IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p2683-d363113.html
   My bibliography  Save this article

A New Model for Discriminating the Source of Produced Water from Cyclic Steam Stimulation Wells in Edge-Bottom Water Reservoirs

Author

Listed:
  • Yuanrui Zhu

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Shijun Huang

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Lun Zhao

    (PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China)

  • Menglu Yang

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

  • Tong Wu

    (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China)

Abstract

Heavy oil reservoirs with edge-bottom water represent a huge portion of the world’s reserves, and the effective development of such reservoirs with cyclic steam stimulation (CSS) is significant for the petroleum supply. However, the water cut of some CSS wells increases, and production decreases, with the increase of circulation turns. Discerning the source of the produced water is the basis of targeted treatment measures. In this paper, a new model is established for discriminating the source of produced water from CSS wells in edge-bottom water reservoirs. The model combines traditional hydrochemical characteristics analysis and factor analysis, and considers the quality change in injected water. The coefficient of formation water and injected water in produced water can thus be obtained. In addition, the normal distribution method is used to further divide interlayer water and edge-bottom water. The model was applied to a field case, and the results showed that one well was severely invaded by edge-bottom water. The results are consistent with field production performance, which further verifies the accuracy of the model. This model is of great significance for not only discriminating the source of produced water in an edge-bottom water reservoir, but also providing a basis for further the provision of further treatment measures.

Suggested Citation

  • Yuanrui Zhu & Shijun Huang & Lun Zhao & Menglu Yang & Tong Wu, 2020. "A New Model for Discriminating the Source of Produced Water from Cyclic Steam Stimulation Wells in Edge-Bottom Water Reservoirs," Energies, MDPI, vol. 13(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2683-:d:363113
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/2683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/11/2683/
    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.
    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. Wang, Sijia & Jiang, Lanlan & Cheng, Zucheng & Liu, Yu & Zhao, Jiafei & Song, Yongchen, 2021. "Experimental study on the CO2-decane displacement front behavior in high permeability sand evaluated by magnetic resonance imaging," Energy, Elsevier, vol. 217(C).
    2. Li, Jing & Zhang, Lisong & Yang, Feiyue & Sun, Luning, 2020. "Positive measure and potential implication for heavy oil recovery of dip reservoir using SAGD based on numerical analysis," Energy, Elsevier, vol. 193(C).
    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).
    4. 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).
    5. Zhang, Lisong & Li, Jing & Sun, Luning & Yang, Feiyue, 2021. "An influence mechanism of shale barrier on heavy oil recovery using SAGD based on theoretical and numerical analysis," Energy, Elsevier, vol. 216(C).
    6. Yiwei Wang & Yuan Wang & Sunhua Deng & Qiang Li & Jingjing Gu & Haoche Shui & Wei Guo, 2022. "Numerical Simulation Analysis of Heating Effect of Downhole Methane Catalytic Combustion Heater under High Pressure," Energies, MDPI, vol. 15(3), pages 1-23, February.
    7. 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.
    8. Ying Shi & Hong He & Yu Li & Fei Ding & Zhuo Zhou & Nuolin Xiong, 2023. "High-Temperature-Resistant Epoxy Resin Gel Behavior and Profile Control in Heavy Oil Steam Drive," Energies, MDPI, vol. 17(1), pages 1-14, December.
    9. 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).
    10. 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).
    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. Anufriev, I.S., 2021. "Review of water/steam addition in liquid-fuel combustion systems for NOx reduction: Waste-to-energy trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    13. Zhengbin Wu & Hanzhao Chen & Xidong Cai & Qiyang Gou & Liangliang Jiang & Kai Chen & Zhangxin Chen & Shu Jiang, 2023. "Current Status and Future Trends of In Situ Catalytic Upgrading of Extra Heavy Oil," Energies, MDPI, vol. 16(12), pages 1-29, June.
    14. 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.
    15. Boris V. Malozyomov & Nikita V. Martyushev & Vladislav V. Kukartsev & Vadim S. Tynchenko & Vladimir V. Bukhtoyarov & Xiaogang Wu & Yadviga A. Tyncheko & Viktor A. Kukartsev, 2023. "Overview of Methods for Enhanced Oil Recovery from Conventional and Unconventional Reservoirs," Energies, MDPI, vol. 16(13), pages 1-48, June.
    16. 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).
    17. Yang, Min & Liu, Yishan & Lu, Ning & Chai, Maojie & Wang, Sen & Feng, Qihong & Chen, Zhangxin, 2023. "Integration of ramped temperature oxidation and combustion tube tests for kinetic modeling of heavy oil in-Situ combustion," Energy, Elsevier, vol. 274(C).
    18. Yu, Peng, 2022. "Posterior probability-based hydraulic unit division and prediction: A case study," Energy, Elsevier, vol. 246(C).
    19. Kang, Shijie & Zhang, Shijing & Wang, Zhendong & Li, Shengli & Zhao, Fangci & Yang, Jie & Zhou, Lingbo & Deng, Yang & Sun, Guidong & Yu, Hongdong, 2023. "Highly efficient catalytic pyrolysis of oil shale by CaCl2 in subcritical water," Energy, Elsevier, vol. 274(C).
    20. Huang, Lijuan & Wang, Yu & Li, Zongfa & Zhang, Liang & Yin, Yuchuan & Chen, Chao & Ren, Shaoran, 2021. "Experimental study on piloted ignition temperature and auto ignition temperature of heavy oils at high pressure," Energy, Elsevier, vol. 229(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:13:y:2020:i:11:p:2683-:d:363113. 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.