| dc.description.abstract | This research is about the prediction of reservoir properties, namely Porosity, Water Saturation,
and Horizontal Log Permeability using Machine Learning regression models, with the purpose
to measure the accuracy of reservoir properties predictions obtained from well log data using
regression model and analyze the benefits of using regression model to determine reservoir
properties. The method used is by importing the well log dataset for data preprocessing (data
cleaning, normalization, and outlier removal) and after that, the dataset is split into train and test
dataset. Three regression models were used in this research, namely Random Forest, Extreme
Gradient Boosting, and Light Gradient Boosting Machine. The regression models are trained
with the train dataset, after that the trained regression models predicted reservoir properties from
the test dataset. Performance of each model was tested, and after comparison of each model’s
accuracy, Random Forest model shown the best result. The results showed that the model was
able to find correlations between well log data and its reservoir properties, and was able to
predict the reservoir properties accurately; indicated by error evaluation and plots of comparison
of actual and predicted data values which are nearly aligned overall. | en_US |
