Determining the Place of Depressurization of Underground Pipelines (Gas Pipelines): New Solutions in Industry based on Thermal Image Analysis Using Computer Vision
Abstract
An analysis of the analytical ratios of the mathematical model, which characterizes the development processes of a corrosion cavern on the surface of an underground metal pipeline, which is placed in the environment of moist soil with an electrolyte solution, is performed. A neural network method for estimating the main informative parameters for determining the place of gas depressurization on the surface of an underground pipe and an expression for calculating the change in gas pressure around a crack after its formation have been developed. The principles of determining the limit values of the parameters of the “pipe-cathodic protection” system are formulated, considering the metal's quality and strength criteria at the top of the cavern.
Depressurization causes fluid to flow from the pipeline to the surface. Thermal imaging devices make it possible to detect the place of damage to the pipeline based on the temperature properties of the surrounding objects. Thermal imaging can be used to analyze the location of a fluid leak or warn of it using computer vision. Thus, preventing an accident or even a catastrophe in the pipeline. In the work, the colour gamuts of the thermal image in the places of depressurization are considered, and the regularities of detecting damaged sections of the pipeline are established.Keywords
Full Text:
PDFReferences
Sopilnyk, L., Skrynkovskyy, R., Lozovan, V., Yuzevych, V., & Pawlowski, G. (2019). Determination of economic losses of gas transportation companies from accidents on gas transmission pipelines. Path of Science, 5(1), 1008–1017. doi: 10.22178/pos.42-4
Paliichuk, L. (2004). Rozghermetyzatsiia hazoprovodiv – dzherelo zabrudnennia dovkillia [Depressurization of gas pipelines is a source of environmental pollution]. Naukovyi visnyk Ivano-Frankivskoho natsionalnoho tekhnichnoho universytetu nafty i hazu, 3(9), 149–150 (in Ukrainian).
Honcharuk, M. (2003). Analiz prychyn vtrat pryrodnoho hazu [Analysis of causes of natural gas losses]. Naftova i hazova promyslovist, 1, 51–53 (in Ukrainian).
Mandryk, O. (2013). Ekolohichni ta ekonomichni naslidky avarii na mahistralnykh hazoprovodakh [Environmental and economic consequences of accidents on main gas pipelines]. Ekolohichna bezpeka ta zbalansovane resursokorystuvannia, 1, 160–165 (in Ukrainian).
Hovdiak, R., & Kosnyriev, Yu. (2007). Kilkisnyi analiz avariinoho ryzyku hazotransportnykh ob’iektiv pidvyshchenoi nebezpeky [Quantitative analysis of emergency risk of high-risk gas transportation facilities]. Lviv: n. d. (in Ukrainian).
Fedorovych, I., & Horal, L. (2010). Metodychni aspekty vyznachennia ekonomichnykh vtrat vid vynyknennia avarii ta vidmov na mahistralnykh hazoprovodakh [Methodological aspects of determining economic losses from accidents and failures on main gas pipelines]. Zbirnyk naukovykh prats NUK, 5(434), 150–155 (in Ukrainian).
Honcharuk, M., Kryzhanivskyi, Ye., & Poberezhnyi, L. (2003). Koroziino-mekhanichna povedinka metalu hazoprovodu [Corrosion-mechanical behavior of gas pipeline metal]. Naukovyi visnyk Natsionalnoho tekhnichnoho universytetu nafty i hazu, 1(5), 54–59 (in Ukrainian).
Kovalko, M., Hrudz, V., Mykhalkiv, V., Tymkiv, D., Shlapak, L., & Kovalko, O. (2002). Truboprovidnyi transport hazu [Pipeline gas transportation]. Kyiev: Ahenstvo z ratsionalnoho vykorystannia enerhii ta ekolohii (in Ukrainian).
Rudnik, A. (2001). Tranzytni postavky hazu cherez terytoriiu Ukrainy: problemy ta perspektyvy [Transit gas supplies through the territory of Ukraine: problems and prospects]. Rozvidka ta rozrobka naftovykh i hazovykh rodovyshch, 1, 9–11 (in Ukrainian).
Honcharuk, M. (2003). Koroziia ta rozghermetyzatsiia hazoprovodiv [Corrosion and depressurization of gas pipelines]. Naftova i hazova promyslovist, 2, 56–57 (in Ukrainian).
Kryzhanivsky, Ye. I. (2005). Corrosive-Mechanical Behaviour of Buried Steel Gas Pipelines of Low and Average Pressure. Nauka Ta Innovacii, 1(5), 123–131. doi: 10.15407/scin1.05.123
Arsenin, V. (1974). Metody matematicheskoj fiziki i special'nye funkcii [Methods of mathematical physics and special functions]. Moscow: Nauka (in Russian).
