基于图像处理的河流相储层边缘特征增强方法研究

张军华; 胡逸甫; 于正军; 任瑞军; 刘烜良

doi:10.15953/j.ctta.2022.174

基于图像处理的河流相储层边缘特征增强方法研究

doi: 10.15953/j.ctta.2022.174

张军华^{1, 2, ,},
胡逸甫^{1, 2},
于正军³,
任瑞军³,
刘烜良^{1, 2}

1.
中国石油大学（华东）地球科学与技术学院, 山东青岛266580
2.
中国石油大学（华东）深层油气重点实验室, 山东青岛266580
3.
中国石化胜利油田分公司物探研究院, 山东东营257022

详细信息

作者简介:
张军华：男，中国石油大学（华东）地球科学与技术学院教授、博士生导师，长期从事储层精细描述与预测工作，E-mail：zjh_upc@163.com

通讯作者:
张军华，

中图分类号: P 631；P 315；O 242
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- 文章访问数: 311
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-30
- 修回日期: 2022-11-28
- 录用日期: 2022-12-11
- 网络出版日期: 2023-01-04

Research on the Edge Feature Enhancement of Fluvial Reservoirs Based on Image Processing

ZHANG Junhua^{1, 2
, ,},
HU Yifu^{1, 2},
YU Zhengjun³,
REN Ruijun³,
LIU Xuanliang^{1, 2}

1.
School of Geosciences, China University of Petroleum, Qingdao 266580, China
2.
Key Laboratory of Deep Oil and Gas, China University of Petroleum, Qingdao 266580, China
3.
Geophysical Research Institute of Shengli Oil Field Branch, SINOPEC, Dongying 257022, China

摘要

摘要: 河道边缘的识别是河流相储层精细描述的重点。受河道叠置、交叉，砂体厚度薄，地震信噪比、分辨率低等因素影响，传统的切片解释、相干技术难以满足精细勘探的需求，新发展的基于算子处理的边缘检测仍存在应用误区。本文以分析河道边缘的几何特征为切入点，明确一阶导数、模值及二阶导数的物理含义；建立具有不同速度特征的三维河道模型，并通过模型与实际资料相干属性的提取，指出该项技术在河道精细描述中存在的问题。针对该问题以Sobel算子为例，图解说明使用此项技术处理后河道边缘的标志性特征，提出采用直方图均衡化与模糊集理论的河道边缘相干增强技术，并取得较好的应用效果。该方法技术对深化河道边缘特征的认识，提高河流相储层识别能力，有一定的借鉴作用。
- 河流相储层 /
- 边缘特征 /
- Sobel算子 /
- 直方图均衡化 /
- 模糊增强
Abstract: The identification of channel edge is the key aspect of fine description of fluvial reservoirs. Affected by the factors such as channel overlaying and crossing, thin sand body thickness, low seismic signal-to-noise ratio, and low resolution, the traditional slice interpretation and coherence technology can barely meet the requirements of fine exploration, and the newly developed edge detection based on operator processing still has application misconceptions. In this study, the geometric characteristics of river edges are analyzed as the entry point, and the physical meanings of first derivatives, module values, and second derivatives are clarified. Three-dimensional channel models with different velocity characteristics are established by extracting the coherence attributes of the model and real data; the existing problems of this technique in the fine description of channels are identified. To solve this problem, considering the Sobel operator as an example, the symbolic characteristics of the channel edge after using this technology are illustrated. The coherent enhancement technique for channel edge identification using histogram equalization and the fuzzy set theory is proposed, and good application results are obtained. This method can be used as reference to deepen the understanding of channel edge characteristics and improve the ability to identify fluvial reservoirs.
- fluvial reservoir /
- edge features /
- Sobel operator /
- histogram equalization /
- fuzzy enhancement

HTML全文

图 1 河道边缘几何特征与数学含义

Figure 1. Geometric characteristics and mathematical implication of the channel edge

下载: 全尺寸图片幻灯片

图 2 用于方法测试的三维河道模型

Figure 2. Three-dimensional channel model for method testing

下载: 全尺寸图片幻灯片

图 3 模型相干切片与原始振幅切片比较

Figure 3. Comparison between model coherence and original amplitude slices

下载: 全尺寸图片幻灯片

图 4 实际资料相干切片与原始振幅切片比较

Figure 4. Comparison between coherence and original amplitude slices of the actual data

下载: 全尺寸图片幻灯片

图 5 Sobel算子及核函数解析图

Figure 5. Analytical diagram of the Sobel operator and kernel function

下载: 全尺寸图片幻灯片

图 6 理论模型Sobel算子处理及效果比较

Figure 6. Sobel operator processing and effect comparison of the theoretical model

