吉林大学学报(地球科学版) ›› 2017, Vol. 47 ›› Issue (6): 1904-1912.doi: 10.13278/j.cnki.jjuese.201706307

• 地球探测与信息技术 • 上一篇    

基于多分辨率图像融合的多尺度多组分数字岩心构建

崔利凯1, 孙建孟1, 闫伟超1, 高银山2, 王洪君2, 宋丽媛3   

  1. 1. 中国石油大学(华东)地球科学与技术学院, 山东 青岛 266580;
    2. 中国石油长庆油田分公司第五采油厂, 西安 710200;
    3. 中国石油长庆油田分公司第八采油厂, 西安 710021
  • 收稿日期:2017-03-07 出版日期:2017-11-26 发布日期:2017-11-26
  • 作者简介:崔利凯(1988),男,博士研究生,主要从事测井解释、数字岩心建模与数字岩石物理实验研究,E-mail:cuilikai1988@163.com
  • 基金资助:
    国家自然科学基金项目(41374124,41574122);国家科技重大专项(2016ZX05006002-004)

Construction of Multi-Scale and -Component Digital Cores Based on Fusion of Different Resolution Core Images

Cui Likai1, Sun Jianmeng1, Yan Weichao1, Gao Yinshan2, Wang Hongjun2, Song Liyuan3   

  1. 1. School of Geosciences, China University of Petroleum, Qingdao 266580, Shandong, China;
    2. No.5 Oil Production Plant, Changqing Oilfield Company, PetroChina, Xi'an 710200, China;
    3. No.8 Oil Production Plant, Changqing Oilfield Company, PetroChina, Xi'an 710021, China
  • Received:2017-03-07 Online:2017-11-26 Published:2017-11-26
  • Supported by:
    Supported by National Natural Science Foundation of China (41374124, 41574122) and Major Project of National Science and Technology (2016ZX05006002-004)

摘要: 针对单一分辨率的数字岩心模型无法完整描述岩心不同尺度结构信息的问题,以砂岩样品为例,通过对岩心多分辨率CT成像,采用基于特征的图像配准方法实现了不同分辨率岩心图像的精确匹配。通过融合不同分辨率岩心扫描图像进行孔隙分割和矿物分割,构建多尺度、多组分的数字岩心模型。结果表明:多尺度多组分数字岩心模型可以表征跨尺度的岩心孔隙结构,孔隙分布与核磁测量结果有较好的一致性;与Qemscan相比,数字岩心矿物体积分数同X衍射测定结果更吻合,真实地还原了岩心不同组分的结构信息。

关键词: 图像配准, 数字岩心, 多尺度, 多组分

Abstract: It is difficult to represent the multi-scale structure feature of rocks by using a single resolution digital core model. Through taking a sandstone sample, by means of core multi-resolution CT imaging,the accurate matching of core images with different resolutions is realized by the feature based image registration method. The multi-scale and -component digital core model is constructed along with the segmentation of pores and minerals in the registered images based on image fusion. The results show that the multi-scale and -component digital core model could represent the cross scale pore structures; and the pore distribution agrees with the results from NMR. The mineral contents of digital core are more consistent with the results from XRD than that from Qemscan. The multi-scale and -component digital core model factually restores the structure features of all rock components.

Key words: image registration, digital core, multi-scale, multi-component

中图分类号: 

