IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7379-d673153.html
   My bibliography  Save this article

Effects of Diffusion, Adsorption, and Hysteresis on Huff-n-Puff Performance in Ultratight Reservoirs with Different Fluid Types and Injection Gases

Author

Listed:
  • Khaled Enab

    (The Petroleum Engineering Program, School of Engineering, Texas A&M International University, Laredo, TX 78041, USA)

  • Hamid Emami-Meybodi

    (John and Willie Leone Department of Energy and Mineral Engineering, The Pennsylvania State University, University Park, PA 16802, USA)

Abstract

Cyclic solvent injection, known as solvent huff-n-puff, is one of the promising techniques for enhancing oil recovery from shale reservoirs. This study investigates the huff-n-puff performance in ultratight shale reservoirs by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, and black oil) and different injection gases (CO 2 , C 2 H 6 , and C 3 H 8 ). A dual-porosity compositional model is utilized to comprehensively evaluate the impact of multicomponent diffusion, adsorption, and hysteresis on the production performance of each reservoir fluid and the retention capacity of the injection gases. The results show that the huff-n-puff process improves oil recovery by 4–6% when injected with 10% PV of gas. Huff-n-puff efficiency increases with decreasing gas-oil ratio (GOR). C 2 H 6 provides the highest recovery for the black oil and volatile oil systems, and CO 2 provides the highest recovery for retrograde condensate fluid type. Diffusion and adsorption are essential mechanisms to be considered when modeling gas injection in shale reservoirs. However, the relative permeability hysteresis effect is not significant. Diffusion impact increases with GOR, while adsorption impact decreases with increasing GOR. Oil density reduction caused by diffusion is observed more during the soaking period considering that the diffusion of the injected gas caused a low prediction error, while adsorption for the injected gas showed a noticeable error.

Suggested Citation

  • Khaled Enab & Hamid Emami-Meybodi, 2021. "Effects of Diffusion, Adsorption, and Hysteresis on Huff-n-Puff Performance in Ultratight Reservoirs with Different Fluid Types and Injection Gases," Energies, MDPI, vol. 14(21), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7379-:d:673153
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7379/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/21/7379/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan Zhang & Jinghong Hu & Qi Zhang, 2019. "Simulation Study of CO 2 Huff-n-Puff in Tight Oil Reservoirs Considering Molecular Diffusion and Adsorption," Energies, MDPI, vol. 12(11), pages 1-15, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhengdong Lei & Yishan Liu & Rui Wang & Lei Li & Yuqi Liu & Yuanqing Zhang, 2022. "A Microfluidic Experiment on CO 2 Injection for Enhanced Oil Recovery in a Shale Oil Reservoir with High Temperature and Pressure," Energies, MDPI, vol. 15(24), pages 1-15, December.
    2. Li, Zongfa & Liu, Jiahui & Su, Yuliang & Fan, Liyao & Hao, Yongmao & kanjibayi, Bahedawulieti & Huang, Lijuan & Ren, Shaoran & Sun, Yongquan & Liu, Ran, 2023. "Influences of diffusion and advection on dynamic oil-CO2 mixing during CO2 EOR and storage process: Experimental study and numerical modeling at pore-scales," Energy, Elsevier, vol. 267(C).
    3. Aaditya Khanal & Md Fahim Shahriar, 2023. "Optimization of CO 2 Huff-n-Puff in Unconventional Reservoirs with a Focus on Pore Confinement Effects, Fluid Types, and Completion Parameters," Energies, MDPI, vol. 16(5), pages 1-23, February.

    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. Kang Ma & Hanqiao Jiang & Junjian Li & Rongda Zhang & Kangqi Shen & Yu Zhou, 2020. "A Novel Assisted Gas–Oil Countercurrent EOR Technique for Attic Oil in Fault-Block Reservoirs," Energies, MDPI, vol. 13(2), pages 1-15, January.
    2. Minxing Si & Ling Bai & Ke Du, 2021. "Discovering Energy Consumption Patterns with Unsupervised Machine Learning for Canadian In Situ Oil Sands Operations," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    3. Diego Manfre Jaimes & Ian D. Gates & Matthew Clarke, 2019. "Reducing the Energy and Steam Consumption of SAGD Through Cyclic Solvent Co-Injection," Energies, MDPI, vol. 12(20), pages 1-28, October.

    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:14:y:2021:i:21:p:7379-:d:673153. 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.