Data-Driven Prediction of D-Exponent in Drilling Operations Using XGBoost and LightGBM Algorithms

Accurate prediction of key parameters in drilling operations particularly the D-Exponent plays a crucial role in optimizing processes and reducing operational costs. In this study, real-time drilling data from two directional wells, including parameters such as Weight on Bit (WOB), Equivalent Circulating Density (ECD), Rotary Speed (RPM), and other relevant variables were used to evaluate the performance of two machine learning algorithms: XGBoost and LightGBM. To train and evaluate the models, ۸۰% of the dataset was used for training and the remaining ۲۰% for testing. The results demonstrated that both models are capable of accurately predicting the D-Exponent value. Specifically, the XGBoost model achieved an accuracy of ۹۸% with a Mean Absolute Error (MAE) of ۰.۰۷۳, while the LightGBM model reached an accuracy of ۹۷% with an MAE of ۰.۰۷۴. These methods not only enable real-time prediction but also support drilling engineers in making timely and effective decisions to enhance operational efficiency and reduce costs. The findings of this study suggest that integrating artificial intelligence and machine learning into drilling processes can significantly improve productivity, enhance safety, and minimize operational risks in the oil and gas industry.

Keywords:

Machine learning , Artificial intelligence , Extreme gradient boosting , LightGBM , D-Exponent , Ensemble Learning