  • P313.1
[1] 刘学锋, 张伟伟, 孙建孟. 三维数字岩心建模方法综述[J]. 地球物理学进展, 2013, 28(6):3066-3072. Liu Xuefeng, Zhang Weiwei, Sun Jianmeng. Methods of Constructing 3-D Digital Cores:A Review[J]. Progress in Geophysics, 2013, 28(6):3066-3072.
[2] 姚军, 赵秀才, 衣艳静, 等. 数字岩心技术现状及展望[J]. 油气地质与采收率, 2005, 12(6):52-54. Yao Jun, Zhao Xiucai, Yi Yanjing, et al. The Current Situation and Prospect on Digital Core Technology[J]. Petroleum Geology and Recovery Efficiency, 2005, 12(6):52-54.
[3] Quiblier J A. A New Three-Dimensional Modeling Te-chnique for Studying Porous Media[J]. Journal of Colloid and Interface Science, 1984, 98(1):84-102.
[4] Roberts A. Statistical Reconstruction of Three-Dimen-sional Porous Media from Two-Dimensional Images[J]. Physical Review E, 1999, 56(3):3203-3212.
[5] 刘学锋, 孙建孟, 王海涛, 等. 顺序指示模拟重建三维数字岩心的准确性评价[J]. 石油学报, 2009, 30(3):391-395. Liu Xuefeng, Sun Jianmeng, Wang Haitao, et al. The Accuracy Evaluation on 3D Digital Cores Reconstructed by Sequence Indicator Simulation[J]. Acta Petrolei Sinica, 2009, 30(3):391-395.
[6] 张丽, 孙建孟, 孙志强, 等. 多点地质统计学在三维岩心孔隙分布建模中的应用[J]. 中国石油大学学报(自然科学版), 2012, 36(2):105-109. Zhang Li, Sun Jianmeng, Sun Zhiqiang, et al. Application of Multiple-Point Geostatistics in 3D Pore Structure Model Reconstruction[J]. Journal of China University of Petroleum, 2012, 36(2):105-109.
[7] Wu K,Nunan N, Crawford J W, et al. An Efficient Markov Chain Model for The Simulation of Heterogeneous Soil Structure[J]. Soil Science Society of America Journal, 2004, 68(2):346-351.
[8] Kanit T, Forest S, Galliet I, et al. Determination of the Size of the Representative Volume Element for Random Composites:Statistical and Numerical Approach[J]. International Journal of Solids and Structures, 2003, 40(13):3647-3679.
[9] Gitman I M, Askes H, Sluys L J. Representative Vo-lume:Existence and Size Determination[J]. Engineering Fracture Mechanics, 2007, 74(16):2518-2534.
[10] Sok R M, Varslot T, Ghous A, et al. Pore Scale Characterization of Carbonates at Multiple Scales:Integration of MicroCT, BSEM and FIBSEM[J]. Petrophysics, 2010, 51(6):379-387.
[11] Khalili A D,Arns J Y, Hussain F, et al. Permeability Upscaling for Carbonates from the Pore Scale by Use of Multiscale X-Ray-CT Images[J]. SPE Reservoir Evaluation and Engineering, 2013, 16(4):353-368.
[12] Khalili A D,Yanici S, Cinar Y, et al. Formation Factor for Heterogeneous Carbonate Rocks Using Multi-Scale Xray-CT Images[J]. Journal of Engineering Research, 2013, 1(2):5-28.
