Advanced Search
Volume 45 Issue 5
Sep.  2023
Turn off MathJax
Article Contents
Article Navigation >  PETROLEUM GEOLOGY & EXPERIMENT  > 2023  >  45(5): 1026-1038
YU Xiaolu, LI Longlong, JIANG Hong, LU Longfei, DU Chongjiao. Application of sparry grain limestone petrographic analysis combining image processing and deep learning[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2023, 45(5): 1026-1038. doi: 10.11781/sysydz2023051026
Citation: YU Xiaolu, LI Longlong, JIANG Hong, LU Longfei, DU Chongjiao. Application of sparry grain limestone petrographic analysis combining image processing and deep learning[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2023, 45(5): 1026-1038. doi: 10.11781/sysydz2023051026
shu

Application of sparry grain limestone petrographic analysis combining image processing and deep learning

doi: 10.11781/sysydz2023051026
  • 1.

    SINOPEC Key Laboratory of Petroleum Accumulation Mechanisms, Wuxi, Jiangsu 214126, China

  • 2.

    Wuxi Research Institute of Petroleum Geology, Wuxi, Jiangsu 214126, China

  • Received Date: 2023-05-25
  • Rev Recd Date: 2023-08-11
  • Publish Date: 2023-09-28
    Abstract
  • Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images.

     

    • All authors disclose no relevant conflict of interests.
    The study was designed by YU Xiaolu. The experimental operation was completed by DU Chongjiao and JIANG Hong. The manuscript was drafted and revised by LI Longlong and LU Longfei. All the authors have read the last version of paper and consented for submission.
    FullText(HTML)
  • loading
    • References(25)
  • [1]
    刘宝珺. 沉积岩石学[M]. 北京: 地质出版社, 1980.

    LIU Baojun. Sedimentary rocks[M]. Beijing: Geology Press, 1980.
    [2]
    MOORE C H. Carbonate reservoirs: porosity, evolution and diagenesis in a sequence stratigraphic framework[M]. Amsterdam: Elsevier, 2001.
    [3]
    FLÜGELE. Microfacies of carbonate rocks: analysis, interpretation and application[M]. Berlin: Springer, 2010.
    [4]
    LIU Jingtuo, DENG Yafeng, BAI Tao, et al. Targeting ultimate accuracy: face recognition via deep embedding[EB/OL]. 2015[2023-06-28]. https://arxiv.org/abs/1506.07310.
    [5]
    陈利. 基于深度学习的车牌识别系统设计[J]. 计算机技术与发展, 2018, 28(6): 85-89. doi: 10.3969/j.issn.1673-629X.2018.06.019

    CHEN Li. Design of license plate recognition system based on deep learning[J]. Computer Technology and Development, 2018, 28(6): 85-89. doi: 10.3969/j.issn.1673-629X.2018.06.019
    [6]
    ESTEVA A, KUPREL B, NOVOA R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. doi: 10.1038/nature21056
    [7]
    张新钰, 高洪波, 赵建辉, 等. 基于深度学习的自动驾驶技术综述[J]. 清华大学学报(自然科学版), 2018, 58(4): 438-444. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201804017.htm

    ZHANG Xinyu, GAO Hongbo, ZHAO Jianhui, et al. Overview of deep learning intelligent driving methods[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(4): 438-444. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201804017.htm
    [8]
    PERRING C S, BARNES S J, VERRALL M, et al. Using automated digital image analysis to provide quantitative petrographic data on olivine-phyric basalts[J]. Computers & Geosciences, 2004, 30(2): 183-195.
    [9]
    叶润青, 牛瑞卿, 张良培. 基于多尺度分割的岩石图像矿物特征提取及分析[J]. 吉林大学学报(地球科学版), 2011, 41(4): 1253-1261. https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201104043.htm

    YE Runqing, NIU Ruiqing, ZHANG Liangpei. Mineral features extraction and analysis based on multiresolution segmentation of petrographic image[J]. Journal of Jilin University (Earth Science Edition), 2011, 41(4): 1253-1261. https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201104043.htm
    [10]
    刘春, 许强, 施斌, 等. 岩石颗粒与孔隙系统数字图像识别方法及应用[J]. 岩土工程学报, 2018, 40(5): 925-931. https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201805022.htm

    LIU Chun, XU Qiang, SHI Bin, et al. Digital image recognition method of rock particle and pore system and its application[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(5): 925-931. https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201805022.htm
    [11]
    张吉群, 胡长军, 和冬梅, 等. 孔隙结构图像分析方法及其在岩石图像中的应用[J]. 测井技术, 2015, 39(5): 550-554. https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS201505003.htm

    ZHANG Jiqun, HU Changjun, HE Dongmei, et al. Image analysis method of pore structure and its application in rock image[J]. Well Logging Technology, 2015, 39(5): 550-554. https://www.cnki.com.cn/Article/CJFDTOTAL-CJJS201505003.htm
    [12]
    胡祺. 融合多维信息的岩石薄片图像深度学习分类方法[D]. 杭州: 浙江大学, 2019.

