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طراحی مدل شبکه ای فازی به منظور ارزیابی کارایی مراکز بهره برداری نفت و گاز کشور مبتنی بر خروجی های نامطلوب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری رشته مدیریت صنعتی (تولید و عملیات)، دانشکده مدیریت و حسابداری، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران

2 دانشیار گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران

3 استاد گروه آمار، دانشکده علوم ریاضی، دانشگاه گیلان، رشت،ایران

10.22054/jims.2024.81623.2930

چکیده

مراکز بهره برداری نه تنها در صنعت نفت و گاز بلکه در بسیاری از صنایع دیگر نقشی حیاتی داشته و یکی از مهمترین عوامل صادرتی در تولید درآمد کشور محسوب می گردد. نفت و گاز استخراج شده برای بسیاری از بخش های صنعتی و مصرف کنندگان نهایی ضروری است. با این وجود، عملیات بهره برداری و تصفیه نفت خام سنگین به دلیل تغییرات ایجاد شده در محصولات برای پاسخگویی به تقاضای بازار و مقررات زیست محیطی، شاهد دگرگونی های قابل توجهی بوده است. در این مطالعه به طراحی مدل شبکه ای فازی به منظور ارزیابی کارایی مراکز بهره برداری نفت و گاز کشور مبتنی بر خروجی های نامطلوب در مراکز بهره برداری نفت استان خوزستان پرداخته شده است. در این پژوهش جهت ارزیابی کارایی مراکز مورد مطالعه ، از تحلیل پوششی داده های شبکه ای استفاده شده که گازهای سمی ماننده co2 و so2 به عنوان خروجی های نامطلوب هر مرحله تعیین شدند. نتایج حاصل از تحلیل داده های 9 مرکز نشان داد که هیچ یک از واحدها از کارایی یک برخوردار نبوده در این میان بیشترین کارایی مربوط به واحد 1 عدد کارایی 0.8244 بوده و کمترین کارایی مربوط به واحدهای 2 عدد کارایی 0.6868، واحد 3 با عدد کارایی 0.6701 و واحد 7 با عدد کارایی 0.6265می باشد. یکی از مهمترین دلایل عدم کارایی واحدهای مذکور ، تحریم های مربوط جهت خرید ماشین آلات و تجهیزات مرتبط با بهره برداری نفت خام و تولید نفت و گاز خالص از مواد استخراج شده از زیر زمین می باشد

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Performance evaluation, coverage analysis of network data, undesirable outputs, and oil and gas exploitation centers.

نویسندگان [English]

  • Mehrab Hasanvand 1
  • Mohammad Taleghani 2
  • Behrouz Fathi Vajargah 3

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

چکیده [English]

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.

کلیدواژه‌ها [English]

