Zoning pipeline routes according to the degree of danger of accidents using geoinformation systems and artificial neural networks

The article describes a method of zoning the territory along the pipeline route according to the degree of potential danger of accidents. The forecast is based on a multivariate analysis performed in an integrated system: GIS and artificial neural networks (ANN) (software package Advangeo). The pipeline in the Northern Urals (Russia) was taken as an object of research. As a result of initial data processing and neural network training, a map of potential accidents along the pipeline route was obtained. The results were compared with the results of mathematical and cartographic modeling in the GIS MapInfo.

Kiselev V. A., Guseva N. V. Zoning pipeline routes according to the degree of danger of accidents using geoinformation systems and artificial neural networks. MIAB. Mining Inf. Anal. Bull. 2022;(10-2):185—192. [In Russ]. DOI: 10.25018/0236_1493_2022_102_0_185.

Kiselev V. A.¹ , Cand. Sci. (Eng.), Associate Professor of the Department of Mine-surveying, e-mail: kiselev_VA@pers.spmi.ru, ORCID ID: 0000-0003-0188-0313;
Guseva N. V.¹ , Cand. Sci. (Eng.), Senior researcher, e-mail: guseva_NV@pers.spmi.ru, ORCID ID: 0000-0003-4210-9824.
¹ Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia.

Kiselev V. A., e-mail: kiselev_VA@pers.spmi.ru.

1. Askarov, R. M. et al. (2017). Features of stress corrosion cracking at the present stage of operation of magi-stralny gas pipelines Gas Industry, 10, 40−45.

2. Official site of the company Advangeo Beak Consultants. URL: https://www.beak.de/ advangeo/advangeo_prediction/home (Access date: 10.02.2022).

3. Bidenko, S. I. et al. (2019). Assessment of the territorial situation with the use of artificial neural networks. Scientific Notes of the Russian State Hydrometeorological University (RSMU), 54, 109−123.

4. Grebenyuk, G. N. et al. (2011). Trunk oil pipelines and their impact on the environment. Izvestiya Samara Scientific Center of the Russian Academy of Sciences, 13 1(5), 1260−1263.

5. Gumerov, A. G. et al. (1998). Emergency repair of trunk oil pipelines. Moscow: Nedra — Businesscenter LLC, 271.

6. Kasyanova, N. A., Kuzmin, Yu. O. (1996). Modern anomalous geodynamics of the subsurface and its impact on the objects of the oil and gas complex. Moscow: Geoinformmark, 55.

7. Melnikov, E. K., Shabarov, A. N. (2010). Assessment of the role of geodynamic factor in accidents-news of pipeline systems. Journal of Mining Institute, 188, 203−206.

8. Mohammed N. E. et al. (2010). Framework for integration between Artificial Neural Network & Geographical Information System. Slum prediction as the case study. International Journal of Electrical & Computer Sciences IJECS-IJENS, 10(1), 25−39.

9. Orlova, A. V. (1975). Block structures and relief. Moscow: Nedra, 230.

10. Osipov, A. G., Dmitriev, V. V., Maslennikov, S. A., Plastinin, L. A., & Plyusnin, V. M. (2017). Cartographic modeling of pipeline routes in the arctic zone of siberia. Geography and Natural Resources, 38(1), 12−19. doi:10.1134/S1875372817010024.

11. Pimakhin, A. N. et al. (2003) Accidents at oil fields and main gas pipelines. On the state of the environment of the Khanty-Mansi Autonomous Okrug (inform. byul.) Yugra in 2003. Khanty-Mansiysk: Polygraphist, 104.

12. Rasputin, A. N. et al (2009) Application of geo-information systems for assessing the influence of natural factors on the technical condition of the main gas pipelines of Gazprom Transgaz Yekaterinburg LLC. Gas Industry, 11, 81−83.

13. Solodovnikov, A. Yu. (2007). The impact of oil and gas pipelines on the environment in the Tyumen region: factors and consequences. Bulletin of St. Petersburg State University, 7(4), 85−95.

14. Dvoynikov, M., Buslaev, G., Kunshin, A., Sidorov, D., Kraslawski, A., & Budovskaya, M. (2021). New Concepts of Hydrogen Production and Storage in Arctic Region. Resources, 10(1), 3. DOI: 10.3390/resources10010003.

15. Strokova, L. A., & Ermolaeva, A. V. (2016). Zoning according to the hazard level of earth surface subsidence when designing the main gas pipeline in south yakutia. Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering, 327(10), 59−68.

16. Terekhov, S. A. et al. (2000). An approach to modeling elements of the main gas pipeline based on artificial neural networks. Radioelectronics, informatics, management, 1(3), 116−122.

17. Khizhnyakov, V. I. et al. (2011). Corrosion cracking — deformed pipeline transportation of oil and gas. Bulletin of Tomsk Polytechnic University, 319(3), 84−89.

18. Hill, R. T., Catmur, J. R. (1995). Risks from Hazardous Pipelines in the United Kingdom. HSE Contract Research Report no. 82/1994.

19. Chikharev, V. A. (2012). The use of geoinformation technologies in conducting geotechnical monitoring of pipeline transport. Pipeline transport. Monitoring and transport, 4(32), 1−6.

20. Egorov, A. S., Prischepa, O. M., Nefedov, Y. V., Kontorovich, V. A., & Vinokurov, I. Y. (2021). Deep structure, tectonics and petroleum potential of the western sector of the Russian Arctic. Journal of Marine Science and Engineering, 9(3), 1−26. doi:10.3390/jmse9030258.

21. Dumitrescu, A., Minescu, M., Dinita, A., & Lambrescu, I. (2021). Corrosion repair of pipelines using modern composite materials systems: A numerical performance evaluation. Energies, 14(3). doi:10.3390/en14030615.

22. Yannikov A. M., Yannikova S. A., Korepanov A. Yu. Influence of tectonic faults on the projected advanced water reduction systems on the example of the “Yubileynaya” pipe. MIAB. Mining Inf. Anal. Bull. 2022;(5–2):174—186. [In Russ]. DOI: 10.25018/0236_1493_ 2022_52_0_174