Utilizza questo identificativo per citare o creare un link a questo documento: http://elartu.tntu.edu.ua/handle/lib/31729
Titolo: Determination of the Place Depressurization of Underground Pipelines in the Monitoring of Oil and Gas Enterprises
Autori: Yuzevych, Volodymyr
Horbonos, Fedir
Rogalskyi, Roman
Yemchenko, Iryna
Yasinskyi, Mykhailo
Bibliographic description (Ukraine): Yuzevych, V., Horbonos, F., Rogalskyi, R., Yemchenko, I., & Yasinskyi, M. (2020). Determination of the Place Depressurization of Underground Pipelines in the Monitoring of Oil and Gas Enterprises // International Journal of Recent Technology and Engineering (IJRTE), 9(1), 2274–2281. (ISSN 2277-3878).
Journal/Collection: International Journal of Recent Technology and Engineering (IJRTE)
Data: 15-mag-2020
Date of entry: 23-mag-2020
Parole chiave: depressurization of gas pipelines
gas transportation enterprises
gas
corrosion
pressure sensors
neural network
non-destructive testing
anode current
Page range: 2274–2281
Abstract: Analytical correlations of mathematical model that characterizes the processes of development of corrosive cavity on the surface of underground metallic pipeline are analysed. A pipeline in the environment of moist soil with solution of electrolyte is placed. In the top of cavity a crack appears and translates the system “underground pipeline (UP) – pumping station (PS)” in the state of depressurization. A new approach is proposed for diagnosing places of underground pipeline depressurization on the basis of two types of devices: pressure sensors and non-destructive testing devices, with the help of which we measure potentials and corrosion currents in surface defects. The presence of only pressure sensors makes it possible to set the coordinates of depressurization with a large error – 20…25%. Diagnosis of the pipeline only by devices BVS-K, VPP-M (at the first stage) allows to reveal surface defects. But this information is not enough for a qualitative experiment, as shown by the qualimetric quality criterion. Taking into account the pressure data in the second stage allows to determine which of the defects is the leakage point. A test example for the distance L1 = 6km from the pumping station of the pipeline is considered. The results of the experiment are used for the example and the growth time of the corrosion crack t* = 0.62 year is established. On the basis of computational experiment the errors of estimating of crack growth time t* and coordinates of the leakage points was established. They present 5…7 %. Based on the method of neural networks, the main informative parameters for determining the places of depressurization on the surface of the underground pipe are estimated. A method for estimating the gas pressure change in the vicinity of the crack after depressurization of the pipeline was proposed. The principles of determining the limit values of the parameters of the system “pipeline – pumping station” taking into account the criteria of quality and strength of the metal are formulated.
URI: http://elartu.tntu.edu.ua/handle/lib/31729
ISSN: 2277-3878
URL for reference material: http://www.ijrte.org/archive/
https://www.scopus.com/sourceid/21100889873
References (International): 1. Paliichuk, L. V. (2004). Rozghermetyzatsiia hazoprovodiv – dzherelo zabrudnennia dovkillia. Naukovyi visnyk Ivano-Frankivskoho natsionalnoho tekhnichnoho universytetu nafty i hazu, 3(9), 149–150. URL: http://elar.nung.edu.ua/handle/123456789/1057. (In Ukr.).
2. Honcharuk, M. I. (2003). Analiz prychyn vtrat pryrodnoho hazu. Naftova i hazova promyslovist, 1, 51–53. (In Ukr.).
3. Mandryk, O. M. (2013). Ekolohichni ta ekonomichni naslidky avarii na mahistralnykh hazoprovodakh. Ekolohichna bezpeka ta zbalansovane resursokorystuvannia, 1, 160–165. URL: http://nbuv.gov.ua/UJRN/ebzp_2013_1_35. (In Ukr.).
4. Hovdiak, R. M., & Kosnyriev, Yu. M. (2007). Kilkisnyi analiz avariinoho ryzyku hazotransportnykh obiektiv pidvyshchenoi nebezpeky: [praktychni rekomendatsii]. Lviv, 158 p. (In Ukr.).
5. Yuzevych, L., Skrynkovskyy, R., Yuzevych, V., Lozovan, V., Pawlowski, G., Yasinskyi, M., & Ogirko, I. (2019). 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, 4(5(100)), 56–64. doi: http://dx.doi.org/10.15587/1729-4061.2019.174488.
6. Honcharuk, M. I. (2003). Koroziia ta rozghermetyzatsiia hazoprovodiv. Naftova i hazova promyslovist, 2, 56–57. (In Ukr.).
7. Yuzevych, V. M., Dzhala, R. M., & Koman, B. P. (2017). Analysis of Metal Corrosion under Conditions of Mechanical Impacts and Aggressive Environments. Metallofizika i Noveishie Tekhnologii, 39(12), 1655–1667. doi: https://doi.org/10.15407/mfint.39.12.1655.
