Constructing Cluster-Network Relations in the Oil Sector Based on a Neural Network Model in the Context of Digitalization

Abstract

The formation of stable and effective cluster-network connections inevitably increases with the digitalization of all socio-economic relations. The oil sector is one of the key ones in the Russian economy, affecting the determining pace and path of the state socio-economic development, and is subject to the government s greatest regulation as compared to most other industries. Oil companies in Russia are striving to take a dominant role in the global market. The international expansion allows oil companies to diversify state risks and opens up new opportunities. Amid global digitalization, the issue of optimizing big data using new approaches that could be based on classical fundamental knowledge is becoming increasingly critical. One of the methods proposed in the article for working with a broad array of regional indicators in dynamics is neural networks. The paper considers a neural network efficiency model of the cluster-network policy process in the oil sector. The analysis of classical mathematical models allows characterizing the influence of cluster-network connections on the oil and gas industry in the first approximation. The article considers the industry structure and analyzes the Volga Federal District s regions for indicators that characterize the economic state of the regions.