Published: 2021-11-23

Page: 1274-1295


Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan, China.


Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan, China.


Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan, China.

*Author to whom correspondence should be addressed.


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.

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

How to Cite



Download data is not yet available.


Global oil industry - Statistics & facts. Available: (accessed on 18 December 2020).

The global oil industry is experiencing a shock like no other in its history. Available: (accessed on 18 December 2020).

The world seeks to save the oil and gas industry. Available online: (accessed on 18 December 2020). Oil 2020. Available: (accessed on 18 December 2020).

oil and gas industry outlook. Available: (accessed on 18 December 2020).

Russian oil industry - statistics & facts. Available: (accessed on 18 December 2020).

Russia's oil and gas production sharply down in 2020, but here comes a new Arctic offshore platform. Available:,a%20drop%20in%20petroleum%20production.&text=However%2C%20the%20development%20of%20new,regions%20are%20in%20full%20swing (accessed on 18 December 2020).

Russia's annual oil production tumbles for first time in 12 years. Available: (accessed on 18 December 2020).

Russia faces challenges cutting oil output while keeping the industry alive. Available: (accessed on 18 December 2020).

Putin sets deadline for plan to support Russian oil industry. Available: (accessed on 18 December 2020). Energy; 2021. Available: (accessed on 18 December 2020)

Bowlin WF. Measuring performance: An introduction to data envelopment analysis (DEA). The Journal of Cost Analysis. 1998;15:3-27.

Khare R, Villuri VGK, Chaurasia D. Urban sustainability assessment: The evaluation of coordinated relationship between BRTS and land use in transit-oriented development mode using DEA model. Ain Shams Engineering Journal; 2020.

Wang CN, Tibo H, Nguyen HA. Malmquist productivity analysis of top global automobile manufacturers. Mathematics. 2020;8:580.

Bang S. Performance evaluation of energy research projects using DEA super-efficiency. Energies 2020;13:5318.

Hermoso-Orzáez MJ, García-Alguacil M, Terrados-Cepeda J, Brito P. Measurement of environmental efficiency in the countries of the European Union with the enhanced data envelopment analysis method (DEA) during the period 2005-2012. Environmental Science and Pollution Research. 2020;27:15691-15715.

Song M, Jia G, Zhang P. An evaluation of air transport sector operational efficiency in china based on a three-stage DEA Analysis. Sustainability. 2020;12:4220.

Julong D. Introduction to grey system theory. Journal of Grey System. 1989;1:1-24.

Qian W, Wang J. An improved seasonal GM (1,1) model based on the HP filter for forecasting wind power generation in China. Energy. 2020;209:118499.

Hu YC. Predicting Foreign tourists for the tourism industry using soft computing-based grey-markov models. Sustainability. 2017;9:1228.

Wang CN, Dang TT, Nguyen NAT, Le TTH. Supporting better decision-making: A combined grey model and data envelopment analysis for efficiency evaluation in E-Commerce marketplaces. Sustainability. 2020;12:10385.

Dai S, Niu D, Han Y. Forecasting of energy-related CO2 emissions in China Based on GM (1,1) and least squares support vector machine optimized by modified shuffled frog leaping algorithm for sustainability. Sustainability. 2018;10:958.

Hanrui BAO, Xun AN. Reliability test on oil field efficiency with DEA. Energy Procedia. 2011;5:1473-1477.

Al-Najjar SM, Al-Jaybajy MA. Application of data envelopment analysis to measure the technical efficiency of oil refineries: A case study. International Journal of Business Administration. 2012;3.

Daryanto WM, Wibisono I. Measuring financial performance of national oil and gas companies in Southeast Asia. International Journal of Innovation, Creativity and Change. 2019;6:191-206.

Tavana M, Khalili-Damghani K, Santos Arteaga FJ, Hosseini A. A fuzzy multi-objective multi-period network DEA model for efficiency measurement in oil refineries. Computers & Industrial Engineering. 2019;135:143-155.

Dalei NN, Joshi JM. Estimating technical efficiency of petroleum refineries using DEA and Tobit model: An India perspective. Computers & Chemical Engineering. 2020;142:107047.

Nedaei H, Jalali Naini SG, Makui A. A dynamic DEA model to measure the learning rates of efficient frontier and DMUs: An application to oil and gas wells drilling. Computers & Industrial Engineering. 2020;144:106434.

Sun S, Huang C. Energy structure evaluation and optimization in BRICS: A dynamic analysis based on a slack based measurement DEA with undesirable outputs. Energy. 2021;216:119251.

