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Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs

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  • Pahari, Silabrata
  • Bhandakkar, Parth
  • Akbulut, Mustafa
  • Sang-Il Kwon, Joseph

Abstract

Hydrocarbon recovery from a hydraulic-fractured unconventional reservoir can be enhanced by replacing slickwater with non-Newtonian high-viscosity gel as a fracturing fluid. However, in the literature, the role of gel rheology on hydrocarbon production from a fractured reservoir is not very well understood. This lack of understanding makes it difficult for the industry to choose appropriate viscosity parameters of fracturing gels. Motivated by these limitations, first, we have developed a high-fidelity model of non-Newtonian fluid flow to simulate fracture propagation, fluid leak-off, and gel flowback processes to predict the oil production from a hydraulic fractured reservoir. Simulation results of the high-fidelity model show how fracture geometry, formation damage, and gel cleanup process varies with the fracturing fluid rheology. Hence, the developed model is used to understand the effect of gel rheology on hydrocarbon production via a sensitivity analysis, which provides a set of rheological parameters that maximize the cumulative amount of oil production. Finally, utilizing this optimal high-viscosity gel, a model predictive controller is designed to obtain an optimal pumping schedule necessary for producing a target fracture geometry and proppant concentration. The closed-loop simulation results demonstrate that the obtained pumping schedule successfully achieved the desired fracture geometry and proppant concentration at the end of pumping, leading to the maximum oil production from an unconventional reservoir.

Suggested Citation

  • Pahari, Silabrata & Bhandakkar, Parth & Akbulut, Mustafa & Sang-Il Kwon, Joseph, 2021. "Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs," Energy, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323380
    DOI: 10.1016/j.energy.2020.119231
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    References listed on IDEAS

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    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Wu, Yining & Yan, Xiang & Huang, Yongping & Zhao, Mingwei & Zhang, Liyuan & Dai, Caili, 2024. "Ultra-deep reservoirs gel fracturing fluid with stepwise reinforcement network from supramolecular force to chemical crosslinking," Energy, Elsevier, vol. 293(C).
    3. Hou, Lei & Cheng, Yiyan & Wang, Xiaoyu & Ren, Jianhua & Geng, Xueyu, 2022. "Effect of slickwater-alternate-slurry injection on proppant transport at field scales: A hybrid approach combining experiments and deep learning," Energy, Elsevier, vol. 242(C).
    4. Lv, Mingkun & Guo, Tiankui & Jia, Xuliang & Wen, Duwu & Chen, Ming & Wang, Yunpeng & Qu, Zhanqing & Ma, Daibing, 2024. "Study on the pump schedule impact in hydraulic fracturing of unconventional reservoirs on proppant transport law," Energy, Elsevier, vol. 286(C).

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