8. Makino, H., Sugie, T., Watanabe, H., Kubo, T., Shiwaku, T., Endo, S., & Machida, S. (2001). Natural Gas Decompression Behavior in High Pressure Pipelines. ISIJ International, 41(4), 389–395. doi: https://doi.org/10.2355/isijinternational.41.389.
9. Raimondi, L. (2016). Simulation of Pipeline Depressurization in the Transportation of Oil & Gas With High CO2 and H2S Content. Chemical Engineering Trans, 53, 337–342. URL: https://www.aidic.it/cet/16/53/057.pdf.
10. King, G. G. (1979). Decompression of Gas Pipelines During Longitudinal Ductile Fractures. Journal of Energy Resources Technology, 101(1), 66–73. doi: https://doi.org/10.1115/1.3446864.
11. Osunleke, A. S., Gabbar, H. A., Inoue, A., Miyazaki, S., & Badmus, I. (2007). Mathematical modeling approach to pipeline leaks detection, location and control: Part I – Review of common causes and current popular techniques. IEEE SMC, Okayama University, Japan, Dec-18, 2007. 3rd International Workshop on Computational Intelligence & Applications, P. 4-1–P4-8.
12. Wu, X., Lu, H., Huang, K., Yuan, Z., & Sun, X. (2015). Mathematical Model of Leakage during Pressure Tests of Oil and Gas Pipelines. Journal of Pipeline Systems Engineering and Practice, 6(4), 04015001. doi: https://doi.org/10.1061/(asce)ps.1949-1204.0000195.
13. Rui, Z., Han, G., Zhang, H., Wang, S., Pu, H., & Ling, K. (2017). A new model to evaluate two leak points in a gas pipeline. Journal of Natural Gas Science and Engineering, 46, 491–497.
14. Roy, U. (2017). Leak Detection in Pipe Networks Using Hybrid ANN Method. Water Conservation Science and Engineering, 2(4), 145–152.
15. Reddy, N. S. (2014). Neural Networks Model for Predicting Corrosion Depth in Steels. Indian Journal of Advances in Chemical Science, 2(3), 204-207. URL: https://www.ijacskros.com/artcles/IJACS-M98.pdf.
16. Brones, H., & Schaffhaussen, H. (1972). European methods of leak detection and location. Pipeline Industry, 50–66.
17. Khalifa, A. E., Chatzigeorgiou, D. M., Youcef-Toumi, K., Khulief, Y. A., & Ben-Mansour, R. (2010). Quantifying Acoustic and Pressure Sensing for In-Pipe Leak Detection. Proceedings of the ASME 2010 International Mechanical Engineering Congress and Exposition. Volume 13: Sound, Vibration and Design. Vancouver, British Columbia, Canada. November 12–18, 2010. pp. 489-495. ASME. doi: https://doi.org/10.1115/imece2010-40056.
18. Dzhala, R. М., Verbenets’, B. Y., Mel’nyk, М. І., Mytsyk, А. B., Savula, R. S., & Semenyuk, О. М. (2017). New Methods for the Corrosion Monitoring of Underground Pipelines According to the Measurements of Currents and Potentials. Materials Science, 52(5), 732–741.
19. 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(97)), 20–27. doi: http://dx.doi.org/10.15587/1729-4061.2019.154999.
20. 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: https://doi.org/10.21303/2461-4262.2017.00392.
21. 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: https://doi.org/10.1109/dsmp.2018.8478560.
22. 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., & 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: https://doi.org/10.1109/iecon.2009.5415324.
23. 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: https://doi.org/10.1590/s0104-66322014000100014.
24. 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(98)), 41–48. doi: http://dx.doi.org/10.15587/1729-4061.2019.161484.
25. 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: https://doi.org/10.3390/mca23040070.
26. 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(102)), 23–29. doi: http://dx.doi.org/10.15587/1729-4061.2019.184247.
27. Silvestrov, A.S., Bulkin, V.A., & Anvarov, A.D. (2011). Thermocyclic processes as the cause of SCC on gas pipelines. Bulletin of Kazan State Technological University, 18, 168–173 (in Russ.).
28. Koman, B., & Yuzevvch, V. (2019). Synergetic Processes in Uniaxially Deformed Crystals. 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT). doi: https://doi.org/10.1109/elit.2019.8892301.
29. Pavlenchyk, N., Mekhovych, S., Bohoslavets, O., Opanashchuk, Y., Hotra, V., & Gayvoronska, I. (2019). Integration of partial least squares path modeling for sustainable tourism development. International Journal of Recent Technology and Engineering, 8(2), 4309–4312.
30. Tkach, V., Pavlenchyk, A., Sadchenko, О., Nikola, S., Drozdova, V., & Davydenko, I. (2019). Modelling buying demand in the tourism industry based on machine training methods. International Journal of Recent Technology and Engineering, 8(2), 744–747.
31. Skrynkovskyy, R., Sopilnyk, L., Heorhiadi, N., & Kniaz, S. (2018). Improvement of the model of the innovative development of the production system of industrial enterprises. Technology Audit and Production Reserves, 1(4(45)), 51–53. doi: http://dx.doi.org/10.15587/2312-8372.2019.159227.
