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Modelling the costs of non-conventional oil: A case study of Canadian bitumen

Author

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  • Aurélie Méjean

  • Chris Hope

Abstract

High crude oil prices, uncertainties about the consequences of climate change and the eventual decline of conventional oil production raise the issue of alternative fuels, such as non-conventional oil and biofuels. This paper describes a simple probabilistic model of the costs of non-conventional oil, including the role of learning-by-doing in driving down costs. This forward-looking analysis quantifies the effects of both learning and production constraints on the costs of supplying bitumen, which can then be upgraded into synthetic crude oil, a substitute to conventional oil. The results show large uncertainties in the future costs of supplying bitumen from Canadian oil sands deposits, with a 90% confidence interval of $7-12 in 2030, and $6-15 in 2060 (2005 US$). The influence of each parameter on the supply costs is examined, with the minimum supply cost, the learning rate (LR), and the depletion curve exponent having the largest influence. Over time, the influence of the LR on the supply costs decreases, while the influence of the depletion curve exponent increases.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Aurélie Méjean & Chris Hope, 2008. "Modelling the costs of non-conventional oil: A case study of Canadian bitumen," Working Papers EPRG 0804, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg0804
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    Cited by:

    1. Giacchetta, Giancarlo & Leporini, Mariella & Marchetti, Barbara, 2015. "Economic and environmental analysis of a Steam Assisted Gravity Drainage (SAGD) facility for oil recovery from Canadian oil sands," Applied Energy, Elsevier, vol. 142(C), pages 1-9.
    2. Botelho, Tatiana & Magrini, Alessandra & Schaeffer, Roberto, 2014. "Plumbing the depths: Utilizing O&G reserve profiles to develop forward-looking risk assessments for exploration and production activities," Energy Policy, Elsevier, vol. 69(C), pages 489-500.
    3. Aurélie Méjean & Chris Hope, 2010. "The Effect of CO2 Pricing on Conventional and Non-Conventional Oil Supply and Demand," Working Papers EPRG 1029, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Chi Yong & Mu Tong & Zhongyi Yang & Jixian Zhou, 2023. "Conventional Natural Gas Project Investment and Decision Making under Multiple Uncertainties," Energies, MDPI, vol. 16(5), pages 1-30, February.
    5. Méjean, Aurélie & Hope, Chris, 2013. "Supplying synthetic crude oil from Canadian oil sands: A comparative study of the costs and CO2 emissions of mining and in-situ recovery," Energy Policy, Elsevier, vol. 60(C), pages 27-40.
    6. McGlade, C.E., 2012. "A review of the uncertainties in estimates of global oil resources," Energy, Elsevier, vol. 47(1), pages 262-270.
    7. Sapkota, Krishna & Oni, Abayomi Olufemi & Kumar, Amit & Linwei, Ma, 2018. "The development of a techno-economic model for the extraction, transportation, upgrading, and shipping of Canadian oil sands products to the Asia-Pacific region," Applied Energy, Elsevier, vol. 223(C), pages 273-292.
    8. Luo, Dongkun & Zhao, Xu, 2012. "Modeling the operating costs for petroleum exploration and development projects," Energy, Elsevier, vol. 40(1), pages 189-195.
    9. Sena, Marcelo Fonseca Monteiro de & Rosa, Luiz Pinguelli & Szklo, Alexandre, 2013. "Will Venezuelan extra-heavy oil be a significant source of petroleum in the next decades?," Energy Policy, Elsevier, vol. 61(C), pages 51-59.
    10. 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.
    11. Rui, Zhenhua & Wang, Xiaoqing & Zhang, Zhien & Lu, Jun & Chen, Gang & Zhou, Xiyu & Patil, Shirish, 2018. "A realistic and integrated model for evaluating oil sands development with Steam Assisted Gravity Drainage technology in Canada," Applied Energy, Elsevier, vol. 213(C), pages 76-91.
    12. Bo Xu & Lianyong Feng & William X. Wei & Yan Hu & Jianliang Wang, 2014. "A Preliminary Forecast of the Production Status of China’s Daqing Oil field from the Perspective of EROI," Sustainability, MDPI, vol. 6(11), pages 1-21, November.
    13. Bremson, Joel, 2012. "Using Gaming Simulation to Explore Long Range Fuel and Vehicle Transitions," Institute of Transportation Studies, Working Paper Series qt91v2j57d, Institute of Transportation Studies, UC Davis.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q32 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Exhaustible Resources and Economic Development

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