Abstract
The Extreme gradient boosting algorithm XGBoost has been confirmed to be an accurate method for predicting rock stiffnesses and anisotropic parameters from basic input features such as rock porosity, density, vertical compression stress, pore pressure and burial depth (Nguyen-Sy, T., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T., 2020. Study the elastic properties and the anisotropy of rocks using different machine learning methods. Geophysical Prospecting, 68(8), 2557–2578). This study has the following contributions: reducing the R2-error score (that is, 1-R2) by 35 %, RMSE by 21 % and MAE by 16 % comparing to the previous study by considering an advanced CatXG hybrid boosting model in combination with the optimizer Optuna for predicting C13 (the most difficult stiffness to accurately predict); 2-conduct a reliability analysis for the predicted stiffness C13 with respect to the randomness of the input features. We also discuss the use of C11 or C33 as additional input features for accurately predicting C13 as well as the prediction of the related anisotropic parameter δ. This significant improvement of predicted stiffness C13 is extremely important because it encourages petrophysical engineers to use machine learning for predicting the elastic stiffnesses of rocks.
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