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Designing a Fuzzy Network Model to Evaluate the Efficiency of Oil and Gas Production Centers in the Country Based on Undesirable Outputs

Document Type : Research Paper

Authors

1 Ph.D. student of Industrial Management (Production and Operation), Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran

2 Associate Professor of Industrial Management, Faculty of Management and Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran

3 Professor of Statistics Department, Faculty of Mathematical Sciences, Gilan University, Rasht, Iran

10.22054/jims.2024.81623.2930

Abstract

Operational centers play a vital role not only in the oil and gas industry but also in many other industries, serving as one of the key export factors contributing to national revenue. The extracted oil and gas are essential for many industrial sectors and end consumers. However, the operations of extracting and refining heavy crude oil have undergone significant transformations due to changes in products designed to meet market demand and environmental regulations. This study focuses on designing a fuzzy network model to evaluate the efficiency of oil and gas operational centers in the country, based on undesirable outputs at the oil extraction centers in Khuzestan province. In this research, Data Envelopment Analysis (DEA) of network models was used to assess the efficiency of the centers, with toxic gases such as CO2 and SO2 identified as undesirable outputs at each stage. The results of the analysis of data from 9 centers showed that none of the units achieved an efficiency score of one, with the main reasons being the use of outdated equipment due to sanctions and the lack of use of liquefied and natural gas as alternatives to diesel and gasoline in machinery for oil extraction and refining. Finally, it was recommended to utilize renewable energy sources and appropriate filters in the equipment to improve efficiency and reduce harmful emissions.

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References
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Industrial Management Studies
Volume 22, Issue 74
November 2024
Pages 175-219
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