IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4805-d852725.html
   My bibliography  Save this article

A Study on the Adaptability of Nonhydrocarbon Gas-Assisted Steam Flooding to the Development of Heavy Oil Reservoirs

Author

Listed:
  • Yong Huang

    (Oil Production Technology Research Institute, Petro China Xinjiang Oilfield Company, Karamay 834000, China)

  • Wulin Xiao

    (Heavy Oil Development Company, PetroChina Xinjiang Oilfield Company, Karamay 834000, China)

  • Sen Chen

    (Oil Production Technology Research Institute, Petro China Xinjiang Oilfield Company, Karamay 834000, China)

  • Boliang Li

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Liping Du

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Binfei Li

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

In view of the serious heat loss in the process of steam injection for heavy oil recovery, nonhydrocarbon gas combined with steam has attracted much attention in recent years to realize the efficient development of heavy oil. Due to the wide variety of nonhydrocarbon gases, their performance in pressurization, dissolution, viscosity reduction, and heat loss decrease is changeable. In this paper, four groups of one-dimensional physical simulation experiments on different nonhydrocarbon gas-assisted steam flooding methods were carried out, and the effect on oil displacement characteristics under high temperature and pressure conditions was studied. Moreover, the differences in N 2 , CO 2 , and flue gas in energy supplementation, heat transfer, and oil recovery efficiency were also analyzed. The results showed that the three nonhydrocarbon gas-assisted steam flooding methods could significantly improve the oil displacement efficiency, which was specifically embodied as a faster oil production rate and longer production period. Compared with pure steam flooding, the recovery was increased by 12.13%, 16.71% and 13.01%, respectively. The effects of N 2 in energy supplementation and heat transfer reinforcement were the greatest among the three nonhydrocarbon gases, followed by those of flue gas, and the CO 2 effects were the worst. The temperature at the end of the sandpack model increased by 14.3 °C, 8.8 °C and 13.1 °C, respectively. In addition, CO 2 -assisted steam flooding had a prominent oil recovery effect, and the oil content of the sands in the front and middle of the model was significantly lower than that of other displacement methods. Most importantly, combined with the analysis of the remaining oil in the oil sands after displacement, we explained the contrasting contradictions of the three non-hydrocarbon gases in terms of recovery and energy supply/heat transfer, and further confirmed the gas properties and reservoir adaptability of the three non-hydrocarbon gases. The results may provide a theoretical basis for the selection of nonhydrocarbon gases for heavy oil reservoirs with different production requirements.

