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Oil price future regarding unconventional oil production and its near-term deployment: A system dynamics approach

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  • Hosseini, Seyed Hossein
  • Shakouri G., Hamed
  • Kazemi, Aliyeh

Abstract

Heretofore, oil prices have been continuously predicted by many researchers using various methods. Among so many factors affecting the oil market, this article foresees the future variations in oil price respecting the new emerging competitors in the oil market, namely unconventional oil, that has received less attention in previous works. The dynamics of oil prices in the global market is investigated via a system approach, regarding the strategies of different oil market players, i.e. OECD/non-OECD member countries, including the mutual causal relationships and mechanisms. Three scenarios are defined to run the model: oil market growth, current situation (base run), and oil market downturn. The results indicate that oil price, which has shown a deep decay in recent years, will gradually reach around $70 per barrel ($41 per barrel in 1993 USD) in the next five years. While there exists oversupply in the global oil market due to unconventional oil production, which will be the case in the near-term, the oil price will not increase considerably up to the year 2025. Surely, an unanticipated shock or crisis on either of the supply side or demand side (e.g. a pandemic disease) can significantly affect both spot and future prices.

Suggested Citation

  • Hosseini, Seyed Hossein & Shakouri G., Hamed & Kazemi, Aliyeh, 2021. "Oil price future regarding unconventional oil production and its near-term deployment: A system dynamics approach," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001274
    DOI: 10.1016/j.energy.2021.119878
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    1. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    2. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
    3. Farzanegan, Mohammad Reza & Raeisian Parvari, Mozhgan, 2014. "Iranian-Oil-Free Zone and international oil prices," Energy Economics, Elsevier, vol. 45(C), pages 364-372.
    4. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
    5. Qudrat-Ullah, Hassan & Seong, Baek Seo, 2010. "How to do structural validity of a system dynamics type simulation model: The case of an energy policy model," Energy Policy, Elsevier, vol. 38(5), pages 2216-2224, May.
    6. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    7. E, Jianwei & Bao, Yanling & Ye, Jimin, 2017. "Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 412-427.
    8. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    9. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    10. Nademi, Arash & Nademi, Younes, 2018. "Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases," Energy Economics, Elsevier, vol. 74(C), pages 757-766.
    11. Ani Shabri & Ruhaidah Samsudin, 2014. "Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, July.
    12. Hosseini, Seyed Hossein & Shakouri G., Hamed, 2016. "A study on the future of unconventional oil development under different oil price scenarios: A system dynamics approach," Energy Policy, Elsevier, vol. 91(C), pages 64-74.
    13. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    14. Taiyong Li & Min Zhou & Chaoqi Guo & Min Luo & Jiang Wu & Fan Pan & Quanyi Tao & Ting He, 2016. "Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels," Energies, MDPI, vol. 9(12), pages 1-21, December.
    15. Chai, Jian & Guo, Ju-E. & Meng, Lei & Wang, Shou-Yang, 2011. "Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model," Energy Policy, Elsevier, vol. 39(12), pages 8022-8036.
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