A.R. Mukhutdinov¹, Z.R. Vakhidova², M.G. Efimov³

1,3 Kazan State Technological University (Kazan, Russia)

2 University of Management "TISBI" (Kazan, Russia)

An increase in the productivity of oil wells is possible with the use of a promising technology based on implosion and a device for its implementation. It is known that the effectiveness of the technology depends on the design parameters of the device. Currently, a promising way to study processes is computer modeling based on modern information technologies. Therefore, solving forecasting problems using modern software based on artificial neural networks (ANNs) is an urgent task of scientific and practical interest. In this regard, the aim of the work is to develop a neural network model and its application to identify the features of the influence of the diameter and length of the implosion chamber of the device on the pressure of a water hammer during implosion. In the software environment, the following have been created and tested: a method for developing a neural network model; a method of conducting a computational experiment with it. The possibility of neural network modeling of the implosion process has been studied. The results of predicting the output parameter, in this case the pressure of the water hammer, on a pre-trained network, with a relative error of 3.5%, using the knowledge base are demonstrated. The results of applying the methodology for solving forecasting problems using software based on artificial neural networks are presented. It was found that the diameter and length of the implosion chamber significantly affect the pressure of the water hammer. The practical significance of the work lies in the ability to determine the required values of the diameter and length of the implosion chamber of the device at a given level of water hammer pressure.

Mukhutdinov A.R., Vakhidova Z.R., Efimov M.G. Neural network modeling of the implosion process in the tasks of optimization of oil production process. Nonlinear World. 2021. V. 19. № 1. 2021. P. 29−35. DOI: https://doi.org/10.18127/j20700970202101-03 (In Russian)

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