A method for judging sand body connectivity based on seismic fusion attributes: A case of the Bozhong Sag
-
摘要:
关于渤中凹陷区中浅层明化镇组下段砂体精细化描述的薄互层以及地震、测井资料较少，砂体连通性的判定问题难以解决。基于研究区实际地震、测井和地质资料，根据砂体实际特征建立了对应模型，通过正演模拟技术中的波动方程法验证、优选适合的地震属性。由地震属性分析技术探究出砂体在最小尺度横向距离$ \frac{1}{8}\mathrm{\lambda } $、纵向叠置$ \frac{1}{6}\mathrm{\lambda } $的情况下，均方根振幅属性、甜点属性和主频属性在中浅层层段比地震资料判断砂体连通性的效果更好。研究区的X5井验证得到，地震融合属性与砂体厚度相关性明显提升，可以定量计算出砂体本身的厚度以及砂体间接触点的距离，对研究区砂体的连通性起到良好的判定作用。
Abstract:The fine description of sand bodies in the lower member of the Minghuazhen Formation (Neogene) in the middle and shallow layers of the Bozhong Sag, Bohai Bay, is limited due to thin interbeds and insufficient seismic and logging data, making it difficult to determine the connectivity of sand bodies. Based on the actual seismic, logging, and geological data in the study area, a corresponding model was established based on the actual characteristics of the sand bodies in the study area, and suitable seismic attributes were verified and optimized through the wave equation method in forward modeling technology. By using a minimum horizontal distance of 1/8 of wavelength and a vertical overlap of 1/6 the wavelength, the root mean square amplitude attribute, sweet spot attribute and main frequency attribute were more effective than using seismic data in determining the connectivity of sand bodies in mid-to-shallow layers. According to the verification of Well X5 logging in the study area, the correlation between seismic fusion attribute and sand body thickness was significantly improved, and the thickness of sand body itself and the distance of contact points among sand bodies could be quantitatively calculated, which played a good role in determining the connectivity of sand bodies in the study area.
-
Key words:
- forward simulation /
- seismic attributes /
- sand body connectivity /
- Bozhong Sag
-
-
表 1 优选属性融合权重统计表
Table 1. Statistics in the weight of priority attribute fusion
属性类型 均方根振幅 主频 甜点属性 相关系数R 0.7288 0.6615 0.6285 融合权重 0.361 0.3277 0.3113 
下载: 导出CSV
表 2 融合地震属性与砂岩厚度数据关系表
Table 2. Relationship between fusion of seismic attributes and sandstone thickness
砂体层 砂岩厚度/m 融合属性 预测厚度/m 绝对误差/m 相对误差 1 7.2 6.5 −0.7 0.097 2 2.4 2.9 0.5 0.208 3 2.9 2.3 −0.6 0.207 4 7.5 8.2 0.7 0.093 5 5.2 3.9 −1.3 0.25 6 9.1 8.2 −0.9 0.099 7 5 6.2 1.2 0.24
下载: 导出CSV
-
[1] 梁文富, 余兴华, 贾春雨, 等. 完善单砂体注采关系的做法及效果[J]. 大庆石油地质与开发, 2002, 21(3):38-40,51
LIANG Wenfu, YU Xinghua, JIA Chunyu, et al. Comprehensive use of pattern to improve the injection-production relation of single sand body [J]. Petroleum Geology & Oilfield Development in Daqing, 2002, 21(3): 38-40,51.
[2] 斯扬. 姬塬油田盐67区长8储层连通性研究[D]. 西安石油大学硕士学位论文, 2015.
SI Yang. Research on reservoir connectivity of Chang 8 Reservoir in Yan 67 area, Jiyuan oilfield[D]. Master Dissertation of Xi'an Shiyou University, 2015.
[3] Fielding C R, Crane R C. An application of statistical modelling to the prediction of hydrocarbon recovery factors in fluvial reservoir sequences[M]//Ethridge F G, Flores R M, Harvey M D. Recent Developments in Fluvial Sedimentology. Tulsa: SEPM Society for Sedimentary Geology, 1987.
