Machine Learning Assists Bit Selection and Optimization
This paper highlights the potential of machine learning to be used as a tool in assisting the drilling engineer in bit selection through data insights previously overlooked.
The project outlined in the complete paper describes machine learning as a powerful tool for bit selection and parameter optimization to improve drilling performance. Machine learning will become a significant part of well planning, design, and operations in the future. The study demonstrates how artificial neural networks (ANNs) can be used to learn from previous operations and influence planning decisions to improve bit performance.
Multiple wells have been drilled in an onshore field in Iraq using different bit designs and with a variety of downhole conditions. To improve the rate of penetration (ROP) in a significant manner, a radical shift in how drill bits are selected, as well as a closer look at bit characteristics, is needed.