Review on Optimization and Fusion of Seismic Attributes for Fluvial Reservoir Characterization
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摘要: 地震属性分析已广泛应用于河流相砂体预测并取得良好效果.地震属性分析技术主要包括属性提取、属性优选与属性融合，总结了河流相砂体预测中常见的地震属性提取方式、优选及融合方法，分析了由围岩干扰、地震分辨率限制导致的属性提取与分析误区，阐述了不同属性优选与融合方法的优缺点、适用条件与发展前景.总体而言，基于线性模型的地震属性融合提升效果较差，适用于少井区域；基于非线性模型的属性融合效果较好，但仅适用于钻井较多的地区，如油气开发阶段；无监督智能属性融合可应用于无井或少井区域，是未来无井或少井条件下属性融合的重要发展趋势之一.同时，重点阐述了新提出的分频属性融合与降低围岩干扰的属性融合方法.Abstract: Seismic attribute analyses have been applied widely in hydrocarbon exploration and development of fluvial reservoirs, and obtained good results. The analysis procedure of seismic attributes mainly includes the extraction, the optimization and the fusion of attributes. In this paper it summarizes the main methods of attribute extraction, optimization and fusion, and evaluates their advantages, disadvantages and application conditions. Besides, the common misunderstanding genetically related to seismic resolution and interference of neighboring zone in attribute extraction is also analyzed. Generally, fusion methods of seismic attributes using linear models cannot significantly improve the results, and are suitable for areas with several or a few wells; fusion methods with intelligent models (mainly for supervised learning) are commonly suitable for the areas with dozens of wells, such as areas within oil development stage. Fusion methods based on unsupervised learning are suitable for areas with few and even no wells, which have an optimistic development prospect since they can make full use of the seismic information, and are not limited by wells. In addition, the new fusion methods of frequency-decomposed attributes, and of attributes from target and neighboring zones are also summarized in this paper.
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Key words:
- fluvial reservoir /
- seismic attribute /
- attribute optimization /
- attribute fusion /
- machine learning /
- sedimentology
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图 1 地震层属性提取方式
a. 层间属性；b. 沿层（顶）属性；c. 沿层（底）属性；d. 沿层切片
Fig. 1. Extraction methods of horizon seismic attributes
图 2 基于地震正演模拟的围岩干扰分析图
据 Li et al.（2021）.a. 地质正演模型；b. 地震正演模型；c. 常见地震子波；d. 上、下围岩干扰层示意图
Fig. 2. Analysis diagrams of interference from neighboring zones based on seismic forward modeling
图 3 秦皇岛32-6油田河流相储层某层地震属性融合效果对比图
a. 地震属性聚类分析；b.基于多元回归的RMS、MPA与AIF属性线性融合结果（效果较差）；c.基于监督学习的RMS、MPA与AIF属性融合结果（效果好）；d. 基于非监督学习的RMS、MPA与AIF属性融合结果（效果居中）
Fig. 3. Comparison between the fused seismic attributes using different methods in fluvial reservoir of the Qinhuangdao 32-6 oilfield
图 4 基于监督学习（a）与无监督学习（b）的地震属性智能融合技术流程
Fig. 4. Intelligent fusion workflow of seismic attributes using supervised learning (a) and unsupervised learning (b)
图 5 基于颜色透明度的分频融合属性与相干属性叠合图（据Li et al., 2019a）
Fig. 5. Map co-rendering the frequency-decomposed attributes and coherence attributes (Li et al., 2019a)
图 6 不同频率地震子波的振幅与厚度相关关系（AVF曲线）(Li et al., 2019b)
Fig. 6. Tuning curves between the sand thickness and amplitude with different frequency wavelets (AVF curve) (Li et al., 2019b)
图 7 分频地震属性智能融合技术路线
Fig. 7. Workflow diagram outlining the intelligent fusion of frequency-decomposed seismic attributes
图 8 埕岛油田分频地震属性融合图
a. 埕岛油田某小层分频属性智能融合图（Li et al.，2019b）；b. 埕岛油田某小层分频属性 RGB 融合图
Fig. 8. Maps of fused frequency-decomposed attributes in the Chengdao oilfield
图 9 河流相储层正演模型及其正演模拟结果
据Li et al.（2020）修改；a. 地质模型；b. 地震正演模拟结果；c. 上部相邻层砂体厚度与RMS对比；d. 目的层砂体厚度与RMS对比；e. 下部相邻层砂体厚度与RMS对比
Fig. 9. Forward geological model of fluvial reservoirs and its seismic responses
图 10 降低围岩干扰的地震属性智能融合技术路线
Fig. 10. Workflow diagram outlining methodology of reducing the interference from neighboring zones
图 11 降低围岩干扰前、后的地震属性对比图
据Li et al.（2020）修改；a.三维地震正演模型；b.上部相邻层正演模型实际砂体厚度；c.目的层正演模型实际砂体厚度；d.下部相邻层正演模型实际砂体厚度；e.受围岩干扰的目的层RMS振幅属性；f.降低围岩干扰后的目的层地震属性
Fig. 11. Comparison of seismic attributes before and after reducing the interference of neighboring zones
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