CN104793245B - Method for recognizing gas reservoirs by utilizing wavelet phase features - Google Patents

Abstract

The invention relates to a method for recognizing gas reservoirs by utilizing wavelet phase features. The method includes the steps: building a plural convolution model; calculating according to the plural convolution model to acquire a time frequency energy spectrum and a time frequency phase spectrum; selecting reservoir effective seismic response starting main frames f1 and f2, and stacking complex reflection coefficient within a frequency range from f1 to f2 along the frequency direction to acquire a reservoir-related complex reflection coefficient spectrum; performing modulus square and anti-tangent operation on the reservoir-related complex reflection coefficient to respectively acquire a frequency abnormality spectrum and a phase abnormality spectrum; utilizing the frequency abnormality spectrum and the phase abnormality spectrum for gas reservoir recognition. The method can be used for detecting gas reservoirs with high attenuation features, capability of describing thin reservoirs can be improved effectively, and solution multiplicity of explaining can be lowered. The method can be widely applied in the process of oil-gas exploration.

Description

Method for identifying gas reservoir by utilizing wavelet phase characteristics

Technical Field

The invention relates to a seismic hydrocarbon detection method, in particular to a method for identifying a gas reservoir by utilizing wavelet phase characteristics in the petroleum exploration process.

Background

Petrophysical modeling and exploration practices have shown that waves propagating in a fluid-containing medium are affected by the attenuation of the medium, and that high frequencies are more easily attenuated than low frequencies, so that the prior art often uses low frequency anomaly profiles for hydrocarbon predictions. However, in actual exploration, this method often fails because the water layer also attenuates the seismic waves and produces a spectral response similar to that of a hydrocarbon reservoir. In practice, attenuation and dispersion not only change the frequency of the wavelet, but also change the phase of the wavelet, but the phase characteristics have not been exploited. The main reasons are that the current knowledge of the phase response characteristics caused by reservoirs is not clear enough, and a technical means for obtaining the phase information of the time-varying wavelet from the seismic section is lacked.

The seismic spectrum decomposition technology can decompose a time domain signal into a two-dimensional function of time and frequency, and is widely applied to the field of frequency division seismic interpretation. Commonly used spectral decomposition methods are short-time Fourier transform, Gabor transform, Continuous Wavelet Transform (CWT), S transform, and the like. These methods have played a very important role in reservoir prediction and hydrocarbon detection. However, these methods have two disadvantages: firstly, the low temporal resolution limits the application of the method to the scribing of thin layers; second, wavelet phase information is difficult to obtain, which limits the application of phase to hydrocarbon detection.

Disclosure of Invention

In view of the above problems, the present invention provides a method for identifying a gas reservoir by using wavelet phase characteristics, which can be used for gas reservoir detection with high attenuation characteristics, effectively improve the description capability of a thin reservoir, and reduce the ambiguity of interpretation.

In order to achieve the purpose, the invention adopts the following technical scheme: a method for identifying a gas reservoir using wavelet phase characteristics, comprising the steps of: 1) establishing a wavelet base composed of zero-phase Ricker wavelets with different frequenciesK is 1,2, … K, K is the number of wavelets participating in calculation, and t represents time; wavelet baseTransforming into complex wavelet base w by Hilbert transform_k(t) establishing a plurality of convolution models; 2) calculating to obtain a time-frequency energy spectrum F according to a complex convolution model_stfSum-time-frequency phase spectrum3) Selecting effective seismic response starting dominant frequency f of reservoir₁And f₂A 1 is to f₁To f₂Overlapping the complex reflection coefficients among the frequency bands along the frequency direction to obtain a complex reflection coefficient spectrum related to the reservoir; 4) complex reflection coefficient related to reservoirPerforming modular square and arc tangent operation to obtain frequency abnormal spectrum and phase abnormal spectrum; 5) the gas reservoir identification is carried out by utilizing the frequency anomaly spectrum and the phase anomaly spectrum, and the identification method comprises the following steps: when the work area to be identified has drilled information, when the frequency abnormal spectrum is not zero, if the phase abnormal spectrum is the same as or close to the phase response characteristic contained in the drilled information, judging that the work area has a gas reservoir; when the work area to be identified has only seismic data, there isIf the frequency is abnormal and the phase spectrum angle of the reflection wavelet at the top of the reservoir is between 120 and 200 degrees, judging that the work area has gas reservoir; in a work area to be identified, when the reflection at the top and the bottom of a reservoir cannot be distinguished, the identification is carried out according to the comprehensive response, namely, if the frequency is abnormal and the phase spectrum angle of the reservoir reflection wavelet is between 120 degrees and 200 degrees, the work area is judged to have a gas reservoir.

