Machine learning-enhanced fully coupled fluid–solid interaction models for proppant dynamics in hydraulic fractures

Machine learning-enhanced fully coupled fluid–solid interaction models for proppant dynamics in hydraulic fractures

Abstract This study presents a hybrid modeling framework for predicting proppant settling rate (PSR) in hydraulic fracturing by integrating symbolic physics-based derivations, parametric simulations, and ensemble machine learning. Symbolic expressions were formulated using Stokes’ law, drag equation...

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Bibliographic Details
Main Authors: Dennis Delali Kwesi Wayo, Sonny Irawan, Lei Wang, Leonardo Goliatt
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Hydraulic fracturing
Machine learning
Fluid–solid
Proppant
Computational geomechanics
Online Access:https://doi.org/10.1038/s41598-025-15837-5
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https://doi.org/10.1038/s41598-025-15837-5

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