A high rate of penetration (ROP) is considered one of the most sought-after targets when drilling a well. While physics-based models determine the importance of drilling parameters, they fail to capture the extent or degree of influence of the interplay of the different dynamic drilling features. Parameters such as WOB, RPM, and flowrate, (Mechanical Specific Energy) MSE, bit run distance, gamma ray for each rock formation in West Texas were examined. Ensuring an adequate ROP while controlling the tool face orientation is quite challenging. Nevertheless, its helps follow 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, Artificial Neural Networks, Support Vector Regression, Ridge Regression, and Gradient Boosting Machine and the appropriate hyperparameters were selected.
A successful model was chosen based a minimized deviation from planned trajectory, minimized tortuosity, and maximized ROP. A MAE of 10% was achieved using Random Forest.
The algorithms have demonstrated competence in the historical dataset, accordingly it will be further tested on blind data to serve as a real-time system for directional drilling optimization to enable a fully automated system.