Designing a Fuzzy Network Model to Evaluate the Efficiency of Oil and Gas Production Centers in the Country Based on Undesirable Outputs
الموضوعات :
مهراب حسنوند
1
,
Mohammad Taleghani
2
,
Behrouz Fathi Vajargah
3
1 - روه مدیریت صنعتی(تولید و عملیات) ، واحد رشت، دانشگاه آزاد اسلامی ،رشت،ایران
2 - دانشیار گروه مدیریت صنعتی و عضو هیأت علمی دانشگاه آزاد اسلامی واحد رشت
3 - Department of statistics, guilan university
الکلمات المفتاحية: Efficiency evaluation, network data envelopment analysis, undesirable outputs, weak disposability, oil and gas exploitation centers.,
ملخص المقالة :
Abstract
Exploitation centers hold a critical role not only within the oil and gas sector but also across various industries, constituting one of the paramount export factors in generating national revenue. The extracted oil and gas are indispensable to numerous industrial sectors and end consumers. Nevertheless, heavy crude oil exploitation and refining operations have undergone substantial transformations in response to product changes aimed at meeting market demand and adhering to environmental regulations.
The objective of this paper is to introduce a fuzzy network model designed to assess the efficiency of the country's oil and gas exploitation centers, taking into account undesirable outputs and weak disposability, specifically within the oil exploitation centers of Khuzestan province.
In this study, network data envelopment analysis was utilized to evaluate the efficiency of the centers, identifying toxic gases such as CO2 and SO2 as undesirable outputs at each stage.
The results of the data analysis of the nine centers indicated that none of the units achieved an efficiency score of one. The primary reasons for this inefficiency were attributed to the use of outdated equipment due to sanctions, as well as the failure to utilize liquefied and natural gases in place of diesel and gasoline in the machinery employed for exploiting and refining crude oil.
Finally, the model was extended to the oil exploitation centers of Khuzestan province as a case study, and its functionality was validated. The results and outputs of the model analysis demonstrated its capability to effectively evaluate the efficiency of current units. Based on these results, the use of renewable energy and the replacement of appropriate filters in the equipment were suggested.
1. Afolarin, A.E., (2022). Redefining the Corporate Responsibility of Fossil Fuel Corporations Towards the Attainment of a Clean Economy. Available at SSRN 4202798.
2. Amirteimoori,A., Allahviranloo,T., Ibrahim Khalaf,O., Algburi,S., Nematizadeh,M., Hamam,H.(2024). Sustainability assessment in the presence of undesirable outputs: A stochastic slack-based data envelopment analysis approach.Research square. https: doi.org.10.21203.rs.3.rs-3942354.v1
3. Al-Najjar, S.M. and Al-Jaybajy, M.A. (2012), “Application of data envelopment analysis to measure the technical efficiency of oil refineries: a case study”, International Journal of Business Administration, Vol. 3 No. 5, pp. 64-77.
4. Atris, A.M. Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis. Resour. Policy 2020, 65, 101543
5. Azadeh, A., Salehi, V., Mirzayi, M., Roudi, E., 2017. Combinatorial optimization of resilience engineering and organizational factors in a gas refinery by a unique mathematical programming approach. Hum. Factors Ergon. Manuf. 27, 53–65.
6. AZADEH, A.; SERAJ, O.; ASADZADEH, S. M.; SABERI, M. An integrated fuzzy regression-data Envelopment Analysis algorithm for optimum oil consumption estimation with ambiguous data. Aplied soft computing. Vol.12, 2012, p.2614-2630.
7. Azadeh, A., Roohani, A., Motevali Haghighi, S., 2015. Performance optimization of gas refineries by ANN and DEA based on financial and operational factors. World. J. Eng. 12 (2), 109–134.
8. Ali, B., & Kumar, A. (2017). Development of life cycle water footprints for oil sands-based transportation fuel production. Energy, 131, 41-49
9. Arinze,C.A; Jacks,B.S.(2024). A COMPREHENSIVE REVIEW ON AI-DRIVEN OPTIMIZATION TECHNIQUES ENHANCING SUSTAINABILITY IN OIL AND GAS PRODUCTION PROCESSES. Engineering Science & Technology Journal, Volume 5, Issue 3, March 2024. DOI: 10. 51594.estj. v5i3.950.
10. Agudelo, M.A.L., Johannsdottir, L. and Davidsdottir, B., (2020). Drivers that motivate energy companies to be responsible. A systematic literature review of Corporate Social Responsibility in the energy sector. Journal of cleaner production, 247, p.119094.
11. BARROS, C.P.; ASSAF, A. (2009). Bootstrapped efficiency measures of oil blocks in Angola. Energy Policy. Vol.37, 2009. p.4098-4103.
12. Bevilacqua, M. and Braglia, M. (2002), “Environmental efficiency analysis for ENI oil refineries”, Journal of Cleaner Production, Vol. 10 No. 1, pp. 85-92.
13. Bezerra,p., Marques Vieira,M., Rodrigues de Almeida,M.(2017). COMPARATIVE ANALYSIS ABOUT THE APPLICATION OF METHODS OF THE DATA ENVELOPMENT ANALYSIS (DEA) IN THE OIL INDUSTRY. International Journal of Engineering Sciences & Research Technology. DOI: 10. 5281.zenodo.345699
14. Bozorgi Gerdvisheh,F., Soufi,M., Amirteimoori, A., Homayounfar.M(2023). Efficiency Analysis of Banking Sector in Presence of Undesirable Factors Using Data Envelopment Analysis.Advances in Mathematical Finance and Applications 8 (2), 589-604.
