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Effect of slickwater-alternate-slurry injection on proppant transport at field scales: A hybrid approach combining experiments and deep learning

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  • Hou, Lei
  • Cheng, Yiyan
  • Wang, Xiaoyu
  • Ren, Jianhua
  • Geng, Xueyu

Abstract

Proppant transport in underground fractures plays a key role in mitigating sand screen-out and enhancing the stimulated production for hydraulic fracturing. The effects of field pumping schedules, however, are not fully studied. We investigate the effect of slickwater-alternate-slurry injection on proppant transport at field-practical scales. A new hybrid approach is proposed to directly connect experimental studies with field operations, which consists of observation experiments, calculations, and deep learning (DL) workflow. The experiments reveal that the alternate injection induces the unexpected proppant ridge. The modified calculations (considering the ridge height) are proposed to extract features for training the DL algorithm. The workflow predicts the downhole pressure (mainly governed by proppant injection) for error analyses. Approximately 20.2% of the error is eliminated by considering the proppant ridge, thus demonstrating its effect on proppant injection. The predictions are significantly improved in early and late periods of fracturing operations when the fracture is initially created and highly filled. The sensitivity analysis suggests that the pump rate may dominate the ridge height compared with other hydraulic parameters. The study of proppant ridge complements the mechanisms of proppant transport, which is essential for controlling fracturing pressure and boosting the proppant injection.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032369
    DOI: 10.1016/j.energy.2021.122987
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    References listed on IDEAS

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    1. Zhao, Liqiang & Chen, Yixin & Du, Juan & Liu, Pingli & Li, Nianyin & Luo, Zhifeng & Zhang, Chencheng & Huang, Fushan, 2019. "Experimental Study on a new type of self-propping fracturing technology," Energy, Elsevier, vol. 183(C), pages 249-261.
    2. 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).
    3. Ma, Lin & Fauchille, Anne-Laure & Chandler, Michael R. & Dowey, Patrick & Taylor, Kevin G. & Mecklenburgh, Julian & Lee, Peter D., 2021. "In-situ synchrotron characterisation of fracture initiation and propagation in shales during indentation," Energy, Elsevier, vol. 215(PB).
    4. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
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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).

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