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Optimising Flowback Strategies in Unconventional Reservoirs: The Critical Role of Capillary Forces and Fluid Dynamics

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  • Hamid Reza Nasriani

    (School of Engineering & Computing, University of Central Lancashire, Preston PR1 2HE, UK)

  • Mahmoud Jamiolahmady

    (Institute of Geoenergy Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK)

Abstract

This study delves into the complexities of fluid cleanup processes post-hydraulic fracturing in unconventional gas deposits, focusing on the pivotal role of capillary pressure (P c ) correlations in tight and ultra-tight formations. Utilising Geo2Flow software, this research evaluates the efficacy of existing P c models, identifying the Brooks and Corey model as notably precise for these formations, albeit recommending an adjustment to the pore size distribution index for a more accurate representation of rock behaviours. Further investigation centres on the cleanup process in multiple fractured horizontal wells, examining the impact of the P c , matrix permeability, drawdown pressure, and fracturing fluid volume. A significant portion of this study addresses the influence of interfacial tension-reducing chemicals on post-fracturing production, highlighting their utility in ultra-tight formations, but advising against their use in tight formations due to environmental concerns and limited efficacy. The findings underscore the nuanced interplay between geological parameters and fracturing fluid dynamics, advocating for tailored fluid cleanup strategies that enhance the hydraulic fracturing efficiency while minimising the environmental impact. This comprehensive analysis offers valuable insights into optimising fracture cleanup and understanding the underlying physics, thereby contributing to more effective hydraulic fracturing practices.

Suggested Citation

  • Hamid Reza Nasriani & Mahmoud Jamiolahmady, 2024. "Optimising Flowback Strategies in Unconventional Reservoirs: The Critical Role of Capillary Forces and Fluid Dynamics," Energies, MDPI, vol. 17(23), pages 1-30, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5822-:d:1526035
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    References listed on IDEAS

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    1. Niu, Wente & Sun, Yuping & Zhang, Xiaowei & Lu, Jialiang & Liu, Hualin & Li, Qiaojing & Mu, Ying, 2023. "An ensemble transfer learning strategy for production prediction of shale gas wells," Energy, Elsevier, vol. 275(C).
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