Crack prediction method and device, and storage medium

Abstract

The invention discloses a crack prediction method and device and a storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: a computer device can determine n seismic spectrums in one-to-one correspondence with n seismic signals at the ith sampling time point in m sampling time points after acquiring pre-stack seismic data; in this way, any seismic spectrum can be selected from the n seismic spectrums to serve as the reference seismic spectrum, and (n-1) attenuation anisotropy characteristic values are obtained. Due to the fact that at the same sampling time point, all seismic signals in the obtained pre-stack seismic data have the same noise. Therefore, after the logarithmic ratio of the two seismic spectrums corresponding to the seismic signals of the seismic waves in the two different propagation orientations at the same sampling time point is taken, the noise at the same sampling time point can be eliminated. In this way, the influence of noise on the calculation result can be effectively eliminated, and then the accuracy of predicting the cracks in the stratum can be improved.

Description

Technical Field

[0001] This application relates to the technical field of geophysical exploration, and particularly relates to a method and device for predicting fractures and a storage medium. Background Art

[0002] Fractures are important spaces for underground oil and gas accumulation and migration. Among them, medium-scale fractures play a major role in the migration, accumulation, and production of oil and gas. Therefore, in modern oil and gas reservoir exploration and development, it is very important to predict medium-scale fractures in reservoirs.

[0003] Fracture prediction refers to predicting the direction of fractures or obtaining relevant parameters of fractures based on the predicted fracture direction. Currently, the attenuation anisotropy fracture prediction technology is the most effective method for predicting medium-scale fractures.

[0004] However, when there is noise in actual seismic data, the current attenuation anisotropy fracture prediction technology has a poor prediction effect on medium-scale fractures. Summary of the Invention

[0005] Embodiments of this application provide a method and device for predicting fractures and a storage medium. The problem that the current attenuation anisotropy fracture prediction technology has a poor prediction effect on medium-scale fractures in the prior art can be solved. The technical solutions are as follows:

[0006] On the one hand, a method for predicting fractures is provided. The method is characterized in that the method includes:

[0007] Obtain pre-stack seismic data, where the pre-stack seismic data contains seismic signals of n seismic waves transmitted in a formation. Each seismic signal contains signals collected at m sampling time points, and the seismic waves of each channel propagate in different azimuths in the formation. Both n and m are integers greater than 2;

[0008] At the i-th sampling time point among the m sampling time points, determine n seismic spectra corresponding one-to-one to the n seismic signals;

[0009] Arbitrarily select one seismic spectrum from the n seismic spectra as a reference seismic spectrum, and based on the reference seismic spectrum and the other seismic spectra except the reference seismic spectrum at the i-th sampling time point, obtain (n - 1) attenuation anisotropy eigenvalues. Each attenuation anisotropy eigenvalue is used to characterize the fitting relationship between the spectral logarithmic ratio of seismic waves in two different propagation azimuths and the frequency of the seismic waves at the i-th sampling time point;

[0010] Based on the (n - 1) attenuation anisotropy eigenvalues, obtain the prediction result of the fracture azimuth in the formation at the i-th sampling time point.

[0011] Optionally, obtaining any one of the attenuation anisotropy eigenvalues includes:

[0012] Obtaining the variation relationship between the spectral logarithmic ratio of any one of the n seismic spectra except the reference seismic spectrum and the reference seismic spectrum, and the frequency of the seismic wave;

[0013] Based on the variation relationship, linearly fitting the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue.

[0014] Optionally, linearly fitting the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue includes:

[0015] Using the first fitting formula to linearly fit the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue; the first fitting formula is:

[0016]

[0017] where A1(f) is the reference seismic spectrum, A2(f) is any one of the seismic spectra except the reference seismic spectrum, f is the frequency of the seismic wave, t i is the i-th sampling time point, Δ(1 / Q) is the attenuation anisotropy eigenvalue, and C is a constant.

[0018] Optionally, after obtaining the pre-stack seismic data, the method further includes:

[0019] Arbitrarily selecting b seismic signals from the n seismic signals, and determining the stable frequency interval of the seismic wave based on the b seismic signals, where b is an integer greater than 2 and less than or equal to n;

[0020] where linearly fitting the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue includes:

[0021] Based on the variation relationship, obtaining the corresponding stable spectral logarithmic ratio interval within the stable frequency interval, and linearly fitting the spectral logarithmic ratio in the stable spectral logarithmic ratio interval and the frequency in the stable frequency interval to obtain the attenuation anisotropy eigenvalue.

