Identification of point bar and abandoned channel of meandering river by spectral decomposition inversion based on machine learning
LI Honghui1,2, YUE Dali1,2, LI Wei1,2, GUO Changchun3, LI Xiang3, LYU Mei1,2
1. State Key Laboratory of Petroleum Resources and Prospecting, Beijing 102249, China; 2. College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China; 3. Research Institute of Exploration & Development, SINOPEC Shengli Oilfield Company, Dongying, Shandong 257000, China
Abstract：Characterizing the hierarchical architecture of point bars and abandoned channels of a meandering river is of great significance for enriching the architecture pattern of the meandering river reservoir and guiding the efficient development of oil and gas fields. Therefore, the Guantao Formation of Zhong12-Xiejian 3011 well block in Gudao Oilfield in Bohai Bay Basin is taken as an example, and the point bars and abandoned channels of the meandering river are identified based on spectral decomposition inversion technology. Firstly, with the seismic data processed by spectral decomposition, the optimal seismic data of frequency bands are selected according to the relationship between the amplitude and the thickness of the sand body, and the machine learning algorithm of support vector regression (SVR) is used for spectral decomposition inversion. Then, on the basis of describing the distribution law of sand bodies in composite channels by the plane attribute of inversed data volume, the single meander belt is predicted according to the seismic, well logging, and other response characteristics of channel boundaries. Finally, guided by the muddy semi-filling pattern of abandoned channels, this paper selects the inversion attribute slices of the upper, middle, and lower parts of the target layer for RGB fusion, so as to establish the identification pattern of abandoned channels and identify point bars and abandoned channels under the constraints of the quantitative model. The results show that:① The spectral decomposition inversion technology based on machine learning can make full use of seismic information and logging information of different frequency bands, which improves the resolution of inversion results and can guide channel boundary identification; ② RGB fusion technology is used to fuse the inversion attribute slices at different positions of the channel, which can help recognize the spatial combination relationship between sand bodies and identify inter-well abandoned channels; ③ Based on the seismic data with a dominant frequency of 38 Hz, four single meander belts, 13 abandoned channels, and 15 point bars are identified in the composite meander belts of the target layer by using the identification technology of point bars and abandoned channels of meandering river based on spectral decomposition inversion.The injection well data verifies the accuracy of this method, which has a good application prospect.
李洪辉, 岳大力, 李伟, 郭长春, 李响, 吕梅. 基于分频智能反演的曲流河点坝与废弃河道识别[J]. 石油地球物理勘探, 2023, 58(2): 358-368.
LI Honghui, YUE Dali, LI Wei, GUO Changchun, LI Xiang, LYU Mei. Identification of point bar and abandoned channel of meandering river by spectral decomposition inversion based on machine learning. Oil Geophysical Prospecting, 2023, 58(2): 358-368.
岳大力,吴胜和,刘建民.曲流河点坝地下储层构型精细解剖方法[J].石油学报,2007,28(4):99-103.YUE Dali,WU Shenghe,LIU Jianmin.An accurate method for anatomizing architecture of subsurface reservoir in point bar of meandering river[J].Acta Petrolei Sinica,2007,28(4):99-103.
[2]
岳大力.曲流河储层构型分析与剩余油分布模式研究——以孤岛油田馆陶组为例[D].北京:中国石油大学(北京),2006.YUE Dali.The Study on Architecture Analysis and Remaining Oil Distribution Patterns of Meandering River Reservoir:A Case Study of Guantao Formation,Gudao Oilfield[D].China University of Petroleum (Beijing),Beijing,2006.
[3]
何文祥,吴胜和,唐义疆,等.地下点坝砂体内部构型分析——以孤岛油田为例[J].矿物岩石,2005,25(2):81-86.HE Wenxiang,WU Shenghe,TANG Yijiang,et al.The architecture analysis of the underground point bar:Taking Gudao oilfield as an example[J].Minera-logy and Petrology,2005,25(2):81-86.
[4]
COLOMBERA L,MOUNTNEY N P,RUSSELL C E,et al.Geometry and compartmentalization of fluvial meander-belt reservoirs at the bar-form scale:quantitative insight from outcrop,modern and subsurface analogues[J].Marine and Petroleum Geology,2017,82:35-55.