32. Skrynkovskyy, R., Sopilnyk, R., Seliverstova, L., Koropetskyi, O., & Protsiuk, T. (2018). Improvement of the system of indicators for the efficiency evaluation of the production capacity of industrial enterprises. Technology Audit and Production Reserves, 2(4(46)), 49-51. doi:http://dx.doi.org/10.15587/2312-8372.2019.162670.
33. 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: http://dx.doi.org/10.22178/pos.42-4.
34. Popova, N., Kataiev, A., Skrynkovskyy, R., & Nevertii, A. (2019). Development of trust marketing in the digital society. Economic Annals-XXI, 176(3-4), 13–25.
35. Skrynkovskyy, R. M., Yuzevych, V. M., Kataev, A. V., Pawlowski, G., Protsiuk, T. B. (2019). Analysis of the methodology of constructing a production function using quality criteria. Journal of Engineering Sciences, 6(1), B1–B5. doi: https://doi.org/10.21272/jes.2019.6(1).b1.
36. Skrynkovskyy, R. M., Yuzevych, L. V., Ogirko, O. I., & Pawlowski, G. (2018). Big Data Approach Application for Steel Pipelines in the Conditions of Corrosion Fatigue. Journal of Engineering Sciences, 5(2), E27–E32. doi: https://doi.org/10.21272/jes.2018.5(2).e6.
37. Skrynkovskyi, R. M. (2011). Methodical approaches to economic estimation of investment attractiveness of machine-building enterprises for portfolio investors. Actual Problems of Economics, 118(4), 177–186.
38. Skrynkovskyi, R. (2008). Investment attractiveness evaluation technique for machine-building enterprises. Actual Problems of Economics, 7(85), 228–240.
39. Klyuvak, A., Kliuva O., & Skrynkovskyy, R. (2018). Partial Motion Blur Removal. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/DSMP.2018.8478595.
40. Koman, B., Skrynkovskyy, R., & Yuzevych, V. (2018). Information Parameters of Synergetic Processes in Structures with Interfractional Boundaries. 2018 IEEE 8th International Conference Nanomaterials: Application & Properties (NAP). doi: https://doi.org/10.1109/nap.2018.8914983.
41. Yuzevych, V., Klyuvak, O., & Skrynkovskyy, R. (2016). Diagnostics of the system of interaction between the government and business in terms of public e-procurement. Economic Annals-ХХI, 160(7-8), 39–44. doi: https://doi.org/10.21003/ea.v160-08.
42. Skrynkovskyy, R., Pawlowski, G., Harasym, L., & Haleliuk, M. (2017). Improvement of the Model of Enterprise Management Process on the Basis of General Management Functions. Path of Science, 3(12), 4007–4014. doi: http://dx.doi.org/10.22178/pos.29-7.
43. Skrynkovskyy, R., Leskіv, S., & Yuzevych, V. (2017). Development of Information Support of the Automated System for Monitoring the State of the Gas Transportation System’s Industrial Safety. Path of Science, 3(8), 3028–3035. doi: http://dx.doi.org/10.22178/pos.25-8.
44. Kramar, R., Kovaliv, M., Yesimov, S., & Skrynkovskyy, R. (2018). The Specifics of Legal Regulation of Relations in the Field of Transportation of Oil and Gas by Trunk-Line. Path of Science, 4(2), 4001–4010. doi: http://dx.doi.org/10.22178/pos.31-5.
45. Yuzevych, L., Skrynkovskyy, R., & Mykyychuk, M. (2017). Improvement of Regulatory Requirements for Ensuring the Quality of Underground Gas Pipelines in Conditions of Corrosion Fatigue. Path of Science, 3(9), 1001–1008. doi: http://dx.doi.org/10.22178/pos.26-1.
46. Dzhala, R. М., & Yuzevych, L. V. (2019). Modeling of Relationships Between the Mechanoelectrochemical Parameters of the Metal Surface. Materials Science, 54, 753–759.
47. Dzhala, R. M., Verbenets’, B. Y., Mel’nyk, M. I., & Shevchuk, T. I. (2009). Determination of parameters of corrosion protection of underground pipelines from noncontact measurements of current. Materials Science, 45(3), 441–447.
48. Dzhala, R., Dzhala, V., Horon, B., Senyuk, O., & Verbenets, B. (2019). Information Technology of Surveys and Diagnostics of Underground Pipelines. 2019 11th International Scientific and Practical Conference on Electronics and Information Technologies, ELIT 2019 – Proceedings (pp. 214–218).
49. Dikmarova, L. P., & Dzhala, R. M. (2000). Mathematical models of the underground pipeline for problem of the corrosion control. Journal of Automation and Information Sciences, 32(2), 42–50.
Content type: Article
È visualizzato nelle collezioni:Зібрання статей

File in questo documento:
File Descrizione DimensioniFormato 
A2941059120.pdf824,21 kBAdobe PDFVisualizza/apri


Tutti i documenti archiviati in DSpace sono protetti da copyright. Tutti i diritti riservati.