Deep Neural Network Prediction of Mechanical Drilling Speed

Rate of penetration (ROP) prediction is critical for the optimization of drilling parameters and ROP improvement during drilling. However, it is still challenging to accurately predict ROP based on traditional empirical formula methods. This is usually the case for the development of the Wushi 17-2 oilfield block in the South China Sea. The Liushagang Formation is complex and the ROP is relatively low and difficult to increase. Ordinary data-driven ROP prediction models are not applicable because they do not take into account the complexity of formation conditions. In this work, we characterize the formation with acoustic transit time and build a data-driven ROP prediction model based on a deep neural network approach. By using the exploratory well data of the Wushi 17-2 oilfield for training and testing, the matching degree of the established model with the real data can reach 82%. In addition, we have developed a drilling parameter optimization process based on the ROP prediction model to improve ROP. Through on-site simulation, we found that the process can well meet the construction requirements. The established models and process flow are also applicable to the development of other formations and fields.

Automated summary

This study focuses on characterizing the formation using acoustic transit time and establishing a data-driven ROP prediction model based on deep neural network approach. By training and testing with the exploratory well data, the established model achieves a matching degree of 82% with real data. Additionally, a drilling parameter optimization process is developed based on the ROP prediction model, which is applicable to other formations and fields.