A method and device for detecting fluid fluidity based on second-order synchronous extraction wavelet transform

Abstract

The present invention belongs to the field of seismic exploration technology, and specifically provides a method and device for detecting fluid fluidity based on second-order synchronous extraction wavelet transform. The main purpose is to solve the problem that the recognition accuracy of fluid detection based on traditional time-frequency analysis methods in the prior art is not high and the location of the reservoir cannot be accurately indicated. The main scheme includes obtaining seismic data, processing the seismic data to obtain the second-order synchronous extraction wavelet transform time-frequency analysis results of each seismic record; taking out the instantaneous amplitude spectrum of each time point of the single-channel time-frequency analysis result, and finding the peak frequency at each time point; taking the peak frequency as the end point of the frequency band, setting a fixed frequency band in the low-frequency direction, and calculating the slope of the frequency band at each time point of the single channel; after calculating the slope of the instantaneous amplitude spectrum at each time point of each seismic record, square the slope and multiply it by the peak frequency of the instantaneous amplitude spectrum, and then perform maximum value normalization on the entire profile to obtain the relative fluidity profile.

Description

Technical Field

[0001] The invention belongs to the technical field of seismic exploration, and specifically provides a method and a device for calculating fluid flowability by time-frequency attributes. Background Art

[0002] The mobility attribute is an important attribute for detecting reservoir fluids. The location of oil and gas reservoirs can be determined through the mobility attribute, thus providing an effective basis for reservoir prediction. Mobility is one of the key parameters for oil and gas identification, reflecting the interaction between the permeability of the pore structure in the reservoir rock skeleton and the viscosity of the pore fluid. Specifically, mobility is the ratio of permeability to fluid viscosity, reflecting the ability of the fluid to flow in porous media. The larger the mobility value, the stronger the fluid's ability to flow in underground reservoirs. Therefore, the mobility attribute plays a symbolic role in the classification of conventional and unconventional oil and gas reservoirs, and plays a vital role in oil and gas exploration.

[0003] In oil and gas detection and reservoir prediction, the application of mobility information helps to determine the permeability of the reservoir and the impact of pore structure on fluid flow, thereby guiding the formulation of exploration and development strategies. Accurate assessment and understanding of mobility is crucial for effective oil and gas exploration and development. However, existing methods mainly use conventional time-frequency analysis methods (such as short-time Fourier transform, continuous wavelet transform, S transform, etc.) to perform time-frequency analysis on seismic data and extract the energy of the effective frequency band for mobility detection. However, the resolution of these methods cannot meet the needs of fine detection, which affects the accuracy of mobility extraction.

[0004] In the prior art, fluid detection based on traditional time-frequency analysis methods (such as short-time Fourier transform, continuous wavelet transform, S transform, etc.) has the following technical problems:

[0005] Fluid detection based on traditional time-frequency analysis methods (such as short-time Fourier transform, continuous wavelet transform, S transform, etc.) has the problem of low recognition accuracy and inability to accurately indicate the location of the reservoir, which makes it difficult to meet the current task requirements of unconventional oil and gas exploration. Summary of the invention

[0006] In response to the above-mentioned research problems, the purpose of the present invention is to provide a method and device for detecting fluid fluidity with high resolution, so as to solve the problem in the prior art that fluid detection based on traditional time-frequency analysis methods (such as short-time Fourier transform, continuous wavelet transform, S transform, etc.) has low recognition accuracy and cannot accurately indicate the location of the reservoir.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A method for detecting fluid fluidity based on second-order synchronous extraction wavelet transform, comprising the following steps:

[0009] Step 1: Obtain seismic data, process the seismic data to obtain seismic post-stack records, and obtain the second-order synchronous extraction wavelet transform time-frequency analysis results of each seismic record;

[0010] Step 2: Take out the instantaneous amplitude spectrum at each time point of the single-channel time-frequency analysis result, and find the peak frequency at each time point;

[0011] Step 3: With the peak frequency as the end point of the frequency band, set a fixed frequency band in the low-frequency direction, and calculate the slope of the frequency band at each time point of the single channel;

[0012] Step 4: After obtaining the slope of the instantaneous amplitude spectrum at each time point of each seismic record, square the slope and multiply it by the peak frequency of the instantaneous amplitude spectrum, and then perform maximum value normalization on the entire profile to obtain the relative flow profile.

