Application of Boosting Machine Learning for Mud Loss Prediction During Drilling Operations

Abstract

Abstract Lost circulation during drilling operations is a persistent challenge in the oil and gas industry, leading to significant financial losses and increased non-productive time. The common use of lost circulation materials (LCMs) in drilling fluids helps mitigate mud loss only to an extent. However, predicting the extent of mud loss before drilling specific formations would greatly benefit engineers. This study aims to predict mud loss using advanced boosting machine learning frameworks, addressing the need for more accurate forecasting tools. We evaluated three ensemble boosting algorithms—Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost)—and compared them to Random Forest, a baseline bagging algorithm. Utilizing a dataset of over 7,000 data points with 27 features from drilling operations in Well MXY at the Utah FORGE field, we found that XGBoost and Random Forest were the most accurate models, with R2 scores of 0.935 and 0.934, respectively. These results indicate that while XGBoost is the top-performing framework, Random Forest remains a robust and reliable method for predicting lost circulation, providing valuable insights for drilling engineers.