[0022] Optionally, determining the stable frequency interval of the seismic wave based on the b seismic signals includes:

[0023] At the s-th sampling time point among the m sampling time points, determining b seismic spectra corresponding one-to-one to the b seismic signals;

[0024] Arbitrarily select one seismic spectrum from the b seismic spectra as the auxiliary reference seismic spectrum, and based on the auxiliary reference seismic spectrum and each of the other seismic spectra except the auxiliary reference seismic spectrum at the s-th sampling time point, obtain the variation curve of (b - 1) spectral log ratios with respect to the frequency of the seismic wave;

[0025] Based on (j - 1) of the variation curves, determine the stable frequency interval.

[0026] On the other hand, a crack prediction device is provided, which is characterized in that the device includes:

[0027] A first acquisition module for acquiring prestack seismic data, where the prestack seismic data contains seismic signals of n seismic waves transmitted in a formation, each seismic signal contains signals collected at m sampling time points, and the azimuths of propagation of each of the n seismic waves in the formation are different, and both n and m are integers greater than 2;

[0028] A first determination module for determining n seismic spectra corresponding one by one to the n seismic signals at the i-th sampling time point among the m sampling time points;

[0029] A second acquisition module for arbitrarily selecting one seismic spectrum from the n seismic spectra as the reference seismic spectrum, and based on the reference seismic spectrum and each of the other seismic spectra except the reference seismic spectrum at the i-th sampling time point, obtain (n - 1) attenuation anisotropy eigenvalues, and each attenuation anisotropy eigenvalue is used to characterize the fitting relationship between the spectral log ratio of seismic waves in two different propagation azimuths and the frequency of the seismic wave at the i-th sampling time point;

[0030] A third acquisition module for obtaining a prediction result of the crack azimuth in the formation at the i-th sampling time point based on the (n - 1) attenuation anisotropy eigenvalues.

[0031] Optionally, the second acquisition module includes:

[0032] A first acquisition unit for obtaining the variation relationship between the spectral log ratio of any one seismic spectrum except the reference seismic spectrum in the n seismic spectra and the reference seismic spectrum and the frequency of the seismic wave;

[0033] A fitting unit for linearly fitting the spectral log ratio and the frequency based on the variation relationship to obtain the attenuation anisotropy eigenvalue.

[0034] Optionally, the fitting unit is configured to: linearly fit the spectral logarithmic ratio and the frequency by using a first fitting formula to obtain the attenuation anisotropy eigenvalue; the first fitting formula is:

[0035]

[0036] where A1(f) is the reference seismic spectrum, A2(f) is any one seismic spectrum other than the reference seismic spectrum, f is the frequency of the seismic wave, t i is the i-th sampling time point, Δ(1 / Q) is the attenuation anisotropy eigenvalue, and C is a constant.

[0037] Optionally, the apparatus further includes:

[0038] A second determination module, configured to, after the first acquisition module acquires the prestack seismic data, arbitrarily select b seismic signals from the n seismic signals and determine a stable frequency interval of the seismic wave based on the b seismic signals, where b is an integer greater than 2 and less than or equal to n;

[0039] wherein the fitting module is configured to: based on the variation relationship, obtain a stable spectral logarithmic ratio interval corresponding to the stable frequency interval, and linearly fit the spectral logarithmic ratio in the stable spectral logarithmic ratio interval and the frequency in the stable frequency interval to obtain the attenuation anisotropy eigenvalue.

[0040] In another aspect, a computer-readable storage medium is provided, characterized in that at least one instruction, at least one program, a code set or an instruction set is stored in the computer-readable storage medium, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method for predicting a crack as described in any one of the above.

[0041] The beneficial effects brought by the technical solution provided in the embodiments of the present application at least include:

[0042] After obtaining pre-stack seismic data, a computer device can determine n seismic spectra corresponding to n seismic signals one by one at the i-th sampling time point among m sampling time points. In this way, any one of the n seismic spectra can be selected as a reference seismic spectrum, and based on the reference seismic spectrum and the other seismic spectra except the reference spectrum at the i-th sampling time point among the m sampling time points, (n - 1) attenuation anisotropy eigenvalues can be obtained. Since each of the (n - 1) attenuation anisotropy eigenvalues characterizes the fitting relationship between the spectral logarithm ratio of seismic waves at the same sampling time point (for example, at the i-th sampling time point) in two different propagation directions and the frequency of the seismic waves, and since at the i-th sampling time point, the seismic signals of each channel in the obtained pre-stack seismic data have the same noise. Therefore, after taking the logarithm ratio of the two seismic spectra corresponding to the seismic signals of seismic waves in two different propagation directions at the same sampling time point, the noise at the i-th sampling time point can be eliminated. In this way, the influence of noise on the calculation result can be effectively eliminated, and thus the accuracy of predicting fractures in the formation can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 is a flowchart of a method for predicting fractures provided by an embodiment of the present application;

