石油地球物理勘探2026，Vol.61Issue(1)：24-33,10.DOI:10.13810/j.cnki.issn.1000-7210.20250043
基于岩屑图像的岩性智能识别系统开发及应用
Development and application of intelligent lithology identification system based on cutting images
摘要
Abstract
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.关键词
岩性识别/神经网络/岩屑图像/智能识别/YOLOv5Key words
lithology identification/neural network/cuttings image/intelligent identification/YOLOv5分类
天文与地球科学引用本文复制引用
杨凯程,姚志刚,黄万国,张学忠,向晓,杨飞龙..基于岩屑图像的岩性智能识别系统开发及应用[J].石油地球物理勘探,2026,61(1):24-33,10.基金项目
本项研究受国家自然科学基金项目"三维斜井井间地震逆菲涅尔枣共反射点叠加成像"(42304135)资助. （42304135）