[0013] Further, the step 1 comprises the following steps:

[0014] Step 1.1: Process the seismic data to obtain the post-stack seismic record profile;

[0015] Step 1.2: Take each post-stack seismic recording signal x(t), first perform continuous wavelet transform to obtain the continuous wavelet transform result of the trace. The formula for continuous wavelet transform of signal x(t) is:

[0016]

[0017] In the formula is the time-frequency result of wavelet transform, a is the wavelet scale factor, t is the translation factor,

[0018] represents the complex conjugate of the wavelet, represents the wavelet basis function, τ represents the time variable;

[0019] Step 1.3: Based on Parseval's theorem, rewrite equation (1) to obtain the first-order instantaneous frequency estimate of the seismic signal x(t):

[0020]

[0021] In the formula is the first-order instantaneous frequency estimate, φ(t) is the true frequency, is the partial derivative of wavelet transform with respect to time, i is the imaginary unit;

[0022] Step 1.4: The second-order instantaneous frequency estimation is expressed as follows through the local delay estimation, chirp rate estimation and first-order instantaneous frequency estimation:

[0023]

[0024] In the formula is the first-order instantaneous frequency estimate, represents the local delay estimation and chirp rate estimation, and their calculation formulas are respectively expressed as:

[0025]

[0026] In the formula is the partial derivative of the first-order instantaneous frequency, is the partial derivative of the local delay estimate;

[0027] Substituting equations (4) and (5) into equation (3) yields the expression for second-order instantaneous frequency estimation:

[0028]

[0029] In the formula They represent x(t) and is the continuous wavelet transform of the mother wavelet;

[0030] Step 1.5: Describe the second-order synchronous extraction wavelet transform as extracting the time-frequency coefficients of the wavelet transform around the second-order instantaneous frequency, expressed as:

[0031]

[0032] In the formula is the time-frequency analysis result of the second-order synchronous extraction wavelet transform, f is the frequency axis, σ is the Dirac function, expressed as:

[0033]

[0034] Further, the step 2 comprises the following steps:

[0035] Step 2.1: Modulo the time-frequency analysis results of the second-order synchronous extraction wavelet transform of the single-channel seismic record;

[0036] Step 2.2: Modulo the time-frequency analysis results obtained in step 2.1, and extract the instantaneous amplitude spectrum at each time point;

[0037] Step 2.3: Obtain the peak frequency position of the instantaneous amplitude spectrum obtained in step 2.2.

[0038] Further, the step 3 comprises the following steps:

[0039] Step 3.1: According to the instantaneous amplitude spectrum of each time point of the single-channel seismic record, the frequency band is set in the low-frequency direction with the peak frequency as the end point of the fixed frequency band;

[0040] Step 3.2: According to the frequency band set in step 3.1, calculate the slope from the frequency band start point to the frequency band end point of each instantaneous amplitude spectrum.

[0041] Further, the step 4 comprises the following steps:

[0042] Step 4.1: The magnitude of the fluidity attribute is proportional to the square of the partial derivative of the reflection coefficient with respect to the frequency;

[0043]

[0044] Where ω is the angular frequency, M is the fluidity of the fluid, expressed as: η represents the fluid viscosity coefficient, K represents the reservoir permeability;

[0045] Step 4.2: In actual calculation, the partial derivative of the reflection coefficient with respect to the diagonal frequency is approximated by the time-frequency result A(ω, t) obtained by the second-order synchronous extraction wavelet transform, that is,

[0046]

[0047] Where R(ω, t) represents the time-frequency spectrum of the reflection coefficient, and A(ω, t) represents the time-frequency spectrum of the seismic signal;

[0048] Step 4.3: Obtain the flow calculation results of each channel and perform profile normalization to obtain the relative flow profile, which is expressed as:

[0049]

[0050] Where I is the relative flow profile, M max is the calculated maximum flow rate.

[0051] A device for detecting fluid fluidity based on second-order synchronous extraction wavelet transform, comprising:

[0052] Time-frequency preprocessing module: obtain seismic data, process the seismic data to obtain seismic post-stack records, and obtain the second-order synchronous extraction wavelet transform time-frequency analysis results of each seismic record;

[0053] Peak frequency search module: extract the instantaneous amplitude spectrum at each time point of the single-channel time-frequency analysis result, and find the peak frequency at each time point;

[0054] Frequency band calculation module: Set a fixed frequency band towards the low frequency direction with the peak frequency end point, and calculate the slope of the frequency band at each time point of a single channel.

