Prediction of asphaltene adsorption capacity of clay minerals using machine learning
Author:
Affiliation:
1. Petroleum Engineering Program, School of Mining and Geosciences Nazarbayev University Nur‐Sultan Kazakhstan
2. Department of Petroleum Engineering Kazakh British Technical University Almaty Kazakhstan
Funder
Nazarbayev University
Publisher
Wiley
Subject
General Chemical Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.24675
Reference81 articles.
1. Experimental study and modelling of asphaltene deposition on metal surfaces with superhydrophobic and low sliding angle inner coatings
2. Adsorption Behavior of Asphaltenes and Resins on Kaolinite
3. Asphaltene precipitation and deposition in oil reservoirs – Technical aspects, experimental and hybrid neural network predictive tools
4. New Molecular Insights into Aggregation of Pure and Mixed Asphaltenes in the Presence of n-Octylphenol Inhibitor
5. Crude oil and asphaltene characterization for prediction of wetting alteration
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