    HU Qi. Thinsection image classification method using deep learning of integrated multi-dimensional information[D]. Hangzhou: Zhejiang University, 2019.
    [13]
    郝慧珍, 顾庆, 胡修棉. 基于机器学习的矿物智能识别方法研究进展与展望[J]. 地球科学, 2021, 46(9): 3091-3106. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX202109005.htm

    HAO Huizhen, GU Qing, HU Xiumian. Research advances and prospective in mineral intelligent identification based on machine learning[J]. Earth Science, 2021, 46(9): 3091-3106. https://www.cnki.com.cn/Article/CJFDTOTAL-DQKX202109005.htm
    [14]
    余晓露, 叶恺, 杜崇娇, 等. 基于卷积神经网络的碳酸盐岩生物化石显微图像识别[J]. 石油实验地质, 2021, 43(5): 880-885. doi: 10.11781/sysydz202105880

    YU Xiaolu, YE Kai, DU Chongjiao, et al. Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network[J]. Petroleum Geology & Experiment, 2021, 43(5): 880-885. doi: 10.11781/sysydz202105880
    [15]
    张涛, 雷丹博, 王宾, 等. 陕南寒武系底部宽川铺组微体化石人工智能识别[J]. 古生物学报, 2019, 58(2): 141-151. https://www.cnki.com.cn/Article/CJFDTOTAL-GSWX201902001.htm

    ZHANG Tao, LEI Danbo, WANG Bin, et al. Artificial intelligence identification of microfossils from the Lower Cambrian Kuchuanpu Formation in southern Shaanxi, China[J]. Acta Palaeontologica Sinica, 2019, 58(2): 141-151. https://www.cnki.com.cn/Article/CJFDTOTAL-GSWX201902001.htm
    [16]
    姜枫, 顾庆, 郝慧珍, 等. 基于语义特征提取的砂岩薄片图像颗粒分割方法[J]. 中国科学: 信息科学, 2020, 50(1): 109-127. https://www.cnki.com.cn/Article/CJFDTOTAL-PZKX202001005.htm

    JIANG Feng, GU Qing, HAO Huizhen, et al. Grain segmentation of sandstone thin section images based on semantic feature extraction[J]. Science China Information Sciences, 2020, 50(1): 109-127. https://www.cnki.com.cn/Article/CJFDTOTAL-PZKX202001005.htm
    [17]
    RUBO R A, DE CARVALHO CARNEIRO C, MICHELON M F, et al. Digital petrography: mineralogy and porosity identification using machine learning algorithms in petrographic thin section images[J]. Journal of Petroleum Science and Engineering, 2019, 183: 106382.
    [18]
    KOESHIDAYATULLAH A, MORSILLI M, LEHRMANN D J, et al. Fully automated carbonate petrography using deep convolutional neural networks[J]. Marine and Petroleum Geology, 2020, 122: 104687.
    [19]
    IZADI H, SADRI J, MEHRAN N A. A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering[J]. Computers & Geosciences, 2015, 81: 38-52.
    [20]
    国家能源局. 岩石薄片鉴定: SY/T 5368-2016[S]. 北京: 石油工业出版社, 2017.

    National Energy Administration. Identification for thin section of rocks: SY/T 5368-2016[S]. Beijing: Petroleum Industry Press, 2017.
    [21]
    GONZALEZ R C, WOODS R E. Digital image processing[M]. 3rd ed. A Wiley-Interscience Publication, 2010.
    [22]
    张顺, 龚怡宏, 王进军. 深度卷积神经网络的发展及其在计算机视觉领域的应用[J]. 计算机学报, 2019, 42(3): 453-482. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJX201903001.htm

    ZHANG Shun, GONG Yihong, WANG Jinjun. The development of deep convolution neural network and its applications on computer vision[J]. Chinese Journal of Computers, 2019, 42(3): 453-482. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJX201903001.htm
    [23]
    隋微波, 程思. 基于卷积神经网络的砂岩数字岩心绝对渗透率计算方法[J]. 油气地质与采收率, 2022, 29(1): 128-136. https://www.cnki.com.cn/Article/CJFDTOTAL-YQCS202201016.htm

    SUI Weibo, CHENG Si. Calculation methods for absolute permeability of sandstone digital cores based on convolutional neural networks[J]. Petroleum Geology and Recovery Efficiency, 2022, 29(1): 128-136. https://www.cnki.com.cn/Article/CJFDTOTAL-YQCS202201016.htm
    [24]
    KRUMBEIN W C, SLOSS L L. Stratigraphy and sedimentation[M]. San Francisco: Freeman, 1963: 676.
    [25]
    孙世强, 左海维, 赵露婷. 联合特征相似性度量和交并比的检测框优选研究[J]. 电脑知识与技术, 2019, 15(29): 190-193. https://www.cnki.com.cn/Article/CJFDTOTAL-DNZS201929085.htm

    SUN Shiqiang, ZUO Haiwei, ZHAO Luting. Research on detection box optimization of joint feature similarity measurement and intersection over union[J]. Computer Knowledge and Technology, 2019, 15(29): 190-193. https://www.cnki.com.cn/Article/CJFDTOTAL-DNZS201929085.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(2)

    Citation
    XML
    PDF-CN

    Article Metrics

    Article views (163) PDF downloads(58) Cited by()
    Proportional views
    Related

    Website Copyright © Wuxi Research Institute of Petroleum Geology, SINOPEC 苏ICP备15009037号 苏公网安备32021102000780号

    Address：No. 2060, Lihu Avenue, Wuxi, Jiangsu   (214126) Tel：0510-68787204，68787203 Fax：0510-68787113 Email：sysydz.syky@sinopec.com

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return