IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2446-d1087471.html
   My bibliography  Save this article

Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs

Author

Listed:
  • Wenchao Liu

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Yuejie Yang

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Chengcheng Qiao

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Chen Liu

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Boyu Lian

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qingwang Yuan

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA)

Abstract

With the continuous development of conventional oil and gas resources, the strategic transformation of energy structure is imminent. Shale condensate gas reservoir has high development value because of its abundant reserves. However, due to the multi-scale flow of shale gas, adsorption and desorption, the strong stress sensitivity of matrix and fractures, the abnormal condensation phase transition mechanism, high-speed non-Darcy seepage in artificial fractures, and heterogeneity of reservoir and multiphase flows, the multi-scale nonlinear seepage mechanisms are extremely complicated in shale condensate gas reservoirs. A certain theoretical basis for the engineering development can be provided by mastering the percolation law of shale condensate gas reservoirs, such as improvement of productivity prediction and recovery efficiency. The productivity evaluation method of shale condensate gas wells based on empirical method is simple in calculation but poor in reliability. The characteristic curve analysis method has strong reliability but a great dependence on the selection of the seepage model. The artificial intelligence method can deal with complex data and has a high prediction accuracy. Establishing an efficient shale condensate gas reservoir development simulation technology and accurately predicting the production performance of production wells will help to rationally formulate a stable and high-yield mining scheme, so as to obtain better economic benefits.

Suggested Citation

  • Wenchao Liu & Yuejie Yang & Chengcheng Qiao & Chen Liu & Boyu Lian & Qingwang Yuan, 2023. "Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs," Energies, MDPI, vol. 16(5), pages 1-30, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2446-:d:1087471
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2446/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2446/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fengshuang Du & Bahareh Nojabaei, 2019. "A Review of Gas Injection in Shale Reservoirs: Enhanced Oil/Gas Recovery Approaches and Greenhouse Gas Control," Energies, MDPI, vol. 12(12), pages 1-33, June.
    2. Hongming Zhan & Feifei Fang & Xizhe Li & Zhiming Hu & Jie Zhang, 2022. "Shale Reservoir Heterogeneity: A Case Study of Organic-Rich Longmaxi Shale in Southern Sichuan, China," Energies, MDPI, vol. 15(3), pages 1-14, January.
    3. Tian, Weibing & Wu, Keliu & Chen, Zhangxin & Gao, Yanling & Li, Jing & Wang, Muyuan, 2022. "A relative permeability model considering nanoconfinement and dynamic contact angle effects for tight reservoirs," Energy, Elsevier, vol. 258(C).
    4. Wang, Hui & Chen, Li & Qu, Zhiguo & Yin, Ying & Kang, Qinjun & Yu, Bo & Tao, Wen-Quan, 2020. "Modeling of multi-scale transport phenomena in shale gas production — A critical review," Applied Energy, Elsevier, vol. 262(C).
    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. Ming Yue & Quanqi Dai & Haiying Liao & Yunfeng Liu & Lin Fan & Tianru Song, 2024. "Prediction of ORF for Optimized CO 2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning," Energies, MDPI, vol. 17(6), pages 1-20, March.