15. Bouzon, M., Govindan, K., Rodriguez, C. M. T., & Campos, L. M. S. (2016). Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, Conversation and Recycle, 1 – 16. https: doi.org. 10.1016.j. resconrec.2015.05.021.
16. Craig, J. and Quagliaroli, F. (2020). The oil & gas upstream cycle: Exploration activity. In EPJ Web of Conferences (Vol. 246, p. 00008). EDP Sciences.
17. Dalei, N.N.; Joshi, J.M.(2020). Estimating technical efficiency of petroleum refineries using DEA and tobit model: An India perspective. Computer. Chem. Engineering., 142, 107047
18. ElAlfy, A., Palaschuk, N., El-Bassiouny, D., Wilson, J. and Weber, O. (2020). Scoping the evolution of corporate social responsibility (CSR) research in the sustainable development goals (SDGs) era. Sustainability, 12(14), p.5544.
19. Eller, S.L., Hartley, P.R. and Medlock, K.B. III (2011), “Empirical evidence on the operational efficiency of national oil companies”, Empirical Economics, Vol. 40 No. 3, pp. 623-643.
20. Fakhru’l-Razi, A., Pendashteh, A., Abdullah, L.C., Biak, D.R.A., Madaeni, S.S., & Abidin, Z.Z., 2009. Review of technologies for oil and gas produced water treatment. Journal of Hazardous Materials, 170(2-3), 530-551.
21. Francisco, C., Almeida, M., Silva, D., (2012). Efficiency in Brazilian refineries under different DEA technologies regular paper. Int. J. Eng. Bus. Manage. 4 (35), 1–11.
22. Jones, C.M. (2018). The oil and gas industry must break the paradigm of the current exploration model. Journal of Petroleum Exploration and Production Technology, 8, 131-142.
23. Khalili-Damghani, K., Tavana, M., Haji-Saami, E. (2015). A data envelopment analysis model with interval data and undesirable output for combined cycle power plant performance assessment. Expert Systems with Applications, 42(2), 760–773.
24. Longxin, M.U., & Zhifeng, J.I. (2019). Technological progress and development directions of PetroChina overseas oil and gas exploration. Petroleum Exploration and Development, 46(6), 1088-1099.
25. Li, H., Dong, K., Sun, R., Yu, J., Xu, J., (2017). Sustainability assessment of refining enterprises using a DEA-based model. Sustainability 9 (620), 1–15.
26. LEE, Seong K.; MOGI, Gento; HUI, K.S. (2013) A fuzzy analytic hierarchy process. Data Envelopment Analysis hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices. Renewable and Sustainable Energy Reviews. May, 2013, Vol.21, p.347-355
27. Mekaroonreung, M., Johnson, A.L., (2010). Estimating the efficiency of American petroleum refineries under varying assumptions of the disposability of bad outputs. Int. J. Energy Sect. manage. 4 (3), 356–398, 2010.
28. Movahedi,M., Homayounfar,M., Fadaei Eshkiki,M., Soufi,M.(2023). Development of a model based on fuzzy cognitive mapping to analyze the performance of stock exchange firms. Securities & Exchange Organization, Research, Development & Islamic Studies (RDIS) Journal of Securities and Exchange, Spring 2023, V. 16, No.61, pp. 57-90. http:..dx.doi.org.10.22034.JSE.2022.11688.178.
29. Mo,R., Huang,H., Yang,L.(2020).” An Interval Efficiency Measurement in DEA When considering Undesirable Outputs”. Hindawi Complexity Volume 2020, Article ID 7161628, 12 pages.
30. Mohammed Atris,A.(2020). Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis. Resources Policy 65 (2020) 101543. https:..doi.org.10.1016.j.resourpol.2019.101543.
31. Oliveira, M.S.d.; Lizot, M.; Siqueira, H.; Afonso, P.; Trojan, F. (2023). Efficiency Analysis of Oil Refineries Using DEA Window Analysis, Cluster Analysis, and Malmquist Productivity Index. Sustainability 2023, 15, 13611. https: doi.org. 10.3390.su151813611
32. Sueyoshi, T.; Wang, D. (2014) Sustainability development for supply chain management in U.S. petroleum industry by DEA environmental assessment. Energy Econ. 2014, 46, 360–374.
33. SONG, Malin; ZHANG, Jie; WANG Shuhong. (2015). Review of the network environmental efficiencies of listed petroleum enterprises in China. Renewable and sustainable energy reviews. Vol.43, 2015, p.65- 71.
34. Tabatabaei, M., Kazemzadeh, F., Sabah, M., & Wood, D.A. (2022). Sustainability in natural gas reservoir drilling: A review on environmentally and economically friendly fluids and optimal waste management. Sustainable Natural Gas Reservoir and Production Engineering, 269-304.
35. Wang, Y.; Zhu, Z.; Liu, Z. (2019). Evaluation of technological innovation efficiency of petroleum companies based on BCC–Malmquist index model. J. Pet. Explor. Prod. Technol. 2019, 9, 2405–2416.
36. Zohuri, B. (2023). Navigating the Global Energy Landscape Balancing Growth, Demand, and Sustainability. J. Mat. Sci. Apl. Eng, 2(7).
37. ZHANG, Hai Xia; PAN, Cai Xin; DONG, Xiu-cheng. (2013). Study on refined oil operating efficiency of international oil companies. International Business, 2013
