Transformative Ensemble Learning Strategies for Accurate Prediction of Drill and Volume Depths in Petroleum Engineering

In petroleum engineering, accurate prediction of drill depth and volume is essential for optimizing resource extraction, reducing costs, and improving drilling efficiency. Traditional methods often fail to account for the complexity of rock layers and subsurface conditions, leading to suboptimal performance. This paper proposes a novel ensemble learning approach that combines XG Boost (Extreme Gradient Boosting) and Ada Boost (Adaptive Boosting) to enhance prediction accuracy and robustness in drilling operations. By leveraging the strengths of these advanced techniques, the proposed system handles large-scale datasets, captures complex relationships, and provides more reliable predictions. Data preprocessing and normalization are applied to ensure the model efficiently processes real world drilling data. Empirical evaluations determine significant improvements in prediction accuracy and operational optimization. This approach is developed using Python, demonstrates the application of advanced Machine Learning (ML) techniques in XG Boost and Ada Boost model excels in error prediction for drilling well production, achieving an RMSE of 7.95 and MAE of 6.26, offering petroleum engineers powerful tool for informed decision making, reducing overfitting, and optimizing drilling performance.