基于岩屑图像的岩性智能识别系统开发及应用OA

Traditional manual cutting logging faces on-site technical challenges,including inability to quantita-tively identify cutting components and accurately name the lithology,thereby hindering the comprehensive and precise acquisition of cutting lithology information.To this end,this paper develops an intelligent lithology identification system based on cutting images collected during the logging-while-drilling process.Firstly,by es-tablishing unified standards for image acquisition and annotation,systematic sample annotation is conducted manually to generate standard samples.Secondly,the deep convolutional neural network algorithm YOLOv5 is adopted for sample training,inference,and post-processing,and an attention mechanism for small target identi-fication is added,with the focus on the influence of Fitness function adjustment on target identification accuracy.Finally,the ONNX(Open Neural Network Exchange)model is adopted for cross-platform support,developing an intelligent lithology identification system based on cutting images.Practical applications show that the system can identify six major lithologies(mudstone,sandstone,limestone,dolomite,coal,and carbonaceous mud-stone),with the overall accuracy exceeding 85％.Meanwhile,the system can analyze the component contents of each lithology and characteristics of sandstone cuttings including the roundness,grain size,and sorting,thus en-abling the accurate description of cutting component characteristics and developing a new lithology identification technology.