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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 and Geng, Xueyu (2022) Effect of slickwater-alternate-slurry injection on proppant transport at field scales : a hybrid approach combining experiments and deep learning. Energy, 242 . 122987. doi:10.1016/j.energy.2021.122987

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Official URL: http://dx.doi.org/10.1016/j.energy.2021.122987

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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.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TD Environmental technology. Sanitary engineering
T Technology > TJ Mechanical engineering and machinery
T Technology > TN Mining engineering. Metallurgy
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Hydraulic fracturing, Hydraulic fracturing -- Data Processing, Fluid mechanics -- Mathematical models, Slurry -- Fluid dynamics, Deep learning (Machine learning)
Journal or Publication Title: Energy
Publisher: Elsevier Ltd
ISSN: 0360-5442
Official Date: 1 March 2022
Dates:
DateEvent
1 March 2022Published
22 December 2021Available
20 December 2021Accepted
Volume: 242
Article Number: 122987
DOI: 10.1016/j.energy.2021.122987
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
846775Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661

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