Hybrid Physics-machine Learning Models For Predicting Rate Of Penetration In The Halahatang Oil Field, Tarim Basin

This article discusses the development of a hybrid physics-ML model for predicting the rate of penetration (ROP) in the Halahatang oil field. The model combines the strengths of traditional physics-based modeling approaches with state-of-the-art machine learning techniques to achieve high accuracy and interpretability. Four different hybrid modeling approaches are proposed and the results show potential for improving drilling parameter optimization and ROP management in the future.