[5]
胡光义,范廷恩,陈飞,等.从储层构型到"地震构型相"——一种河流相高精度概念模型的表征方法[J].地质学报,2017,91(2):465-478.HU Guangyi,FAN Ting'en,CHEN Fei,et al.From reservoir architecture to seismic architecture facies:characteristic method of a high-resolution fluvial fa-cies model[J].Acta Geologica Sinica,2017,91(2):465-478.
[6]
李伟,岳大力,胡光义,等.分频段地震属性优选及砂体预测方法——秦皇岛32-6油田北区实例[J].石油地球物理勘探,2017,52(1):121-130.LI Wei,YUE Dali,HU Guangyi,et al.Frequency-segmented seismic attribute optimization and sandbody distribution prediction:an example in North Block,Qinghuangdao 32-6 Oilfield[J].Oil Geophysical Prospecting,2017,52(1):121-130.
[7]
于建国,韩文功,刘力辉.分频反演方法及应用[J].石油地球物理勘探,2006,41(2):193-197.YU Jianguo,HAN Wengong,LIU Lihui.Frequency-divided inversion and application[J].Oil Geophysical Prospecting,2006,41(2):193-197.
[8]
岳大力,李伟,王军,等.基于分频融合地震属性的曲流带预测与点坝识别——以渤海湾盆地埕岛油田馆陶组为例[J].古地理学报,2018,20(6):941-950.YUE Dali,LI Wei,WANG Jun,et al.Prediction of meandering belt and point-bar recognition based on spectral-decomposed and fused seismic attributes:a case study of the Guantao Formation,Chengdao Oilfield,Bohai Bay Basin[J].Journal of Palaeogeography (Chinese Edition),2018,20(6):941-950.
[9]
陶倩倩,李达,杨希冰,等.利用分频反演技术预测烃源岩[J].石油地球物理勘探,2015,50(4):706-713,722.TAO Qianqian,LI Da,YANG Xibing,et al.Hydrocarbon source rock prediction with frequency-divided inversion[J].Oil Geophysical Prospecting,2015,50(4):706-713,722.
[10]
VEEKEN P C,PRIEZZHEV I I,SCHMARYAN L E,et al.Nonlinear multitrace genetic inversion applied on seismic data across the Shtokman field,offshore northern Russia[J].Geophysics,2009,74(6):WCD49-WCD59.
[11]
LI W,YUE D,WU S,et al.Characterizing meander belts and point bars in fluvial reservoirs by combining spectral decomposition and genetic inversion[J].Marine and Petroleum Geology,2019,105:168-184.
[12]
LI W,YUE D,WANG W,et al.Fusing multiple frequency-decomposed seismic attributes with machine learning for thickness prediction and sedimentary facies interpretation in fluvial reservoirs[J].Journal of Petroleum Science and Engineering,2019,177:1087-1102.
[13]
PARTYKA G,GRIDLEY J,LOPEZ J.Interpretational applications of spectral decomposition in reservoir characterization[J].The Leading Edge,1999,18(3):353-360.
[14]
SINHA S,ROUTH P S,ANNO P D,et al.Spectral decomposition of seismic data with continuous-wavelet transform[J].Geophysics,2005,70(6):P19-P25.
[15]
ZENG H.Thickness imaging for high-resolution stratigraphic interpretation by linear combination and color blending of multiple-frequency panels[J].Inter-pretation,2017,5(3):T411-T422.
[16]
CASTAGNA J P,SUN S.Comparison of spectral decomposition methods[J].First Break,2006,24(3):75-79.
[17]
VAPNIK V,LEVIN E,CUN Y.Measuring the VC-dimension of a learning machine[J].Neural Computation,1994,6(5):851-876.
杜炳毅,张广智,王磊,等.基于机器学习的复杂储层微小断裂系统识别方法研究与应用[J].石油物探,2021,60(4):621-631.DU Bingyi,ZHANG Guangzhi,WANG Lei,et al.Research and application of micro-fault system detection based on machine learning[J].Geophysical Prospecting for Petroleum,2021,60(4):621-631.