[0055] Relative flow calculation module: After obtaining the slope of the instantaneous amplitude spectrum at each time point of each seismic record, the slope is squared and multiplied by the peak frequency of the instantaneous amplitude spectrum, and the entire profile is normalized to the maximum value to obtain the relative flow profile.

[0056] Furthermore, the time-frequency preprocessing module specifically implements the steps including:

[0057] Step 1.1: Process the seismic data to obtain the post-stack seismic record profile;

[0058] Step 1.2: Take each post-stack seismic recording signal x(t), first perform continuous wavelet transform to obtain the continuous wavelet transform result of the trace. The formula for continuous wavelet transform of signal x(t) is:

[0059]

[0060] In the formula is the time-frequency result of wavelet transform, a is the wavelet scale factor, t is the translation factor,

[0061] represents the complex conjugate of the wavelet, represents the wavelet basis function, τ represents the time variable;

[0062] Step 1.3: Based on Parseval's theorem, rewrite equation (1) to obtain the first-order instantaneous frequency estimate of the seismic signal x(t):

[0063]

[0064] In the formula is the first-order instantaneous frequency estimate, φ(t) is the true frequency, is the partial derivative of wavelet transform with respect to time, i is the imaginary unit;

[0065] Step 1.4: The second-order instantaneous frequency estimation is expressed as follows through the local delay estimation, chirp rate estimation and first-order instantaneous frequency estimation:

[0066]

[0067] In the formula is the first-order instantaneous frequency estimate, represents the local delay estimation and chirp rate estimation, and their calculation formulas are respectively expressed as:

[0068]

[0069]

[0070] In the formula is the partial derivative of the first-order instantaneous frequency, is the partial derivative of the local delay estimate;

[0071] Substituting equations (4) and (5) into equation (3) yields the expression for second-order instantaneous frequency estimation:

[0072]

[0073] In the formula They represent x(t) and is the continuous wavelet transform of the mother wavelet;

[0074] Step 1.5: Describe the second-order synchronous extraction wavelet transform as extracting the time-frequency coefficients of the wavelet transform around the second-order instantaneous frequency, expressed as:

[0075]

[0076] In the formula is the time-frequency analysis result of the second-order synchronous extraction wavelet transform, f is the frequency axis, σ is the Dirac function, expressed as:

[0077]

[0078] Furthermore, the peak frequency finding module specifically implements the steps including:

[0079] Step 2.1: Modulo the time-frequency analysis results of the second-order synchronous extraction wavelet transform of the single-channel seismic record;

[0080] Step 2.2: Modulo the time-frequency analysis results obtained in step 2.1, and extract the instantaneous amplitude spectrum at each time point;

[0081] Step 2.3: Obtain the peak frequency position of the instantaneous amplitude spectrum obtained in step 2.2.

[0082] Furthermore, the frequency band calculation module specifically implements the steps including:

[0083] Step 3.1: According to the instantaneous amplitude spectrum of each time point of the single-channel seismic record, the frequency band is set in the low-frequency direction with the peak frequency as the end point of the fixed frequency band;

[0084] Step 3.2: According to the frequency band set in step 3.1, calculate the slope from the frequency band start point to the frequency band end point of each instantaneous amplitude spectrum.

[0085] Furthermore, the relative flow calculation module specifically implements the steps including:

[0086] Step 4.1: The magnitude of the fluidity attribute is proportional to the square of the partial derivative of the reflection coefficient with respect to the frequency;

[0087]

[0088] Where ω is the angular frequency, M is the fluidity of the fluid, expressed as: η represents the fluid viscosity coefficient, κ represents the reservoir permeability;

[0089] Step 4.2: In actual calculation, the partial derivative of the reflection coefficient with respect to the diagonal frequency is approximated by the time-frequency result A(ω, t) obtained by the second-order synchronous extraction wavelet transform, that is,

[0090]

[0091] Where R(ω, t) represents the time-frequency spectrum of the reflection coefficient, and A(ω, t) represents the time-frequency spectrum of the seismic signal;

[0092] Step 4.3: Obtain the flow calculation results of each channel and perform profile normalization to obtain the relative flow profile, which is expressed as:

[0093]

[0094] Where I is the relative flow profile, M max is the calculated maximum flow rate.

