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An efficient optimization framework of cyclic steam stimulation with experimental design in extra heavy oil reservoirs

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  • Luo, Erhui
  • Fan, Zifei
  • Hu, Yongle
  • Zhao, Lun
  • Bo, Bing
  • Yu, Wei
  • Liang, Hongwei
  • Liu, Minghui
  • Liu, Yunyang
  • He, Congge
  • Wang, Jianjun

Abstract

The Jurassic sandstone reservoir of the Pre-Caspian basin has abundant heavy oil resources. It has characteristics of high porosity, high permeability, shallow depth, and high crude oil viscosity. So the cyclic steam stimulation (CSS) is necessary to produce oil in an effective and economic way. However, the optimization of injection-production parameters for CSS is the premise of the technical strategies and development schemes. The conventional method usually adopts the one-parameter-at-a-time approach to perform sensitivity studies. This method is lack of interactions between different factors and difficult to determine the optimal level, in some situations even the main controlling factor is ignored.

Suggested Citation

  • Luo, Erhui & Fan, Zifei & Hu, Yongle & Zhao, Lun & Bo, Bing & Yu, Wei & Liang, Hongwei & Liu, Minghui & Liu, Yunyang & He, Congge & Wang, Jianjun, 2020. "An efficient optimization framework of cyclic steam stimulation with experimental design in extra heavy oil reservoirs," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322960
    DOI: 10.1016/j.energy.2019.116601
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    References listed on IDEAS

    as
    1. Akbilgic, Oguz & Zhu, Da & Gates, Ian D. & Bergerson, Joule A., 2015. "Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics," Energy, Elsevier, vol. 93(P2), pages 1663-1670.
    2. Baghernezhad, Danial & Siavashi, Majid & Nakhaee, Ali, 2019. "Optimal scenario design of steam-assisted gravity drainage to enhance oil recovery with temperature and rate control," Energy, Elsevier, vol. 166(C), pages 610-623.
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    Cited by:

    1. Alade, Olalekan S. & Mahmoud, Mohamed & Al Shehri, Dhafer & Mokheimer, Esmail M.A. & Sasaki, Kyuro & Ohashi, Ryo & Kamal, Muhammad Shahzad & Muhammad, Isah & Al-Nakhli, Ayman, 2022. "Experimental and numerical studies on production scheme to improve energy efficiency of bitumen production through insitu oil-in-water (O/W) emulsion," Energy, Elsevier, vol. 244(PA).
    2. Zhang, Qitao & Liu, Wenchao & Dahi Taleghani, Arash, 2022. "Numerical study on non-Newtonian Bingham fluid flow in development of heavy oil reservoirs using radiofrequency heating method," Energy, Elsevier, vol. 239(PE).
    3. Zhou, Yuhao & Wang, Yanwei, 2022. "An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs," Energy, Elsevier, vol. 253(C).

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