Application of data science and machine learning algorithms for ROP prediction: Turning data into knowledge
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Copyright © 2019, Offshore Technology Conference A high 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. 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 unwanted wellbore deviations. Five different Machine Learning algorithms were tested and trained on forty wells to optimize ROP and create a less tortuous borehole. The collected data was cleaned, preprocessed and used to structure and train Random Forest, Artificial Neural Networks, Support Vector Regression, K-Nearest Neighbor, and Gradient Boosting Machine and the appropriate hyperparameters were selected. Parameters such as WOB, RPM, flowrate, MSE, bit run distance, gamma ray for each rock formation in the data set were examined. 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 prediction and optimization to enable a fully automated system.
author list (cited authors)
Noshi, C. I., & Schubert, J. J.