SEDIMENTARY FACIES ANALYSIS UPON SEISMIC ATTRIBUTES BY K-MEANS CLUSTERING ALGORITHM IN LOW-EXPLORATION AREAS: A CASE STUDY OF WENCHANG FORMATION IN BAIYUN SAG
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摘要:
始新统文昌组是白云凹陷的潜在烃源层系，为了解决沉积研究中基础资料少和多地震数据体联合研究的难题，提出了基于K-means方法的地震属性聚类分析方法。在可靠的层位解释基础上，针对不同地层接触关系，合理的进行地震属性的提取、优选和聚类分析，结合单井相分析、地震相识别和邻区资料的类比研究，综合进行文昌组沉积相分析工作。研究选取了均方根振幅、校偏带宽比和能量半衰时等3种地震属性进行聚类分析，划分了蓝色、亮绿色和红色3类区域，分别对应于亚平行—平行中高频中强振幅中好连续相、亚平行中弱振幅差连续相、楔状强振幅差连续—杂乱相等3类地震相。白云凹陷文昌组主要发育滨湖、浅湖、中深湖和(扇)三角洲等沉积相类型；文昌SQ1层序时，周缘隆起在凹陷内发育多个扇三角洲，凹陷内水体相对较浅，以滨湖和浅湖亚相发育为主；文昌SQ2层序时水体加深，在主洼东、西2个深洼区中深湖亚相已连片展布，总面积近600 km2。该方法合理可靠，有力支持了白云凹陷资源潜力评价工作。
Abstract:The Wenchang Formation is a potential hydrocarbon source sequence in the Baiyun Sag, which has been penetrated by drilling one time only up to date and little geological and seismic data are available. In order to tackle these difficulties, the clustering algorithm with seismic attributes based on the method of K-means is recommended in this paper. On the premise of reliable interpretation, seismic attributes should be calculated with different methods for different units. As the results of attributes extraction, optimization and clustering algorithm, a distribution map of seismic attributes clustering would be prepared. By facies analysis of well data, using the seismic profiles and sedimentary models from adjacent areas as references, a comprehensive analysis of Wenchang Formation in the Baiyun Sag could be conducted. We pick RMS, Bandwidth Rating (Debias), and Energy half time for the K-means clustering algorithm and suggest that the research area can be divided into three units: Blue unit, Green unit and Red unit, which represent three kinds of seismic facies: smooth parallel or sub-parallel reflection with middle-high frequency and middle-high amplitude, discontinued sub-parallel reflection with weak-middle amplitude and chaotic high-amplitude reflection with wedge shape. Consequently, it is deduced that there were lakes and fan-deltas in the Eocene Baiyun Sag. In the earlier Wenchang stage, there occurred fan-deltas around the shallow Baiyun lake, as the lake-level rising in the later Wenchang stage, deep-lake area expanded up to 600km2. The study gives a powerful contribution to the resource potential evaluation of the Baiyun Sag, which proves that this method is a reasonable and reliable one and useful for similar occasions.
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表 1 白云凹陷文昌组地震属性相关系数矩阵
Table 1. Correlation Matrix of seismic attributes of Wenchang Formation in Baiyun Sag
Correlation Matrix Bandwidth Rating Debias Energy Half Time RMS Ampitude Seismic Arc Length Bandwidth Dominant Frequency Instantaneous Frequency Instantaneous Phase Bandwidth Rating Debias -0.445 1 0.069 34 0.254 4 0.612 41 0.196 07 0.658 5 -0.021 89 Energy Half Time -0.445 1 -0.328 86 -0.342 24 -0.191 13 -0.017 96 -0.174 79 0.040 57 RMS Ampitude 0.069 34 -0.328 86 0.896 99 0.037 49 0.062 4 -0.026 54 -0.057 33 Seismic Arc Length 0.254 4 -0.342 24 0.896 99 0.133 84 0.270 21 0.207 15 -0.043 52 Bandwidth 0.612 41 -0.191 13 0.037 49 0.133 84 0.100 98 0.393 58 -0.014 19 Dominant Frequency 0.196 07 -0.017 96 0.062 4 0.270 21 0.100 98 0.401 38 0.004 18 Instantaneous Frequency 0.658 5 -0.174 79 -0.026 54 0.207 15 0.393 58 0.401 38 0.009 33 Instantaneous Phase -0.021 89 0.040 57 -0.057 33 -0.043 52 -0.014 19 0.004 18 0.009 33 
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表 2 白云凹陷文昌组沉积相、地震相与属性聚类分析对应关系
Table 2. The relationship between sedimentary facies, seismic facies and cluster analysis of attributes of Wenchang Formation in Baiyun Sag
沉积相类型 地震相类型 属性聚类分析图 扇三角洲 楔状强振幅差连续相 Ⅲ类 辫状河三角洲 前积中高频中振幅差—中连续相 Ⅲ类 滨湖亚相 亚平行中弱振幅差连续相 SQ1层序以Ⅱ类为主 浅湖亚相 亚平行高频中振幅中好连续相 SQ1层序以I类为主SQ2层序为II类、Ⅱ1类 中深湖亚相 席状平行低频强振幅好连续相 I类
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