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Forecast of oil reserves and production in Daqing oilfield of China

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  • Tang, Xu
  • Zhang, Baosheng
  • Höök, Mikael
  • Feng, Lianyong

Abstract

As China’ largest oilfield, Daqing is of great importance to China, this paper analyzes the status of the Daqing oilfield and forecasts its ultimate recoverable reserves by use of the URR model. The forecast results are presented for three scenarios which show that the ultimate recoverable reserves in Daqing oilfield are 3574.0million tons in the optimistic scenario, 3169.3million in the base case scenario and 3033.3million in the pessimistic scenario, respectively. A system dynamics model is established and the quantitative relationships between variables in the model are determined. Total oil production, remaining recoverable reserves, annual newly discovered reserves, and the degree of reserves recovery before 2060 are simulated under the three scenarios by use of the system dynamics model. The forecast results show that the future oil production in Daqing oilfield will continue declining, under the base case scenario, from 41.6million tons in 2007 to 8.0million tons in 2060. For Chinese policy-makers, it is worth paying attention to the problem of whether oil production in new oilfields can effectively make up for the decline in production of the large, old oilfields.

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  • Tang, Xu & Zhang, Baosheng & Höök, Mikael & Feng, Lianyong, 2010. "Forecast of oil reserves and production in Daqing oilfield of China," Energy, Elsevier, vol. 35(7), pages 3097-3102.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:7:p:3097-3102
    DOI: 10.1016/j.energy.2010.03.043
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    Cited by:

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    2. Armin Leopold, 2016. "Energy related system dynamic models: a literature review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(1), pages 231-261, March.
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    7. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    8. Xu Tang & Benjamin C. McLellan & Simon Snowden & Baosheng Zhang & Mikael Höök, 2015. "Dilemmas for China: Energy, Economy and Environment," Sustainability, MDPI, vol. 7(5), pages 1-13, May.
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    12. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    13. Wang, Ke & Feng, Lianyong & Wang, Jianliang & Xiong, Yi & Tverberg, Gail E., 2016. "An oil production forecast for China considering economic limits," Energy, Elsevier, vol. 113(C), pages 586-596.
    14. 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.

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