Intelligent information technologies for natural gas transportation: current state and future prospects
DOI:
https://doi.org/10.31471/1993-9965-2025-2(59)-144-155Keywords:
gas transmission system, gas flow modeling, pipeline network optimization, gas demand forecasting, numerical simulation, digital twinsAbstract
The article examines the relevance, current state, and prospects of applying intelligent information technologies (IIT) in natural gas transportation. In view of the global increase in energy demand, the need for decarbonization of the fuel and energy sector, and growing requirements for efficiency and safety, IITs play a significant role in ensuring the reliable operation of gas transmission systems (GTS). The analysis covers the condition of Ukraine’s GTS, its technical capacities, and its role in strengthening European energy security, particularly under the transformation of the natural gas market and the cessation of Russian gas transit. The experience of recent studies is summarized, where IITs are applied for gas flow modeling, transportation optimization, gas demand forecasting, leak detection, accident prevention, and infrastructure cybersecurity. Special attention is paid to digital twins, SCADA supervisory control and data acquisition systems, machine learning methods, and artificial intelligence algorithms, which are integrated to achieve a synergistic effect in managing complex engineering systems. The prospects for introducing neural networks and deep learning methods to improve operational safety are outlined, as well as the potential of IITs for integrating GTS with hydrogen transportation systems within the EU decarbonization policy. It is emphasized that combining intelligent algorithms with economic and mathematical models enables the simultaneous solution of tasks related to technical reliability, environmental sustainability, and economic efficiency. The purpose of the study is to develop scientific approaches to the application of IIT in natural gas transportation, considering current challenges, in particular the strengthening of energy security and the integration of Ukraine’s GTS into the European energy space.
Downloads
References
1. World Nuclear Association. (n.d.). World energy needs and nuclear power. Current and future generation. https://world-nuclear.org/information-library/current-and-future-generation/world-energy-needs-and-nuclear-power
2. State Statistics Service of Ukraine. (n.d.). Enerhetychnyi balans Ukrainy. Ekonomichna statystyka / Ekonomichna diialnist / Enerhetyka [Energy balance of Ukraine. Economic statistics / Economic activity / Energy]. https://www.ukrstat.gov.ua/operativ/operativ2012/energ/en_bal/
arh_2012.htm [in Ukrainian]
3. National Institute for Strategic Studies. (2020). Enerhetychna bezpeka Ukrainy: metodolohiia systemnoho analizu ta stratehichnoho planuvannia [Energy security of Ukraine: Methodology of system analysis and strategic planning]. https://niss.gov.ua/sites/default/files/2020-12/
sukhodolia_energy_security_sayt-1.pdf [in Ukrainian]
4. Gas TSO of Ukraine. (n.d.). Tekhnichni dani. Mozhlyvosti HTS [Technical data. GTS capabilities]. https://tsoua.com/gts-infrastruktura/mozhlyvosti-gts/tehnichni-dani/ [in Ukrainian]
Ukrtransgaz. (n.d.). Pidzemni skhovyshcha hazu. Kliientam PSH [Underground gas storages. For UGS clients]. https://utg.ua/utg/psg/underground-gas-storages/ [in Ukrainian
5. Ukrtransgaz. (n.d.). Pidzemni skhovyshcha hazu. Kliientam PSH [Underground gas storages. For UGS clients]. https://utg.ua/utg/psg/underground-gas-storages/ [in Ukrainian]
