基于改进粒子群算法的VTI介质裂隙预测

doi:10.11720/wtyht.2024.0025

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摘要

裂隙产生的各向异性在地层介质中广泛存在,基于各向异性的裂隙参数反演和预测在一定程度上能提高裂隙反演的精度和预测的可靠性。本文通过建立基于VTI介质的反射系数方程,引入模拟退火算法中的Metropolis准则设置跳出概率对粒子群标准算法进行改进,实现对VTI介质中纵、横波速度及各向异性参数等属性的反演;并通过各向异性相关的属性、泊松比和泊松速度的联合,实现对裂隙充填物的预测。通过二层模型和Marmousi2模型对算法的稳定性和抗噪性进行测试,论证了方法的可行性;并进一步将方法应用于煤矿实际资料,对裂隙含水性进行预测,论证了方法的有效性。

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	李勤
	杨晓迎
	姜星宇
	李江

关键词 ： VTI介质, 粒子群算法, 地震反演, 裂隙预测

Abstract：

The anisotropy caused by fractures is ubiquitous in formation media.The inversion and prediction of fracture parameters based on anisotropy can somewhat improve the inversion accuracy and prediction reliability of fractures.This study established a reflection coefficient equation based on vertical transverse isotropy(VTI) media.Then,it improved the standard particle swarm optimization algorithm by setting the exit probability based on the Metropolis criterion of the simulated annealing algorithm.Consequently,it obtained the inversion results of compressional- and shear-wave velocities and anisotropy parameters in VTI media.By combining anisotropy-related attributes,Poisson's ratio,and Poisson's velocity, this study predicted the fillers in fractures.The improved algorithm was tested for stability and noise resistance using a two-layer model and a Marmousi2 model,demonstrating its feasibility.Furthermore,the improved algorithm was applied to predict the water-bearing property of fractures using real coal mine data,validating its effectiveness.

Key words： vertical transverse isotropy(VTI) media particle swarm optimization algorithm seismic inversion fracture prediction

收稿日期: 2024-02-23 修回日期: 2024-05-10 出版日期: 2024-08-20

ZTFLH:

P631.4

基金资助:国家自然科学基金项目“煤层地震波各向异性响应及裂隙预测”(41674135);陕西省自然科学基础研究项目“煤矿隐蔽致灾地质体地震波场模拟与深度域成像方法研究”(2024JC-YBMS-236)

通讯作者: 杨晓迎(1997-),女,硕士研究生,主要从事各向异性介质裂隙预测研究工作。Email:aircraftyxy@163.com

作者简介: 李勤(1979-),女,博士,副教授,主要从事地震波各向异性研究工作。Email:eriliqin@126.com

引用本文:

李勤, 杨晓迎, 姜星宇, 李江. 基于改进粒子群算法的VTI介质裂隙预测[J]. 物探与化探, 2024, 48(4): 1054-1064.
LI Qin, YANG Xiao-Ying, JIANG Xing-Yu, LI Jiang. Prediction of fractures in VTI media based on the improved particle swarm optimization algorithm. Geophysical and Geochemical Exploration, 2024, 48(4): 1054-1064.

链接本文:

https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2024.0025 或 https://www.wutanyuhuatan.com/CN/Y2024/V48/I4/1054

Table 1 二层模型参数^[39-40]

Fig.1 二层模型的角道集

Fig.2 二层模型反演结果

Fig.3 信噪比为10 dB时的角道集

Table 2 二层模型下层VTI介质的反演结果误差分析

Fig.4 Marmousi2原始模型

Fig.5 对目标位置提取的角道集
a—含水区域;b—含气区域;c—含油区域

Fig.6 选定Marmousi2模型待反演区域一(图中裂隙为含水区域)的原始剖面与反演结果剖面
a、b、c、g、i—分别为纵波速度、横波速度、密度、ε、δ原始剖面; d、e、f、h、j—分别为纵波速度、横波速度、密度、ε、δ反演剖面

Fig.7 选定Marmousi2模型待反演区域二(图中裂隙为含气区域)的原始剖面与反演结果剖面
a、b、c、g、i—分别为纵波速度、横波速度、密度、ε、δ原始剖面;d、e、f、h、j—分别为纵波速度、横波速度、密度、ε、δ反演剖面

Fig.8 选定Marmousi2模型待反演区域三(图中裂隙为含油区域)的原始剖面与反演结果剖面
a、b、c、g、i—分别为纵波速度、横波速度、密度、ε、δ原始剖面;d、e、f、h、j—分别为纵波速度、横波速度、密度、ε、δ反演剖面

Fig.9 区域一(图中裂隙为含水区域)A、B、C属性剖面

Fig.10 区域二(图中裂隙为含气区域)A、B、C属性剖面

Fig.11 区域三(图中裂隙为含油区域)A、B、C属性剖面

Fig.12 Marmousi2模型中截取的3个区域的泊松比

Fig.13 Marmousi2模型中截取的3个区域的泊松速度

Fig.14 叠加剖面(Inline317)

Fig.15 角道集

Fig.16 目标区域参数剖面
a—属性A;b—属性B;c—属性C;d—泊松比;e—泊松速度;f—测井曲线

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