Simulation Study for Flow Assurance of Thai Binh Liquefied Natural Gas Supply Pipeline by Using OLGA Software
Main Article Content
Abstract
The natural gas supply in Vietnam is progressively declining; therefore, the strategy of importing LNG (Liquefied Natural Gas) is currently an urgent solution to ensure national energy security. The main component of LNG is methane, which is liquefied through deep refrigeration for storage and transportation. According to Power Development Plan VIII, by 2030, the country is expected to have about 24 GW of power generated from LNG. Ensuring stable flow (flow assurance) is a key factor in the LNG transportation system. This study focuses on calculating the operating conditions in the LNG supply pipeline using OLGA software. Based on the transported gas flow rate, the study conducts steady-state simulations as the basis for calculating the transient state of the pipeline. The transient results indicate the need to reinforce the initial segment of the pipeline with thermally insulated alloy steel, control hydrate formation using hydrate inhibitors, conduct regular pigging, assess the risk of system shutdown, and manage depressurization operations. These results serve as a foundation for the effective and safe design and operation of the LNG supply system.
Keywords
Flow assurance, liquefied natural gas, modeling, pipeline.
Article Details
References
[2] G. Cooper, M. Keane, and H. Nguyen, Report: Vietnam LNG sector update, Allens, Oct 18, 2023. [Online]. Available: https://www.allens.com.au/insights-news/insights/2020/10/report-vietnam-lng-sector-update/.
[3] J. Fu, S. Zou, J. Sun, Q. Xua, Q. Wu And L. Guo, Self‑adaptive early warning of undesirable gas‑liquid flow pattern in offshore oil and gas pipeline‑riser system, Process Safety and Environmental Protection, vol. 182, pp. 254–278, 2024.
https://doi.org/10.1016/j.psep.2023.11.055
[4] I. Animah and M. Shafiee, Application of risk analysis in the liquefied natural gas (LNG) sector: an overview, Journal of Loss Prevention in the Process Industries, vol. 63, 2020, Art. no. 103980.
[5] A. Atienza-Márquez, D. S. Ayou, J. C. Bruno, and A. Coronas, Energy polygeneration systems based on LNG‑regasification: comprehensive overview and techno‑economic feasibility, Thermal Science and Engineering Progress, vol. 20, 2020, Art. no. 100677.
[6] T. H. Nguyen and U. Turksen, LNG‑to‑Power projects in Vietnam: a critical assessment of potentials, gains and risks, Oil Gas and Energy Law, vol. 21, pp. 2–17, 2023.
[7] T. Vu, The full title is Beyond the Noise: Setting the Right Expectations for Vietnam’s LNG Project Pipeline, The report is published by the Institute for Energy Economics and Financial Analysis (IEEFA), Jan. 2021. [Online]. Available: https://ieefa.org/sites/default/files/resources/Setting-the-Right-Expectations-for-Vietnams-LNG-Project-Pipeline_January-2021.pdf
[8] O. S. Obaro, Effect of ethylene‑glycol on hydrate formation in gas pipeline, Global Journal of Engineering and Technology Advances, vol. 12, no. 2, pp. 86–95, 2022.
[9] A. A. Olajire, Flow assurance issues in deep‑water gas well testing and mitigation strategies with respect to gas hydrates deposition in flowlines-a review, Journal of Molecular Liquids, vol. 318, Art. no. 114203, 2020.
[10] S. L. Caceres, Safety problems caused by hydrate formation in deepwater production operation, Master of Science thesis, Department of Safety Engineering, Texas A&M University, College Station, Texas, USA, Aug. 2017.
[11] S. Boumaraf, Vision-based air-flow monitoring in an industrial flare system design using deep convolutional neural networks, Expert Systems with Applications, vol. 272, Art. no. 126733, 2025.
[12] Ministry of Construction, Circular No. 02/2022/TT-BXD, Ministry of Construction, 2022.
[13] L. Basharova, Features of thermal and hydraulic calculations in designing of cryogenic pipeline for LNG, M.Sc. thesis, Montanuniversität Leoben, Leoben, Austria, Jun. 2021. [Online]. Available: https://pure.unileoben.ac.at/files/7474885/AC16307033.pdf.
[14] V. C. G. D. Freitas, V. G. D. Araujo, D. C. D. C. Crisóstomo, G. F. D. Lima, A. D. D. Neto and A. O. Salazar, Velocity prediction of a pipeline inspection gauge (PIG) with machine learning,vi Sensors, vol. 22, no. 23, Art. no. 9162, Nov. 2022.
[15] L. Dykhno, J. Hudson, J. Harris, and M. Seay, Modeling of pigging with production fluids in a single flowline, in Proceedings of the Offshore Technology Conference (OTC), Houston, TX, USA, May 2002.