地质模式约束的SVR属性融合在浅水三角洲储层描述中的应用: 以西湖凹陷X气田为例

李文俊; 岳大力; 何贤科; 段冬平; 龙涛; 王伟; 常吟善

doi:10.19509/j.cnki.dzkq.2021.0611

地质模式约束的SVR属性融合在浅水三角洲储层描述中的应用: 以西湖凹陷X气田为例

doi: 10.19509/j.cnki.dzkq.2021.0611

基金项目:

国家自然科学基金项目 U19B2006

中海石油(中国)有限公司科技项目"西湖凹陷中深层河流相储层表征及剩余油气挖潜研究" YXKY-2019-SH-01

详细信息

作者简介:
李文俊(1989-), 男, 工程师, 主要从事开发地质与开发地震方面的研究工作。E-mail: liwj41@cnooc.com.cn

中图分类号: TE13
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- 文章访问数: 266
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- 被引次数: 0
出版历程
- 收稿日期: 2021-07-13

Application of SVR attribute fusion constrained by geological model in reservoir description of shallow water delta: A case study of X gas field in Xihu Sag

摘要

摘要: X气田位于东海盆地西湖凹陷中央反转构造带，主要目的层H4层为浅水三角洲沉积环境，气田地震资料主频较低（25 Hz），而H4层埋深较大（3 300~3 400 m），储层低孔低渗，常规地震反演预测砂体厚度吻合度较低。针对X气田三维地震资料全覆盖及钻井较少的特点，通过地质模式指导下的正反演结合设置虚拟井来弥补SVR（Support Vector Regression）算法中样本点的不足，通过提取地震属性并优选表征砂体厚度的敏感属性，利用SVR算法进行多属性融合，完成了H4层砂体的定量预测。基于储层预测成果，提出H4层为浅水三角洲曲流型分流河道沉积，并进一步完成了砂体沉积模式解剖，成功指导了开发调整井部署，实钻砂体厚度与预测砂体厚度吻合度高达84%以上。探索得到了海上少井条件下地质模式约束的SVR算法储层定量预测方案，对X气田中深层分流河道储层完成了精准预测，亦对同类型油气田的储层描述具有一定指导意义。
- 西湖凹陷 /
- 分流河道 /
- SVR属性融合 /
- 储层预测
Abstract: X gas field is located in the central inversion structural belt of Xihu Sag, East China Sea Basin.The main target layer H4 is in shallow water delta sedimentary environment.The dominant frequency of seismic data of the gas field is low (25 Hz), while the buried depth of H4 layer is large (3 300-3 400 m).The reservoir has low porosity and low permeability, and the consistency of sand body thickness predicted by conventional seismic inversion is low.Aiming at the characteristics of full coverage of 3D seismic data and less drilling in X gas field, this paper makes up for the lack of sample points in SVR algorithm through forward and inverse modeling under the guidance of geological model and setting up virtual wells.Then, by extracting seismic attributes and optimizing sensitive attributes characterizing sand body thickness, SVR algorithm is used for multi-attribute fusion to complete the quantitative prediction of H4 sand body.Based on the reservoir prediction results, it is proposed that H4 layer is a distributary channel deposit of shallow water delta meandering flow type, and further completed the dissection of sand body deposition model.After the development adjustment wells drilled, the coincidence between the actual drilled sand body thickness and the predicted sand body thickness is more than 84%.This paper explores and obtains the quantitative reservoir prediction scheme of SVR algorithm constrained by geological model under the condition of few wells on the sea, which completes the accurate prediction of the middle and deep distributary channel reservoir in X gas field, and also has certain guiding significance for the reservoir description of the same type of oil and gas fields.
- Xihu Sag /
- distributary channel /
- SVR attribute fusion /
- reservoir prediction

HTML全文

图 1 研究区地理位置和地质剖面分布图

Figure 1. Location and geological section of the study area

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图 2 主要目的层砂体对比图

Figure 2. Comparison of sand bodies in the target layer

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图 3 储层预测技术路线图

Figure 3. Technology roadmap of reservoir prediction

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图 4 X3井H4层柱状图

Figure 4. H4 layer histogram of Well X3

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图 5 X3-X1-X2井正演初始模型示意图

a.初始正演剖面；b.原始地震剖面；c.初始地质模型

Figure 5. Initial forward modeling of wells X3-X1-X2

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图 6 X3-X1-X2井正演模型优化

a.原始地震剖面; b.初始地质模型; c.初始地质模型正演剖面; d.优化地质模型; e.优化地质模型正演剖面

Figure 6. Optimization of forward modeling for wells X3-X1-X2

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图 7 井间虚拟井设计

Figure 7. Virtual well design between wells

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图 8 SVR多属性融合图

Figure 8. Graph of SVR multi-attribute fusion

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图 9 H4层砂体厚度预测图

Figure 9. Prediction chart of sand thickness of H4 layer

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图 10 H4层连井砂体对比图

Figure 10. Comparison diagram of continuous wells sand body of H4 layer

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图 11 平均振幅属性与砂体厚度相关性

Figure 11. Correlation between average amplitude attributes and sand thickness

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图 12 融合属性与砂体厚度相关性

Figure 12. Correlation between attribute fusion and sand thickness

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图 13 X气田H4层曲流型分流河道储层构造解剖

Figure 13. Structure anatomy of meandering distributary channel reservoir of H4 Layer in X gas field

