Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs

Ramanzani S. Kalule, Hamid Ait Abderrahmane, Waleed AlAmeri, Mohamed Sassi

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This study employs a stacked ensemble machine learning approach to predict carbonate rocks' porosity and absolute permeability with various pore-throat distributions and heterogeneity. Our dataset consists of 2D slices from 3D micro-CT images of four carbonate core samples. The stacking ensemble learning approach integrates predictions from several machine learning-based models into a single meta-learner model to accelerate the prediction and improve the model's generalizability. We used the randomized search algorithm to attain optimal hyperparameters for each model by scanning over a vast hyperparameter space. To extract features from the 2D image slices, we applied the watershed-scikit-image technique. We showed that the stacked model algorithm effectively predicts the rock's porosity and absolute permeability. © 2023, The Author(s).
Original languageUndefined/Unknown
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - 2023

Keywords

  • carbonic acid
  • algorithm
  • machine learning
  • permeability
  • porosity
  • Algorithms
  • Carbonates
  • Machine Learning
  • Permeability
  • Porosity

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