Please use this identifier to cite or link to this item:
|Title:||Diagnostics of Temperature Regime of Technological Environments of Underground Pipelines in the Monitoring System of Oil and Gas Enterprises for Providing of Safe Exploitation|
|Bibliographic description (Ukraine):||Yuzevych, V., Pavlenchyk, A., Lozovan, V., Mykhalitska, N., & Bets, M. (2020). Diagnostics of Temperature Regime of Technological Environments of Underground Pipelines in the Monitoring System of Oil and Gas Enterprises for Providing of Safe Exploitation // International Journal of Recent Technology and Engineering (IJRTE), 9(1), 1301–1307. (ISSN 2277-3878).|
|Journal/Collection:||International Journal of Recent Technology and Engineering (IJRTE)|
|Date of entry:||9-May-2020|
oil and gas enterprises
|Abstract:||The diagnosed density of corrosion was diagnosed on the outer surface of the underground metal pipeline, depending on the distance L to the compressor station, taking into account the influence of soil, defects, thermal impulses, mechanical vibrational vibrations and corrosion fatigue. The basic relations of the mathematical model for the description of thermal processes and mechanical vibrational vibrations that lead to low-cycle corrosion fatigue in the pipe are proposed. It is noted that the measurement of corrosion currents and polarization potentials at the boundary of the metal pipeline–soil can be detected by devices of types BVS (noncontact current meter), VPP-M (polarization potential meter) and equipment for for diagnostic inspections and monitoring of corrosion protection of underground pipelines (UGPL). Consider for compare the distribution of corrosion current densities and accidents for the pipeline at a distance of L=0..30 km from the compressor station. It is found that the correlation coefficient between them KLD=0,76 is not enough to establish causation. A difference is formed in which the corresponding corrosion current density distribution for a non-oscillating temperature background is subtracted from the total corrosion current density distribution in the range L=0…30 km. In this case, the part of the distribution that is related to the frequency of thermal pulses is highlighted.The correlation coefficient of KWD=0.92 is established between the part of the distribution that is related to the frequency of thermal pulses and the distribution of accidents for the pipeline at a distance of L=0…30 km from the compressor station. Based on KWD, it can be argued that the causal relationship between the distribution of heat pulses and accidents is quite plausible. The noted information is important for improving the methods of operation of compressor stations of oil and gas enterprises, taking into account changes in the frequency of heat pulses with regard to improving the quality of by-laws on labor protection regarding gas supply systems.|
|URL for reference material:||http://www.ijrte.org/archive/|
|References (International):||1. 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.|
2. 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: https://doi.org/10.15587/1729-4061.2019.154999.
3. 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. doi: https://doi.org/10.21003/ea.V176-02.
4. 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.
5. Skrynkovskyi, R. (2008). Investment attractiveness evaluation technique for machine-building enterprises. Actual Problems of Economics, 7(85), 228–240.
6. Lu, T., & Wang, K. (2008). Numerical analysis of the heat transfer associated with freezing/solidifying phase changes for a pipeline filled with crude oil in soil saturated with water during pipeline shutdown in winter. Journal of Petroleum Science and Engineering, 62(1-2), 52–58. doi: https://doi.org/10.1016/j.petrol.2008.07.004.
7. Akhmetova, G., & Chichirova, N. D. (2016). Evaluation of Thermal Insulation Type Impact on the Value of Regulatory Heat Losses in Heat and Power Systems. Journal of Engineering and Applied Sciences, 11, 2946–2949.
8. Shkrebko, S. V. (1998). The effect of soil moisture on the thermal conditions of channelless heating mains. Bulletin of the Rostov State University of Civil Engineering, 2, 174–175 (in Russ.).
9. 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.
10. Kuznetsov, G. V., & Polovnikov, V. Y. (2011). The conjugate problem of convective-conductive heat transfer for heat pipelines. Journal of Engineering Thermophysics, 20(2), 217–224. doi: https://doi.org/10.1134/s181023281102010x.
11. Parfentieva, N., Valančius, K., Samarin, O., Paulauskaitė, S., & Čiuprinskienė, J. (2015). Solving the problem of pipeline freezing with respect to external heat exchange. Mechanics, 21(5), 393–396. doi: https://doi.org/10.5755/j01.mech.21.5.11411.
12. Barletta, A., Zanchini, E., Lazzari, S., & Terenzi, A. (2008). Numerical study of heat transfer from an offshore buried pipeline under steady-periodic thermal boundary conditions. Applied Thermal Engineering, 28(10), 1168–1176. doi: https://doi.org/10.1016/j.applthermaleng.2007.08.004.
13. Fürtbauer, D., & Cheng, C. (2019). Investigation of Pipeline Heat Loss to Ground Influenced by Adjacent Pipelines. Pipeline Technology Conference. Berlin, 1–6. URL: https://www.researchgate.net/publication/332396916_Investigation_of_Pipeline_Heat_Loss_to_Ground_Influenced_by_Adjacent_Pipelines.