Vikas V, Bansal R. Efficiency evaluation of Indian oil and gas sector: data envelopment analysis. International Journal of Emerging Markets. 2019;14:362-378.

Wang Y, Zhu Z, Liu Z. Evaluation of technological innovation efficiency of petroleum companies based on BCC-Malmquist index model. Journal of Petroleum Exploration and Production Technology. 2019;9: 2405-2416.

Hosseini K, Stefaniec A. Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure. Energy. 2019;189:116112.

Kashani HA. Regulation and efficiency: an empirical analysis of the United Kingdom continental shelf petroleum industry. Energy Policy. 2005;33:915-925.

Lu L, Zhang J, Yang F, Zhang Y. Evaluation and prediction on total factor productivity of Chinese petroleum companies via three-stage DEA model and time series neural network model. Sustainable Computing: Informatics and Systems. 2020;27:100397.

Xia P, Wu J, Ji X, Xi P. A DEA-based empirical analysis for dynamic performance of China's regional coke production chain. Science of The Total Environment. 2020;717:136890.

Ghazi A, Hosseinzadeh Lotfi F. Assessment and budget allocation of Iranian natural gas distribution company- A CSW DEA based model. Socio-Economic Planning Sciences. 2019;66:112-118.

Hawdon, D. Efficiency, performance and regulation of the international gas industry-a bootstrap DEA approach. Energy Policy. 2003;31:1167-1178.

Huang, Y, Sun, H. Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic. IEEE Transactions on Dielectrics and Electrical Insulation. 2013;20:974-981.

Tang KHD, Md Dawal SZ, Olugu EU. Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia. Journal of Loss Prevention in the Process Industries. 2018;56:32-45.

Akbas H, Bilgen B. An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs. Energy. 2017;125:484-497.

Rahdari AH. Developing a fuzzy corporate performance rating system: a petrochemical industry case study. Journal of Cleaner Production. 2016;131:421-434.

Khojastehmehr M, Madani M, Daryasafar A. Screening of enhanced oil recovery techniques for Iranian oil reservoirs using TOPSIS algorithm. Energy Reports. 2019;5:529-544.

Elhuni RM, Ahmad MM. Key performance indicators for sustainable production evaluation in oil and gas sector. Procedia Manufacturing. 2017;11:718-724.

Al-Marri AN, Nechi S, Ben-Ayed O, Charfeddine L. Analysis of the performance of TAM in oil and gas industry: Factors and solutions for improvement. Energy Reports. 2020;6:2276-2287.

Rabbani A, Zamani M, Yazdani-Chamzini A, Zavadskas EK. Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies. Expert Systems with Applications. 2014;41:7316-7327.

Sun C, Luo Y, Huang Y, Ouyang X. A comparative study on the production efficiencies of China's oil companies: A true fixed effect model considering the unobserved heterogeneity. Journal of Cleaner Production. 2017;154:341-352.

Eller SL, Hartley PR, Medlock KB. Empirical evidence on the operational efficiency of National Oil Companies. Empirical Economics. 2011;40:623-643.

Wang CN, Nguyen TL, Dang TT. Analyzing operational efficiency in real estate companies: An application of GM (1,1) and DEA Malmquist Model. Mathematics 2021;9:202.

Wang CN, Dang TT, Tibo H, Duong DH. Assessing renewable energy production capabilities using DEA Window and Fuzzy TOPSIS Model. Symmetry. 2021;13:334.

Wang CN, Nguyen TL, Dang TT, Bui TH. Performance evaluation of fishery enterprises using data envelopment analysis-a malmquist model. Mathematics 2021;9:469.

Song J, Zhang Z. Oil refining enterprise performance evaluation based on DEA and SVM. In 2009 Second International Workshop on Knowledge Discovery and Data Mining 2009;401-404.

Ike CB, Lee H. Measurement of the efficiency and productivity of national oil companies and its determinants. Geosystem Engineering. 2014;17:1-10.

Atris AM. Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis. Resources Policy. 2020;65:101543. Morningstar. Available: (accessed on 10 November 2020).

RussNeft to rise oil production by 2% to 6.5 million tons in 2021. Available: (accessed on 15 January 2021).

Coronavirus (COVID-19) implications for Russian oil and refining industries. Available: (accessed on 15 January 2021).

Russian economy hit by COVID-19 and oil market turmoil; 2020. Available: (accessed on 15 January 2021).