薄互層儲(chǔ)層地震響應(yīng)時(shí)頻分析研究

1、薄互層儲(chǔ)層地震響應(yīng)時(shí)頻分析研究        【中文摘要】現(xiàn)階段,石油勘探主要以薄互層儲(chǔ)層為主,如何確定薄互層儲(chǔ)層的空間展布規(guī)律及其性質(zhì)已成為一個(gè)急需解決的題目。在我國(guó)東部的盡大多數(shù)中、新生代陸相含油盆地大都以薄層砂、泥巖沉積為主,夾有少量薄層碳酸鹽巖、頁(yè)巖及膏鹽層,地層巖性和厚度橫向變化均較大,而且這些地層的厚度遠(yuǎn)遠(yuǎn)低于常規(guī)地震勘探的垂向分辨率。為了解決薄互層儲(chǔ)層猜測(cè)題目,近年來(lái),地球物理學(xué)家們發(fā)展了很多新方法、新技術(shù),基于時(shí)頻分析理論的頻譜分解為其一種。頻譜分解不依靠子波相位,較Widess提出的波峰波谷法具有

2、更好的穩(wěn)健性。本文綜合分析了短時(shí)傅立葉變換(STFT)、連續(xù)小波變換(CWT)、S變換(ST)及其Wigner-Ville分布(WVD)的優(yōu)缺點(diǎn),研究了影響薄互層地震波特征的因素及其單層的厚度、互層數(shù)、反射系數(shù)大小及其反射系數(shù)極性對(duì)波形特征的影響,通過(guò)對(duì)單道理論信號(hào)、楔形模型、Marmousi2模型及實(shí)在際資料分析,得到如下結(jié)論:(1)薄互層反射系數(shù)序列頻譜存在陷頻現(xiàn)象,陷頻周期由單層時(shí)間厚度決定,而薄互層互層數(shù)、反射系數(shù)大小及其反射系數(shù)極性只影響反射系數(shù)序列振幅譜的形態(tài)。利用這一周期性特征,可以對(duì)薄互層儲(chǔ)層厚度量化。(2)通過(guò)對(duì)單道合成地震信號(hào)及其楔形模型測(cè)試,結(jié)果表明WVD有最高的時(shí)頻分辨

3、率,然而存在嚴(yán)重的交叉項(xiàng);為了分析薄互層,用于短時(shí)傅立葉變換的窗長(zhǎng)要短,窗長(zhǎng)過(guò)短會(huì)損失頻率分辨率,而且當(dāng)頻率為0時(shí),時(shí)頻譜存在較強(qiáng)的直流分量;楔形模型測(cè)試表明,傳統(tǒng)的Morlet小波和S變換不適合分析薄層,本文采用改進(jìn)的Morlet小波,通過(guò)引進(jìn)一個(gè)控制頻帶寬度的量,公道選擇該參數(shù),可以較好的分析薄層。因此,本文主要采用改進(jìn)的Morlet小波作為時(shí)頻譜分析的工具,為了進(jìn)步計(jì)算速度,在頻率域?qū)崿F(xiàn)連續(xù)小波變換。(3)Marmousi2模型及其DQ實(shí)際資料分析表明,基于改進(jìn)的Morlet連續(xù)小波變換不僅能夠較好的刻畫(huà)薄層信息,而且能夠識(shí)別不同的巖性。對(duì)于薄層分析而言,當(dāng)?shù)卣鹦盘?hào)主頻與連續(xù)小波變換標(biāo)準(zhǔn)

4、相匹配時(shí),可以達(dá)到最佳薄層識(shí)別,地震信號(hào)主頻越高,頻帶寬度越寬,分頻剖面的時(shí)間分辨率就越高;對(duì)于油氣躲低頻異常檢測(cè)而言,由于油氣躲對(duì)高頻信號(hào)吸收,使地震波頻率衰減,勢(shì)必會(huì)造成低頻異常,通過(guò)選擇合適的標(biāo)準(zhǔn),這種低頻異常就在低頻分頻剖面上顯現(xiàn)出來(lái)。然而造成低頻異常的因素是多方面的,我們可以將低頻異?，F(xiàn)象作為佐證油氣躲的存在的一個(gè)條件,但不是唯一條件。');【Abstract】 Currently, Oil exploration mainly aims at thin interbedded reservoirs, it has become an urgent problem to de

5、termine the regularities of the spatial distribution of thin interbedded reservoirs and their characters. In eastern China, the vast majority of Mesozoic and Cenozoic continental oil basins are dominated by thin-layer sand and shale depositing, filling with a few thin-layer carbonate rock, shale and

6、 plaster layers. The lithology and thickness of the stratums varies largely in the transverse direction, and the thicknesses of these stratums are far less than the vertical resolution of conventional seismic exploration.In order to predict thin interbedded reservoir effectively, geophysicists had d

7、eveloped many new methods and technologies recently, and one of which was spectrum decomposition based on time-frequency analysis theory. Spectrum decomposition was independent of the phase of wavelet, hence more stable than wave peak-trough method presented by Widess.This * analyzed the advantage a

8、nd disadvantage of common time-frequency methods such as STFT, CWT, S transformation, WVD etc, and analyzed the characteristic of spectral of reflection coefficient series and its influence factors, emphasizing on the influence of time thickness of single layer, number of thin interbedded layers, ma

9、gnitude of reflectance, and polarity of reflectance on spectrum, and we draw such conclusions:(1)The number of thin interbedded layers, magnitude of reflectance and polarity of reflectance could only affect the amplitude of the spectrum of reflectance, and the period of the notch was determined by t

10、he time thickness of single layer.(2)Through the testing of synthetic seismic signal and that of wedge-shaped model, the results shows that although WVD was highest in time-frequency resolution, it presents badly cross-term; to analyze thin interbedded layers, the window of STFT should be short, tha

11、t is at the expense of the frequency resolution hence the window could not be too short, and there would be a strong direct current component when the frequency is zero; conventional Morlet wavelet was unsuitable to analyze thin layers, and an improved Morlet wavelet was adopted in this *, by introd

12、ucing an item to control the width of the frequency band, it avoided the difficulty in selecting parameters in three-parameters wavelet application, the results of the model testing indicated that improved Morlet wavelet could better analyze thin layers; the time-frequency resolution of S transforma

13、tion was between that of STFT and CWT. Hence, in this *, improved Morlet wavelet was applied as the tool for time-frequency analyzing, to save CPU time, a fast CWT was carried out in frequency domain.(3)The analysis of Marmosi2 model and actual data from DQ indicates: the CWT based on improved Morle

14、t wavelet could not only better descript the information of thin layers, but also be used to detect the difference of lithology. For thin layer analyzing, the example shows that, it could optimally distinguish the thin layers when the dominant frequency of seismic signal matches with the scale of CW

15、T, the higher the dominant frequency and the wider the frequency band, the higher the time resolution of decomposed frequency profiles; for the anomaly low frequency indication of oil or gas reservoirs, since oil or gas reservoirs could absorb high frequency, causing the seismic wave to attenuate, which certainly will present low frequency anomaly. By selecting appropriate scales, the low frequency anomaly could be highlighted on decomposed frequency