... vary significantly, thus it is of paramount importance to accurately detect lithology changes and formation tops while drilling. In order to do so, geologic data and logs are often utilized by experts and operators to identify lithological variations. Machine learning algorithms and random forest have...

... Clustering Analysis. For electrofacies classification, two supervised machine-learning techniques, K-Nearest Neighbors (KNN) and Random Forests (RF), were adopted to model the resulting electrofacies given the CPI well logging data for a well to predict at other wells that have missing data. These two...

... the planned well trajectory and eliminates excessive doglegs that lead to wellbore deviations. Five different Machine Learning algorithms were implemented to optimize ROP and create a less tortuous borehole. The collected data was cleaned and preprocessed and used to structure and train Random Forest...