Thomas Philip Fagerli

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

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...