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Назва:	Diagnostics of oil leaks caused by malicious damage to the linear part of oil pipelines: innovative solutions for the oil industry
Автори:	Obshta, Anatoliy Yuzevych, Volodymyr Pohrebniak, Andrii Mysiuk, Roman Chorniy, Bogdan
Бібліографічний опис:	Obshta A., Yuzevych V., Pohrebniak A., Mysiuk R., Chorniy B. Diagnostics of oil leaks caused by malicious damage to the linear part of oil pipelines: innovative solutions for the oil industry // International scientific journal "Internauka". 2024. № 2. doi: https://doi.org/10.25313/2520-2057-2024-2-9590
Журнал/збірник:	International scientific journal "Internauka". 2024. № 2.
Дата публікації:	2024
Дата подання:	2024
Дата внесення:	30-січ-2024
Країна (код):	UA
DOI:	https://doi.org/10.25313/2520-2057-2024-2-9590
URI (Уніфікований ідентифікатор ресурсу):	https://www.inter-nauka.com/en/issues/2024/2/9590/ http://elartu.tntu.edu.ua/handle/lib/44236
URL-посилання пов’язаного матеріалу:	https://www.inter-nauka.com/en/issues/2024/2/9590/ https://doi.org/10.25313/2520-2057-2024-2-9590
Перелік літератури:	1. Lozovan V., Skrynkovskyy R., Yuzevych V., Yasinskyi M., Pawlowski G. Forming the toolset for development of a system to control quality of operation of underground pipelines by oil and gas enterprises with the use of neural networks. Eastern-European Journal of Enterprise Technologies. 2019. Vol. 2, No. 5(98). P. 41–48. doi: http://dx.doi.org/10.15587/1729-4061.2019.161484. 2. Lozovan V., Dzhala R., Skrynkovskyy R., Yuzevych V. Detection of specific features in the functioning of a system for the anti-corrosion protection of underground pipelines at oil and gas enterprises using neural networks. Eastern-European Journal of Enterprise Technologies. 2019. Vol. 1, No. 5(97). P. 20–27. doi: https://doi.org/10.15587/1729-4061.2019.154999. 3. Yuzevych L., Yankovska L., Sopilnyk L., Yuzevych V., Skrynkovskyy R., Koman B., Yasinska-Damri L., Heorhiadi N., Dzhala R., Yasinskyi M. Improvement of the toolset for diagnosing underground pipelines of oil and gas enterprises considering changes in internal working pressure. Eastern-European Journal of Enterprise Technologies. 2019. Vol. 6, No. 5(102). P. 23–29. doi: https://doi.org/10.15587/1729-4061.2019.184247. 4. Kuchin N. L., Vishnyakov Yu. M., Emel’yanov S. I. Radiation Diagnostics of Pipelines and Equipment of Oil and Gas Production Complexes. Atomic Energy. 2019. Vol. 127(2). P. 115–119. doi: https://doi.org/10.1007/s10512-019-00595-1. 5. Xu Z.-D., Zhu C., Shao L.-W. Damage Identification of Pipeline Based on Ultrasonic Guided Wave and Wavelet Denoising. Journal of Pipeline Systems Engineering and Practice. 2021. Vol. 12, Issue 4. doi: https://doi.org/10.1061/(asce)ps.1949-1204.0000600. 6. Li J., Zheng Q., Qian Z., Yang X. A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Safety and Environmental Protection. 2019. Vol. 123. P. 309–316. doi: https://doi.org/10.1016/j.psep.2019.01.010. 7. Ndalila P. D., Li Y., Liu C., Nasser A. H. A., Mawugbe E. A. Modeling Dynamic Pressure of Gas Pipeline With Single and Double Leakage. IEEE Sensors Journal. 2021. Vol. 21, No. 9. P. 10804–10810. doi: https://doi.org/10.1109/jsen.2021.3058507. 8. Ling K., Han G., Ni X., Xu C., He J., Pei P., Ge J. A New Method for Leak Detection in Gas Pipelines. Oil and Gas Facilities. 2015. Vol. 4, Issue 02. P. 097–106. doi: https://doi.org/10.2118/2014-1891568-pa. 9. Marino M., Chiappa F., Giunta G. A vibroacoustic integrated technology for the detection of a wide spectrum of illegal activities. 16th Pipeline Technology Conference. 2021. Berlin, Germany. URL: https://www.pipeline-conference.com/abstracts/vibroacoustic-integrated-technology-detection-wide-spectrum-illegal-activities (date of access: 05.01.2024). 10. Thiberville C. J., Wang Y., Waltrich P., Williams W. C., Kam S. I. Evaluation of Software-Based Early Leak-Warning System in Gulf of Mexico Subsea Flowlines. SPE Production & Operations. 2018. Vol. 33, Issue 04. P. 802–828. doi: https://doi.org/10.2118/187417-pa. 11. Adegboye M. A., Karnik A., Fung W.-K. Numerical study of pipeline leak detection for gas-liquid stratified flow. Journal of Natural Gas Science and Engineering. 2021. Vol. 94. 104054. doi: https://doi.org/10.1016/j.jngse.2021.104054. 12. Vladimirsky A. A., Vladimirsky I. A. Correlation Parametric Methods for Determining the Coordinates of Leaks in Underground Pipelines. Electronic Modeling. 2021. Vol. 43, No. 3. P. 03–16. doi: https://doi.org/10.15407/emodel.43.03.003. 13. Grudz V. Y. Modern software products as a means of diagnosing non-isothermal oil pipelines. Exploration and Development of Oil and Gas Fields. 