A Tuned-Filtration and Supervised Machine-Learning Framework for Robust ROP Prediction and Drilling Optimization

Abstract

Improving drilling efficiency remains a central objective in petroleum operations due to its direct impact on time and cost. This study develops a predictive framework for estimating the Rate of Penetration (ROP) using supervised machine learning techniques combined with systematic data conditioning. The analysis is based on more than 11,000 measurements obtained from four horizontal wells. A rigorous preprocessing strategy was implemented to enhance data reliability, including removal of invalid entries and statistical outliers using the interquartile range method. This procedure reduced the dataset to 7,297 high-quality observations. In addition, target stabilization was introduced through Exponential Moving Average smoothing (spans of 5 and 10), which reduced short-term fluctuations and improved the learnability of the ROP signal. Three tree-based regression models—Decision Tree, Random Forest, and Gradient Boosting—were evaluated under both default configurations and optimized settings. Results show that model performance is strongly influenced by data conditioning. The Random Forest model achieved the highest accuracy, with a coefficient of determination (R2) of 0.96 and a mean squared error (MSE) of 26 when trained on the EMA-10 dataset. Gradient Boosting exhibited the largest improvement from hyperparameter tuning, with R2 increasing from 0.86 to 0.95. To bridge the gap between model development and practical use, the trained models were implemented in interactive applications for real-time prediction and parameter optimization. The outcomes demonstrate that careful preprocessing and noise-aware modeling significantly enhance predictive capability.