从储层物性到叠前叠后地震数据的分步正演模拟方法与油气识别

李胜阁; 张季; 耶合亚·扎依尔; 刘桂萍; 程远锋

doi:10.13568/j.cnki.651094.651316.2024.12.27.0002

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从储层物性到叠前叠后地震数据的分步正演模拟方法与油气识别

更新时间：2026-01-23

- 从储层物性到叠前叠后地震数据的分步正演模拟方法与油气识别
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 42, Issue 3, Pages: 257-268(2025)
- 作者机构：
  
  新疆大学地质与矿业工程学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2024.12.27.0002
  CLC： P631.44;P618.13
- Published：2025
- 稿件说明：
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[1]李胜阁,张季,耶合亚·扎依尔,等.从储层物性到叠前叠后地震数据的分步正演模拟方法与油气识别[J].新疆大学学报(自然科学版中英文),2025,42(03):257-268.
[1]李胜阁,张季,耶合亚·扎依尔,等.从储层物性到叠前叠后地震数据的分步正演模拟方法与油气识别[J].新疆大学学报(自然科学版中英文),2025,42(03):257-268. DOI： 10.13568/j.cnki.651094.651316.2024.12.27.0002.

DOI：10.13568/j.cnki.651094.651316.2024.12.27.0002.

摘要

地震数据的正演模拟方法对揭示地下地层岩性和含流体特征至关重要，但将微观储层物理特性与宏观地震数据直接关联仍面临挑战．通过引入弹性参数作为桥梁，结合岩石物理理论，成功连接了微观储层参数与宏观地震数据．基于此，利用Python脚本建立了从储层物性到地震AVO数据的分步正演模拟方法．通过叠后楔形体模型算例和叠前3层蛋糕模型算例，分析了薄层调谐效应及AVO道集的褶积结果与精确Zoeppritz方程理论结果的差异．结果显示：高孔隙度储层在叠前AVO道集上的响应比低孔隙度储层更显著，AVO截距高4.1倍，斜率高4.6倍，利于油水识别；天然气储层的振幅响应比液态石油强烈，AVO截距高32.9%，斜率高8.3%，因此天然气识别更加准确．

Abstract

Forward modeling methods for seismic data are crucial for revealing the lithology and fluid-bearing characteristics of underground formations

but directly correlating microscopic reservoir physical properties with macroscopic seismic data is extremely challenging. By introducing elastic parameters as a bridge and combining rock physics theory

a successful connection between microscopic reservoir parameters and macroscopic seismic data has been established. Based on this

a step-by-step forward modeling method from reservoir physical properties to seismic Amplitude Versus Offset(AVO) data was developed using Python scripting. Through case studies of a post-stack wedge model and a pre-stack three-layer cake model

the thin-layer tuning effect and the differences between the convolution results of the AVO gather and the theoretical results of the exact Zoeppritz equation were analyzed. The results show that high-porosity reservoirs exhibit more significant responses on pre-stack AVO gathers than low-porosity reservoirs

with AVO intercepts being 4.1 times higher and slopes being 4.6 times steeper

which facilitates oil-water identification. Furthermore

amplitude responses are stronger for natural gas reservoirs compared to liquid oil reservoirs

with AVO intercepts being 32.9% higher and slopes being 8.3% steeper

making natural gas identification more accurate.

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