Kartashov, Je. (1985). Analiticheskie metody v teorii teploprovodnosti tverdyh tel [Analytical methods in the theory of thermal conductivity of solids]. Moscow: Vysshaja shkola (in Russian).
Avelino, A. M., de Paiva, J. A., da Silva, R. E. F., de Araujo, G. J. M., de Azevedo, F. M., de O. Quintaes, F., Maitelli, A. L., Neto, A. D. D., & Salazar, A. O. (2009). Real time leak detection system applied to oil pipelines using sonic technology and neural networks. 2009 35th Annual Conference of IEEE Industrial Electronics. doi: 10.1109/iecon.2009.5415324
Santos, R. B., Sousa, E. O. de, Silva, F. V. da, Cruz, S. L. da, & Fileti, A. M. F. (2014). Detection and on-line prediction of leak magnitude in a gas pipeline using an acoustic method and neural network data processing. Brazilian Journal of Chemical Engineering, 31(1), 145–153. doi: 10.1590/s0104-66322014000100014
Dzhala, R. M., Verbenets’, B. Ya., & Melnyk, M. I. (2016). Measuring of Electric Potentials for the Diagnostics of Corrosion Protection of the Metal Structures. Materials Science, 52(1), 140–145. doi: 10.1007/s11003-016-9936-y
Bermúdez, J.-R., López-Estrada, F.-R., Besançon, G., Valencia-Palomo, G., Torres, L., & Hernández, H.-R. (2018). Modeling and Simulation of a Hydraulic Network for Leak Diagnosis. Mathematical and Computational Applications, 23(4), 70. doi: 10.3390/mca23040070
Yuzevych, L., Skrynkovskyy, R., & Koman, B. (2017). Development of information support of quality management of underground pipelines. EUREKA: Physics and Engineering, 4, 49–60. doi: 10.21303/2461-4262.2017.00392
Yuzevych, V., Skrynkovskyy, R., & Koman, B. (2018). Intelligent Analysis of Data Systems for Defects in Underground Gas Pipeline. 2018 IEEE Second International Conference on Data Stream Mining; Processing (DSMP). doi: 10.1109/dsmp.2018.8478560
Lozovan, V., Skrynkovskyy, R., Yuzevych, V., Yasinskyi, M., & Pawlowski, G. (2019). 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, 2(5), 41–48. doi: 10.15587/1729-4061.2019.161484
Lozovan, V., Dzhala, R., Skrynkovskyy, R., & Yuzevych, V. (2019). 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, 1(5), 20–27. doi: 10.15587/1729-4061.2019.154999
Yuzevych, L., Yankovska, L., Sopilnyk, L., Yuzevych, V., Skrynkovskyy, R., Koman, B., Yasinska-Damri, L., Heorhiadi, N., Dzhala, R., & Yasinskyi, M. (2019). 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, 6(5), 23–29. doi: 10.15587/1729-4061.2019.184247
Dinh, T. H., Ha, Q. P., & La, H. M. (2016). Computer vision-based method for concrete crack detection. 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). doi: 10.1109/icarcv.2016.7838682
Maintworld. (n. d.). Kuva-4. Retrieved September 10, 2022, from https://www.maintworld.com/var/ezwebin_site/storage/images/media/images/kuva-4/2810-1-eng-GB/Kuva-4_large.jpg
Maintworld. (n. d.). Kuva-3. Retrieved September 10, 2022, from https://www.maintworld.com/var/ezwebin_site/storage/images/media/images/kuva-3/2806-1-eng-GB/Kuva-3_large.jpg
Tcorr Inspection. (2016). Pipeline. Retrieved September 10, 2022, from https://www.tcorr.com.au/dev/wp-content/uploads/2016/10/Pipeline2.gif
Workswell Thermal Imaging System. (2020). Pipeline. Retrieved September 10, 2022, from https://www.drone-thermal-camera.com/wp-content/uploads/2020/01/Photo-03-01-2020-2-59-19-PM.jpg
InfraTec. Pipeline. (n. d.). Thermal imaging image of the pipeline. Retrieved September 10, 2022, from https://cdn.infratec.eu/en/thermography/service/thermography-service-support-glossar-leak-search-infratec.jpg
Jenoptic. (n. d.). Thermal imaging image of the pipeline. Retrieved September 10, 2022, from https://www.jenoptik.com/-/media/websiteimages/optics/optics-sys/evidir/thermal-image-rotary-kiln.jpg?impolicy=aoiv1&width=620&height=465
Tcorr Inspection. (2016). The thermal image of the pipeline. Retrieved September 10, 2022, from https://www.tcorr.com.au/dev/wp-content/uploads/2016/10/Pipeline-corosion-under-insulation.jpgArticle Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Roman Mysiuk, Iryna Mysiuk, Volodymyr Yuzevych, Grzegorz Pawlowski

This work is licensed under a Creative Commons Attribution 4.0 International License.