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| Research on identification and analysis method of rock pore structure based on optical images of borehole walls |
| WANG Chao1，2，WANG Chuanying1，WANG Yiteng1，WANG Jinchao1，3，CHEN Wei3，4，HAN Zengqiang1，2 |
| (1. State Key Laboratory of Geomechanics and Geotechnical Engineering，Institute of Rock and Soil Mechanics，Chinese Academy of Sciences，Wuhan，Hubei 430071，China；2. University of Chinese Academy of Sciences，Beijing 100049，China；3. Key Laboratory of Carbonate Reservoirs，CNPC，Hangzhou，Zhejiang 310023，China；4. PetroChina Hangzhou Research Institute of Geology(HIPG)，Hangzhou，Zhejiang 310023，China) |
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Abstract Pore structure is an important factor in rock mass quality evaluation，reservoir classification and seepage characteristics research. Continuous quantitative analysis of pore structure in whole well section is still a difficult problem. Based on the feature that the optical image of the hole wall continuously records the structural information of rock mass in the whole well section，a method for accurate identification and quantitative analysis of rock pore structure is proposed. Firstly，according to the difference of different structure information in the optical image of the hole wall，the effective identifications of drilling fluid，dark gray shading of rock and pore structure are realized by reasonably adjusting the R，B and G components of the image and converting the color space. Then，the best threshold binarization and morphological operation are carried out on the optical image of the hole wall，and the accurate recognition of the rock pore structure is completed. On the basis of accurate identification of the pore structure，combined with the depth and orientation information attached in the optical image of the hole wall，a calculation method of the surface porosity and the linear porosity is proposed. Combined with specific cases，the pore structure distribution analysis and particle size statistics along the depth and orientation of boreholes are completed. The research shows that based on the rock structure information in the optical image of the hole wall，the automatic identification and statistical analysis of the pore structure can be realized，which provides a new idea and method for the continuous research of rock pore structure in the whole well section.
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[1] 李 静，孔祥超，宋明水，等. 储层岩石微观孔隙结构对岩石力学特性及裂缝扩展影响研究[J]. 岩土力学，2019，40(11)：4 149– 4 156.(LI Jing，KONG Xiangchao，SONG Mingshui，et al. Study on the influence of reservoir rock micro-pore structure on rock mechanical properties and crack propagation[J]. Rock and Soil Mechanics，2019，40(11)：4 149–4 156.(in Chinese))
[2] 程志林，隋微波，宁正福，等. 数字岩芯微观结构特征及其对岩石力学性能的影响研究[J]. 岩石力学与工程学报，2018，37(2)：449–460.(CHENG Zhilin，SUI Weibo，NING Zhengfu，et al. Microstructure characteristics and its effects on mechanical properties of digital core[J]. Chinese Journal of Rock Mechanics and Engineering，2018，37(2)：449–460.(in Chinese))
[3] 房 涛，张立宽，刘乃贵，等. 核磁共振技术定量表征致密砂岩气储层孔隙结构——以临清坳陷东部石炭系—二叠系致密砂岩储层为例[J]. 石油学报，2017，38(8)：902–915.(FANG Tao，ZHANG Likuan，LIU Naigui，et al. Quantitative characterization of pore structure of tight gas sandstone reservoirs by NMR T2 spectrum technology：a case study of Carboniferous-Permian tight sandstone reservoir in Linqing depression[J]. Acta Petrolei Sinica，2017，38(8)：902–915.(in Chinese))
[4] PAN J N，ZHANG Z Z，LI M，et al. Characteristics of multi-scale pore structure of coal and its influence on permeability[J]. Journal of Natural Gas Science and Engineering，2019，6(4)：457–365.
[5] 李 乐，刘爱武，漆智先，等. 潜江凹陷王场背斜潜四下段盐韵律层页岩储层孔隙结构特征[J]. 地球科学，2020，45(2)：602–616.(LI Yue，LIU Aiwu，QI Zhixian，et al. Pore structure characteristics of shale reservoir of the lower Qian 4 member in the Wangchang anticline of the qianjiang sag[J]. Earth Science，2020，45(2)：602–616.(in Chinese))
[6] CUNNINGHAM K J，CARLSON J I，HURLEY N F. New method for quantification of vuggy porosity from digital optical borehole images as applied to the karstic Pleistocene limestone of the Biscayne aquifer，southeastern Florida[J]. Journal of Applied Geophysics，2003，55(1)：77–90.
[7] 刘晓鹏，肖 亮，张 伟. 储层毛管压力曲线构造方法及其应用[J]. 西南石油大学学报：自然科学版，2008，30(6)：126–130.(LIU Xiaopeng，XIAO Liang，ZHANG Wei. Constructing method and application of reservoir capillary pressure curve[J]. Journal of Southwest Petroleum University：Science and Technology，2008，30(6)：126–130.(in Chinese))
[8] 牛 涛，王 晖，胡晓庆，等. 基于复杂岩性识别的低渗储层综合分类评价[J]. 西南石油大学学报：自然科学版，2017，39(3)：47–56.(LIU Tao，WANG Hui，HU Xiaoqing，et al. Comprehensive classification and evaluation of low permeability reservoirs based on the identification of complex lithological characteristics[J]. Journal of Southwest Petroleum University：Science and Technology，2017，39(3)：47–56.(in Chinese))
[9] 王家禄，高 建，刘 莉. 应用CT技术研究岩石孔隙变化特征[J]. 石油学报，2009，30(6)：887–893.(WANG Jialu，GAO Jian，LIU Li. Porosity characteristics of sandstone by X-ray CT scanning system[J]. Acta Petrolei Sinica，2009，30(6)：887–893.(in Chinese))
[10] JU Y，YANG Y M，SONG Z D. A statistical model for porous structure of rocks[J]. Science in China Series E：Technological Sciences，2008，51(11)：2 040–2 058.