[13] 王晨晨, 姚军, 杨永飞, 等. 碳酸盐岩双孔隙数字岩心结构特征分析[J]. 中国石油大学学报(自然科学版), 2013, 37(2):71-74. Wang Chenchen, Yao Jun, Yang Yongfei, et al. Structure Characteristics Analysis of Carbonate Dual Pore Digital Rock[J]. Journal of China University of Petroleum, 2013, 37(2):71-74.
[14] 白斌, 朱如凯, 吴松涛, 等. 利用多尺度CT成像表征致密砂岩微观孔喉结构[J]. 石油勘探与开发, 2013, 40(3):329-333. Bai Bin, Zhu Rukai, Wu Songtao, et al. Multi-Scale Method of Nano(Micro)-CT Study on Microscopic Pore Structure of Tight Sandstone of Yanchang Formation, Ordos Basin[J]. Petroleum Exploration and Development, 2013, 40(3):329-333.
[15] Sain R. Numerical Simulation of Pore-Scale Hetero-geneity and Its Effects on Elastic, Electrical and Transport Properties[D]. Stanford:Stanford University, 2010.
[16] Shabro V, Kelly S, Torres-Verdín C, et al. Pore-Scale Modeling of Electrical Resistivity and Permeability in FIB-SEM Images of Organic Mudrock[J]. Geophysics, 2014, 79(5):D289-D299.
[17] 邹才能, 朱如凯, 吴松涛, 等. 常规与非常规油气聚集类型、特征、机理及展望:以中国致密油和致密气为例[J]. 石油学报, 2012, 33(2):173-187. Zou Caineng, Zhu Rukai, Wu Songtao, et al. Types, Characteristics, Genesis and Prospects of Conventional and Unconventional Hydrocarbon Accumulations:Taking Tight Oil and Tight Gas in China as an Instance[J]. Acta Petrolei Sinica, 2012, 33(2):173-187.
[18] 李易霖, 张云峰, 丛琳, 等.X-CT扫描成像技术在致密砂岩微观孔隙结构表征中的应用:以大安油田扶余油层为例[J]. 吉林大学学报(地球科学版), 2016, 46(2):379-387. Li Yilin, Zhang Yunfeng, Cong Lin, et al. Application of X-CT Scanning Technique in the Characterization of Micro Pore Structure of Tight Sandstone Reservoir:Taking the Fuyu Oil Layer in Daan Oilfield as an Example[J]. Journal of Jilin University (Earth Sciense Edition), 2016, 46(2):379-387.
[19] HolzerL, Münch B, Rizzi M, et al. 3D-Microstru-cture Analysis of Hydrated Bentonite with Cryo-Stabilized Pore Water[J]. Applied Clay Science, 2010, 47(3):330-342.
[20] Latham S, Varslot T, Sheppard A. Image Regist-ration:Enhancing and Calibrating X-Ray Micro-CT Imaging[C]//International Symposium of the Society of Core Analysts. Abu Dhabi:[s. n.], 2008.
[21] Lowe D G. Distinctive Image Feature from Scale-Invariant Key Points[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[22] Buades A, Coll B, Morel J M. A Non-Local Al-gorithm for Image Denoising[C]//Computer Vision and Pattern Recognition, 2005.[S. l.]:IEEE, 2005:60-65.
[23] Andrä H, Combaret N, Dvorkin J, et al. Digital Rock Physics Benchmarks:Part I:Imaging and Segmentation[J]. Computers and Geosciences, 2013, 50(1):25-32.
