Drilling in the Digital Age: Machine Learning Assisted Bit Selection and Optimization

Abstract

Abstract Conventionally, a bit is selected from offset well bit run summaries. This method of selection is not always accurate since each bit is run under different conditions which might not be reflected in an offset study analysis. The large quantities of data generated from real time measurements in offset wells makes machine learning the ideal tool for analysis and comparison. Artificial Neural Network (ANN) is a relatively simple machine learning tool that combines inputs and calculation layers to compute a specified output layer. The ANN is fed over thousands of data points from 17-1/2 in hole sections across multiple wells. A specific model is then trained for every bit with weight on bit (WOB), rotary speed (RPM), bit hydraulics, and lithological properties as inputs and rate of penetration (ROP) as output. The model is finalized when a satisfactory statistical set of KPI's are achieved. Using a combination of Monte-Carlo analysis and sensitivity analysis, different bits are compared by varying parameters for the same bit and varying the bit under the same parameters. A bit and its optimized parameters are proposed, resulting in an average instantaneous ROP improvement of 32%. Performance benchmarked with individual drilling parameters shows improved ROP response to WOB, RPM, and bit hydraulics in the optimized run. This project solidifies 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. This study demonstrates how ANN's can be used to learn from previous operations and influence planning decisions to improve bit performance.