Seismic facies analysis of tight sandstone reservoir based on SOM and HSV color technique

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  • 1.School of Energy Resources,China University of Geosciences(Beijing),Key Laboratory of Marine
    Reservoir Evolution and Hydrocarbon Enrichment Mechanism,Ministry of Education,Beijing 100083,China;
    2.Beijing Zhongdirunde Petroleum Technology Co. Ltd.,Beijing 100083,China;
    3.School of Science,China University of Geosciences(Beijing),Beijing 100083,China

Received date: 2017-08-20

  Revised date: 2017-09-29

  Online published: 2018-02-10

Abstract

Seismic facies analysis is an important technique for predicting tight sandstone reservoirs,which can be used to describe the changes between sedimentary facies.For the problem of poor visualization and low precision of interpretation in the interpretation process of seismic facies,a seismic facies analytical technique based on SOM and HSV color technique is carried out.Firstly,the coordinates of the grid are calculated firstly using the contraction topological algorithm,and then the HSV model is used to color the nodes according to the coordinates of the nodes.In this way,the closer positions have the same color,which can clearly show the distribution of the high-dimensional input space.In order to verify the validity of the method,the Z30 area in the east of the Sulige Gas Field is taken as an example.Firstly,through the overlaid map between the seismic attributes and the thickness of the gas layer and also the AVO forward modeling,seismic attributes,such as RMS,average instantaneous frequency,AVO intercept and gradient,are chosen which are sensitive to the reservoir.Finally,the SOM and HSV color techniques are carried out in the process of seismic facies analysis.On the basis,the K-means method is used to cluster the grid nodes.By analyzing the distribution characteristics of sedimentary facies in He 8 member,the channel is clearly described from north to south and the results are in good agreement with the prior geological knowledge.Through analyzing the process,we can see that this method provides a better visualization method for the analysis of sedimentary facies distribution,which has certain value in the practical application.

Cite this article

Zhang Yan,Zhang Chun-lei,Gao Shi-chen . Seismic facies analysis of tight sandstone reservoir based on SOM and HSV color technique[J]. Natural Gas Geoscience, 2018 , 29(2) : 259 -267 . DOI: 10.11764/j.issn.1672-1926.2017.09.016

