ArticleScientific reports2023
Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (NProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Delineating homogeneous zones for rock joint wall mechanical properties in open-pit mine slope based on a multi-indicator stacked generalization model.Scientific reports · 2026Article
- Machine learning improves detection of alpha thalassemia carriers compared to clinical features.Scientific reports · 2025Article
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- Acoustic impedance inversion via voting stacked regression (VStaR) algorithms.Scientific reports · 2025Article
- STEMI-OP in-hospital mortality prediction algorithms: Frailty-integrated machine learning in older patients undergoing primary PCI.npj aging · 2025Article
- A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers.CPT: pharmacometrics & systems pharmacology · 2024Article
- Investigation of wettability and IFT alteration during hydrogen storage using machine learning.Heliyon · 2024Article
- Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes.Scientific reports · 2024Article
- Comprehensive input models and machine learning methods to improve permeability prediction.Scientific reports · 2024Article
- Absolute permeability estimation from microtomography rock images through deep learning super-resolution and adversarial fine tuning.Scientific reports · 2024Article
- Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems.Sensors (Basel, Switzerland) · 2024Article
- Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters.ACS omega · 2024Article
- Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.PloS one · 2024Article
- Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.Frontiers in plant science · 2024Article
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Authors and funding
4 authors.
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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.
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