In the step 2), the time-frequency energy spectrum F_s,tfSum-time-frequency phase spectrumThe calculation method is as follows: the complex convolution model is arranged into a linear form: s ═ Wr + n, where W ═ W (W)₁W₂…W_k)，k＝1,2,…K，W_kRepresenting complex wavelets w_kThe convolution matrix of (a); r ═ r (r)₁r₂…r_K)^TT represents a matrix rotation operation; l is performed on a linear form complex convolution model₁Norm regularization:

wherein,is L2 norm, | · | | non-woven₁Is L₁Norm, λ is a regularization parameter that adjusts sparsity; solving the formula (1) by a sparse inversion algorithm to obtain a complex reflection coefficient r, and performing modular square and arc tangent operations on the complex reflection coefficient r to respectively obtain a time-frequency energy spectrum F_s,tfSum-time-frequency phase spectrum

In the step 4), the frequency anomaly spectrum and the phase anomaly spectrum are as follows:

wherein,in order to be a frequency anomaly spectrum,in order to be a spectrum of phase anomalies,is a complex reflection coefficient spectrum associated with the reservoir.

Due to the adoption of the technical scheme, the invention has the following advantages: 1. the time-frequency spectrum obtained by the traditional spectrum decomposition method has lower resolution ratio, particularly low time resolution ratio, and is difficult to depict a thin reservoir stratum; by means of the sparse inversion method, the time-frequency distribution time resolution obtained is high, and the thin reservoir layer depicting capability is improved. 2. The existing method can only extract the frequency information of the sub-waves from the seismic data, and is difficult to extract the phase information; the invention provides a spectral decomposition method capable of accurately obtaining frequency and phase information of time-varying wavelets. 3. When the existing-stage spectral decomposition is applied to hydrocarbon detection, only frequency characteristics are considered, but a water-bearing reservoir and a hydrocarbon-bearing reservoir often generate similar frequency responses, and the multi-solution property is strong only through frequency information identification; the invention uses the phase characteristics of the reflection wavelets for gas reservoir identification, thereby reducing the multi-solution of interpretation. 4. Since the prior art only uses attenuation caused by the propagation of a wave in a fluid-containing medium, the present invention proposes that attenuation-related phase changes occur not only during the propagation of the wave, but also at interfaces where impedance and attenuation differences exist. At the interface, the impedance difference only causes the polarity of the reflected wavelet to change, while the attenuation difference is a key factor that causes the phase rotation of the wavelet. Therefore, the wavelet phase change characteristic can be used for gas reservoir detection with high attenuation characteristic. The invention can be widely applied in the process of oil exploration.

Drawings

FIG. 1 is a schematic diagram of the wavelet base construction of the present invention, wherein FIG. 1(a) is a real wavelet base composed of zero-phase Ricker wavelets of different frequencies; FIG. 1(b) is a complex wavelet base obtained by Hilbert transform of a real wavelet base, in which a solid line is a real part of the complex wavelet base and a dotted line is an imaginary part of the complex wavelet base;

FIG. 2 is a schematic diagram of the ability of spectral decomposition to resolve thin layers when fitting signals according to the present invention, wherein fitting signal 3 in FIG. 2(a) is the fitting signal used for calculation, and is the combination of fitting signal 1 and fitting signal 2, and is used for characterizing the top and bottom reflection of formations of different thicknesses; FIG. 2(b) is a time-frequency energy spectrum obtained by the continuous wavelet transform method; FIG. 2(c) is a time-frequency energy spectrum obtained by a complex spectrum decomposition method;

FIG. 3 is a schematic diagram of an example of a fitted signal complex spectral decomposition algorithm of the present invention, wherein FIG. 3(a) is a fitted seismic signal composed of Ricker wavelets of different frequencies and phases, the wavelet frequencies being 60Hz,40Hz,20Hz,30Hz and 30Hz in sequence from top to bottom, and the phases being 0 °, -90 °, 45 °, -180 ° and-180 °; FIGS. 3(b) and (c) are time-frequency energy spectrum and time-frequency phase spectrum obtained by the continuous wavelet transform method; FIG. 3(d) and FIG. 3(e) are the time-frequency energy spectrum and the time-frequency phase spectrum obtained by the complex spectrum decomposition method, respectively;

fig. 4(a) to 4(b) are schematic diagrams of the cause of phase change due to numerical simulation attenuation of the present invention, in which the wave impedance of the medium on the left side of the interface is low and the attenuation degree is high with respect to the wave impedance of the medium on the right side, and the solid lines in fig. 4(a) and 4(b) represent the transmitted wave and the reflected wave on the interface where only the impedance difference exists; the broken line in fig. 4(a) indicates that there are only transmitted and reflected waves at the interface where the difference in attenuation exists; the broken line in FIG. 4(b) indicates the transmission and reflection waves at the interface where both the impedance difference and the attenuation difference exist;

