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 equations, and pressure-gradient dynamics. A symbolic dataset was synthetically generated by sampling realistic physical ranges: proppant density [Formula: see text], fluid viscosity [Formula: see text], and particle diameter [Formula: see text]. Complementary CFD-informed datasets were simulated to represent complex flow behavior. Both datasets were used to train stacked ensemble regressors comprising five base learners: Random Forest, Extra Trees, Gradient Boosting, XGBoost, and Support Vector Regression (SVR)

SUBMITTER: Wayo DDK 

PROVIDER: S-EPMC12368236 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature