Innovative AI Agent for Real-Time Drill Bit Selection Optimization

Objectives/Scope Optimizing drill bit selection constitutes a critical challenge within the domain of oil and gas drilling, with substantial implications for operational efficiency and cost-effectiveness. Traditional methodologies, often reliant on empirical guidelines or experiential knowledge, are prone to biases and inadequacies in addressing the complexities of contemporary drilling data. This study presents an innovative artificial intelligence (AI) agent that leverages advanced machine learning techniques, specifically utilizing Multi-Layer Perceptron (MLP) and Random Forest (RF) algorithms, to enhance the real-time selection of drill bits as informed by the International Association of Drilling Contractors (IADC) Code. Methods, Procedures, Process The AI agent independently employs both MLP and RF models to analyze a comprehensive dataset comprising over 1 million drilling records sourced from Middle Eastern oilfields. Key input parameters include Measured Depth (MD), Weight-On-Bit (WOB), Rotational Speed (RPM), Rotary Torque (TQ), Rate of Penetration (ROP), Pump Pressure, Flow Rate, Mud Weight (MW), True Vertical Depth (TVD), Bit Size, Drilled Interval, Total Flow Area (TFA), Jet Number, and Formation Types. The analysis targets the prediction of the IADC Code. Results, Observations, Conclusions Field implementation of the AI agent yielded significant improvements in the accuracy and consistency of drill bit selection outcomes. The MLP model achieved an impressive overall accuracy of 99.3%, with an F1 score of 96.0%, whereas the RF model attained an accuracy of 95.1% and an F1 score of 93.4%. Both models demonstrated robust precision and recall rates; however, the MLP model exhibited superior performance in accurately classifying IADC Codes, particularly for less frequent categories. The probabilistic outputs produced by these models empower drilling engineers to make informed decisions based on quantifiable confidence metrics, thereby enhancing the decision-making process in dynamic drilling environments. Novel/Additive Information This study introduces a novel AI agent framework that systematically employs MLP and RF methodologies to provide real-time, autonomous, and context-sensitive recommendations for drill bit selection. This approach signifies a substantial advancement in digital drilling optimization, establishing a new benchmark for intelligent decision support systems within the petroleum industry and facilitating the ongoing digital transformation of drilling operations.