FIGS. 5(a) -5 (d) are schematic diagrams of hydrocarbon detection algorithms for actual data of the present invention, wherein FIG. 5(a) is a seismic profile of a target zone through a borehole, projected logs are water saturation, and well-revealing and geologically-interpreted gas and water layer locations are indicated in the figures; FIG. 5(b) is a frequency anomaly profile for hydrocarbon detection obtained by the continuous wavelet transform method; FIG. 5(c) is a frequency anomaly profile obtained by using the complex spectrum decomposition method proposed by the present invention; FIG. 5(d) is a time-frequency phase spectrum abnormal section obtained by the complex spectrum decomposition method.

Detailed Description

The invention is described in detail below with reference to the figures and examples.

As shown in fig. 1 to 5(d), the present invention provides a method for identifying a gas reservoir by using wavelet phase characteristics, which comprises the following steps:

1) establishing a wavelet base composed of zero-phase Ricker wavelets with different frequenciesK is 1,2, … K, K is the number of wavelets participating in calculation, and t represents time; wavelet baseTransforming into complex wavelet base w by Hilbert transform_k(t) and establishing a complex convolution model as follows:

where s denotes seismic record, w_kRepresenting the kth complex wavelet in the frequency band involved in the computation, with dominant frequency and phase f and f, respectivelyr_kFor corresponding information carrying wavelet main frequency information f and phase information in seismic recordComplex reflection coefficient of (d); k represents the total number of wavelets participating in the calculation, x represents convolution operation, and n represents random noise.

2) Arranging the complex convolution model established in the step 1) into the following linear form:

s＝Wr+n， (2)

wherein W ═ W₁W₂…W_k)，k＝1,2,…K，W_kRepresenting complex wavelets w_kThe convolution matrix of (a); r ═ r (r)₁r₂…r_K)^TT represents a matrix rotation operation;

to the formula (2) is subjected to L₁Norm regularization, as follows:

wherein,is L2 norm, | · | | non-woven₁Is L₁Norm, λ is a regularization parameter that adjusts sparsity;

solving the formula (3) by a sparse inversion algorithm to obtain a complex reflection coefficient r, and performing modular square and arc tangent operations on the complex reflection coefficient r to respectively obtain a time-frequency energy spectrum F_s，tfSum-time-frequency phase spectrumThe following were used:

3) selecting effective seismic response starting dominant frequency f of reservoir₁And f₂A 1 is to f₁To f₂And superposing the complex reflection coefficients among the frequency bands along the frequency direction to obtain a reservoir related complex reflection coefficient spectrum as follows:

wherein,a complex reflection coefficient spectrum related to the reservoir; f. of₁And f₂And determining according to the actual data background frequency band and the reservoir effective response frequency band.

4) Complex reflection coefficient related to reservoirAnd performing modular square and arc tangent operation to obtain a frequency abnormal spectrum and a phase abnormal spectrum respectively as follows:

wherein,the frequency of the abnormal spectrum of the frequency,is a phase anomaly spectrum; the process of extracting frequency and phase information from seismic records using a library of complex wavelets is known as complex spectral decomposition.

5) The gas reservoir identification is carried out by utilizing the frequency anomaly spectrum and the phase anomaly spectrum, and the identification method comprises the following steps:

when the work area to be identified has drilled information, when the frequency abnormal spectrum is not zero, namely the frequency is abnormal, if the phase abnormal spectrum is the same as or close to the phase response characteristic contained in the drilled information, the work area is judged to have a gas reservoir;

when the work area to be identified only has seismic data, if frequency abnormality exists and the phase spectrum angle of the top reflection wavelet of the reservoir is 120-200 degrees, judging that the work area has a gas reservoir;

in a work area to be identified, when the reflection at the top and the bottom of a reservoir cannot be distinguished, the identification is carried out according to the comprehensive response, namely, if the frequency is abnormal and the phase spectrum angle of the reservoir reflection wavelet is between 120 degrees and 200 degrees, the work area is judged to have a gas reservoir.

In the step 5), the gas reservoir identification is to assume that the original incident seismic wavelet is a zero-phase wavelet close to a Ricker wavelet form, and if the incident wavelet has a certain angle of phase rotation, the phase of the incident wavelet is subtracted from the abnormal profile of the phase spectrum and then used for gas reservoir prediction.