[0095] Compared with the prior art, the present invention has the following beneficial effects:

[0096] Compared with traditional methods, the technical solution proposed by the present invention can more accurately identify changes in the movement state of reservoir fluids, thereby achieving more accurate identification of reservoir fluid properties. The high-resolution time-frequency spectrum obtained not only provides a more detailed description of the characteristics of seismic signals, but also achieves a more refined characterization of fluid fluidity by calculating the derivatives of the frequency band, and can more accurately identify the location of the reservoir. In summary, the method and device of the present invention not only technically break through the traditional seismic exploration methods, but also show significant advantages in practical applications, and have broad market prospects and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 The present invention is a flowchart of a method for detecting fluid fluidity based on second-order synchronous extraction wavelet transform.

[0098] Figure 2 It is the post-stack seismic record in the present invention.

[0099] Figure 3 It is a single-channel seismic record in the present invention.

[0100] Figure 4 It is the time-frequency result of continuous wavelet transform of single-channel seismic record in the present invention.

[0101] Figure 5 This is the time-frequency result of the second-order synchronous extraction wavelet transform of the single-channel seismic record in the present invention.

[0102] Figure 6It is a schematic diagram of the relative fluidity profile calculated by using continuous wavelet transform for post-stack seismic records in the present invention.

[0103] Figure 7 This is a schematic diagram of the relative flow profile calculated using the second-order synchronous extraction wavelet transform of the post-stack seismic records in the present invention.

[0104] Specific implementation method

[0105] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods.

[0106] Combine the following Figure 1-7 The present invention is described in detail.

[0107] By using the second-order synchronous extraction wavelet transform technology, the present invention effectively solves the problems of low recognition accuracy and inaccurate reservoir position indication in traditional time-frequency analysis methods in fluid detection. The method first processes the seismic data to obtain post-stack seismic records, and applies the second-order synchronous extraction wavelet transform to perform time-frequency analysis to obtain a high-resolution time-frequency spectrum. Then, by calculating the slope of the frequency band at the peak frequency, the relative fluidity profile is obtained. This step has a higher resolution than the existing technology. Finally, the present invention achieves a more accurate identification of reservoir fluid properties through the analysis and calculation of this high-resolution time-frequency spectrum, providing an effective basis for the identification and prediction of oil and gas reservoirs. Compared with traditional methods, the present invention has achieved significant technical improvements, especially in improving the precision of seismic signal feature description and the refined characterization of fluid fluidity, thus showing broad application prospects in the field of oil and gas exploration.

[0108] Specifically, the present invention provides a method and device for detecting fluid fluidity based on second-order synchronous extraction wavelet transform, such as Figure 1 As shown, the following steps are included:

[0109] Step 1: Obtain seismic data, process the seismic data to obtain seismic post-stack records, and obtain the second-order synchronous extraction wavelet transform time-frequency analysis results of each seismic record;

[0110] Step 1.1: Process the seismic data to obtain the post-stack seismic record profile (such as Figure 2 shown);

[0111] Step 1.2: Take out each post-stack seismic record signal x(t) (such as Figure 3 As shown), firstly, continuous wavelet transform is performed to obtain the continuous wavelet transform result of the channel (as shown Figure 4 As shown). The formula for continuous wavelet transform of signal x(t) is:

[0112]

[0113] In the formula is the time-frequency result of wavelet transform, a is the wavelet scale factor, t is the translation factor,

[0114] represents the complex conjugate of the wavelet, represents the wavelet basis function, τ represents the time variable;

[0115] Step 1.3: Based on Parseval's theorem, rewrite equation (1) to obtain the first-order instantaneous frequency estimate of the seismic signal x(t):

[0116]

[0117] In the formula is the first-order instantaneous frequency estimate, φ(t) is the true frequency, is the partial derivative of wavelet transform with respect to time, i is the imaginary unit;

[0118] Step 1.4: The second-order instantaneous frequency estimation is expressed as follows through the local delay estimation, chirp rate estimation and first-order instantaneous frequency estimation:

[0119]

[0120] In the formula is the first-order instantaneous frequency estimate, represents the local delay estimation and chirp rate estimation, and their calculation formulas are respectively expressed as:

[0121]

[0122] In the formula is the partial derivative of the first-order instantaneous frequency, is the partial derivative of the local delay estimate;

[0123] Substituting equations (4) and (5) into equation (3) yields the expression for second-order instantaneous frequency estimation:

[0124]

[0125] In the formula They represent x(t) and is the continuous wavelet transform of the mother wavelet;

[0126] Step 1.5: Describe the second-order synchronous extraction wavelet transform as extracting the time-frequency coefficients of the wavelet transform around the second-order instantaneous frequency, expressed as:

[0127]

[0128] In the formula is the time-frequency analysis result of the second-order synchronous extraction wavelet transform, f is the frequency axis, σ is the Dirac function, expressed as:

[0129]

[0130] Step 2: Take out the instantaneous amplitude spectrum at each time point of the single-channel time-frequency analysis result, and find the peak frequency at each time point;

[0131] The specific steps are:

[0132] Step 2.1: Modulo the time-frequency analysis results of the second-order synchronous extraction wavelet transform of the single-channel seismic record;

[0133] Step 2.2: Modulo the time-frequency analysis results obtained in step 2.1, and extract the instantaneous amplitude spectrum at each time point;

[0134] Step 2.3: Obtain the peak frequency position of the instantaneous amplitude spectrum obtained in step 2.2.

[0135] Step 3: With the peak frequency as the end point of the frequency band, set a fixed frequency band in the low-frequency direction, and calculate the slope of the frequency band at each time point of the single channel;

[0136] The specific steps are:

[0137] Step 3.1: According to the instantaneous amplitude spectrum of each time point of the single-channel seismic record, the frequency band is set in the low-frequency direction with the peak frequency as the end point of the fixed frequency band;

[0138] Step 3.2: According to the frequency band set in step 3.1, calculate the slope from the frequency band start point to the frequency band end point of each instantaneous amplitude spectrum.

[0139] Step 4: After obtaining the slope of the instantaneous amplitude spectrum at each time point of each seismic record, square the slope and multiply it by the peak frequency of the instantaneous amplitude spectrum, and then perform maximum value normalization on the entire profile to obtain the relative flow profile.

[0140] The specific steps are:

[0141] Step 4.1: The magnitude of the fluidity attribute is proportional to the square of the partial derivative of the reflection coefficient with respect to the frequency;

[0142]

[0143] Where ω is the angular frequency, M is the fluidity of the fluid, expressed as: η represents the fluid viscosity coefficient, κ represents the reservoir permeability;

[0144] Step 4.2: In actual calculation, the partial derivative of the reflection coefficient with respect to the diagonal frequency is approximated by the time-frequency result A(ω, t) obtained by the second-order synchronous extraction wavelet transform, that is,

[0145]

[0146] Where R(ω, t) represents the time-frequency spectrum of the reflection coefficient, and A(ω, t) represents the time-frequency spectrum of the seismic signal;

[0147] Step 4.3: Obtain the flow calculation results of each channel and perform profile normalization to obtain the relative flow profile, which is expressed as:

[0148]

[0149] Where I is the relative flow profile, M max is the calculated maximum flow rate.

[0150] Effect analysis: Figure 2-7 As shown, Figure 5 The time-frequency resolution of the second-order synchronous extraction wavelet transform result is significantly higher than Figure 4 In the continuous wavelet transform, the second-order synchronous extraction wavelet transform has a stronger time-frequency concentration than the synchronous extraction wavelet transform. Figure 6 and Figure 7 The red arrow points to the reservoir location. The vertical and horizontal resolutions of the relative fluidity profile calculated using the second-order synchronous extraction wavelet transform are significantly higher than those calculated using the continuous wavelet transform. The fluid position reflected by the fluidity calculated using the second-order synchronous extraction wavelet transform is closer to the geological model, proving the correctness of this method. The fluid fluidity calculated using this method has a higher resolution than the traditional method, making the present invention have a wide range of application potentials in oil and gas reservoir identification. It improves the recognition accuracy of fluid detection and can more accurately indicate the location of the reservoir.

[0151] The above are only representative embodiments of the present invention in many specific application scopes, and do not constitute any limitation on the protection scope of the present invention. Any technical solutions formed by transformation or equivalent replacement fall within the protection scope of the present invention.