References

[1]Zhang Jie,Zhang Zhenhong,Xin Honggang,et al.Application of seismic facies analysis technique for reservoir prediction in the Maling Oilfield[J].Natural Gas Geoscience,2012,23(3):590-595.
张杰,张振红,辛红刚,等.地震相分析技术在马岭油田储层预测中的应用[J].天然气地球科学,2012,23(3):590-595.
[2]Du H K,Cao J X,Xue Y J,et al.Seismic facies analysis based on self-organizing map and empirical mode decomposition[J].Journal of Applied Geophysics,2015,112(1):52-61.
[3]Wang Yongwei,Li Rongxi,Lai Shenghua.Depositional system distribution characteristics based on seismic data:Case study of the lower Benxi Formation,Gaojiahe 3-D block,Yanchang exploration area,Orodos Basin,China[J].Natural Gas Geosciences,2017,28(6):888-897.
王永炜,李荣西,赖生化.基于地震信息的沉积体系平面分布特征研究——以鄂尔多斯盆地延长探区高家河三维区本溪组下部沉积体系为例[J].天然气地球科学,2017,28(6):888-897.
[4]Matos M C D,Osorio P L M,Johann P RS.Unsupervised seismic facies analysis using wavelet transform and self-organizing maps[J].Geophysics,2007,72(1):9-21.
[5]Song C,Liu Z,Wang Y,et al.Multi-waveform classification for seismic facies analysis[J].Computers & Geosciences,2016,101(1):1-9.
[6]Himberg J.A SOM based cluster visualization and its application for false coloring[C].Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks,2000,587-592.
[7]Matos M C D,Marfurt K J,Johann P R S.Seismic interpretation of self-organizing maps using 2D color displays[J].Revista Brasileira de Geofisica,2010,28(4):631-642.
[8]Roden R,Smith T,Sacrey D.Geologic pattern recognition from seismic attributes:Principal component analysis and self-organizing maps[J].Interpreation,2015,3(4):SAE59-SAE83.[ZK)]
[9]Zhang Yan,Zheng Xiaodong,Li Jinsong,et al.Unsupervised seismic facies analysis technology based on SOM and PSO[J].Chinese Journal of Geophysics,2015,58(9):3412-3423.
张,郑晓东,李劲松,等.基于SOM和PSO的非监督地震相分析技术[J].地球物理学报,2015,58(9):3412-3423.
[10]Kohonen T.Self-organized formation of topologically correct feature maps[J].Biological Cybernetics,1982,43(1):59-69.
[11]Xu Jianhua,Cai Rui.Application of the supervised SOM neural network to oil and gas prediction[J].Geophysical Prospecting for Petroleum,1998,37(3):71-76.许建华,蔡瑞.有监督SOM神经网络在油气预测中的应用[J].石油物探,1998,37(3):71-76.
[12]Villmann T.Topology preservation in self-organizing maps[J].Kohonen Maps,1999,27(2):279-292.
[13]Roy A,Matos M,Marfurt K J.Automatic seismic facies classification with Kohonen self organizing maps-a tutorial[J].Geohorizons,2010,10(1):6-14.
[14]Li Dailin,Wan Jie,Zhu Huafeng,et al.A new technique for recognition of underwater object based on HSV color space[J].Scientia Sinica:Physica,Mechanica & Astronomica,2015,45(8):084209-1-084209-5.
李代林,万杰,朱化凤,等.基于HSV颜色空间的水下物体识别技术研究[J].中国科学:物理学,力学,天文学,2015,45(8):084209-1-084209-5.
[15]Wang Shaofei,An Wenhong,Chen Peng,et al.Characteristics and development techniques of Sulige tight gas pool[J].Natural Gas Geoscience,2013,24(1):138-145.
王少飞,安文宏,陈鹏,等.苏里格气田致密气藏特征与开发技术[J].天然气地球科学,2013,24(1):138-145.
[16]Yuan Zhaowei,Qiang Xiaolong,Gao Shichen,et al.Modeling of different sedimentary facies and assessments for features of spatial structures in the Sulige Gasfield[J].Special Oil and Gas Reservoirs,2017,24(1):77-83.
袁照威,强小龙,高世臣,等.苏里格气田不同沉积相建模方法及空间结构特征评价[J].特种油气藏,2017,24(1):77-83.
[17]Wang Z,Gao J,Wang D,et al.3D seismic attributes for a tight gas sand reservoir characterization of the eastern Sulige Gasfield,Ordos Basin,China[J].Geophysics,2015,80(2):35-43.
[18]Yuan Zhaowei,Chen Long,Gao Shichen,et al.A method of sedimentary facies modeling through integration of multi-seismic attributes based on Markov-Bayes model:An example from Su10 area in the north of Sulige Gasfield [J].Petroleum Geology and Recovery Efficiency,2017,24(3):37-43.
袁照威,陈龙,高世臣,等.基于马尔科夫—贝叶斯模拟算法的多地震属性沉积相建模方法——以苏里格气田苏10区块为例[J].油气地质与采收率,2017,24(3):37-43.
[19]Gao Shichen,Yuan Zhaowei.Seismic attribute in facies predicton-sequential stochastic pattern recognition methods[J].Progress in Geophysics,2016,31(3):1066-1072.
高世臣,袁照威.地震属性在沉积相预测中的方法研究—序贯随机模式识别[J].地球物理学进展,2016,31(3):1066-1072.
[20]Li Jianhua,Liu Baihong,Zhang Yanqing,et al.Oil-bearing reservoir prediction with prestack AVO inversion[J].Oil Geophysical Porspecting,2016,51(6):1180-1186.
李建华,刘百红,张延庆,等.叠前AVO反演在储层含油气性预测中的应用[J].石油地球物理勘探,2016,51(6):1180-1186.

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