Using Artificial Neural Networks for Analyzing Efficiency of Advanced Recovery Methods
Irik Galikhanovich Fattakhov, Ramzis Rakhimovich Kadyrov, Ildar Danilovich Nabiullin, Ramis Rasilevich Sakhibgaraev, Anatolij Nikolaevich Fokin
FSBEI of “Ufa State Petroleum Technological University” 1, Kosmonavtov Street, Ufa, 450062, Republic of Bashkortostan, FSBEI of Ufa State Petroleum Technological University”, Oktyabrsky branch, 54à, Devon Street, Oktyabrsky, 452607, Republic of Bashkortostan
ABSTRACT: A method of using neural networks for analyzing efficiency of advanced recovery methods has been proposed. In the course of creating a model of artificial neural networks, optimal parameters of the network for training on an array of data by the layers of Solonets deposit have been defined. The architecture of multilayer Rosenblatt's perceptron has been disclosed. Its optimal parameters for solving the task have been justified. It has been proven that the productivity coefficients calculated in this way are most approximated to the actual values.
KEYWORDS: Artificial neural network; advanced recovery method; Rosenblatt's perceptron; productivity coefficient; geological; technical measuresDownload this article as:
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Fattakhov I. G, Kadyrov R. R, Nabiullin I. D, Sakhibgaraev R. R, Fokin A. N. Using Artificial Neural Networks for Analyzing Efficiency of Advanced Recovery Methods. Biosci Biotechnol Res Asia 2015;12(2)