The method for identifying gas reservoirs using wavelet phase characteristics of the present invention is further described below by way of example.

Example 1: fitting data examples

This embodiment is an example of the ability to resolve thin layers using the method of the present invention, as shown in fig. 2. FIG. 2(a) is a fitting signal composed of pairs of Ricker wavelets at different time intervals, which is used to characterize the top-bottom reflection of formations of different thicknesses. Fig. 2(b) is a time-frequency energy spectrum obtained by the CWT method. When the formation is thick (60ms interval), the CWT method can distinguish formation top-bottom reflections; as the formation thickness becomes thinner (<30ms interval), the CWT spectra do not discriminate well between top and bottom reflections. Fig. 2(c) shows the time-frequency energy spectrum obtained by the complex spectrum decomposition method, which shows that the time resolution of the complex spectrum decomposition method of the present invention is very high.

An example of a fitted seismic signal consisting of Ricker wavelets of different frequencies and phases is shown in FIG. 3. As shown in fig. 3(a), the wavelet frequencies of the fitting signals are 60Hz,40Hz,20Hz,30Hz and 30Hz in order from top to bottom, and the phases are 0 °, -90 °, 45 °, -180 ° and-180 ° in order. Fig. 3(b) is a time-frequency energy spectrum obtained by the CWT method, which represents the resolution level of the conventional spectral decomposition. Fig. 3(c) shows a time-frequency phase spectrum obtained by the CWT method, from which wavelet phase information relating to attenuation is difficult to extract. Fig. 3(d) and fig. 3(e) are a time-frequency energy spectrum and a time-frequency phase spectrum obtained by the seismic complex spectrum decomposition method, respectively. The time-frequency distribution obtained by the seismic complex spectrum decomposition has high time resolution, and the solved result is consistent with the real situation of the fitting signal. The fitting calculation example shows that the complex spectrum decomposition method has the advantages of not only having high time-frequency resolution characteristics, but also being capable of accurately calculating the phase information of the time-varying wavelet.

As shown in fig. 4(a) and 4(b), the numerical simulation proves that the phase change related to attenuation occurs not only in the process of wave propagation but also at the interface where the impedance and attenuation difference exists. Impedance differences only cause the polarity of the reflected wavelets to change, while attenuation differences are a key factor in causing phase rotation of the wavelets. Typically, the gas reservoir has significant impedance and attenuation differences from the cap layer, and thus, the gas reservoir can be predicted from the phase information.

Example 2: example of actual data

As shown in fig. 5(a) to 5(d), this embodiment is an example of actual data. FIG. 5(a) is a seismic section of a target area through a well log, wherein the projected well log on the section is a water saturation curve. The gas and water layer locations are indicated in the figures according to exploratory well discovery and geological interpretation. Fig. 5(b) is a frequency anomaly profile for hydrocarbon detection obtained by the CWT method, where the time resolution is lower than the seismic resolution and the thin layer horizon cannot be accurately depicted; FIG. 5(c) is a frequency anomaly profile obtained using the complex spectral decomposition method proposed herein, with very high time resolution, and consistent with the formation locations revealed on the log. Gas reservoirs respond in frequency anomaly profiles, but water layers also respond in frequency anomaly profiles, so this interferes with gas reservoir predictions, causing ambiguity of interpretation. Fig. 5(d) is a time-frequency phase spectrum abnormal section obtained by the complex spectrum decomposition method, and it can be seen from the figure that there is a certain difference between the gas layer and water layer responses on the phase abnormal section, so that the multi-resolution of hydrocarbon detection by using only frequency abnormality is reduced.

In summary, the present invention is obviously different from the prior art, and mainly includes the following points: 1) the time-frequency spectrum obtained by the traditional spectrum decomposition method has lower resolution ratio, particularly low time resolution ratio, and is difficult to depict a thin reservoir stratum; by means of the sparse inversion technology, the time-frequency distribution time resolution obtained is high, and the thin reservoir characterization capability is improved (as shown in figure 2). 2) The existing method can only extract the frequency information of the sub-waves from the seismic data, and is difficult to extract the phase information; the invention provides a spectral decomposition method (as shown in fig. 3) capable of accurately obtaining frequency and phase information of a time-varying wavelet. 3) When the existing-stage spectral decomposition is applied to detection of hydrocarbons, only frequency abnormality is considered, but a water-bearing reservoir and a hydrocarbon-bearing reservoir often generate similar frequency response, and the multi-solution property is strong only through frequency information identification; the invention uses the reflection phase characteristics for gas reservoir identification, and reduces the interpretation ambiguity (as shown in fig. 5(a) to fig. 5 (d)). 4) Generally, only the attenuation characteristics caused by the wave propagating in the fluid-containing medium are utilized, and the invention proves that the phase change related to attenuation not only occurs in the wave propagation process, but also occurs at the interface where impedance and attenuation difference exists. The impedance difference at the interface only causes the polarity of the reflected wavelet to change, and the attenuation difference is a key factor causing the phase rotation of the wavelet. Therefore, the wavelet phase change can be used for gas reservoir detection with high attenuation characteristics (as shown in fig. 4(a) and 4 (b)).