[4] 林伟强, 曲丽丽, 朱露, 等. 井震藏结合判定井间砂体连通性研究及应用: 以南堡油田M区中深层为例[J]. 油气藏评价与开发, 2022, 12(2):373-381
LIN Weiqiang, QU Lili, ZHU Lu, et al. Evaluation of inter-well sand body connectivity by combination of well, seismic, and reservoir and its application: Taking the middle and deep layers of M area of Nanpu Oilfield as an example [J]. Petroleum Reservoir Evaluation and Development, 2022, 12(2): 373-381.
[5] 张钊, 陈明强, 高永利. 应用示踪技术评价低渗透油藏油水井间连通关系[J]. 西安石油大学学报:自然科学版, 2006, 21(3):48-51
ZHANG Zhao, CHEN Mingqiang, GAO Yongli. Estimation of the connectivity between oil wells and water injection wells in low-permeability reservoir using tracer detection technique [J]. Journal of Xi'an Shiyou University (Natural Science Edition), 2006, 21(3): 48-51.
[6] 刘文岭, 牛彦良, 李刚, 等. 多信息储层预测地震属性提取与有效性分析方法[J]. 石油物探, 2002, 41(1):100-106
LIU Wenling, NIU Yanliang, LI Gang, et al. Seismic attribute extraction and effectiveness analysis of multi-attribute reservoir prediction [J]. Geophysical Prospecting for Petroleum, 2002, 41(1): 100-106.
[7] 王开燕, 徐清彦, 张桂芳, 等. 地震属性分析技术综述[J]. 地球物理学进展, 2013, 28(2):815-823
WANG Kaiyan, XU Qingyan, ZHANG Guifang, et al. Summary of seismic attribute analysis [J]. Progress in Geophysics, 2013, 28(2): 815-823.
[8] Chopra S, Marfurt K J. Emerging and future trends in seismic attributes [J]. The Leading Edge, 2008, 27(3): 298-318. doi: 10.1190/1.2896620
[9] 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(S1):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.
[10] 冯金义. 时频分析技术识别河道砂体在埕海一区明化镇组中的应用[J]. 中国石油和化工标准与质量, 2016, 36(6):31-32
FENG Jinyi. Application of time-frequency analysis technique to identify channel sand body in Minghuazhen formation in Chenghai No. 1 District [J]. China Petroleum and Chemical Standard and Quality, 2016, 36(6): 31-32.
[11] 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, 2022, 37(2):844-850
LIU Chuanqi, SONG Junting, XUE Mingxing. Application of edge detection technology in sand connectivity research [J]. Progress in Geophysics, 2022, 37(2): 844-850.
[12] 周连敏. 倾角方位属性在曲流河河道砂体预测中的应用[J]. 断块油气田, 2017, 24(4):471-473
ZHOU Lianmin. Application of DipAzi attribute to predicting channel sandstone of meandering river [J]. Fault-block Oil and Gas Field, 2017, 24(4): 471-473.
[13] 安鹏, 刘专, 马云海, 等. 基于地质甜点的地震相控敏感属性分析技术在河道砂体识别中的应用[C]//中国石油学会2021年物探技术研讨会论文集. 成都: 《中国学术期刊(光盘版)》电子杂志社有限公司, 2020.
AN Peng, LIU Zhuan, MA Yunhai, et al. Application of seismic phase-controlled sensitive attribute analysis technique based on geological sweet spot in channel sand body identification[C]. Chengdu: China Academic Journal (CD) Online magazine Co. , Ltd, 2020.
[14] 井涌泉, 栾东肖, 张雨晴, 等. 基于地震属性特征的河流相叠置砂岩储层预测方法[J]. 石油地球物理勘探, 2018, 53(5):1049-1058
JING Yongquan, LUAN Dongxiao, ZHANG Yuqing, et al. Fluvial facies inter-bedded sand reservoir prediction with seismic multi-attributes [J]. Oil Geophysical Prospecting, 2018, 53(5): 1049-1058.
[15] 印兴耀, 周静毅. 地震属性优化方法综述[J]. 石油地球物理勘探, 2005, 40(4):482-489
YIN Xingyao, ZHOU Jingyi. Summary of optimum methods of seismic attributes [J]. Oil Geophysical Prospecting, 2005, 40(4): 482-489.