Claims

1. A method for detecting fluid fluidity based on second-order synchronous extraction wavelet transform, characterized in that: The following steps are involved: Step 1: Obtain seismic data, process the seismic data to obtain seismic post-stack records, and obtain the second-order synchronous extraction wavelet transform time-frequency analysis results of each trace; Step 2: Take out the instantaneous amplitude spectrum at each time point of the single-channel time-frequency analysis result, and find the peak frequency at each time point; Step 3: With the peak frequency as the end point of the frequency band, set a fixed frequency band in the low-frequency direction, and calculate the slope of the frequency band at each time point of the single channel; Step 4: After obtaining the slope of the instantaneous amplitude spectrum at each time point of each seismic record, square it and multiply it by the peak frequency of the instantaneous amplitude spectrum, and then perform maximum value normalization on the entire profile to obtain the relative flow profile; The step 1 comprises the following steps: Step 1.1: Process the seismic data to obtain the post-stack seismic record profile; Step 1.2: Take out each post-stack seismic record First, continuous wavelet transform is performed to obtain the continuous wavelet transform result of the signal. The formula for continuous wavelet transform is: （1） In the formula is the time-frequency result of wavelet transform, a is the wavelet scale, represents the complex conjugate of the wavelet, represents the mother wavelet; Step 1.3: Based on Parseval's theorem, rewriting equation (1) can give the earthquake signal: First-order instantaneous frequency estimate of : （2） In the formula is the first-order instantaneous frequency estimate, is the true frequency, is the partial derivative of the wavelet transform with respect to time, is an imaginary unit; Step 1.4: The second-order instantaneous frequency estimation can be expressed as follows through the local delay estimation, chirp rate estimation and first-order instantaneous frequency estimation: （3） In the formula is the first-order instantaneous frequency estimate, , represents the local delay estimation and chirp rate estimation, and their calculation formulas can be expressed as: （4） （5） In the formula is the partial derivative of the first-order instantaneous frequency, is the partial derivative of the local delay estimate; Substituting (4) and (5) into (3) yields the expression for second-order instantaneous frequency estimation: (6) In the formula , , , Respectively by , , , is the continuous wavelet transform of the mother wavelet; Step 1.5: According to the synchronous extraction idea, the second-order synchronous extraction wavelet transform can be described as extracting the time-frequency coefficients of the wavelet transform coefficients around the second-order instantaneous frequency, expressed as: （7） In the formula For the second order synchronous extraction of wavelet transform, is the frequency axis, is the Dirac function, expressed as: （8）。 2. According to claim 1, a method for detecting fluid fluidity based on second-order synchronous extraction wavelet transform is characterized by: The step 2 comprises the following steps: Step 2.1: Modulo the time-frequency analysis results of the second-order synchronous extraction wavelet transform of the single-channel seismic record; Step 2.2: Take the time-frequency analysis results obtained in step 2.1 and extract the instantaneous amplitude spectrum at each time point; Step 2.2: Obtain the peak frequency position of the instantaneous amplitude spectrum obtained in step 2.

2.

3. According to claim 2, a method for detecting fluid fluidity based on second-order synchronous extraction wavelet transform is characterized in that: The step 3 comprises the following steps: Step 3.1: According to the instantaneous amplitude spectrum of each time point of the single-channel seismic record, the frequency band is set in the low-frequency direction with the peak frequency as the end point of the fixed frequency band; Step 3.2: According to the frequency band set in step 3.1, calculate the slope from the frequency band start point to the frequency band end point of each instantaneous amplitude spectrum.

4. The method for detecting fluid fluidity based on second-order synchronous extraction wavelet transform according to claim 3, characterized in that: The step 4 comprises the following steps: Step 4.1: The magnitude of the fluidity attribute is proportional to the square of the partial derivative of the reflection coefficient with respect to the frequency; （9） In the formula is the angular frequency, is the fluidity of the fluid, expressed as: ; Step 4.2: In actual calculation, the partial derivative of the reflection coefficient with respect to the diagonal frequency can be extracted using the time-frequency result obtained by the second-order synchronous wavelet transform. To approximate, that is （10） Step 4.3: Obtain the flow calculation results of each channel and perform profile normalization to obtain the relative flow profile, which is expressed as: （11） In the formula is the relative flow profile, is the calculated maximum flow rate.