下载: 全尺寸图片幻灯片

图 7 实际资料Sobel算子处理及效果比较

Figure 7. Sobel operator processing and effect comparison of the actual data

下载: 全尺寸图片幻灯片

图 8 理论模型灰度均衡化处理前后切片及直方图对比

Figure 8. Comparison of slices and histograms before and after grayscale equalization of the theoretical model

下载: 全尺寸图片幻灯片

图 9 理论模型图像模糊增强处理及效果对比

Figure 9. Image fuzzy enhancement processing and effect comparison of the theoretical model

下载: 全尺寸图片幻灯片

图 10 实际资料图像处理前后相干切片对比

Figure 10. Comparison of coherent slices before and after image processing of the actual data

下载: 全尺寸图片幻灯片

表 1 特征值相干不同表征公式

Table 1. Different characterization formulas of eigenvalue coherence

时间	作者	表征公式	物理含义
1999	Gersztenkorn 和Marfurt	${C_{31}} = \displaystyle \dfrac{{{\lambda _1}}}{{\sum\limits_{j = 1}^J {{\lambda _j}} }}$	用最大特征值在所有特征值中的占比来表示相干
2000	Randen等	${C_{32}} = \displaystyle \dfrac{{2{\lambda _2}}}{{{\lambda _1} + {\lambda _3}}} - 1 = \dfrac{{{\lambda _2} - {\lambda _3} - （ {{\lambda _1} - {\lambda _2}} ）}}{{{\lambda _1} + {\lambda _3}}}$	被称为“chaos”的相干属性
2002	Bakker	${C_{33}} = \displaystyle \dfrac{{2{\lambda _2}（ {{\lambda _2} - {\lambda _3}} ）}}{{（ {{\lambda _1} + {\lambda _2}} ）（ {{\lambda _2} + {\lambda _3}} ）}}$	重点考虑第2特征值和第3特征值的差异
2007	Donias等	${C_{34}} = 1 - \displaystyle \dfrac{3}{2}\dfrac{{{\lambda _2} + {\lambda _3}}}{{{\lambda _1} + {\lambda _2} + {\lambda _3}}} = \dfrac{{{\lambda _1} - {\lambda _2} + {\lambda _1} - {\lambda _3}}}{{2（ {{\lambda _1} + {\lambda _2} + {\lambda _3}} ）}}$	“disorder”相干属性
2017	Wu	${C_{35}} = \displaystyle \dfrac{{{\lambda _1}{{ - }}{\lambda _2}}}{{{\lambda _1}}}$	利用第1特征值和第2特征值的差异

下载: 导出CSV

参考文献(16)

