海相油气地质2023，Vol.28Issue(4)：337-348,12.DOI:10.3969/j.issn.1672-9854.2023.04.001
基于深度学习的碳酸盐岩薄片人工智能鉴定方法研究
Research on artificial intelligence identification approach for carbonate thin sections based on deep learning
摘要
Abstract
Thin section identification is the basis of various geological work such as research on sedimentation,diagenesis,and reservoir of carbonate rocks.Carbonate rocks have strong heterogeneity,various structural components and particle types.The artificial thin section identification is subjective,difficult,time-consuming and labor-intensive,and not easy to be widely popularized.In the big data and artificial intelligence(AI)background,it is promising to increase the efficiency by applying AI identification technology.This study summarized the research status and analyzed the existed problems in AI identification of carbonate thin sections.The main contents of AI identification of carbonate thin sections include:(1)Preparation of thin sections and image processing.Dyeing thin sections with no-cover glass are the basis of later recognition.The blue casting thin sections are significant for pore recognition.Photos should be captured under different optical property including PPL and XPL with different rotation degree.Image pre-processing and segmentation can help to increase the later identification.The establishment of carbonate thin section database is the basis of AI identification.(2)Based on the prior knowledge of carbonate professionals,the structural components,mineral components and pore types of the image are classified,label classification is established,and manual labeling is carried out by carbonate professionals.It is established that the classification chart of major component labels in carbonate thin sections.The establishment of label database can contribute to further machine learning.(3)The convolution neural network and deep learning are introduced into the labeled thin section images,which can learn and discriminate the morphology and internal structure of various components.The knowledge graph of the thin section image labels is established by combination of machine learning and manual correction,which can classify rock types,recognize sedimentary structures and grain types.(4)It is performed that intelligence recognition of structural components,mineral components and pore types and contents.The denomination specification for AI identification of carbonate thin sections is established.Automatically denomination would be achieved.There are still problems including label sample amount,indeterminate semantic object segmentation,diagenesis,etc,which need further research.The future development directions of AI carbonate identification include the identification of core-outcrop-microscopic image,geochemical image(CT,SEM,FL,etc.),interpretation of logging and geophysical data.关键词
碳酸盐岩薄片/人工智能识别/岩石结构组分/知识图谱/标签数据库Key words
carbonate thin section/artificial intelligence identification/rock structural components/knowledge graph/la-bel database分类
能源科技引用本文复制引用
张杰,沈安江,胡安平,周进高,佘敏,韩明珊..基于深度学习的碳酸盐岩薄片人工智能鉴定方法研究[J].海相油气地质,2023,28(4):337-348,12.基金项目
本文受中国石油天然气股份有限公司前瞻性基础性技术攻关项目"油气勘探开发人工智能关键技术研究"(编号:2023DJ84)和中国石油海外重点领域油气勘探关键技术研究项目(编号:2021DJ3104)资助 （编号:2023DJ84）