[20]
张彦周,刘叶玲,谢宝英.支持向量机在储层厚度预测中的应用[J].勘探地球物理进展,2005,28(6):422-424,431.ZHANG Yanzhou,LIU Yeling,XIE Baoying.Application of SVM in prediction of reservoir thickness[J].Progress in Exploration Geophysics,2005,28(6):422-424,431.
[21]
邴萍萍,曹思远,路交通.基于支持向量机的非线性AVO反演[J].地球物理学报,2012,55(3):1025-1032.BING Pingping,CAO Siyuan,LU Jiaotong.Non-linear AVO inversion based on support vector machine[J].Chinese Journal of Geophysics,2012,55(3):1025-1032.
[22]
吴胜和,纪友亮,岳大力,等.碎屑沉积地质体构型分级方案探讨[J].高校地质学报,2013,19(1):12-22.WU Shenghe,JI Youliang,YUE Dali,et al.Discussion on hierarchical scheme of architectural units in clastic deposits[J].Geological Journal of China Universities,2013,19(1):12-22.
[23]
MIALL A D.Architectural-element analysis:a new method of facies analysis applied to fluvial deposits[J].Earth-Science Reviews,1985,22(4):261-308.
[24]
岳大力,胡光义,李伟,等.井震结合的曲流河储层构型表征方法及其应用——以秦皇岛32-6油田为例[J].中国海上油气,2018,30(1):99-109.YUE Dali,HU Guangyi,LI Wei,et al.Meandering fluvial reservoir architecture characterization method and application by combining well logging and seismic data:a case study of QHD32-6 oilfield[J].China Offshore Oil and Gas,2018,30(1):99-109.
[25]
王海峰,范廷恩,宋来明,等.高弯度曲流河砂体规模定量表征研究[J].沉积学报,2017,35(2):279-289.WANG Haifeng,FAN Ting'en,SONG Laiming,et al.Quantitative characterization study on sand body scale in high sinuosity meandering river[J].Acta Se-dimentologica Sinica,2017,35(2):279-289.
[26]
LEEDER M R.Fluviatile fining-upwards cycles and the magnitude of palaeochannels[J].Geological Magazine,1973,110(3):265-276.
[27]
LORENZ J C,HEINZE D M,CLARK J A.Determination of widths of meander-belt sandstone reservoirs from vertical downhole data,Mesaverde group,Piceance Creek basin,Colorado[J].AAPG Bulletin,1985,69(5):710-721.
[28]
张本华.曲流河储层构型中废弃河道的识别及其分布模式——以孤岛油田馆上段为例[J].油气地质与采收率,2013,20(3):18-21.ZHANG Benhua.Discussion on abandoned channels recognition and distribution models on meandering river reservoir architecture research:case study of upper member of Guantao formation in Gudao oilfield[J].Petroleum Geology and Recovery Efficiency,2013,20(3):18-21.
[29]
周新茂,高兴军,季丽丹,等.曲流河废弃河道的废弃类型及机理分析[J].西安石油大学学报(自然科学版),2010,25(1):19-23.ZHOU Xinmao,GAO Xingjun,JI Lidan,et al.Analysis on the types and the sedimentation mechanism of the abandoned channel in meandering river[J].Journal of Xi'an Shiyou University (Natural Science Edition),2010,25(1):19-23.
[30]
李婷婷,王钊,马世忠,等.地震属性融合方法综述[J].地球物理学进展,2015,30(1):378-385.LI Tingting,WANG Zhao,MA Shizhong,et al.Summary of seismic attributes fusion method[J].Progress in Geophysics,2015,30(1):378-385.
[31]
王楠,杜鹏,赵悦伊,等.地震RGB分频属性融合技术在J工区河道砂体空间展布中的应用[J].海洋石油,2016,36(3):8-12.WANG Nan,DU Peng,ZHAO Yueyi,et al.Application of the technology of decomposed frequency RGB blending seismic attribute in interpretation of spatial distribution of channel sand body of J area[J].Offshore Oil,2016,36(3):8-12.
[32]
岳大力,李伟,杜玉山,等.河流相储层地震属性优选与融合方法综述[J].地球科学,2022,47(11):3929-3943.YUE Dali,LI Wei,DU Yushan,et al.Review on optimization and fusion of seismic attributes for fluvial reservoir characterization[J].Earth Science,2022,47(11):3929-3943.
