Researchers base their analysis on basic drilling parameters obtained during mud logging and demonstrate impressive results. However, due to limitations imposed by data quality often present during drilling, those solutions often tend to lose their stability and high levels of predictivity. In this work, the concept of hybrid modeling was introduced which allows to integrate the analytical correlations with algorithms of machine learning for obtaining stable solutions consistent from one data set to another.
Keywords:machine learning, artificial intelligence, blind test, drilling data acquisition, accuracy, reservoir characterization, drilling operation, positive class, torque, roc auc
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