| 基于稀疏约束反演谱分解的频变AVO储层识别方法 |
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| 引用本文： | 黄广谭, 李景叶, 陈小宏, 雒聪. 2017. 基于稀疏约束反演谱分解的频变AVO储层识别方法. 地球物理学报, 60(10): 3984-3994, doi: 10.6038/cjg20171025 |
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| 作者姓名： | 黄广谭 李景叶 陈小宏 雒聪 |
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| 作者单位： | 1. 中国石油大学(北京)油气资源与探测国家重点实验室, 北京 102249; 2. 中国石油大学(北京)海洋石油勘探国家工程实验室, 北京 102249 |
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| 基金项目： | 国家自然科学基金项目（U1262207，41390454）和国家科技重大项目（2016ZX05033003-008，2016ZX05024001-004）资助. |
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| 摘 要： | 时频分析技术是描述和分析非平稳信号的有效工具，如何提高谱分解的时-频分辨率受到了国内外学者的广泛关注.基于稀疏约束反演的谱分解方法（ISD）是近些年提出的一种具有高时-频分辨率的谱分解方法.然而传统ISD方法采用复雷克子波作为母小波函数，很大程度限制了信号分析的分辨率和精度.本文创新性地将传统ISD方法拓展到其他线性变换当中，提高了ISD方法的适用性.利用合成数据测试并对比了传统方法与ISD方法的时频分析结果，研究表明ISD方法不但能提供高分辨率的振幅谱，还能提供可靠的时-频相位谱信息.在此基础上，本文提出将高时-频分辨率的ISD方法与频变AVO（FAVO）反演相结合，得到能指示流体的地震波频散属性.将该方法应用于实际资料，结果表明基于ISD时频方法的FAVO反演结果具有更高的分辨率和准确度，对于储层油气具有更好的指示作用.
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| 关 键 词： | 反演谱分解 频散 频变AVO 时-频分辨率 储层识别 |
| 收稿时间： | 2016-12-02 |
| 修稿时间： | 2017-08-24 |
Frequency-dependent AVO reservoir identification method based on sparse constrained inversion spectral decomposition |
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| HUANG Guang-Tan, LI Jing-Ye, CHEN Xiao-Hong, LUO Cong. 2017. Frequency-dependent AVO reservoir identification method based on sparse constrained inversion spectral decomposition. Chinese Journal of Geophysics (in Chinese), 60(10): 3984-3994, doi: 10.6038/cjg20171025 |
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| Authors: | HUANG Guang-Tan LI Jing-Ye CHEN Xiao-Hong LUO Cong |
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| Affiliation: | 1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China; 2. National Engineering Laboratory for Offshore Oil Exploration, China University of Petroleum, Beijing 102249, China |
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| Abstract: | Time-frequency analysis technique is an effective tool to describe and analyze non-stationary signals, and how to improve time-frequency resolution of decomposition results has received a lot of attention. Spectral decomposition based on sparse constrained inversion, which was proposed in recent years, is a time-frequency analysis method with high resolution. While the traditional ISD method uses complex Ricker wavelet as mother wavelet function, which greatly limits the resolution and accuracy of signal analysis result. In this work, we innovatively extended ISD to other linear transforms to improve its applicability. By analyzing the synthetic signal and comparing the result of ISD with that of other common methods, we demonstrate that ISD can provide not only time-frequency amplitude spectrum with high resolution, but also the reliable phase spectrum. Based on this, we propose a new hydrocarbon-identification scheme of combining ISD with frequency-dependent AVO inversion (ISD-FAVO). Application of it to real data shows that frequency dispersion attribute obtained by the ISD-FAVO has higher time resolution and calculation accuracy, and a better indication for oil and gas in reservoirs. |
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| Keywords: | Inversion spectral decomposition Frequency dispersion Frequency dependent AVO Time-frequency resolution Reservoir identification |
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