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表 1 X气田H4层地震属性与砂体厚度相关性分析

Table 1. Correlation analysis of seismic attributes and sand thickness of H4 layer in X gas field

地震属性	相关系数	地震属性	相关系数
最小波谷振幅	0.701 1	最大峰值振幅	0.490 0
均方根振幅	0.558 6	最大绝对值振幅	0.643 3
半时间能量	0.294 1	平均瞬时频率	0.331 5
均方根振幅	0.704 1	平均能量	0.053 0
平均振幅	0.729 7	平均瞬时相位	0.212 8
总绝对值振幅	0.605 4	总能量	0.027 5
总振幅	0.615 9	平均反射强度	0.075 9

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表 2 X气田H4层地震属性相关性分析

Table 2. Correlation analysis of seismic attributes of H4 layer in X gas field

地震属性	最小波谷振幅	平均振幅	均方根振幅
最小波谷振幅	1	\	\
平均振幅	0.851 9	1	\
均方根振幅	0.861 5	0.984 3	1

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参考文献(17)

[1]	丁燕, 杜启振, Qamar Y, 等. 基于深度学习的裂缝预测在S区潜山碳酸盐岩储层中的应用[J]. 石油物探, 2020, 59(2): 267-275. doi: 10.3969/j.issn.1000-1441.2020.02.013 Ding Y, Du Q Z, Qamar Y, et al. Fracture prediction based on deep learning: Application to a buried hill carbonate reservoir in the S[J]. Geophysical Prospecting for Petroleum, 2020, 59(2): 267-275(in Chinese with English abstract). doi: 10.3969/j.issn.1000-1441.2020.02.013
[2]	刘力辉, 陆蓉, 杨文魁. 基于深度学习的地震岩相反演方法[J]. 石油物探, 2019, 58(1): 123-129. doi: 10.3969/j.issn.1000-1441.2019.01.014 Liu L H, Lu R, Yang W K. Seismic lithofacies inversion based on deep learning[J]. Geophysical Prospecting for Petroleum, 2019, 58(1): 123-129(in Chinese with English abstract). doi: 10.3969/j.issn.1000-1441.2019.01.014
[3]	孙永壮, 黄鋆. 多任务深度学习技术在储层横波速度预测中的应用[J]. 地球物理学进展, 2021, 166(2): 799-809. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ202102040.htm Sun Y Z, Huang J. Application of multi task deep learning technology in S-wave velocity prediction of reservoir[J]. Progress in Geophysics Prog Geophys, 2021, 166(2): 799-809(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ202102040.htm
[4]	陈建文, 梁杰, 张银国, 等. 中国海域油气资源潜力分析与黄东海海域油气资源调查进展[J]. 海洋地质与第四纪地质, 2019, 182(6): 1-29. https://www.cnki.com.cn/Article/CJFDTOTAL-HYDZ201906001.htm Chen J W, Liang J, Zhang Y G, et al, Potential analysis of oil and gas resources in China seas and progress of oil and gas resources investigation in the Yellow Sea and East China Sea[J]. Marine Geology and Quaternary Geology, 2019, 182(6): 1-29(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-HYDZ201906001.htm
[5]	赵军, 唐贤君, 蒋一鸣, 等. 东海盆地西湖凹陷西北缘中始新世构造变革厘定及成因探讨[J]. 地质科技通报, 2020, 39(3): 58-67. https://dzkjqb.cug.edu.cn/CN/abstract/abstract10023.shtml Zhao J, Tang X J, Jiang Y M, et al. Determination and genesis of middle Eocene tectonic change in the northwest margin of Xihu Sag, East China Sea Basin[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 58-67(in Chinese with English abstract). https://dzkjqb.cug.edu.cn/CN/abstract/abstract10023.shtml
[6]	张建培, 徐发, 钟韬, 等. 东海陆架盆地西湖凹陷平湖组－花港组层序地层模式及沉积演化[J]. 海洋地质与第四纪地质, 2012, 32(1): 35-41. https://www.cnki.com.cn/Article/CJFDTOTAL-HYDZ201201009.htm Zhang J P, Xu F, Zhong T, et al. Sequence stratigraphic models and sedimentary evolution of Pinghu and Huagang Formation in Xihu trough[J]. Marine Geology and Quaternary Geology, 2012, 32(1): 35-41(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-HYDZ201201009.htm
[7]	周心怀. 西湖凹陷地质认识创新与油气勘探领域突破[J]. 中国海上油气, 2020, 32(1): 1-12. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHSD202001001.htm Zhou X H. Innovation of geological knowledge and breakthrough of oil and gas exploration in Xihu Sag[J]. China Offshore Oil and Gas, 2020, 32(1): 1-12(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZHSD202001001.htm
[8]	高伟中. 西湖凹陷中央反转带中北部天然气高效成藏主控因素及富集规律[J]. 地质科技通报, 2020, 39(3): 40-48. https://dzkjqb.cug.edu.cn/CN/abstract/abstract10021.shtml Gao W Z. Main controlling factors and enrichment law of high efficient gas accumulation in central inversion zone of Xihu Sag[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 40-48(in Chinese with English abstract). https://dzkjqb.cug.edu.cn/CN/abstract/abstract10021.shtml
