地球物理学进展 ›› 2019, Vol. 34 ›› Issue (6): 2346-2352.doi: 10.6038/pg2019CC0408

• 应用地球物理学Ⅰ(油气及金属矿产地球物理勘探) • 上一篇    下一篇

基于粒子群优化算法的AVAF反演方法

王震宇1, 刘俊州1, 时磊1, 刘颖2, 夏红敏1   

  1. 1. 中国石化石油勘探开发研究院,北京 100083;
    2. 中国石油大学(北京),北京 102249;
  • 收稿日期:2019-02-25 修回日期:2019-09-11 出版日期:2019-12-20 发布日期:2019-12-29
  • 作者简介:王震宇,男,1986年生,工程师,主要从事岩石物理建模及地震储层预测等研究工作.(E-mail:wangzy.syky@sinopec.com)
  • 基金资助:
    国家科技重大专项(2016ZX05002-005)资助.

AVAF inversion method based on particle swarm optimization algorithm

WANG Zhen-yu1, LIU Jun-zhou1, SHI Lei1, LIU Ying2, XIA Hong-min1   

  1. 1. Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China;
    2. China University of Petroleum, Beijing 102249, China
  • Received:2019-02-25 Revised:2019-09-11 Online:2019-12-20 Published:2019-12-29

摘要: 岩石物理实验和实际观测研究表明,纵波速度的频散现象通常都与地层的含气性有着密切的关系,它是纵波反射系数随频率变化所导致的.但是,传统的AVA反演方法忽略了这种速度频散现象,因此引入了误差,增加了含气预测的风险.本文我们提出了一种适用于频变反射系数和速度频散的反演方法,采用传播矩阵方程来进行正演模拟.而考虑到加入频散信息的AVAF反演问题具有高度的非线性特征,我们基于粒子群优化(PSO)算法来进行AVAF反演.经过模型与实际数据的测试证明,我们的反演方法适用于包含频散信息的地震数据,且具有一定的抗噪性,即使是在含噪数据下,也能够挖掘出数据中的纵波速度频散信息,为之后利用纵波速度的频散规律来解释储层含气性提供可靠的依据.

关键词: 速度频散, AVAF反演, 含气性解释, 粒子群优化算法

Abstract: Rock-physics experiment and field observation shown that the frequency-dependent P-wave velocity is often associated with hydrocarbon deposit, which results in P-wave reflection coefficients varying with frequency. However, this effects is often neglected in the conventional AVA/AVO (Amplitude Versus Angle or Offset) inversion method, and thus error is introduced to increases risk of detect hydrocarbon bearing formations. In this paper, we propose an inversion method that is suitable for frequency dependent reflection coefficients and velocity dispersion. We employ forward modeling based on propagator matrices that include frequency dependent elastic coefficients. Due to the AVAF inversion is a kind of high non-linear problem, we use Particle Swarm Optimization (PSO) for inversion of layer parameters. Synthetic and field data examples demonstrate the ability and usefulness of this method could digging the information that P-wave velocity dispersion at seismic frequencies, even if the seismic data is noisy. It can be utilized to favorably indicate gas reservoirs.

Key words: Velocity dispersion, AVAF inversion, Gas interpretation, Particle Swarm Optimization (PSO)

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