1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. Key Laboratory of Earth Exploitation and Information Technology, Ministry of Education, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 3. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China; 4. Yangtze Delta Region Institute of University of Electronic Science and Technology of China(Huzhou), Huzhou, Zhejiang 313099, China
Abstract:The effect of characterizing channels by using curvature and coherence is not obvious, and the performance of principal component analysis (PCA) on these attributes is poor. Although seismic texture, coherent energy, spectral peak, frequency division amplitude, and other attributes can identify some paleochannels well, there are some problems such as incomplete paleochannel characterization and limited identification range. Therefore, the method of seismic attribute PCA is proposed to identify paleochannels. The method finely characterizes paleochannels at different scales, and its prediction effect is better than that of a single attribute, which improves the identification accuracy of paleochannels. The following understandings are obtained: ①The three kinds of seismic attributes including seismic texture, coherent energy, and spectral peak can characterize paleochannels well, but their identification results of the paleochannels are not comprehensive. The effective information of channels identified by the three kinds of seismic attributes is fused by PCA, which can highlight the distribution characteristics and boundary morphology of paleochannels. ②According to the identification difference of channel change details by the instantaneous profile of frequency division amplitude, PCA is used to fuse the instantaneous amplitude attributes of different frequency bands to enhance the common detail information, which improves the identification accuracy of paleochannels. The proposed method is used to identify the channel in Shaximiao Formation in western Sichuan, and it accurately reflects the specific location and contour of the paleochannel.
戴晓峰,冯周,王锦芳.川中茅口组岩溶储层地球物理特征及勘探潜力[J].石油地球物理勘探,2017,52(5):1049-1058.DAI Xiaofeng,FENG Zhou,WANG Jinfang.Geologic and geophysical characteristics and exploration potential of Karst reservoirs at Maokou Formation in the Central Sichuan Basin[J].Oil Geophysical Prospecting,2017, 52(5):1049-1058.
[2]
杨国权,高荣涛,雷凌,等.河流相储集体的精细解释与描述[J].石油地球物理勘探,2005,40(3):314-317.YANG Guoquan,GAO Rongtao,LEI Ling,et al.Fine interpretation and description of river facies reservoir[J].Oil Geophysical Prospecting,2005,40(3):314-317.
[3]
云美厚,赵秋芳,李晓斌.地震分辨率思考与高分辨率勘探对策[J].石油地球物理勘探,2022,57(5):1250-1262.YUN Meihou,ZHAO Qiufang,LI Xiaobin.Thought about seismic resolution and countermeasures of high-resolution seismic exploration[J].Oil Geophysical Prospecting,2022, 57(5): 1250-1262.
[4]
CHEN Q,SIDNEY S.Seismic attribute technology for reservoir forecasting and monitoring[J].The Leading Edge, 1997,16(5):445-450.
[5]
李在光,杨占龙,刘俊田,等.多属性综合方法预测含油气性及其效果[J].天然气地球科学,2006,17(5):727-730.LI Zaiguang,YANG Zhanlong,LIU Juntian,et al.Hydrocarbon detection by integrating multiple attri-butes[J].Natural Gas Geoscience,2006,17(5):727-730.
[6]
MIALL A D.Analysis of Fluvial Depositional Systems[M].American Association of Petroleum Geologists,Tulsa, Calgary, 1981.
[7]
王治国,尹成,雷小兰,等.河道纹理属性分析中的灰度共生矩阵参数研究[J].石油地球物理勘探,2012,47(1):100-106.WANG Zhiguo,YIN Cheng, LEI Xiaolan,et al.GLCM parameters in fluvial texture analysis[J].Oil Geo-physical Prospecting, 2012,47(1):100-106.
[8]
王治国,尹成,蒋志斌,等.莱州湾凹陷明化镇组下段河道储层的地震地貌分析[J].石油地球物理勘探,2012,47(4):629-636.WANG Zhiguo,YIN Cheng,JIANG Zhibin,et al.Seismic geomorphology of channel reservoir in lower Minghuazhen formation,Laizhouwan Depression[J].Oil Geophysical Prospecting,2012,47(4):629-636.
[9]
邢成智,胡正舟,蔡义峰,等.GeoEast解释系统在滴南凸起中段储层预测中的应用[J].石油地球物理勘探,2014,49(增刊1):120-125.XING Chengzhi,HU Zhengzhou,CAI Yifeng,et al.Reservoir prediction with GeoEast in the middle of Dinan uplift[J].Oil Geophysical Prospecting,2014,49(S1):120-125.
[10]
蔡涵鹏,胡浩炀,吴庆平,等.基于叠前地震纹理特征的半监督地震相分析[J].石油地球物理勘探,2020,55(3):504-509.CAI Hanpeng,HU Haoyang,WU Qingping,et al. Semi-supervised seismic facies analysis based on prestack seismic texture[J].Oil Geophysical Prospecting, 2020,55(3):504-509.
[11]
于豪,李劲松,张研,等.频谱分解技术在断层与储层识别中的应用[J].石油地球物理勘探,2013,48(6):954-959.YU Hao,LI Jinsong,ZHANG Yan,et al.Spectral decomposition in fault and reservoir identifications[J].Oil Geophysical Prospecting,2013,48(6):954-959.
[12]
韩红涛,贾敬,李慧琳,等.应用GeoEast解释系统中的地震属性技术预测生物礁滩[J].石油地球物理勘探,2014,49(增刊1):160-163.HAN Hongtao,JIA Jing, LI Huilin, et al. Organic reef and bank prediction with seismic attribute approaches provided by GeoEast[J].Oil Geophysical Prospecting,2014,49(S1):160-163.
[13]
CASTAGNA J,SUN S,SIEGFRIED R W.Instantaneous spectral analysis:Detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003, 22(2):120-127.
[14]
牛双晨,程冰洁.基于WVD-MEM的高分辨河道识别方法[J].石油地球物理勘探,2021,56(5):1157-1169.NIU Shuangchen,CHENG Bingjie.High-resolution channel identification method based on WVD-MEN[J].Oil Geophysical Prospecting, 2021,56(5):1157-1169.
曹鉴华.RGB混频显示技术及其在河道识别中的应用[J].勘探地球物理进展,2010,33(5):355-358.CAO Jianhua.RGB color blending and its application in channel recognition[J].Progress in Exploration Geophysics, 2010,33(5):355-358.