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Prediction of Asphaltene Stability in Crude Oils Using Machine Learning Algorithms by Syed Imran Ali; Shaine Mohammadali Lalji; Zahoor Awan; Muhammad Qasim; Thamraa Alshahrani; Firoz Khan; Sami Ullah; Almas Ashraf is a Chemistry article available to read on EtoBox.

What is Prediction of Asphaltene Stability in Crude Oils Using Machine Learning Algorithms about?

The main aim of this study is to predict asphaltene stability in crude oil as a function of its SARA (Saturates, Aromatics, Resins and Asphaltenes) values using Machine learning algorithms. For this purpose, four Machine Learning algorithms namely, Linear discriminant analysis (LDA), Linear regression (LR), decision tree (DT), and random forest (RF) were applied to predict the asphaltene stability of 95 crude oils. SARA values of these oil samples were taken as input parameters for the algorithms. The dataset was also visualized comprehensively using different plots. For ML model implementation, 70% of the dataset was used during the training of the model while the remaining 30% dataset was used during the testing phase. For the model's reliability, a 10-fold cross-validation method was applied. Moreover, Recursive feature elimination (RFE) was also applied to determine the effect of feature elimination on the accuracy of models. The performance of all ML algorithms was determined using various statistical metrics. For all ML models, the accuracy was found in the range of 60–80% during the training and testing phase with LDA having the highest accuracy of about 78% and DT with the

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Author

Syed Imran Ali; Shaine Mohammadali Lalji; Zahoor Awan; Muhammad Qasim; Thamraa Alshahrani; Firoz Khan; Sami Ullah; Almas Ashraf

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Chemistry (Physical Sciences)