Geodesic
Neural network modeling of the process of maintaining temperature after heating the bituminous reservoir
Informacionnye tehnologii i vyčislitelnye sistemy, no. 2 (2022), pp. 84-90.

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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, software module, heating, energized material
Mots-clés : bituminous reservoir, distance, mass. 
@article{ITVS_2022_2_a8,
     author = {A. R. Mukhutdinov and M. G. Efimov and Z. R. Vahidova},
     title = {Neural network modeling of the process of maintaining temperature after heating the bituminous reservoir},
     journal = {Informacionnye tehnologii i vy\v{c}islitelnye sistemy},
     pages = {84--90},
     publisher = {mathdoc},
     number = {2},
     year = {2022},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/ITVS_2022_2_a8/}
}
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A. R. Mukhutdinov; M. G. Efimov; Z. R. Vahidova. Neural network modeling of the process of maintaining temperature after heating the bituminous reservoir. Informacionnye tehnologii i vyčislitelnye sistemy, no. 2 (2022), pp. 84-90. http://geodesic.mathdoc.fr/item/ITVS_2022_2_a8/