[24] Koenderink J. The Structure of Images[J]. Biological Cybernetics, 1984, 50(5):363-370.
[1] 戴振学, 李克英, 陈玮, 刘东. 北山花岗岩矿物的多尺度结构特征与智能识别[J]. 吉林大学学报(地球科学版), 2026, 56(1): 285-294.
[2] 高全明, 尚复庆, 柴进, 王一, 孙伟, 赵静. 基于MagTrack-CNN的磁性目标运动轨迹实时定位跟踪方法[J]. 吉林大学学报(地球科学版), 2025, 55(6): 2088-2099.
[3] 耿鑫, 王长鹏, 张春霞, 张讲社, 熊登. 基于多尺度特征自注意力模型的地震数据重建方法[J]. 吉林大学学报(地球科学版), 2025, 55(3): 1001-1013.
[4] 殷文. 低勘探程度地区层序地层分析及沉积规律——以五墩凹陷侏罗系为例[J]. 吉林大学学报(地球科学版), 2025, 55(1): 70-83.
[5] 吴翔, , 肖占山, , 张永浩, , 王飞, 赵建斌, , 方朝强, . 多尺度数字岩石建模进展与展望[J]. 吉林大学学报(地球科学版), 2024, 54(5): 1736-1751.
[6] 熊超, 孙红月. 基于多因素-多尺度分析的阶跃型滑坡位移预测[J]. 吉林大学学报(地球科学版), 2023, 53(4): 1175-1184.
[7] 安百州, 曾昭发, 闫照涛, 张代磊, 于朝阳, 赵勇, 杜亚男. 鄂尔多斯盆地西缘热储构造模式及地热资源分布特征[J]. 吉林大学学报(地球科学版), 2022, 52(4): 1286-.
[8] 范雨霏, 潘保芝, 郭宇航, 张丽华. 利用数字岩心技术评价含黏土砂岩导电模型[J]. 吉林大学学报(地球科学版), 2021, 51(3): 919-926.
[9] 杨坤, 王付勇, 曾繁超, 赵久玉, 王聪乐. 基于数字岩心分形特征的渗透率预测方法[J]. 吉林大学学报(地球科学版), 2020, 50(4): 1003-1011.
[10] 王璐, 杨胜来, 彭先, 刘义成, 徐伟, 邓惠. 缝洞型碳酸盐岩气藏多类型储集层孔隙结构特征及储渗能力——以四川盆地高石梯-磨溪地区灯四段为例[J]. 吉林大学学报(地球科学版), 2019, 49(4): 947-958.
[11] 林承焰, 王杨, 杨山, 任丽华, 由春梅, 吴松涛, 吴玉其, 张依旻. 基于CT的数字岩心三维建模[J]. 吉林大学学报(地球科学版), 2018, 48(1): 307-317.
[12] 李振苓, 沈金松, 李曦宁, 王磊, 淡伟宁, 郭森, 朱忠民, 于仁江. 用形态学滤波从电导率图像中提取缝洞孔隙度谱[J]. 吉林大学学报(地球科学版), 2017, 47(4): 1295-1307.
[13] 刘财, 杨宝俊, 冯晅, 单玄龙, 田有, 刘洋, 鹿琪, 刘才华, 杨冬, 王世煜. 论油气资源的多元勘探[J]. 吉林大学学报(地球科学版), 2016, 46(4): 1208-1220.
[14] 李易霖, 张云峰, 丛琳, 谢舟, 闫明, 田肖雄. X-CT扫描成像技术在致密砂岩微观孔隙结构表征中的应用——以大安油田扶余油层为例[J]. 吉林大学学报(地球科学版), 2016, 46(2): 379-387.
[15] 赵建如, 初凤友, 金路, 杨克红, 葛倩. 珠江口西部海域表层沉积物重金属元素多尺度空间变化特征[J]. 吉林大学学报(地球科学版), 2015, 45(6): 1772-1780.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 刘建峰,迟效国,周燕,王铁夫,金巍,周建波,董春艳,黎广荣. 小兴安岭东北部金林岩体全岩-角闪石Rb-Sr年龄[J]. J4, 2005, 35(06): 690 -0693 .
[2] 黄冠星, 孙继朝, 张英, 刘景涛, 张玉玺, 荆继红. 珠江三角洲污灌区地下水重金属含量及其相互关系[J]. J4, 2011, 41(1): 228 -234 .
[3] 肖长来,梁秀娟,崔建铭,兰盈盈,张君,李书兰,梁瑞奇,郑策. 确定含水层参数的全程曲线拟合法[J]. J4, 2005, 35(06): 751 -0755 .
[4] 郭振华,王璞珺,印长海,黄玉龙. 松辽盆地北部火山岩岩相与测井相关系研究[J]. J4, 2006, 36(02): 207 -0214 .
[5] 孙永河,付晓飞,吕延防,付广,阎冬. 地震泵抽吸作用与油气运聚成藏物理模拟[J]. J4, 2007, 37(1): 98 -0104 .
[6] 谢忠雷,陈卓,孙文田,尹波. 不同茶园茶叶氟含量及土壤氟的形态分布[J]. J4, 2008, 38(2): 293 -0298 .
[7] 贾军涛,王璞珺,邵 锐,程日辉,张 斌,侯景涛,李金龙,边伟华. 松辽盆地东南缘营城组地层序列的划分与区域对比[J]. J4, 2007, 37(6): 1110 -1123 .
[8] 康立明,任战利. 多参数定量研究流动单元的方法--以鄂尔多斯盆地W93井区为例[J]. J4, 2008, 38(5): 749 -0756 .
[9] 薛永超, 程林松. 白豹油田长8油藏成岩储集相[J]. J4, 2011, 41(2): 365 -371 .
[10] 姜纪沂, 张宇东, 谷洪彪, 左兰丽. 基于灰色关联熵的地下水环境演化模式判别模型研究[J]. J4, 2009, 39(6): 1111 -1116 .