5. A device for detecting fluid fluidity based on second-order synchronous extraction wavelet transform, characterized in that: include: Time-frequency preprocessing module: obtain the seismic post-stack profile and obtain the second-order synchronous extraction transform time-frequency analysis results of each trace in the profile; Peak frequency search module: extract the instantaneous amplitude spectrum at each time point of the single-channel time-frequency analysis result, and find the peak frequency at each time point; Frequency band calculation module: Set a fixed frequency band towards the low frequency direction with the peak frequency end point, and calculate the slope of the frequency band at each time point of a single channel; Relative flow calculation module: After obtaining the slope of the instantaneous amplitude spectrum at each time point of each seismic record, square it and multiply it by the peak frequency of the instantaneous amplitude spectrum, and then perform maximum value normalization on the entire profile to obtain the relative flow profile; The specific implementation steps of the time-frequency preprocessing module are: Step 1.1: Process the seismic data to obtain the post-stack seismic record profile; Step 1.2: Take out each post-stack seismic record First, continuous wavelet transform is performed to obtain the continuous wavelet transform result of the signal. The formula for continuous wavelet transform is: （1） In the formula is the time-frequency result of wavelet transform, a is the wavelet scale, represents the complex conjugate of the wavelet, represents the mother wavelet; Step 1.3: Based on Parseval's theorem, rewriting equation (1) can give the earthquake signal: First-order instantaneous frequency estimate of : （2） In the formula is the first-order instantaneous frequency estimate, is the true frequency, is the partial derivative of the wavelet transform with respect to time, is an imaginary unit; Step 1.4: The second-order instantaneous frequency estimation can be expressed as follows through the local delay estimation, chirp rate estimation and first-order instantaneous frequency estimation: （3） In the formula is the first-order instantaneous frequency estimate, , represents the local delay estimation and chirp rate estimation, and their calculation formulas can be expressed as: （4） （5） In the formula is the partial derivative of the first-order instantaneous frequency, is the partial derivative of the local delay estimate, Substituting (4) and (5) into (3) yields the expression for second-order instantaneous frequency estimation: (6) In the formula , , , Respectively by , , , is the continuous wavelet transform of the mother wavelet; Step 1.5: According to the synchronous extraction idea, the second-order synchronous extraction wavelet transform can be described as extracting the time-frequency coefficients of the wavelet transform coefficients around the second-order instantaneous frequency, expressed as: （7） In the formula For the second order synchronous extraction of wavelet transform, is the frequency axis, is the Dirac function, expressed as: （8）。 6. The device for detecting fluid flowability based on second-order synchronous extraction wavelet transform according to claim 5 is characterized in that: The specific implementation steps of the peak frequency search module are as follows: Step 2.1: Modulo the time-frequency analysis results of the second-order synchronous extraction wavelet transform of the single-channel seismic record; Step 2.2: Take the time-frequency analysis results obtained in step 2.1 and extract the instantaneous amplitude spectrum at each time point; Step 2.2: Obtain the peak frequency position of the instantaneous amplitude spectrum obtained in step 2.

1.

7. The device for detecting fluid flowability based on second-order synchronous extraction wavelet transform according to claim 6, characterized in that: The specific implementation steps of the frequency band calculation module are: Step 3.1: According to the instantaneous amplitude spectrum of each time point of the single-channel seismic record, the frequency band is set in the low-frequency direction with the peak frequency as the end point of the fixed frequency band; Step 3.2: According to the frequency band set in step 3.1, calculate the slope from the frequency band start point to the frequency band end point of each instantaneous amplitude spectrum.

8. The device for detecting fluid flowability based on second-order synchronous extraction wavelet transform according to claim 7, characterized in that: The specific implementation steps of the relative flow calculation module are: Step 4.1: The magnitude of the fluidity attribute is proportional to the square of the partial derivative of the reflection coefficient with respect to the frequency; （9） In the formula is the angular frequency, is the fluidity of the fluid, expressed as: ; Step 4.2: In actual calculation, the partial derivative of the reflection coefficient with respect to the diagonal frequency can be extracted using the time-frequency result obtained by the second-order synchronous wavelet transform. To approximate, that is （10） Step 4.3: Obtain the flow calculation results of each channel and perform profile normalization to obtain the relative flow profile, which is expressed as: （11） In the formula is the relative flow profile, is the calculated maximum flow rate.

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