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December 4, 2025The Canadian Journal of Chemical Engineering

A novel investigation based on the tree‐based machine learning methods on rheological behaviour of waxy crude oils

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Authors

AMAmir Mohammadi

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Overview

Novel models using gradient boosting and random forest predict shear stress and viscosity in waxy crude oils, suggesting effective flow assurance strategies.

Key Points

  • Shear stress was reduced to 9.50 with optimized model hyperparameters, significantly improving flow predictions.
  • Root mean squared error calculated at 14.15 indicates a strong model fit with R² of 0.998 for viscosity predictions.
  • Analysis utilized methods like random forest, gradient boosting, and linear regression to model waxy oils' flow behaviour.
  • Study highlights the impact of ethylene-vinyl acetate copolymer on changing flow behaviour from non-Newtonian to Newtonian.

Cite This Study

Amir Mohammadi (2025) studied this question.

synapsesocial.com/papers/6930e8bdea1aef094cca3257https://doi.org/10.1002/cjce.70173
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Interpretable Viscosity Prediction of Waxy Crude Oils via Physics-guided Machine Learning2026
  2. 2Fitting steady-state and transient models of the temperature and shear-rate-dependent rheology of waxy oils using symbolic regression and physics-informed neural networks2026
  3. 3Thermo-rheological model predicts waxy crude oil behavior2026
  4. 4Predicting the Occurrence of Wax Precipitation in Crude Oil Pipelines Using Machine Learning2025
  5. 5From composition to deposition: A machine learning framework for wax deposition prediction in petroleum fluids using compositional properties2025 · 10 citations