Depth wavenumber spectral decomposition based on orthogonal matching pursuit and its application in hydrocarbon reservoir prediction
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摘要:
常规时间域地震属性分析是利用由叠前深度偏移数据转换至时间域的数据，这种转换将造成有效高频信息的损失，为了充分利用深度域资料成像精度高的优势，有必要开展深度域资料属性分析工作。由于深度域波数与频率和波速有关，如何获得高分辨率深度波数谱是深度域地震属性分析的关键。采用基于稀疏反演的谱分解方法，通过建立深度域过完备子波库，利用正交匹配追踪算法提高深度波数谱计算分辨率，通过理论模型的深度波数谱属性计算，并与时频谱属性进行对比，分析含油气储层的深度波数谱变化特征，通过实际资料的深度波数谱属性分析应用，验证利用深度波数谱进行含油气性预测的实用性。结果表明，基于正交匹配追踪算法的深度波数谱分解方法具有较高分辨率，可作为深度域含油气储层预测的高精度手段之一。实际数据应用表明，深度波数谱含油气储层下方出现低波数伴影现象，可作为深度域指示油气储层存在的标志，基于正交匹配追踪的深度波数谱分解方法可有效识别低波数伴影异常，对深度域地震资料进行含油气储层预测。
Abstract:Objective Conventional seismic attribute analysis in the time domain is based on the conversion from prestack depth migration data to time domain data, which will cause the loss of effective high-frequency information. To make full use of the advantage of the high imaging accuracy of depth domain data, it is necessary to carry out the attribute analysis of depth domain data. Because the wavenumber in the depthdomain is related to the frequency and wave velocity, obtaining a high-resolution depth wavenumber spectrum is the key to seismic attribute analysis in the depth domain.
Methods In this paper, based on the spectral decomposition method of sparse inversion, an overcomplete wavelet dictionary in the depth domain is established, and the orthogonal matching pursuit algorithm is used to improve the computational resolution of the depth wavenumber spectrum. By calculating the attributes of the depth wavenumber spectrum of the theoretical model and comparing them with the attributes of the time-frequency spectrum, the variation characteristics of the depth wavenumber spectrum of the hydrocarbon reservoir are analyzed. Through the application of depth wavenumber spectral attribute analysis of field data, the practicability of using the depth wavenumber spectrum to predict oil and gas reservoirs is verified.
Results The results show that the depth wavenumber spectral decomposition method based on the orthogonal matching pursuit algorithm has high resolution and can be used as a high-precision method for hydrocarbon reservoir prediction in the depth domain.
Conclusion The application of field data shows that the low-wavenumber shadow appears below the oil and gas reservoir in the deep wavenumber spectrum, which can be used as a sign to indicate the existence of oil and gas reservoirs in the depth domain. The depth wavenumber spectral decomposition based on orthogonal matching pursuit can effectively identify the low-wavenumber shadow anomaly, which enables to predict the oil and gas reservoirs by use of the depth domain seismic data.
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图 5 模型单道记录谱分解结果
Figure 5. Spectral decompositionresults of a single trace of the model
图 6 含油气储层模型的等频剖面
Figure 6. Common frequency profiles of the model with hydrocarbon-bearing reservoirs
图 7 含油气储层模型的等波数剖面
Figure 7. Common wavenumber profiles of the model with hydrocarbon-bearing reservoirs
图 8 实际过57井深度(a)和时间(b)剖面
Figure 8. Seismic profile across Well 57 in the depth domain (a) and time domain (b)
图 9 时间域与深度域过57井记录谱分解结果
Figure 9. Spectral decomposition of seismic traces across Well 57 in the time domain and depth domain
图 10 时间域实际资料等频剖面
Figure 10. Common frequency profiles of field data in the time domain
图 11 深度域实际资料等波数剖面
Figure 11. Common wavenumber profiles of field data in the depth domain
图 12 时间域实际资料等频切片
Figure 12. Common frequency sections of field data in the time domain
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