An implementation of XGBoost algorithm to estimate effective porosity on well log data

Abstract

Abstract The application of machine learning methods is aimed at providing efficiency and avoiding subjectivity in estimating reservoir porosity data. This study proposes the eXtreme Gradient Boost (XGBoost) algorithm which is known to be effective in providing accurate predictions in a short time for estimating effective porosity. The model was optimized using the GridSearchCV (GS) module, then applied to 7 wells from Damar field, Indonesia with variations in the separation of training and testing data based on the number of wells. The best evaluation results are achieved when uses 6 training wells and one tested well with a model accuracy around 78.36% and training time around 1.29 seconds. An increasing the amount of training data will increase the model performance. All variations of training and testing did not show any indication of overfitting. Therefore, it can be concluded that the XGBoost model can effectively estimate reservoir porosity in the area.