2012. № 1. P. 7–16. 14. Fu H., Yang L., Liang H., Wang S., Ling K. Diagnosis of the single leakage in the fluid pipeline through experimental study and CFD simulation. Journal of Petroleum Science and Engineering. 2020. Vol. 193. 107437. doi: https://doi.org/10.1016/j.petrol.2020.107437. 15. ICP DAS Products by Category. HOLIT Data Systems (Ukraine). URL: http://icpdas.com.ua/ (date of access: 05.01.2024). 16. Products & Services. Yokogawa Electric Corporation. URL: https://www.yokogawa.com/solutions/products-and-services/ (date of access: 05.01.2024). 17. All Products. Solutions, Software. Schneider Electric. URL: https://www.se.com/in/en/all-products (date of access: 05.01.2024). 18. Ivasiv V. М., Deineha R. О., Faflei О. Ya., Mykhailiuk V. V., Bui V. V., Hovdiak R. M. Investigation of the influence of corrosion defects on the durability of main oil pipelines. Oil and Gas Power Engineering. 2020. No. 2(34). P. 67–74. doi: https://doi.org/10.31471/1993-9868-2020-2(34)-67-74. 19. Products. We are experts in hardware and software. ROSEN Group. URL: https://www.rosen-group.com/global/solutions/products.html (date of access: 05.01.2024). 20. Korlapati N. V. S., Khan F., Noor Q., Mirza S., Vaddiraju S. Review and analysis of pipeline leak detection methods. Journal of Pipeline Science and Engineering. 2022. Vol. 2, Issue 4. 100074. doi: https://doi.org/10.1016/j.jpse.2022.100074. 21. Rai A., Kim J.-M. A novel pipeline leak detection approach independent of prior failure information. Measurement. 2021. Vol. 167. 108284. doi: https://doi.org/10.1016/j.measurement.2020.108284. 22. Koman B., Balitskii O. A., Yuzevych V. The Nature of Intrinsic Stresses in Thin Copper Condensates Deposited on Solid State Substrates. Journal of Nano Research. 2018. Vol. 54. P. 66–74. doi: https://doi.org/10.4028/www.scientific.net/jnanor.54.66. 23. Adegboye M. A., Fung W.-K., Karnik A. Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. Sensors. 2019. Vol. 19, Issue 11. 2548. doi: https://doi.org/10.3390/s19112548. 24. Kumar Vandrangi S., Alemu Lemma T., Muhammad Mujtaba S., Ofei T. N. Developments of leak detection, diagnostics, and prediction algorithms in multiphase flows. Chemical Engineering Science. 2022. Vol. 248, Part B. 117205. doi: https://doi.org/10.1016/j.ces.2021.117205. 25. Lukonge A. B., Cao X. Leak Detection System for Long-Distance Onshore and Offshore Gas Pipeline Using Acoustic Emission Technology. A Review. Transactions of the Indian Institute of Metals. 2020. Vol. 73(7). P. 1715–1727. doi: https://doi.org/10.1007/s12666-020-02002-x. 26. Characteristic ICP-DAS I-7520. ICP DAS CO., LTD. URL: https://www.icpdas.com/en/product/I-7520 (date of access: 05.01.2024). 27. Characteristic ICP-DAS I-7017. ICP DAS CO., LTD. URL: https://www.icpdas.com/en/product/I-7017 (date of access: 05.01.2024). 28. Repianskyi N., Rak T. Client-Server Library Index Automation System. Advances in Cyber-Physical Systems. 2022. Vol. 7, No. 2. P. 147–155. doi: https://doi.org/10.23939/acps2022.02.147. 29. Fu H., Yang L., Liang H., Wang S., Ling K. Diagnosis of the single leakage in the fluid pipeline through experimental study and CFD simulation. Journal of Petroleum Science and Engineering. 2020. Vol. 193. 107437. doi: https://doi.org/10.1016/j.petrol.2020.107437. 30. Toosi T., Sirola M., Laukkanen J., Van Heeswijk M., Karhunen J. Method for detecting aging related failures of process sensors via noise signal measurement. International Journal of Computing. 2019. Vol. 18, Issue 2. P. 135–146. doi: https://doi.org/10.47839/ijc.18.2.1412. 31. Yuzevych L., Skrynkovskyy R., Koman B. Development of information support of quality management of underground pipelines. EUREKA: Physics and Engineering. 2017. No. 4. P. 49–60. doi: https://doi.org/10.21303/2461-4262.2017.00392. 32. Yuzevych L., Skrynkovskyy R., Yuzevych V., Lozovan V., Pawlowski G., Yasinskyi M., Ogirko I. Improving the diagnostics of underground pipelines at oil-and-gas enterprises based on determining hydrogen exponent (PH) of the soil media applying neural networks. Eastern-European Journal of Enterprise Technologies. 2019. Vol. 4, No. 5(100). P. 56–64. doi: https://doi.org/10.15587/1729-4061.2019.174488. 33. Obshta A., Biliak Y., Shugai V. Cyber-Physical System for Diagnostic Along the Controlled Section of the Oil Pipeline. Advances in Cyber-Physical Systems. 2023. Vol. 8, No. 1. P. 66–73. doi: https://doi.org/10.23939/acps2023.01.066. 34. Characteristic ICP-DAS I-7080. ICP DAS CO., LTD. URL: https://www.icpdas.com/en/product/I-7080 (date of access: 05.01.2024).
Тип вмісту:	Article
Розташовується у зібраннях:	Зібрання статей

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