To address the prediction problems of reservoir grain sizes in low permeability oilfields, this study proposes a scheme for predicting reservoir grain sizes in low permeability oilfields with the extreme gradient boosting (XGBoost) in machine learning. First, a proper XGBoost model is built in consideration of the problems. Then, well logging curves suitable for grain size prediction are selected to create a sample database according to the established relationships of the characteristic values of the core reservoir grain size with other logging information. Finally, sample database data are employed to train the newly built XGBoost model. The trained model can predict unknown reservoir grain size characteristics in a study area. The results show that the XGBoost model designed in this study is superior to the back propagation (BP) neural network in calculation efficiency and prediction accuracy of reservoir grain sizes in low permeability oilfields.