Suggested Citation

  • Yong Huang & Wulin Xiao & Sen Chen & Boliang Li & Liping Du & Binfei Li, 2022. "A Study on the Adaptability of Nonhydrocarbon Gas-Assisted Steam Flooding to the Development of Heavy Oil Reservoirs," Energies, MDPI, vol. 15(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4805-:d:852725
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4805/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4805/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dong, Xiaohu & Liu, Huiqing & Chen, Zhangxin & Wu, Keliu & Lu, Ning & Zhang, Qichen, 2019. "Enhanced oil recovery techniques for heavy oil and oilsands reservoirs after steam injection," Applied Energy, Elsevier, vol. 239(C), pages 1190-1211.
    2. Xu, Xiaofeng & Wang, Chenglong & Zhou, Peng, 2021. "GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective," International Journal of Production Economics, Elsevier, vol. 235(C).
    3. Pang, Zhanxi & Wang, Luting & Yin, Fanghao & Lyu, Xiaocong, 2021. "Steam chamber expanding processes and bottom water invading characteristics during steam flooding in heavy oil reservoirs," Energy, Elsevier, vol. 234(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo, Yating & Rosland, Anitha & Ishak, Suryati & Muhammad Senan, Mohammad Khair Afham, 2023. "Public spending and natural resources development: A way toward green economic growth in China," Resources Policy, Elsevier, vol. 86(PB).
    2. Sajad Koochakinia & Amir Ebrahimi-Moghadam & Mahdi Deymi-Dashtebayaz, 2022. "Techno-Environmental Analyses and Optimization of a Utility Boiler Based on Real Data," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    3. Anufriev, I.S. & Kopyev, E.P. & Alekseenko, S.V. & Sharypov, O.V. & Vigriyanov, M.S., 2022. "New ecology safe waste-to-energy technology of liquid fuel combustion with superheated steam," Energy, Elsevier, vol. 250(C).
    4. Lin, Fawei & Luan, Chujun & Mao, Huiyi & Zhong, Chenxu & Han, Xinlu & Chen, Guanyi & Che, Lei, 2025. "NH3 assisted catalytic pyrolysis of low-grade fuel oil with energy self-supply to acquire high-quality products," Applied Energy, Elsevier, vol. 381(C).
    5. Wu, Weitiao & Li, Yu, 2024. "The multi-compartment truck and trailer petrol station replenishment problem with domino hazard risks," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
    6. Camilo Andrés Guerrero-Martin & Angie Tatiana Ortega-Ramírez & Paula Alejandra Perilla Rodríguez & Shalom Jireth Reyes López & Laura Estefanía Guerrero-Martin & Raúl Salinas-Silva & Stefanny Camacho-G, 2023. "Analysis of Environmental Sustainability through a Weighting Matrix in the Oil and Gas Industry," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    7. Yijing Chu & Yingying Wang & Zucheng Zhang & Shengli Dai, 2022. "Decoupling of Economic Growth and Industrial Water Use in Hubei Province: From an Ecological–Economic Interaction Perspective," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    8. Yao, Yue & Sun, Deqiang & Xu, Jin-Hua & Wang, Bin & Peng, Guohong & Sun, Bingmei, 2023. "Evaluation of enhanced oil recovery methods for mature continental heavy oil fields in China based on geology, technology and sustainability criteria," Energy, Elsevier, vol. 278(PB).
    9. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
    10. Muhammad Luqman & Yafei Li, 2024. "Can oil prices affect economic uncertainty and financial stress? Quantile-based evidence from France and Germany," Economic Change and Restructuring, Springer, vol. 57(6), pages 1-23, December.
    11. Chai, Maojie & Nourozieh, Hossein & Chen, Zhangxin & Yang, Min, 2022. "A semi-compositional approach to model asphaltene precipitation and deposition in solvent-based bitumen recovery processes," Applied Energy, Elsevier, vol. 328(C).
    12. Long Guo & Dongsheng Sun & Muhammad Akib Warraich & Abdul Waheed, 2023. "Does industry 5.0 model optimize sustainable performance of Agri‐enterprises? Real‐time investigation from the realm of stakeholder theory and domain," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(4), pages 2507-2516, August.
    13. Feng, Amei & Liu, Liqian, 2023. "Local green economic growth and natural resources development in China," Resources Policy, Elsevier, vol. 86(PB).
    14. Yunqi Jiang & Huaqing Zhang & Kai Zhang & Jian Wang & Shiti Cui & Jianfa Han & Liming Zhang & Jun Yao, 2022. "Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network," Mathematics, MDPI, vol. 10(9), pages 1-22, May.
    15. Ji Tan & S. B. Goyal & Anand Singh Rajawat & Tony Jan & Neda Azizi & Mukesh Prasad, 2023. "Anti-Counterfeiting and Traceability Consensus Algorithm Based on Weightage to Contributors in a Food Supply Chain of Industry 4.0," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    16. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    17. Laura Osma & Luis García & Romel Pérez & Carolina Barbosa & Jesús Botett & Jorge Sandoval & Eduardo Manrique, 2019. "Benefit–Cost and Energy Efficiency Index to Support the Screening of Hybrid Cyclic Steam Stimulation Methods," Energies, MDPI, vol. 12(24), pages 1-16, December.
    18. Peiyue Cheng & Guitao Zhang & Hao Sun, 2022. "The Sustainable Supply Chain Network Competition Based on Non-Cooperative Equilibrium under Carbon Emission Permits," Mathematics, MDPI, vol. 10(9), pages 1-31, April.
    19. Mateus-Rubiano, Camilo & Castillo, Andrea C. & León, Paola & Rueda, Luis & Molina V, Daniel & Leon, Adan Y., 2024. "Effect of hydrotreatment process on the physicochemical properties of a Colombian heavy crude oil post-catalytic aquathermolysis," Energy, Elsevier, vol. 298(C).
    20. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4805-:d:852725. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.