[11] 冯怀平，马德良，刘启塬，等. 基于扫描电镜图像的土体三维视孔隙率定量计算方法[J]. 岩土工程学报，2019，41(3)：574–580. (FENG Huaiping，MA Deliang，LIU Qiyuan，et al. Method for calculating three dimensional apparent porosity of soils based on SEM images[J]. Chinese Journal of Geotechnical Engineering，2019，41(3)：574–580.(in Chinese))
[12] 陈 杰，刘向君，成 竹，等. 利用电阻率测井资料研究砂岩孔隙结构特征[J]. 西南石油学院学报：自然科学版，2005，27(6)：5–7.(CHEN Jie，LIU Xiangjun，CHENG Zhu，et al. Studying the pore structure of sandstone by resistivity logging[J]. Journal of Southwest Petroleum University：Science and Technology，2005，27(6)：5–7.(in Chinese))
[13] 闫建平，温丹妮，李尊芝，等. 基于核磁共振测井的低渗透砂岩孔隙结构定量评价方法——以东营凹陷南斜坡沙四段为例[J]. 地球物理学报，2016，59(4)：1 543–1 552.(YAN Jianping，WEN Danni，LI Zunzhi，et al. The quantitative evaluation method of low permeable sandstone pore structure based on nuclear magnetic resonance(NMR) logging[J]. Chinese Journal of Geophysics，2016，59(4)：1 543–1 552.(in Chinese))
[14] 周 翔，何 生，刘 萍，等. 鄂尔多斯盆地代家坪地区长6致密油储层孔隙结构特征及分类评价[J]. 地学前缘，2016，23(3)：253–265.(ZHOU Xiang，HE Sheng，LIU Ping，et al. Characteristics and classification of tight oil pore structure in reservoir Chang 6 of Daijiaping area，Ordos Basin[J]. Earth Science Frontiers，2016，23(3)：253–265.(in Chinese))
[15] 赖 锦，王贵文，陈 敏，等. 基于岩石物理相的储集层孔隙结构分类评价——以鄂尔多斯盆地姬塬地区长8油层组为例[J]. 石油勘探与开发，2013，40(5)：566–573.(LAI Jin，WANG Guiwen，CHEN Min，et al. Pore structures evaluation of low permeability clastic reservoirs based on petrophysical facies：a case study on Chang 8 reservoir in the Jiyuan region，Ordos Basin[J]. Petroleum Exploration and Development，2013，40(5)：566–573.(in Chinese))
[16] LI H B，ZHANG J J. Well log and seismic data analysis for complex pore- structure carbonate reservoir using 3D rock physics templates[J]. Journal of Natural Gas Science and Engineering，2018，151：175–183.
[17] 陈 杰，周改英，赵喜亮，等. 储层岩石孔隙结构特征研究方法综述[J]. 特种油气藏，2005，12(4)：11–14.(CHEN Jie，ZHOU Gaiying，ZHAO Xiliang，et al. Overview of study methods of reservoir rock pore structure[J]. Special Oil and Gas Reservoirs，2005，12(4)：11–14.(in Chinese))
[18] 汪进超，王川婴，胡 胜，等. 孔壁钻孔图像的结构面参数提取方法研究[J]. 岩土力学，2017，38(10)：3 074–3 080.(WANG Jinchao，WANG Chuanying，HU Sheng，et al. A new method for extraction of parameters of structural surface in borehole images[J]. Rock and Soil Mechanics，2017，38(10)：3 074–3 080.(in Chinese))
[19] 王川婴，邹先坚，韩增强，等. 基于特征函数的钻孔图像结构面识别方法[J]. 岩土力学，2017，38(10)：3 062–3 066.(WANG Chuanying，ZOU Xianjian，HAN Zengqiang，et al. Recognition of structural planes in borehole image based on characteristic function[J]. Rock and Soil Mechanics，2017，38(10)：3 062–3 066.(in Chinese))
[20] 邹先坚，王川婴，韩增强，等. 钻孔图像特征分析与结构面区域划分方法[J]. 天津大学学报：自然科学与工程技术版，2018，51(1)：88–94.(ZOU Xianjian，WANG Chuanying，HAN Zengqiang，et al. Borehole image characteristic analysis and structural planes partitioning method[J]. Journal of Tianjin University：Science and Technology，2018，51(1)：88–94.(in Chinese))
[21] 李清波，杜朋召. 基于边缘阈值分割的钻孔图像RQD自动分析方法研究[J]. 岩土工程学报，2020，42(11)：2 153–2 160.(LI Qingbo，DU Pengzhao. Automatic RQD analysis method based on information recognition of borehole images[J]. Chinese Journal of Geotechnical Engineering，2020，42(11)：2 153–2 160.(in Chinese))
[22] KHAMAR B S，GANESAN P，KALIST V，et al. Comparative study of skin color detection and segmentation in HSV and YCbCr color space[J]. Procedia Computer Science，2015，57：41–48.
[23] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems，Men，and Cybernetics，1979，9(1)：62–66.
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2021, Vol. 40 
