... to improve the quantity and quality of training data and to optimize the hyperparameters of the SVRM. Second, the enhanced training data are used to train the SVRM that predicts the Shmin based on the wellbore breakout geometries. In order to examine the reliability of this technique, the Shmin predicted...

... ABSTRACT Ensemble learning is a recent development in machine learning. Random forest regression (RFR) is one such widely utilized ensemble learning algorithm. However, the current literature lacks studies that primarily focus on the effects of hyperparameter tuning in RFR when predicting...

... compressive test (Alkinani et al., 2021). casing and cementing upstream oil & gas neural network artificial intelligence algorithm strength prediction boualam strength genetic algorithm different artificial intelligence hyperparameter machine learning prediction composition journal...

... porosity and Vp). Based on the collected logging data set, the GRU prediction model for predicting Vs is trained, validated and tested, which can predict Vs in the missing sections according to synthetic logging data and depth correlation. The optimal hyperparameters of the proposed model, including...

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... machine learning reservoir characterization lithology classification vibration neural network lithology classification method sandstone algorithm dataset convolution calculation validation classification lithology gravel sandstone baraboshkin hyperparameter characteristic disturbance...

... drillstring design algorithm hyperparameter engineering rop explanation rop prediction penetration prediction model decision tree learning data mining prediction xgboost algorithm selection torque calculation journal conditional probability ARMA 22 142 A New Rate of Penetration Prediction...