14. Čarnogurská, M., Příhoda, M., Dobáková, R., & Brestovič, T. (2017). Model of heat losses from underground heat distribution system. doi: https://doi.org/10.1063/1.5004337.
15. Golik, V. V., Zemenkov, Y. D., Gladenko, A. A., & Seroshtanov, I. V. (2019). Modeling the heat transfer processes in the pipe-soil system. IOP Conference Series: Materials Science and Engineering, 663, 012012. doi: https://doi.org/10.1088/1757-899x/663/1/012012.
16. Pyanilo, Y., Pyanilo, G. (2009). On the influence of thermophysical parameters on the process of gas motion in pipelines. Physical-mathematical model and information technology, 10, 106–112 (in Ukraine).
17. Belov, D. B. (2013). Analysis of the effect of the temperature of natural gas in a pipeline on its volume. Bulletin of Tula State University. Technical science. Energy and environmental management, 6-1, 25–31 (in Russ.).
18. Balageas, D., Maldague, X., Burleigh, D., Vavilov, V. P., Oswald-Tranta, B., Roche, J.-M., & Carlomagno, G. M. (2016). Thermal (IR) and Other NDT Techniques for Improved Material Inspection. Journal of Nondestructive Evaluation, 35(1). doi: https://doi.org/10.1007/s10921-015-0331-7.
19. Jianguang, Yi (2018). Methods of Heat Transfer Analysis of Buried Pipes in District Heating and Cooling Systems. Applied Engineering, 2(2), 33–38.
20. Oosterkamp, A., Helgaker, J. F., & Ytrehus, T. (2015). Modelling of Natural Gas Pipe Flow with Rapid Transients-case Study of Effect of Ambient Model. Energy Procedia, 64, 101–110. doi: https://doi.org/10.1016/j.egypro.2015.01.013.
21. Dalla Rosa, A., Li, H., & Svendsen, S. (2011). Method for optimal design of pipes for low-energy district heating, with focus on heat losses. Energy, 36(5), 2407–2418. doi: https://doi.org/10.1016/j.energy.2011.01.024.
22. Danielewicz, J., Śniechowska, B., Sayegh, M. A., Fidorów, N., & Jouhara, H. (2016). Three-dimensional numerical model of heat losses from district heating network pre-insulated pipes buried in the ground. Energy, 108, 172–184. doi: https://doi.org/10.1016/j.energy.2015.07.012.
23. Bohm, B. (2000). On transient heat losses from buried district heating pipes. Int. Journal of Energy Research, 24(15), 1311–1334. doi: https://doi.org/10.1002/1099-114x(200012)24:15<1311::aid-er648>3.0.co;2-q.
24. 2012 ASHRAE Handbook – HVAC Systems and Equipment. Chapter 12. District Heating and Cooling. America: ASHRAE, 2012. URL: https://app.knovel.com/web/toc.v/cid:kpASHRAEA2/viewerType:toc/.
25. Garris, N. A., & Askarov, G. A. (2009). Activation of corrosion process on big diameter gas mains under impulse temperature changes. Oil and Gas Business, 2, 1–15.
26. Liu, Z., Jia, W., Liang, L., & Duan, Z. (2019). Analysis of Pressure Pulsation Influence on Compressed Natural Gas (CNG) Compressor Performance for Ideal and Real Gas Models. Applied Sciences, 9(5), 946. doi: https://doi.org/10.3390/app9050946.
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. Tousek, J. (1977). Temperature Dependence of Pitting Corrosion in Cr-Ni stainless steels. Materials and Corrosion/Werkstoffe Und Korrosion, 28(9), 619–622. doi: https://doi.org/10.1002/maco.19770280906.
29. Živica, V. (2002). Significance and influence of the ambient temperature as a rate factor of steel reinforcement corrosion. Bulletin of Materials Science, 25(5), 375–379. doi: https://doi.org/10.1007/bf02708013.
30. Yuzevych, L., Yankovska, L., Sopilnyk, L., Yuzevych, V., Skrynkovskyy, R., Koman, B., & 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: https://doi.org/10.15587/1729-4061.2019.184247.
31. 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.
32. Moffat, R. J. (1988). Describing the uncertainties in experimental results. Experimental Thermal and Fluid Science, 1(1), 3–17. doi: https://doi.org/10.1016/0894-1777(88)90043-x.
33. Cividino, S., Egidi, G., Zambon, I., & Colantoni, A. (2019). Evaluating the Degree of Uncertainty of Research Activities in Industry 4.0. Future Internet, 11(9), 196. doi: https://doi.org/10.3390/fi11090196.
34. 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.
35. 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.
36. 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.
37. 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.
38. 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.
39. 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.
40. Dzhala, R. М., & Yuzevych, L. V. (2019). Modeling of Relationships Between the Mechanoelectrochemical Parameters of the Metal Surface. Materials Science, 54, 753–759. doi: https://doi.org/10.1007/s11003-019-00243-w.
41. 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.
|Appears in Collections:||Зібрання статей|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.