Hydraulic Line Modeling and Pre-Commissioning Optimization of High-Pressure, Low-Pressure, and Corrosion Inhibitor Systems in Subsea Umbilicals
Malvern Iheanyichukwu Odum, Iduate Jason Digitemie, Dazok Donald Jambol
Abstract
This paper presents a comprehensive study on hydraulic line modeling and pre-commissioning optimization for high-pressure, low-pressure, and corrosion inhibitor systems within subsea umbilicals. Emphasizing fluid dynamics, pressure loss mechanisms, and material considerations, the research develops accurate modeling techniques to predict hydraulic behavior under complex offshore conditions. It further explores optimized pre-commissioning strategies, including pressure testing, leak detection, flushing, and corrosion inhibitor injection, to ensure system integrity and operational readiness. Analytical insights reveal critical parameters affecting hydraulic line performance and demonstrate the benefits of optimized procedures in enhancing system reliability and reducing downtime. The study also discusses the implications of these findings for subsea umbilical design and maintenance practices, highlighting opportunities for improved material selection and monitoring technologies. Future research directions focus on real-time monitoring, advanced materials, and automated control systems to further elevate subsea system performance. The integrated approach proposed bridges design and operational challenges, supporting safer and more efficient offshore production.
Keywords
Subsea Umbilicals
Hydraulic Line Modeling
Pre-Commissioning Optimization
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Engineering and Technology, vol. 7, no. 2, pp. 16-30, 2024.
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Business Units in Finance," 2024.
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F. E. Adikwu, C. O. Ozobu, O. Odujobi, F. O. Onyeke, and E. O. Nwulu, "A
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Occupational Health Programs in Large-Scale Manufacturing Plants," 2025.
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for Enhancing Regulatory Compliance and Risk Controls in Smart Transportation
Networks," International Journal of Advanced Multidisciplinary Research and Studies,
vol. 4, 2024.
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Automation Models for Real-Time Regulatory Reporting in Energy Systems."
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a Machine Learning-Based Conceptual Framework for Market Trend Analysis in E-
Commerce: Enhancing Customer Engagement and Driving Sales Growth," Int. J.
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Risk Management," Iconic Research and Engineering Journals, vol. 7, no. 11, 2024.
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D. I. Ajiga, O. Hamza, A. Eweje, E. Kokogho, and P. E. Odio, "Data-driven strategies
for enhanc