[16] 季玉新, 欧钦. 优选地震属性预测储层参数方法及应用研究[J]. 石油地球物理勘探, 2003, 38(S1):57-62
JI Yuxin, OU Qin. Research on the method and application of optimizing seismic attribute prediction of reservoir parameters [J]. Petroleum Geophysical Exploration, 2003, 38(S1): 57-62.
[17] 李娟, 王鑫渊, 杨国栋, 等. 地震属性优化技术在郭家河三维地震资料解释中的应用[J]. 煤炭科技, 2019, 40(6):83-85
LI Juan, WANG Xinyuan, YANG Guodong, et al. Application of seismic attribute optimization technology in three-dimensional seismic data interpretation of Guojiahe [J]. Coal Science & Technology Magazine, 2019, 40(6): 83-85.
[18] Chen Q, Sidney S. Seismic attribute technology for reservoir forecasting and monitoring [J]. The Leading Edge, 1997, 16(5): 445-450. doi: 10.1190/1.1437657
[19] Chopra S, Marfurt K J. Seismic attributes-A historical perspective [J]. Geophysics, 2005, 70(5): 3SO-28SO. doi: 10.1190/1.2098670
[20] 王彦仓, 秦凤启, 杜维良, 等. 地震属性优选、融合探讨[J]. 中国石油勘探, 2013, 18(6):69-73
WANG Yancang, QIN Fengqi, DU Weiliang, et al. Discussions on optimization and fusion of seismic attributes [J]. China Petroleum Exploration, 2013, 18(6): 69-73.
[21] 李德生. 渤海湾含油气盆地的地质构造特征与油气田分布规律[J]. 海洋地质研究, 1981, 1(1):3-20
LI Desheng. Geological structure and hydrocarbon occurrence of Bohai gulf oil and gas basin (China) [J]. Marine Geology & Quaternary Geology, 1981, 1(1): 3-20.
[22] 叶加仁, 吴景富, 舒誉, 等. 中国近海富烃凹陷油气成藏特征[J]. 地质科技情报, 2012, 31(5):105-111
YE Jiaren, WU Jingfu, SHU Yu, et al. Characteristics of hydrocarbon accumulation in the hydrocarbon-rich depressions, offshore China [J]. Geological Science and Technology Information, 2012, 31(5): 105-111.
[23] 刘海涛, 于海涛, 孙雨, 等. 断陷盆地多类型斜坡形成与油气差异富集规律: 以渤海湾盆地为例[J]. 岩石学报, 2022, 38(9):2697-2708
LIU Haitao, YU Haitao, SUN Yu, et al. Formation and differential enrichment of oil and gas on multiple types of slopes in rifted basins: Taking the Bohai Bay Basin as an example, China [J]. Acta Petrologica Sinica, 2022, 38(9): 2697-2708.
[24] 张勇刚, 范国章, 王红平, 等. 地震多属性分析技术预测和评价盐下碳酸盐岩储层厚度分布[J]. 盐湖研究, 2022, 30(3):72-82
ZHANG Yonggang, FAN Guozhang, WANG Hongping, et al. Seismic multi-attributes analysis method to predict and evaluate thickness distribution of carbonate reservoir in Pre-salt [J]. Journal of Salt Lake Research, 2022, 30(3): 72-82.
[25] 张亚志. 薄互层储层地震反射特征识别方法研究[D]. 大庆石油学院硕士学位论文, 2003.
ZHANG Yazhi. Research on seismic reflection characteristic identification method of thin interbed reservoir[D]. Master Dissertation of Daqing Petroleum Institute, 2003.
[26] Dorrington K P, Link C A. Genetic-algorithm/neural-network approach to seismic attribute selection for well-log prediction [J]. Geophysics, 2004, 69(1): 212-221. doi: 10.1190/1.1649389
[27] 刘淑芬, 张海翔, 李占东, 等. 地震属性融合定量储层预测实验设计[J]. 实验技术与管理, 2022, 39(7):181-186,195
LIU Shufen, ZHANG Haixiang, LI Zhandong, et al. Experimental design of quantitative reservoir prediction based on seismic attribute fusion [J]. Experimental Technology and Management, 2022, 39(7): 181-186,195.
-
图(10)
表(2)
计量
- 文章访问数: 218
- PDF下载数: 6
- 施引文献: 0