6. DiXi Group. (2021). Stratehiia YeS z intehratsii enerhetychnoi systemy: mozhlyvosti dlia Ukrainy [EU strategy on energy system integration: Opportunities for Ukraine]. https://dixigroup.org/wp-content/uploads/2021/03/PB_Energy-System-Integration-Strategy.pdf [in Ukrainian]
7. Li, Z., et al. (2023). Review on intelligent pipeline technologies: A life cycle perspective. Computers & Chemical Engineering, 175, 108283. https://doi.org/10.1016/j.compchemeng.2023.108283
8. Su, H., et al. (Eds.). (2023). Advanced intelligent pipeline management technology. Springer Singapore. https://doi.org/10.1007/978-981-19-9899-7
9. Li, J., et al. (2023). An intelligent energy management information system with machine learning algorithms in oil and gas industry. Wireless Communications and Mobile Computing, 2023, 3385453. https://doi.org/10.1155/2023/3385453
10. Hrudz, V., Mykhalkiv, V., Rudko, V., & Tymkiv, D. (1996). Rozrobka systemy operatyvnoho keruvannia rezhymamy hazoprovodu [Development of a system for operational control of gas pipeline modes]. Naftova i hazova promyslovist, (4), 31–32. [in Ukrainian]
11. Chekurin, V. F., Prytula, M. H., & Khymko, O. M. (2013). Struktura ta funktsii intehrovenoho prohramno-tekhnichnoho kompleksu dlia avtomatyzatsii upravlinnia hazotransportnoiu systemoi [Structure and functions of an integrated software and hardware complex for automation of gas transportation system management]. Visnyk Natsionalnoho universytetu "Lvivska politekhnika". Seriia: Avtomatyka, vymiriuvannia ta keruvannia, (774), 51–60. https://ena.lpnu.ua:8443/server/api/core/bitstreams/6427ea91-e1e2-4baa-b483-cc60f0b76ec7/content [in Ukrainian]
12. Fuller, A., et al. (2020). Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/access.2020.2998358
13. Rebello, C. M., et al. (2023). Framework for optimal and autonomous decision-making in cyber-physical systems: Enhancing reliability and adaptability in the oil and gas industry (Preprint). arXiv:2311.12755. https://doi.org/10.48550/arXiv.2311.12755
14. Li, B., Gai, J., & Xue, X. (2020). The digital twin of oil and gas pipeline system. IFAC-PapersOnLine, 53(5), 710–714. https://doi.org/10.1016/j.ifacol.2021.04.162
15. Liu, Z. E., et al. (2024). A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning. Energy and AI, 100434. https://doi.org/10.1016/j.egyai.2024.100434
16. Arya, A. K. (2021). Optimal operation of a multi-distribution natural gas pipeline grid: An ant colony approach. Journal of Petroleum Exploration and Production Technology, 11, 3859–3878. https://doi.org/10.1007/s13202-021-01266-3
17. Chekurin, V. F. (2007). Do pobudovy prohramnoi systemy dlia modeliuvannia ta optymizatsii protsesiv transportuvannia pryrodnoho hazu [Towards the construction of a software system for modeling and optimization of natural gas transportation processes]. Fizyko-matematychne modeliuvannia ta informatsiini tekhnolohii, (5), 158–169. [in Ukrainian]
18. Chekurin, V., Prytula, M., & Khymko, O. (2013). Matematychna model struktury hazot-?ansportnoi systemy [Mathematical model of the gas transportation system structure]. Kompiuterni nauky ta informatsiini tekhnolohii, (771), 187–196. [in Ukrainian]
19. Khymko, O., & Chekurin, V. (2017). Modeliuvannia struktury prohramnoho kompleksu dlia upravlinnia hazotransportnymy systemamy [Modeling the structure of a software complex for managing gas transportation systems]. Informatsiini tekhnolohii ta kompiuterne modeliuvannia, 28–31. [in Ukrainian]
20. Kralik, J., Stiegler, P., Vostry, Z., & Zavorka, J. (1984). Modelling the dynamics of flow in gas pipelines. IEEE Transactions on Systems, Man, and Cybernetics, 14(4), 586–596.
21. Kralik, J., Stiegler, P., Vostry, Z., & Zavorka, J. (1984). A universal dynamic simulation model of gas pipeline networks. IEEE Transactions on Systems, Man, and Cybernetics, 14(4), 597–606.
