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The oil and gas industry is one of the core sectors of the national economy, playing a fundamental role in ensuring energy security. The Covid-19 situation, which has driven companies to innovate, may serve as a catalyst for rethinking the size and role of functional teams, field workers, and management processes required to run an efficient oil and gas company. The slump in global oil consumption due to the pandemic has caused a shock to the Russian economy. To gain comprehensive insights on the performance of the oil and gas industry in Russia, this study aims to develop an integrated methodology that combines the Grey prediction method, a so-called GM (1,1) and Data Envelopment Analysis (DEA) Malmquist model for the prediction and evaluation of the top 10 potential companies in Russia. Grey theory is adopted to predict the companies’ data during 2020–2023, and the Malmquist method is used to evaluate their performance over the whole period of 2016–2023, based on three input factors (total assets, total liabilities, cost of revenue), and two output factors (total revenue and net income). During the research period, “Russneft” was found to have performed the most efficiently while “Slavneft” held the least-effective company, despite its efforts to achieve progressive technological changes. Overall, all companies have achieved excellent technological efficiency. Thus, the average total factor productivity indexes of all companies mainly rely on their technical performance. This study assists policymakers and decision-makers in expediting their recovery plans for further sustainable development in the oil and gas industry.

Oil and gas industry, grey prediction, data envelopment analysis, frontier, efficiency, decision-making

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