Deep Learning–Assisted Parameter Monitoring and Optimization in Rotary-Percussive Drilling

Abstract

As an efficient method for hard rock fracturing, rotary-percussive drilling has been widely used in various scenarios, especially deep drilling. Drilling parameter monitoring and control are necessary to ensure stable and efficient underground drilling processes. However, this may be more difficult in deep, harsh conditions. In this paper, our goal is to establish models based on deep learning for drilling parameter monitoring and optimization. Combining impregnated diamond bits and granite rock samples, we conducted rotary-percussive rock drilling experiments using a rock drilling test rig. Real-time acoustic signals during rotary-percussive drilling were recorded, segmented, and transformed as spectra, which made up a drilling acoustic signal data set. Drilling parameters, including rotational speed (revolutions per minute, RPM), pump flow rate, pump pressure, weight on bit (WOB), torque, and rate of penetration (ROP), were logged in the meantime. Given the acoustic signal as input, we built 1D convolutional neural network (1D-CNN) models for drilling parameter prediction. The prediction results revealed the high efficiency and accuracy of 1D-CNN regression models based on deep learning in drilling condition monitoring. Batch normalization played an essential role in the regression model training processes. Given that these parameters have different units and dimensions, we compared models with different output modes to evaluate the multiparameter prediction performance of the 1D-CNN. Taking RPM, flow rate, pressure, and WOB as independent variables and torque and ROP as dependent variables, we developed a conditional variational autoencoder to realize optimization on drilling parameters based on expected drilling performance.