    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. Xuhua Gao & Junhong Yu & Xinchun Shang & Weiyao Zhu, 2023. "Investigation on Nonlinear Behaviors of Seepage in Deep Shale Gas Reservoir with Viscoelasticity," Energies, MDPI, vol. 16(17), pages 1-23, August.
    2. Yang, Jinghua & Wang, Min & Wu, Lei & Liu, Yanwei & Qiu, Shuxia & Xu, Peng, 2021. "A novel Monte Carlo simulation on gas flow in fractal shale reservoir," Energy, Elsevier, vol. 236(C).
    3. Tianjiao Cheng & Takeji Hirota & Hiroshi Onoda & Andante Hadi Pandyaswargo, 2024. "LCCO 2 Assessment and Fertilizer Production from Absorbed-CO 2 Solid Matter in a Small-Scale DACCU Plant," Energies, MDPI, vol. 17(19), pages 1-16, October.
    4. Bowen Ling & Hasan J. Khan & Jennifer L. Druhan & Ilenia Battiato, 2020. "Multi-Scale Microfluidics for Transport in Shale Fabric," Energies, MDPI, vol. 14(1), pages 1-23, December.
    5. Chenxu Yang & Jintao Wu & Haojun Wu & Yong Jiang & Xinfei Song & Ping Guo & Qixuan Zhang & Hao Tian, 2024. "Research on Gas Injection Limits and Development Methods of CH 4 /CO 2 Synergistic Displacement in Offshore Fractured Condensate Gas Reservoirs," Energies, MDPI, vol. 17(13), pages 1-12, July.
    6. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    7. Wu, Jian & Gan, Yixiang & Shi, Zhang & Huang, Pengyu & Shen, Luming, 2023. "Pore-scale lattice Boltzmann simulation of CO2-CH4 displacement in shale matrix," Energy, Elsevier, vol. 278(PB).
    8. Tao Zhang & Shuyu Sun, 2021. "Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure," Energies, MDPI, vol. 14(22), pages 1-16, November.
    9. Yangbo Lu & Feng Yang & Ting’an Bai & Bing Han & Yongchao Lu & Han Gao, 2022. "Shale Oil Occurrence Mechanisms: A Comprehensive Review of the Occurrence State, Occurrence Space, and Movability of Shale Oil," Energies, MDPI, vol. 15(24), pages 1-16, December.
    10. Kaiyi Zhang & Fengshuang Du & Bahareh Nojabaei, 2020. "Effect of Pore Size Heterogeneity on Hydrocarbon Fluid Distribution, Transport, and Primary and Secondary Recovery in Nano-Porous Media," Energies, MDPI, vol. 13(7), pages 1-22, April.
    11. Xiaomeng Cao & Yuan Gao & Jingwei Cui & Shuangbiao Han & Lei Kang & Sha Song & Chengshan Wang, 2020. "Pore Characteristics of Lacustrine Shale Oil Reservoir in the Cretaceous Qingshankou Formation of the Songliao Basin, NE China," Energies, MDPI, vol. 13(8), pages 1-25, April.
    12. Zhang, Huidong & Liu, Yong & Tang, Jiren & Liu, Wenchuan & Chen, Changjiang, 2022. "Investigation on the fluctuation characteristics and its influence on impact force of supercritical carbon dioxide jet," Energy, Elsevier, vol. 253(C).
    13. Zhou, Wei & Li, Xiangchengzhen & Qi, ZhongLi & Zhao, HaiHang & Yi, Jun, 2024. "A shale gas production prediction model based on masked convolutional neural network," Applied Energy, Elsevier, vol. 353(PA).
    14. Zhang, Xiaoying & Ma, Funing & Yin, Shangxian & Wallace, Corey D & Soltanian, Mohamad Reza & Dai, Zhenxue & Ritzi, Robert W. & Ma, Ziqi & Zhan, Chuanjun & Lü, Xiaoshu, 2021. "Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: A critical review," Applied Energy, Elsevier, vol. 303(C).
    15. Guang, Wenfeng & Zhang, Zhenyu & Zhang, Lei & Ranjith, P.G. & Hao, Shengpeng & Liu, Xiaoqian, 2023. "Confinement effect on transport diffusivity of adsorbed CO2–CH4 mixture in coal nanopores for CO2 sequestration and enhanced CH4 recovery," Energy, Elsevier, vol. 278(PA).
    16. Wang, Tianyu & Tian, Shouceng & Li, Gensheng & Zhang, Liyuan & Sheng, Mao & Ren, Wenxi, 2021. "Molecular simulation of gas adsorption in shale nanopores: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    17. Tian, Weibing & Wu, Keliu & Feng, Dong & Gao, Yanling & Li, Jing & Chen, Zhangxin, 2023. "Dynamic contact angle effect on water-oil imbibition in tight oil reservoirs," Energy, Elsevier, vol. 284(C).
    18. Ping Guo & Jian Zheng & Chao Dong & Zhouhua Wang & Hengjie Liao & Haijun Fan, 2024. "Invasion Characteristics of Marginal Water under the Control of High-Permeability Zones and Its Influence on the Development of Vertical Heterogeneous Gas Reservoirs," Energies, MDPI, vol. 17(18), pages 1-19, September.
    19. Yang, Run & Liu, Xiangui & Yu, Rongze & Hu, Zhiming & Duan, Xianggang, 2022. "Long short-term memory suggests a model for predicting shale gas production," Applied Energy, Elsevier, vol. 322(C).
    20. Hou, Lei & Elsworth, Derek & Wang, Jintang & Zhou, Junping & Zhang, Fengshou, 2024. "Feasibility and prospects of symbiotic storage of CO2 and H2 in shale reservoirs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

    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:16:y:2023:i:5:p:2446-:d:1087471. 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.