<![CDATA[New approaches and methodologies have been developed to petrophysical analysis from well logs data using machine learning. Through this method, a machine learning algorithm is applied to predict the accuracy of the model on effective porosity (∅e) and permeability (K) which implemented using Random Forest and Xtreme Gradient Boosting (XGBoost) algorithm. The dataset used is obtained from well logs data that have been calculated petrophysical analysis. This study proposes the algorithm which is known to be effective in providing accurate predictions in a short time in estimating effective porosity and permeability. The results of the prediction model is optimized by GridSearchCV (GS), validated by the k-fold cross-validation, and evaluated using R2 score and Root Mean Square Error (RMSE). Model is applied to 5 research wells in Fajar Field of south Sumatra Basin, Indonesia with 4 variations of well training and well testing data split. The best evaluation results obtained with evaluation metrics were up to 0.90 (R2 score) and 0.01 (RMSE) for effective porosity and permeability by Random Forest, while evaluation metrics are 0.90 (R2 score) and under 0.68 (RMSE) for effective porosity and permeability by XGBoost. There is no decrease in accuracy until the last variation so that it can be concluded that these algorithm models can effectively estimate reservoir porosity and permeability in the field and contributed an alternative for the problem of many incomplete and dissimilar well logs data.]]>