The above embodiments are only for illustrating the present invention, and the steps may be changed, and on the basis of the technical solution of the present invention, the modification and equivalent changes of the individual steps according to the principle of the present invention should not be excluded from the protection scope of the present invention.

Claims (3)

1. A method for identifying a gas reservoir using wavelet phase characteristics, comprising the steps of:

1) establishing a wavelet base composed of zero-phase Ricker wavelets with different frequenciesK is 1,2, … K, K is the number of wavelets participating in calculation, and t represents time; wavelet baseTransforming into complex wavelet base w by Hilbert transform_k(t) establishing a plurality of convolution models;

2) calculating to obtain a time-frequency energy spectrum F according to a complex convolution model_s,tfSum-time-frequency phase spectrum

3) Selecting effective seismic response starting dominant frequency f of reservoir₁And f₂A 1 is to f₁To f₂Overlapping the complex reflection coefficients among the frequency bands along the frequency direction to obtain a complex reflection coefficient spectrum related to the reservoir;

4) complex reflection coefficient related to reservoirPerforming modular square and arc tangent operation to obtain frequency abnormal spectrum and phase abnormal spectrum;

5) the gas reservoir identification is carried out by utilizing the frequency anomaly spectrum and the phase anomaly spectrum, and the identification method comprises the following steps:

when the work area to be identified has drilled information, when the frequency abnormal spectrum is not zero, if the phase abnormal spectrum is the same as or close to the phase response characteristic contained in the drilled information, judging that the work area has a gas reservoir;

when the work area to be identified only has seismic data, if frequency abnormality exists and the phase spectrum angle of the top reflection wavelet of the reservoir is 120-200 degrees, judging that the work area has a gas reservoir;

in a work area to be identified, when the reflection at the top and the bottom of a reservoir cannot be distinguished, identifying according to the comprehensive response, namely, judging that the work area has a gas reservoir if the frequency is abnormal and the phase spectrum angle of the reservoir reflection wavelet is 120-200 degrees;

the gas reservoir identification is to assume that the original incident seismic wavelet is a zero-phase wavelet close to a Ricker wavelet form, and if the incident wavelet has a certain angle of phase rotation, the phase of the incident wavelet is subtracted from the abnormal section of the phase spectrum and then used for gas reservoir prediction.

2. As in claimThe method of identifying a gas reservoir using wavelet phase features of claim 1, wherein: in the step 2), the time-frequency energy spectrum F_s,tfSum-time-frequency phase spectrumThe calculation method is as follows:

the complex convolution model is arranged into a linear form:

s＝Wr+n，

wherein W ═ W₁W₂… W_k)，k＝1,2,…K，W_kRepresenting complex wavelets w_kThe convolution matrix of (a); r ═ r (r)₁r₂… r_K)^TT represents a matrix rotation operation;

l is performed on a linear form complex convolution model₁Norm regularization:

$\underset{r}{\arg \min }\frac{1}{2}\left|\right|Wr-s|{|}_2^2+\mathrm{\&lambda;}|\left|r\right|{|}_1,\mathrm{\&lambda;}>0---\left(1\right)$

wherein,is L2 norm, | · | | non-woven₁Is the norm of L1, λ is the regularization parameter that adjusts sparsity;

solving the formula (1) by a sparse inversion algorithm to obtain a complex reflection coefficient r, and performing modular square sum arctangent operation on the complex reflection coefficient rRespectively obtain time-frequency energy spectrum F_s,tfSum-time-frequency phase spectrum

F_s,tf＝|r|²，

3. A method of identifying a gas reservoir using wavelet phase characteristics according to claim 1 or 2, wherein: in the step 4), the frequency anomaly spectrum and the phase anomaly spectrum are as follows:

${\hat{F}}_{s,stack}=|{\hat{r}}_{stack}{|}^2,$

wherein,in order to be a frequency anomaly spectrum,in order to be a spectrum of phase anomalies,is a complex reflection coefficient spectrum associated with the reservoir.