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A. R. Mukhutdinov, M. G. Efimov, Z. R. Vakhidova Neural Network Modeling of the Process of Maintaining Temperature after Heating the Bituminous Reservoir |
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Abstract.
Currently, thermal methods of treating productive formations are widely used to intensify oil production in existing wells. It is known that one of the main stages of oil production in the development of bituminous formations is thermal treatment, which is carried out by using the thermite composition of the combustible material. Overcoming the low mobility of bituminous oil and increasing the efficiency of the downhole method of reservoir treatment is possible through the use of modern information technologies that have the widest possibilities for modeling such systems. They allow, based on empirical experience alone, to build neural network models that help extract knowledge from data, identify features and actively use them to solve specific practical problems. In this paper, the possibility of neural network modeling of the process of maintaining temperature after heating a bituminous reservoir has been studied and shown. The results of a study of the influence of various factors on the process of maintaining the temperature after heating are presented.
Keywords:
artificial neural network, modeling, bituminous reservoir, software module, heating, energized material, distance, mass.
PP. 84-90.
DOI 10.14357/20718632220209 References
1. Muslimov R.H. Strategija razvitija neftebitumnogo kompleksa Tatarstana v napravlenii vosproizvodstva resursnoj bazy uglevodorodov / R.H. Muslimov, G. V. Romanov, G. P. Kajukova, T. N. Jusupova, N. I. Iskrickaja, S. M. Petrov // Neft'. Gaz. Novacii. – 2012, № 2. S. 21-29. 2. Kudinov V.I. Novye tehnologii povyshenija nefteotdachi na mestorozhdenijah s vysokovjazkimi neftjami // Neftjanoe hozjajstvo. – 2002, Vyp. 5. S. 92-95. 3. Gupta P. Rezul'taty vnutriplastovogo gorenija / Gupta P., Doriah A., Rjej S. // Neftegazovye tehnologii. – 2008. – Mart. – Vyp. 3. – S. 12-15. 4. Poiskovye issledovanija po sozdaniju izdelij dlja progreva bituminoznogo plasta: otchet o NIR / V.M. Bochkov, I.F.Sadykov; G.I. Sheshukov, G.P. Frolov; I.M. Sabitov, I.A. Gajnutdinov. – Kazan': Kaz. Khim-tehnol. inst. 1997. – S. 6-24. 5. Muhutdinov A.R., Efimov M.G., Vahidova Z.R. Povyshenie jeffektivnosti jenergonasyshhennyh materialov dlja razrabotki mestorozhdenij bituminoznoj nefti za schet ispol'zovanija sovremennyh informacionnyh tehnologij // Avtomatizacija, telemehanizacija i svjaz' v neftjanoj promyshlennosti. 2021. № 6. S. 19-22. 6. Smirnov A., Levashova T., Shilov N., Krizhanovsky A. Knowledge Fusion in Context-aware Decision Support: Ontology-based Modeling and Patterns // In: Recent Developments and New Directions in Soft Computing, eds. by L.A. Zadeh et al. - Studies in Fuzziness and Soft Computing, vol. 317. Springer International Publishing Switzerland, 2014. pp. 35–52. 7. Finn V.K. Intellekt, informacionnoe obshhestvo, gumanitarnoe znanie i obrazovanie. – M.: URSS, 2021. — 504 s. 8. Yue Wang, Intelligent Simulation of Children’s Psychological Path Selection Based on Chaotic Neural Network Algorithm", Computational Intelligence and Neuroscience, 2021, pp. 1-9. 9. Lijuan Luo, Shaozhi Hong, Shanshan Shang, Xiaoli Zhou, Junyu Yang, Yu Pan, Intelligent Boarding Modelling and Evaluation: A Simulation-Based Approach, Journal of Advanced Transportation, 2021, pp. 1-12. 10. Muhutdinov A.R. Nejrosetevoe modelirovanie i optimizacija slozhnyh processov i naukoemkogo teplojenergeticheskogo oborudovanija / A.R. Muhutdinov, G.N. Marchenko, Z.R. Vahidova. – Kazan': Kazan. gos. jenerg. un-t, 2011. – 296 s. 11. Muhutdinov A.R., Efimov M.G. Nejrosetevoj podhod dlja optimizacii sostava tverdogo topliva po skorosti gorenija // Avtomatizacija, telemehanizacija i svjaz' v neftjanoj promyshlennosti. 2019. №4. S. 25-29. 12. Muhutdinov A.R., Sadykov M.I., Efimov M.G. Optimizacija receptury tverdogo topliva s ispol'zovaniem komp'juternyh tehnologij // Strategicheskaja stabil'nost'. 2018. № 4. S. 64-66.
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