Rate of penetration forecasting while drilling using a transformer-based deep learning model

Abstract

A method for forecasting a rate of penetration while drilling includes training a transformer-based machine learning model with historical drilling data obtained from a plurality of drilled wells to establish relationships between measured drilling parameters and ROP; acquiring short context drilling data while drilling the subterranean wellbore, the short context drilling data including a plurality of measured drilling parameters and a corresponding ROP; evaluating the short context drilling data using the trained transformer-based machine learning model to update the relationships between the measured drilling parameters and the ROP; and forecasting a future ROP using the short context drilling data and the updated relationships.