[9]	徐国盛, 崔恒远, 刘勇, 等. 东海盆地西湖凹陷古近系花港组砂岩储层致密化与油气充注关系[J]. 地质科技通报, 2020, 39(3): 20-29. https://dzkjqb.cug.edu.cn/CN/abstract/abstract10019.shtml Xu G S, Cui H Y, Liu Y, et al. Relationship between sandstone reservoir densification and oil and gas filling of Paleogene Huagang Formation in Xihu Sag, East China Sea Basin[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 20-29(in Chinese with English abstract). https://dzkjqb.cug.edu.cn/CN/abstract/abstract10019.shtml
[10]	杜学斌, 陆永潮, 曹强, 等. 东海盆地西湖凹陷深部储层"相-岩-温"三元分级评价原则与效果[J]. 地质科技通报, 2020, 39(3): 10-19. https://dzkjqb.cug.edu.cn/CN/abstract/abstract10018.shtml Du X B, Lu Y C, Cao Q, et al. Principle and effect of "facies-rock -temperature" ternary classification evaluation of deep reservoir in Xihu Sag, East China Sea Basin[J]. Bulletin of Geological Science and Technology, 2020, 39(3): 10-19(in Chinese with English abstract). https://dzkjqb.cug.edu.cn/CN/abstract/abstract10018.shtml
[11]	韩刚, 高红艳, 龙凡, 等. 叠前反演在西湖凹陷致密砂岩储层"甜点"预测中的应用[J]. 石油物探, 2021, 60(3): 471-478. doi: 10.3969/j.issn.1000-1441.2021.03.013 Han G, Gao H Y, Long F, et al. Application of prestack inversion in prediction of "sweet spot" of tight sandstone reservoir in Xihu Sag[J]. Geophysical Prospecting for Petroleum, 2021, 60(3): 471-478(in Chinese with English abstract). doi: 10.3969/j.issn.1000-1441.2021.03.013
[12]	庄建建, 李喆, 巩兴会, 等. 西湖凹陷WBT地区平湖组下段有利储层预测[J]. 海洋石油, 2021, 41(1): 8-14, 21. https://www.cnki.com.cn/Article/CJFDTOTAL-HYSY202101003.htm Zhuang J J, Li Z, Gong X H, et al. Favorable reservoir prediction of lower Pinghu Formation in WBT area of Xihu Sag[J]. Offshore Oil, 2021, 41(1): 8-14, 21(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-HYSY202101003.htm
[13]	申洪源, 马亮, 张雅楠, 等. 基于多传感器和SVR算法的油田多相流实时计量技术研究[J]. 仪器仪表用户, 2019, 26(10): 15-19. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYQ201910009.htm Shen H Y, Ma L, Zhang Y N, et al. Research on real-time measurement technology of oilfield multiphase flow based on multi-sensor and SVR algorithm[J]. Instrumentation Customer, 2019, 26(10): 15-19(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZYQ201910009.htm
[14]	张琳, 汪廷华, 周慧颖. 基于群智能算法的SVR参数优化研究进展[J]. 计算机工程与应用, 2021, 57(16): 1-16. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202116006.htm Zhang L, Wang Y H, Zhou H Y. Research progress of SVR parameter optimization based on swarm intelligence algorithm[J]. Computer Engineering and Applications, 2021, 57(16): 1-16(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202116006.htm
[15]	孙禾, 赵文珍, 赵文辉, 等. 基于SVR的齿廓图像边缘失真补偿算法[J]. 控制工程, 2020, 27(11): 2030-2037. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202011025.htm Sun H, Zhao W Z, Zhao W H, et al. Edge distortion compensation algorithm of tooth profile image based on SVR[J]. Control Engineering of China, 2020, 27(11): 2030-2037(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202011025.htm
[16]	单蕊. 地震多属性分析技术在煤层厚度预测中的应用分析[J]. 物探化探计算技术, 2021, 43(3): 304-310. https://www.cnki.com.cn/Article/CJFDTOTAL-WTHT202103006.htm Shan R. Application analysis of seismic multi-attribute analysis technology in coal seam thickness prediction[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2021, 43(3): 304-310(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-WTHT202103006.htm
[17]	彭作磊, 陈铭. 多元地震属性分析提高薄储层预测精度[J]. 断块油气田, 2020, 27(3): 318-322. https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT202003011.htm Peng Z L, Chen M. Improving prediction accuracy of thin reservoir by multivariate seismic attribute analysis[J]. Fault-Block Oil & Gas Field, 2020, 27(3): 318-322(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DKYT202003011.htm