[1]	于建国, 林春明, 王金铎, 等. 曲流河沉积亚相的地震识别方法[J]. 石油地球物理勘探, 2003,38(5): 547−551. doi: 10.3321/j.issn:1000-7210.2003.05.017 YU J G, LIN C M, WANG J D, et al. Seismic identification of meandering channel sedimentary subfacies[J]. Oil Geophysical Prospecting, 2003, 38(5): 547−551. (in Chinese). doi: 10.3321/j.issn:1000-7210.2003.05.017
[2]	张军华, 李琴, 王延光, 等. 用相干体识别古河道及实际应用[J]. 科学技术与工程, 2020,20(35): 14431−14439. doi: 10.3969/j.issn.1671-1815.2020.35.013 ZHANG J H, LI Q, WANG Y G, et al. Recognition of paleo-river channels by coherence cube and the application[J]. Science Technology and Engineering, 2020, 20(35): 14431−14439. (in Chinese). doi: 10.3969/j.issn.1671-1815.2020.35.013
[3]	张运龙, 丁峰, 尹成, 等. 基于地震波形结构属性识别河流相砂体叠置区[J]. 石油学报, 2018,39(7): 792−801. ZHANG Y L, DING F, YIN C, et al. The identification of sand-body superimposed area based on seismic waveform structure attributes[J]. Acta Petrolei Sinica, 2018, 39(7): 792−801. (in Chinese).
[4]	张军华, 刘杨, 林承焰, 等. 甜点地震属性理论诠释及应用[J]. 石油地球物理勘探, 2018,53(2): 355−360. doi: 10.13810/j.cnki.issn.1000-7210.2018.02.017 ZHANG J H, LIU Y, LIN C Y, et al. Theoretical annotation and application of sweetness[J]. Oil Geophysical Prospecting, 2018, 53(2): 355−360. (in Chinese). doi: 10.13810/j.cnki.issn.1000-7210.2018.02.017
[5]	马跃华, 吴蜀燕, 白玉花, 等. 利用谱分解技术预测河流相储层[J]. 石油地球物理勘探, 2015,50(3): 502−509. MA Y H, WU S Y, BAI Y H, et al. River sedimentary microfacies prediction based on spectral decomposition[J]. Oil Geophysical Prospecting, 2015, 50(3): 502−509. (in Chinese).
[6]	丁峰, 胡光义, 尹成, 等. 基于地质信息约束的概率神经网络地震反射模式预测方法[J]. 中国海上油气, 2018,30(1): 127−131. DING F, HU G Y, YIN C, et al. A method under the constraint of geological information for prediction of seismic reflection patterns with probabilistic neural network[J]. China Offshore Oil and Gas, 2018, 30(1): 127−131. (in Chinese).
[7]	束宁凯, 苏朝光, 石晓光, 等. 胜利埕岛极浅海油田薄储集层地震描述及流体识别[J]. 石油勘探与开发, 2021,48(4): 768−776. doi: 10.11698/PED.2021.04.09 SHU N K, SU C G, SHI X G, et al. Seismic description and fluid identification of thin reservoirs in Shengli Chengdao extra-shallow sea oil field[J]. Petroleum Exploration and Development, 2021, 48(4): 768−776. (in Chinese). doi: 10.11698/PED.2021.04.09
[8]	李俊山, 李旭辉. 数字图像处理[M]. 北京: 清华大学出版社, 2013. LI J S, LI X H. Digital image Processing[M]. Beijinag: Tsinghua University Press, 2013. (in Chinese).
[9]	马承杰. 多尺度边缘检测技术在断层识别及裂缝发育带预测中的应用−以车排子地区排691井区为例[J]. 油气地质与采收率, 2021,28(2): 85−90. MA C J. Application of multi-scale edge detection technology to fault recognition and fracture zone prediction: A case study of Block Well P691, Chepaizi area[J]. Petroleum Geology and Recovery Efficiency, 2021, 28(2): 85−90. (in Chinese).
[10]	CHOPRA S, MARFURT K J. Volumetric fault image enhancement-some applications[J]. Interpretation, 2017, 5(2): T151−T161. doi: 10.1190/INT-2016-0129.1
[11]	宋建国, 孙永壮, 任登振. 基于结构导向的梯度属性边缘检测技术[J]. 地球物理学报, 2013,56(10): 3561−3571. doi: 10.6038/cjg20131031 SONG J G, SUN Y Z, REN D Z. Edge detection technique based on structure-directed gradient attribute[J]. Chinese Journal Geophys, 2013, 56(10): 3561−3571. (in Chinese). doi: 10.6038/cjg20131031
[12]	许辉群, 桂志先, 孙赞东. 边缘检测技术在地震属性中的应用[J]. 石油地球物理勘探, 2011,46(1): 126−128. XU H Q, GUI Z X, SUN Z D. Application of edge detection technique in seismic attribute analysis[J]. Oil Geophysical Prospecting, 2011, 46(1): 126−128. (in Chinese).
[13]	刘传奇, 宋俊亭, 薛明星. 边缘检测技术在砂体连通研究中的应用[J]. 地球物理学进展, 2021,21(2): 1−8. LIU C Q, SONG J T, XUE M X. Application of edge detection technology in sand connectivity research[J]. Progress in Geophysics, 2021, 21(2): 1−8. (in Chinese).
[14]	周连敏, 何书梅, 赵郁文, 等. 复合曲流河道内的单河道识别[J]. 石油地球物理勘探, 2019,54(1): 175−181. doi: 10.13810/j.cnki.issn.1000-7210.2019.01.020 ZHOU L M, HE S M, ZHAO Y W, et al. Single channel identification in a meandering river with compound channels[J]. Oil Geophysical Prospecting, 2019, 54(1): 175−181. (in Chinese). doi: 10.13810/j.cnki.issn.1000-7210.2019.01.020
[15]	张军华. 储层地震精细描述方法及应用研究[M]. 青岛: 中国石油大学出版社, 2022. ZHANG J H. Research on reservoir seismic fine description methods and their applications[M]. Qingdao: China University of Petroleum Press, 2022. (in Chinese).
[16]	PAL S K, KING R A. Image enhancement using fuzzy sets[J]. Electronics Letters, 1980, 16(10): 376−378. doi: 10.1049/el:19800267