一种基于叠后-叠前联合反演的储层表征方法

doi:10.3969/j.issn.2096-1693.2025.01.018

石油科学通报 ›› 2025, Vol. 10 ›› Issue (4): 666-680. doi: 10.3969/j.issn.2096-1693.2025.01.018

一种基于叠后-叠前联合反演的储层表征方法

苗发维¹^,²(), 贺艳晓¹^,²^,^*(), 唐征新¹^,², 依圣博¹^,², 倪京阳¹^,²

1 中国石油大学 (北京) 油气资源与工程全国重点实验室，北京 102249
2 中国石油大学 (北京) 地球物理学院，北京 102249

收稿日期:2025-03-18 修回日期:2025-06-17 出版日期:2025-08-15 发布日期:2025-08-05
通讯作者: *贺艳晓(1985年—)，博士，副教授，从事多频段岩石物理实验、理论与应用，地震反演与储层预测，时移地震与CO₂地质封存监测，裂缝介质数值模拟等方面研究，eeyhe123@163.com。
作者简介:苗发维(1993年—)，博士研究生，从事地震岩石物理反演、储层流体识别方面的研究，mfw1327803442 @163.com。
基金资助:
国家自然科学基金项目(42374129);国家自然科学基金项目(U24B6001);中国石油天然气集团有限公司科技管理部资助项目(物探应用基础实验和前沿理论方法研究2022DQ0604-01);中国石油大学(北京)科研基金项目(2462024PTJS007);教育部复杂油气藏勘探开发工程研究中心开放课题基金项目(ZX20240163)

A seismic reservoir characterization method based on post and pre-stack joint inversion

MIAO Fawei¹^,²(), HE Yanxiao¹^,²^,^*(), TANG Zhengxin¹^,², YI Shengbo¹^,², NI Jingyang¹^,²

1 State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, China
2 College of Geophysics, China University of Petroleum, Beijing 102249, China

Received:2025-03-18 Revised:2025-06-17 Online:2025-08-15 Published:2025-08-05

摘要/Abstract

摘要：

地震岩石物理反演是储层物性评价的有效方法。从地震数据中直接预测储层参数相比于从地震弹性参数估计储层参数具有更低的不确定性和更高的精度，然而现阶段研究对直接储层参数反演中初始模型建立的问题讨论较少，合理的初始模型不仅能提高反演结果的精度也能减少反演过程的计算成本。针对这个问题，本文提出了基于叠后-叠前联合反演的地震储层表征方法，结合叠后阻抗反演和统计岩石物理模型为叠前地震岩石物理反演提供可靠初始模型，充分利用叠后地震数据的高信噪比和叠前地震数据的高分辨率优势来提高储层参数反演的稳定性和精度。首先，通过现有测井数据对临界孔隙度模型进行标定并结合Zoeppritz反射系数方程构建储层参数化的反射系数公式，建立地震数据与储层物性之间的直接联系。接着通过叠后反演获得纵波阻抗，并利用测井数据得到的统计岩石物理模型建立叠前储层物性参数反演的初始模型。最后基于贝叶斯框架和柯西先验分布约束，从叠前地震数据进行孔隙度、泥质含量和含水饱和度等物性参数反演。模型测试结果表明，叠后纵波阻抗反演结果能够为叠前储层参数预测提供可靠的初始模型进而提高物性参数的反演精度。实际数据测试验证了该方法在提高储层物性反演精度和增强横向连续性方面的优势。

关键词: 叠后-叠前联合反演, 地震岩石物理反演, 地震储层表征, 贝叶斯框架, 初始模型

Abstract:

Seismic petrophysical inversion is an effective method for reservoir physical property evaluation. Direct prediction of reservoir parameters from seismic data has lower uncertainty and higher accuracy than estimation of reservoir parameters from seismic elastic parameters. However, at present, there is little discussion on the establishment of initial model in direct reservoir parameter inversion. A reasonable initial model can not only improve the accuracy of inversion results but also reduce the calculation cost of inversion process. To solve this problem, this paper proposes a seismic reservoir characterization method based on pre-stack and post-stack joint inversion, which combines post-stack impedance inversion and statistical rock physical model to provide a reliable initial model for pre-stack seismic rock physical inversion, and makes full use of the high signal-to-noise ratio of post-stack seismic data and the high resolution of pre-stack seismic data to improve the stability and accuracy of reservoir parameter inversion. Firstly, the critical porosity model is calibrated based on the existing logging data, and the reservoir parametric reflection coefficient formula is constructed based on Zoeppritz reflection coefficient equation, which establishes the direct relationship between seismic data and reservoir physical properties. Then the P-wave impedance is obtained by post-stack inversion, and the initial model of reservoir physical parameter inversion is obtained by using the statistical petrophysical model obtained from logging data. Finally, based on Bayesian framework and Cauchy prior constraints, the inversion of physical property parameters such as porosity, shale content and water saturation from pre-stack seismic data is realized. The synthetic tests show that the superior anti-noise performance of post-stack impedance can provide a reliable initial model for reservoir parameter prediction, and can significantly improve the accuracy of physical property inversion. The field data test verifies the advantages of this method in improving inversion accuracy and enhancing lateral continuity in direct estimation of reservoir physical properties.

Key words: combination of post and pre-stack seismic inversion, petrophysical inversion, seismic reservoir characterization, Bayesian framework, initial model

中图分类号:

苗发维, 贺艳晓, 唐征新, 依圣博, 倪京阳. 一种基于叠后-叠前联合反演的储层表征方法[J]. 石油科学通报, 2025, 10(4): 666-680.

MIAO Fawei, HE Yanxiao, TANG Zhengxin, YI Shengbo, NI Jingyang. A seismic reservoir characterization method based on post and pre-stack joint inversion[J]. Petroleum Science Bulletin, 2025, 10(4): 666-680.

https://sykxtb.cup.edu.cn/CN/Y2025/V10/I4/666

图/表 19

图1 岩石物理建模流程图

Fig. 1 Flow chart of petrophysical modeling

表1 岩石物理模型相关参数

Table 1 Parameters of the rock-physics model

基质/流体	体积模量/GPa	剪切模量/GPa	密度/g·cm^-3
石英	36	36	2.65
黏土	21	15	2.45
水	2.25	/	1.03
气	0.0208	/	0.001

图2 叠后-叠前联合反演的地震储层表征方法技术路线图

Fig. 2 Technical roadmap of seismic reservoir characterization method for joint post-stack and pre-stack inversion

表2 岩石物理模型标定后相关参数

Table 2 Parameters of the calibrated petrophysical model

基质/流体	体积模量/GPa	剪切模/GPa	密度/g·cm^-3
石英	45	29	2.70
黏土	36	13	2.65
水	2.25	/	1.10
气	0.03	/	0.001

图3 岩石物理标定结果

Fig. 3 Petrophysical calibration results

图4 岩石物理标定精度分析

Fig. 4 Precision analysis of petrophysical calibration results

图5 无噪条件下的纵波阻抗反演结果

Fig. 5 Inversion results of P-wave impedance without noise

图6 信噪比SNR=5条件下的纵波阻抗反演结果

Fig. 6 Inversion results of P-wave impedance when SNR=5

图7 信噪比SNR=1条件下的纵波阻抗反演结果

Fig. 7 Inversion results of P-wave impedance when SNR=1

图8 储层参数与纵波阻抗交会图

Fig. 8 The crossplot of reservoir parameters and P-wave impedance

图9 基于低频初始模型的叠前地震岩石物理反演结果

Fig. 9 Inversion results of pre-stack seismic petrophysical based on low-frequency initial model

图10 基于本文方法的叠前地震岩石物理反演结果

Fig. 10 Inversion results of pre-stack seismic petrophysical based on proposed method

图11 地震记录含10 dB随机噪声条件下低频初始模型的叠前地震岩石物理参数反演结果

Fig. 11 Inversion results of prestack seismic petrophysical parameters based on the low-frequency initial model under the condition of seismic records containing 10 dB random noise

图12 地震记录含10 dB随机噪声条件下本文方法的叠前地震岩石物理参数反演结果

Fig. 12 Inversion results of prestack seismic petrophysical parameters of the proposed method under the condition of seismic records containing 10 dB random noise

图13 地震叠后叠加剖面

Fig. 13 Seismic stack section

图14 叠后纵波阻抗反演结果

Fig. 14 Inversion results of post-stack P-wave impedance

图15 实际数据孔隙度地震预测

Fig. 15 Porosity prediction of real data

图16 实际数据泥质含量地震预测

Fig. 16 Clay volume prediction of real data

图17 实际数据含水饱和度地震预测

Fig. 17 Water saturation prediction of real data

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