ISSN 1004-4140
CN 11-3017/P

基于波形特征向量的谱聚类地震相分析

秦德文, 张岩, 于杰

秦德文, 张岩, 于杰. 基于波形特征向量的谱聚类地震相分析[J]. CT理论与应用研究(中英文), 2024, 33(1): 13-23. DOI: 10.15953/j.ctta.2023.124.
引用本文: 秦德文, 张岩, 于杰. 基于波形特征向量的谱聚类地震相分析[J]. CT理论与应用研究(中英文), 2024, 33(1): 13-23. DOI: 10.15953/j.ctta.2023.124.
QIN D W, ZHANG Y, YU J. Seismic Facies Analysis Based on Spectral Clustering with Waveform Characteristic Vector[J]. CT Theory and Applications, 2024, 33(1): 13-23. DOI: 10.15953/j.ctta.2023.124. (in Chinese).
Citation: QIN D W, ZHANG Y, YU J. Seismic Facies Analysis Based on Spectral Clustering with Waveform Characteristic Vector[J]. CT Theory and Applications, 2024, 33(1): 13-23. DOI: 10.15953/j.ctta.2023.124. (in Chinese).

基于波形特征向量的谱聚类地震相分析

基金项目: “十四五”科技重大项目(中国近海新区新领域勘探技术(KJGG2022-0304))。
详细信息
    作者简介:

    秦德文: 男,硕士,中海石油(中国)有限公司上海分公司高级工程师,主要从事油藏地球物理方面研究工作,E-mail:qindw@cnooc.com.cn

    通讯作者:

    张岩: 男,硕士,中海石油(中国)有限公司上海分公司高级工程师,主要从事油气地球物理技术研究工作,E-mail:zhangyan65@cnooc.com.cn

  • 中图分类号: O  242;P  631.4;P  315

Seismic Facies Analysis Based on Spectral Clustering with Waveform Characteristic Vector

  • 摘要:

    本文提出一种基于地震沉积学原理沿层提取地震波形特征向量,并以谱聚类(spectral clustering)分析进行地震相划分的方法。谱聚类能够处理非线性的数据结构和高维数据的聚类问题,但其相似度矩阵的构建和谱分解的计算较为复杂,需要较高的计算资源和时间成本。为提高谱聚类算法的效率和可扩展性,本文提出将Mini-batch K-means算法与谱聚类算法结合起来的MKSC算法,在提高谱聚类算法精度的同时大大降低谱聚类空间的复杂度。经过对数值模拟、地球物理模型数据和实际地震资料的处理分析,证明该方法在沉积相划分、沉积相特征识别方面的效果明显,是一种具有良好应用前景的新型沉积特征分析工具。

    Abstract:

    Based on the principle of seismic sedimentology, the feature vectors of seismic waveforms are extracted along stratum slices, and spectral clustering analysis is introduced to classify seismic facies. Spectral clustering is an unsupervised machine learning algorithm. Its essence is to simplify the expression of high-dimensional seismic data in the form of feature vectors, which belongs to the process of dimensionality reduction. Considering the traces with specific time windows in the seismic work area as nodes of the graph and the similarity between traces as the weight of the edges, a graph model can be constructed. Spectral clustering must determine the best segmentation method to complete the segmentation of the graph, so that different types of sedimentary characteristics can be distinguished. Physical model and actual data processing and analysis demonstrate that this method is capable of dividing sedimentary facies characteristics and is a new kind of facies analysis tool for reservoir classification, which has good application prospects.

  • 图  1   无向图及其邻接矩阵特征向量

    Figure  1.   Feature vectors of the undirected graph and its adjacency matrix

    图  2   相同数据集不同聚类方式分类结果

    Figure  2.   Classification results of the same dataset by different clustering methods

    图  3   物理模型的采集尺寸及参数示意图

    Figure  3.   Schematic of the acquisition size and parameters of the physical model

    图  4   模型砂体空间分布

    Figure  4.   Spatial distribution of sand body model

    图  5   三维地震体及控制层位

    Figure  5.   3D seismic body and control horizon

    图  6   物理模型偏移剖面及基于地震沉积学原理解释的层位

    Figure  6.   Migration section of the physical model and horizon interpretation based on the seismic sedimentology principle

    图  7   作为聚类输入的200个波形曲线

    Figure  7.   Two hundred waveform curves as a clustering input

    图  8   物理模型第2至第5层砂、泥岩空间展布及形态

    Figure  8.   Spatial distribution and morphology of sand and mudstone in the second to fifth layers of the physical model

    图  9   为各层的地层切片均方根振幅地震相

    Figure  9.   Slices of root mean square amplitude seismic phase of stratums

    图  10   各层MKSC 聚类地震相分布

    Figure  10.   MKSC clustering seismic facies distribution of stratums

    图  11   目标区基于地震沉积学原理解释的层位

    Figure  11.   Horizon interpretation based on seismic sedimentological principles in the target area

    图  12   H3顶面时间域构造图

    Figure  12.   Time domain structure of H3 top surface

    图  13   A井H3取心段解释图

    Figure  13.   Interpretation diagram of H3 coring section of well A

    图  14   分流河道中心部位不同水动力条件下的相变示意图

    Figure  14.   Schematic diagram of phase transition in the central part of distributary channel under different hydrodynamic conditions

    图  15   E~A~D井连井剖面

    Figure  15.   Connecting well profile of Wells E~A~D

    图  16   H3c段MKSC聚类属性平面图和沉积相平面图

    Figure  16.   MKSC clustering attribute plan and sedimentary facies plan of H3c

    图  17   H3b段MKSC聚类属性平面图和沉积相平面图

    Figure  17.   MKSC clustering attribute plan and sedimentary facies plan of H3b

    表  1   不同聚类分析算法计算效率比较

    Table  1   Comparison of the computational efficiency of different clustering analysis algorithms

    算法类型Mini-batch K-meansSpectral clusteringMKSC
    计算时间/s0.170.210.17+0.09
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-07
  • 修回日期:  2023-07-26
  • 录用日期:  2023-08-03
  • 网络出版日期:  2023-09-10
  • 刊出日期:  2024-01-09

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