A comparative study of deep learning methods for drilling performance prediction

Abstract

Rate of penetration is a key parameter to describe the efficiency of drilling through the formations and it has always been a measure of drilling efficiency. Previous research has demonstrated that machine learning can be applied to improve the prediction of ROP from conventional correlation approaches. More recently, deep learning models have also been used toward that purpose. In this research, the typical used machine learning methods and deep learning methods are tested and compared in terms of ROP prediction. An open-source drilling dataset is used for single well and multiple well modeling and testing. The results demonstrate that deep learning models have more general and accurate results, and LSTM shows solid prediction performance for both single well and multiple well cases. The accurate prediction model for ROP can be further applied for the planning phase and real-time operation optimization.