Study of Intelligent Identification Method for Drilling Condition and Lithology in Underground Coal Mine Based on Deep Learning

Abstract

It is important to accurately master the downhole drilling parameter information and drilling formation lithology for the sake of efficient and reasonable gas extraction, hole arrangement and construction safety. According to different detection information, an identification & interpretation model for drilling engineering parameters and a lithology identification & interpretation model in while-drilling azimuthal Gamma logging were established. Next, the drilling conditions were judged and the lithology was discriminated. However, this method relied heavily upon the professional quality and working experience of workers. By numerically simulating the influence laws of azimuthal Gamma logging on the amplitude values of detection data under different borehole sizes, a more suitable method was chosen and applied to the automatic lithology explanation and identification during the logging. An object identification method combining deep convolutional neural network (CNN) and multi-weight multi-task learning mechanism was established, followed by the residual network design, in an effort to elevate the network training rate.