This study aims to classify pipeline segments based on protective infrastructure conditions using the Block-Based K-Medoids clustering method. The analysis considers six variables: Pipeline Burial, Pipe Along Road, Pipe Guards, Berm/Rail/Guard Condition, Public Road, and ROW HCA along a 59-kilometer pipeline corridor. Data were normalized, and the optimal number of clusters was determined using the Deviation Ratio Index based on Medoid (DRIM), which indicated three clusters as the most representative structure. The results demonstrate clear differentiation among segments in terms of exposure level, protective condition, and HCA involvement, enabling classification into low-, moderate-, and high-risk groups. Spatial visualization further confirms systematic risk distribution along the route. These findings provide a structured basis for prioritizing inspection, maintenance, and mitigation strategies in pipeline infrastructure management.
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Copyright (c) 2026 Dimas Zahran Wicaksana , Kariyam Kariyam, Tri Suryanto
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References
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References
P. Gautam, R. K. Khutey, S. K. Rath, A. Srivastava, and V. K. Singh, “Risk Analysis of Oil and Natural Gas Pipelines Due to Hazards,” Www.Ijres.Org, vol. ISSN, no. 8, pp. 165–176, 2022, [Online]. Available: www.ijres.org
W. Lu, H.; Zhang, J.; Wang, X.; Huang, Y.; Xie, “Trenchless Construction Technologies for Oil and Gas Pipelines: State-of-the-Art Review,” J. Constr. Eng. Manag., vol. 146, no. 6, pp. 1–13, 2020, doi: 10.1061/(ASCE)CO.1943-7862.0001819.
J. A. Ali et al., “Investigating the Influence of Environmental Factors on Corrosion in Pipelines Using Geospatial Modeling,” UHD J. Sci. Technol., vol. 8, no. 1, pp. 1–12, 2024, doi: 10.21928/uhdjst.v8n1y2024.pp1-12.
G. Tiehua, H.; Jingbo, “Development and Application of New Technologies and Equipment for In-line Pipeline Inspection,” Nat. Gas Ind. B, vol. 6, no. 4, pp. 404–411, 2019, doi: 10.1016/j.ngib.2019.01.017.
Q. Ma et al., “Pipeline in-line inspection method, instrumentation and data management,” Sensors, vol. 21, no. 11, 2021, doi: 10.3390/s21113862.
A. Nurissa’adah, E. Ismiyah, and A. W. Rizqi, “Analysis of Occupational Health, and Safety (K3) in the Workshop Area Using the HIRA and 5S Methods at PT. Ravana Jaya,” Motiv. J. Mech. Electr. Ind. Eng., vol. 4, no. 2, pp. 161–174, 2022, doi: 10.46574/motivection.v4i2.122.
Y. J. D. H. Y. Y. C. Xiang, “Risk Assessment of Submarine Pipelines Using Modified FMEA Approach Based on Cloud Model and Extended VIKOR Method,” Process Saf. Environ. Prot., vol. 155, pp. 555–574, 2021, doi: 10.1016/j.psep.2021.09.041.
P. K. Dey, S. O. Ogunlana, and S. Naksuksakul, “Risk-based maintenance model for offshore oil and gas pipelines: A case study,” J. Qual. Maint. Eng., vol. 10, no. 3, pp. 169–183, 2004, doi: 10.1108/13552510410553226.
Z. Zemanova, S. Krocova, and P. Sirotiak, “Risk Management in the Water Industry †,” Eng. Proc., vol. 57, no. 1, 2023, doi: 10.3390/engproc2023057020.
S. E. Bitty, L. A. Hendratta, A. H. Thambas, and G. Malingkas, “Manajemen risiko pada sistem penyediaan air minum ( SPAM ) perpipaan dengan metode failure mode and effect analysis dan fault tree analysis di Kabupaten Minahasa Utara,” vol. 13, no. 2, pp. 138–147, 2024.
D. Fatmawaty, “Analisis Pertanggungjawaban Pencemaran Lingkungan Akibat Tumpahan Minyak (Studi Kasus: Kebocoran Pipa Minyak di Teluk Balikpapan),” Bumi Lestari J. Environ., vol. 20, no. 1, p. 14, 2020, doi: 10.24843/blje.2020.v20.i01.p03.
K. Noussia, “The BP Oil Spill Environmental Pollution Liability and Other Legal Ramifications,” Eur. Energy Environ. Law Rev., vol. 20, no. 3, pp. 98–107, 2011, doi: 10.54648/EELR2011009.
Kariyam, Abdurakhman, Subanar, H. Utami, and A. R. Effendie, “Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy,” Math. Model. Eng. Probl., vol. 9, no. 6, pp. 1613–1621, 2022, doi: 10.18280/MMEP.090622.
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J. J. Soria, O. Poma, D. A. Sumire, J. H. F. Rojas, and S. M. R. Chipa, “Multiple Linear Regression Model of Environmental Variables, Predictors of Global Solar Radiation in the Area of East Lima, Peru,” IOP Conf. Ser. Earth Environ. Sci., vol. 1006, no. 1, 2022, doi: 10.1088/1755-1315/1006/1/012009.
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