22. SIMONE Research Group s.r.o. (n.d.). About company. Solutions for simulation and optimisation in the gas industry. https://www.simone.eu/simone-company-about.asp
23. Zhao, Y., et al. (2025). Research on detection methods for gas pipeline networks under small-hole leakage conditions. Sensors, 25(3), 755. https://doi.org/10.3390/s25030755
24. Zhang, T., Bai, H., & Sun, S. (2022). Intelligent natural gas and hydrogen pipeline dispatching using the coupled thermodynamics-informed neural network and compressor boolean neural network. Processes, 10(2), 428. https://doi.org/10.3390/pr10020428
25. Zhu, C., et al. (2023). AI-based energy transportation safety: Pipeline radial threat estimation using intelligent sensing system (Preprint). arXiv:2312.11583. https://doi.org/10.48550/arXiv.2312.11583
26. Yang, Y., et al. (2023). Intelligent methods for pipeline operation and integrity. Journal of Pipeline Systems Engineering and Practice, 15(1). https://doi.org/10.1061/JPSEA2.PSENG-1490
27. Wang, Y. (2025). Digital twin-based real-time monitoring and intelligent maintenance system for oil and gas pipelines. Applied Mathematics and Nonlinear Sciences, 10(1). https://doi.org/10.2478/amns-2025-0849
28. Wang, M., et al. (2021). Robots for pipeline inspection tasks – A survey of design philosophy and implementation technologies. 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT), 427–432. https://doi.org/10.1109/ACAIT53529.2021.9731152
29. Rachman, A., Zhang, T., & Ratnayake, R. M. C. (2021). Applications of machine learning in pipeline integrity management: A state-of-the-art review. International Journal of Pressure Vessels and Piping, 193, 104471. https://doi.org/10.1016/j.ijpvp.2021.104471
30. Herrera, R. H., Christensen, P., & Elvers, A. (2019). Machine learning in pipeline inspection: Applications of supervised learning in non-destructive evaluation. e-Journal of Nondestructive Testing, 24(10). https://www.ndt.net/?id=24701
31. Khymko, O., Hryniv, O., Prytula, N., & Prytula, M. (2015). Alhorytmichenyi metod diahnostyky vytokiv hazu na diliankakh mahistralnykh hazoprovodiv [Algorithmic method for diagnosing gas leaks in sections of main gas pipelines]. Kompiuterni nauky ta informatsiini tekhnolohii, (826), 404–412. [in Ukrainian]
32. Oshingbesan, A. (2022). Leak detection in natural gas pipeline using machine learning models (Preprint). arXiv:2209.10121. https://doi.org/10.48550/arXiv.2209.10121
33. Kim, J., et al. (2024). Gas pipeline leak detection by integrating dynamic modeling and machine learning under the transient state. Energies, 17(21), 5517. https://doi.org/10.3390/en17215517
34. Al‐Ammari, W. A., et al. (2025). Development of digital/visual twin for real‐time leak detection in gas pipelines under multiphase flow conditions. Greenhouse Gases: Science and Technology. https://doi.org/10.1002/ghg.2379
35. Wang, D., et al. (2023). Research on gas pipeline leakage model identification driven by digital twin. Systems Science & Control Engineering, 11(1). https://doi.org/10.1080/21642583.2023.2180687
36. Nankya, M., Chataut, R., & Akl, R. (2023). Securing industrial control systems: Components, cyber threats, and machine learning-driven defense strategies. Sensors, 23(21), 8840. https://doi.org/10.3390/s23218840
37. Koay, A. M. Y., et al. (2023). Machine learning in industrial control system (ICS) security: Current landscape, opportunities and challenges. Journal of Intelligent Information Systems, 60, 377–405. https://doi.org/10.1007/s10844-022-00753-1
38. Zhang, L., & Wang, J. (2023). Intelligent safe operation and maintenance of oil and gas production systems: Connotations and key technologies. Natural Gas Industry B, 10(3), 293–303. https://doi.org/10.1016/j.ngib.2023.05.006
39. Nemec, R. (2022). Artificial intelligence and pipelines – A smart way to go. P&GJ Online, 249(1). https://pgjonline.com/magazine/2022/january-2022-vol-249-no-1/features/artificial-intelligence-and-pipelines-a-smart-way-to-go
40. Magdin, M. (2025). Literature review: Current trends and advances in the use of artificial intelligence for ensuring the safety and efficiency of gas pipeline operations. Results in Engineering, 28, 107309. https://doi.org/10.1016/j.rineng.2025.107309
41. Lu, H., & Cheng, Y. F. (2025). Artificial intelligence in energy pipelines: Opportunities and risks. Engineering. https://doi.org/10.1016/j.eng.2025.08.032
42. Kovalko, M. P., et al. (2002). Truboprovidnyi transport hazu [Pipeline transportation of gas]. ArenaEKO. [in Ukrainian]
43. Norwegian University of Science and Technology. (n.d.). The role of natural gas in Europe towards 2050. Energy Transition Initiative. https://www.ntnu.edu/energytransition/the-role-of-natural-gas-in-europe-towards-2050
Downloads
Published
How to Cite
Issue
Section
License
Авторські права....
1.png)