  • Performance evaluation
  • coverage analysis of network data
  • undesirable outputs and oil and gas exploitation centers
مراجع
  1. موحدی، م.، همایون فر، م.، فدایی اشکیکی، م.، صوفی، م.(1402). توسعه یک مدل مبتنی بر نگاشت شناختی فازی جهت تحلیل عملکرد شرکت‌های بورس اوراق بهادار. فصلنامه بورس اوراق بهادار، دوره 16، شماره 61، اردیبهشت 1402، صص 57-90.
  2. Afolarin, A.E., (2022). Redefining the Corporate Responsibility of Fossil Fuel Corporations Towards the Attainment of a Clean Economy. Available at SSRN 4202798. https:..doi.org.10.1016.j.uncres .2024.100127
  3. Agudelo,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. https:..doi.org.10.1016.j.jclepro.2019.119094
  4. Ali, B., & Kumar, A. (2017). Development of life cycle water footprints for oil sands-based transportation fuel production. Energy, 131, 41-49. https: doi.org.10.1016.j.energy.2017.05.021
  5. 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. https:..doi.org. 5430.ijba.v3n5p64
  6. Amirteimoori,, 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
  7. Arinze,C., Jacks,B. (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: https:..doi.org.10.51594.estj.v5i3.950
  8. 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. https:..doi.org. 51594.estj.v5i3.950
  9. Atris,M. (2020) Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis. Resource. Policy 2020, 65, 101543.. https: doi.org.10. 1016.j. resourpol.2019.101543.
  10. Azadeh,, 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. https:..doi.org.10.1260.1708-5284.12.2.109
  11. 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.
  12. Azadeh, ; SERAJ, O.; ASADZADEH, S. M.; SABERI, M. (2012) An integrated fuzzy regression-data Envelopment Analysis algorithm for optimum oil consumption estimation with ambiguous data. Applied soft computing. Vol.12, 2012, p.2614-2630. https:..doi.org.10.1016.j.asoc.2012.03.026
  13. BARROS, P.; ASSAF, A. (2009). Bootstrapped efficiency measures of oil blocks in Angola. Energy Policy. Vol.37, 2009. p.4098-4103. https:..doi.org.10.1016.j.enpol.2009.05.007
  14. Bevilacqua, M. and Braglia, M. (2002), Environmental efficiency analysis for ENI oil refineries. Journal of Cleaner Production, Vol. 10 No. 1, pp. 85-92. https:..doi.org.10.1016.S0959-6526(01)00022-1
  15. 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. https:..doi.org.10.21919.remef.v17i2.718
  16. 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. DOI: 10.1016.j.resconrec.2015.05.021
  17. Bozorgi Gerdvisheh, F., Soufi,M., Amirteimoori, , 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. https:..doi.org.10.22034.amfa.2022.1950209.1684
  18. Charnes, A., Cooper, W., Rhodes, E., Measuring efficiency of decision making units. European Journal of Operational Research 2., 1978, 429–444. https:..doi.org.10.1016.0377-2217(78)90138-8
  19. Craig, J. and Quagliaroli, F. (2020). The oil & gas upstream cycle: Exploration activity. In EPJ Web of Conferences (Vol. 246, p. 00008). EDP Sciences. https:..doi.org.10.1051.epjconf.2020246‌00008
  20. Dalei, N.N.; Joshi, J.M. (2020). Estimating technical efficiency of petroleum refineries using DEA and tobit model: An India perspective. Chem. Engineering., 142, 107047. 10.1016.j.compchemeng.2020.107047
  21. 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. https:..doi.org. 3390.su12145544
  22. 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. DOI: 1007.s00181-010-0349-8
  23. 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. doi: 10.1016.j.jhazmat.2009.05.044.
  24. Fare, , Grosskopf, S., Lovell, K., Pasurka, C. (2000). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The Review of Economics and Statistics 71.90–98. https:..doi.org.10.1080.00036846.2010.498368
  25. Francisco, C., Almeida, M., Silva, D., (2012). Efficiency in Brazilian refineries under different DEA technologies regular paper. International Journal of Engineering Business Management. 4 (35), 1–11. DOI: 5772.52799
  26. Fukuyama,H.,  Weber,A. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega,Volume 38, Issue 5, October 2010, Pages 398-409, https:..doi.org.10.1016.j. 2009.10.006
  27. Ishikawa, A; Amagasa, M; Shiga, T; Tomizawa, G; Tatsuta, R & Mieno, H. (1993).The max–min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55, 241–253. https:..doi.org.10.1016.0165-0114(93)90251-C
  28. 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. https:..doi.org.10.1007.s13202-017-0395-2
  29. KAO, C., Liu.c. (2009). Data envelopment analysis with imprecise data: an application of Taiwan machinery firms. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, No. 02, pp. 225-240 (2009), https:..doi.org.10.1142.S0218488505003412
  30. 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. https:..doi.org. 1016.j.eswa.2014.08.02
  31. 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. DOI: 10.1016.j.rser.2012.12.067
  32. 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. https:..doi.org.10.3390.su9040620
  33. 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. https:..doi.org.10.1016.S1876-3804(19)60265-X
  34. 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. 4 (3), 356–398, 2010. DOI:10.1108.17506221011073842
  35. 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. DOI: 1155.2020.7161628
  36. Mohammadzadeh, M., Navabakhsh M., Hafezalkotob. A (2024). Performance Evaluating Energy, Economic and Environmental Performance with an Integrated Approach of Data Envelopment Analysis and Game Theory. IJE TRANSACTIONS B: Applications Vol. 37, No. 05, (May 2024) 959-973. 5829.ije.2024.37.05b.13
  37. 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.
  38. 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
  39. Raj, A.and Samuel, C. (2023).Assessing and overcoming the barriers for healthcare waste management in India: an integrated AHP and Fuzzy TOPSIS approach, Journal of Health Organization and Management, Vol. 37 No. 6.7, pp. 483-501. https:..doi.org.10.1108.JHOM-09-2022-0264.
  40. 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. DOI: 10.1016.j.rser.2014.11.050.
  41. Sueyoshi, ; Jingjing Qu., Aijun Li., Chunping Xie. (2014) Understanding the efficiency evolution for the Chinese provincial power industry: A new approach for combining data envelopment analysis-discriminant analysis with an efficiency shift across periods. Journal of Cleaner Production,Volume 277. https.// doi.org.10.1016.j.jclepro.2020.12237.
  42. Sueyoshi,; Wang, D. (2014). Sustainability development for supply chain management in U.S. petroleum industry by DEA environmental assessment. Energy Econ. 2014, 46, 360–374. DOI: 10.1016.j.eneco.2014.09.022.
  43. 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. DOI: 1016.B978-0-12-824495-1.00008-5
  44. Wang, Q., Zhu, Z. W., & Liu, Z. B. (2019). Evaluation of Technological Innovation Efficiency of Petroleum Companies Based on BCC-Malmquist Index Model. Journal of Petroleum Exploration and Production Technology, 9, 2405-2416.
    https:..doi.org.10.1007.s13202-019-0618-9
  45. Wu J, Liang L, Yang F, Yan H. (2016). Bargaining game model in the evaluation of decision-making units. Expert Systems with Applications. 2016;36(3):4357-62. https:..doi.org.10.1016.j.eswa.2009.05.001
  46. ZHANG, Hai Xia; PAN, Cai Xin; DONG, Xiu-cheng. (2013). Study on refined oil operating efficiency of international oil companies. International Business, 2013. DOI: 10.1016.j.enpol.2020.111491
  47. Zohuri, B. (2023). Navigating the Global Energy Landscape Balancing Growth, Demand, and Sustainability. Journal of Mathematics science Apllication and Engineering, 2(7). DOI: https:..doi.org.10.30574.ijsra.2024.11.1.0029
مطالعات مدیریت صنعتی
دوره 22، شماره 74
مهر 1403
صفحه 175-219
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هم رسانی
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