[0045] Figure 2 is a flowchart of another method for predicting fractures provided by an embodiment of the present application;

[0046] Figure 3 is a flowchart of a method for determining a stable frequency range of seismic waves based on b seismic signals provided by an embodiment of the present application;

[0047] Figure 4 is a curve showing the change of the spectral logarithm ratio of six seismic spectra other than the auxiliary reference spectrum to the auxiliary reference spectrum and the frequency of the seismic waves provided by an embodiment of the present application;

[0048] Figure 5 is a flowchart of a method for obtaining any one of the attenuation anisotropy eigenvalues provided by an embodiment of the present application;

[0049] Figure 6It is a prediction result diagram provided by an embodiment of the present application using an existing crack prediction method;

[0050] Figure 7 It is a prediction result diagram provided by an embodiment of the present application using the crack prediction method of the present application;

[0051] Figure 8 It is one provided by an embodiment of the present application Schematic diagram of change over time;

[0052] Figure 9 It is a schematic diagram of the change of C1 over time provided by an embodiment of the present application;

[0053] Figure 10 It is a block diagram of a crack prediction device provided by an embodiment of the present application;

[0054] Figure 11 It is a structural block diagram of a second acquisition module provided by an embodiment of the present application;

[0055] Figure 12 It is a block diagram of another crack prediction device provided by an embodiment of the present application;

[0056] Figure 13 It is a structural block diagram of a second determination module provided by an embodiment of the present application. Detailed implementation manners

[0057] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0058] Please refer to Figure 1 , Figure 1 It is a flowchart of a crack prediction method provided by an embodiment of the present application. This crack prediction method is applied to a computer device. This crack prediction method may include:

[0059] Step 101, obtain pre-stack seismic data.

[0060] Here, the pre-stack seismic data obtained by the computer device may include seismic signals of n seismic waves transmitted in the formation. Each seismic signal may include signals collected at m sampling time points, and the azimuths of each seismic wave transmitted in the formation are different. Among them, both n and m may be integers greater than 2.

[0061] Step 102, based on the pre-stack seismic data, at the i-th sampling time point among the m sampling time points, determine n seismic frequency spectra corresponding one-to-one to the n seismic signals.

[0062] Among them, the \(i\)-th sampling time point among the \(m\) sampling time points refers to any one of the \(m\) sampling time points.

[0063] Step 103: Based on the \(n\) seismic spectra, arbitrarily select one seismic spectrum from the \(n\) seismic spectra as the reference seismic spectrum, and according to the reference seismic spectrum and each of the other seismic spectra except the reference spectrum at the \(i\)-th sampling time point among the \(m\) sampling time points, obtain \((n - 1)\) attenuation anisotropy eigenvalues.

[0064] Here, each of the \((n - 1)\) attenuation anisotropy eigenvalues can be used to characterize the fitting relationship between the spectral logarithm ratio of seismic waves in two different propagation azimuths and the frequency of the seismic waves at the \(i\)-th sampling time point. That is, each attenuation anisotropy eigenvalue can be used to characterize the fitting relationship between the spectral logarithm ratio between a corresponding one of the other seismic spectra except the reference spectrum and the reference spectrum and the frequency of the seismic waves at the \(i\)-th sampling time point.

[0065] Step 104: Based on the \((n - 1)\) attenuation anisotropy eigenvalues, obtain the prediction result of the fracture azimuth in the formation at the \(i\)-th sampling time point.

[0066] Currently, in the existing fracture prediction methods, after obtaining the pre-stack seismic data, what is determined are the \(m\) seismic spectra corresponding one by one to the \(m\) time sampling points in the seismic signal of the same seismic trace. In this way, one seismic spectrum can be arbitrarily selected from the \(m\) seismic spectra as the reference seismic spectrum, and according to the selected reference seismic spectrum and each of the other seismic spectra except the reference seismic spectrum in the seismic signal of one seismic trace, \((m - 1)\) attenuation anisotropy eigenvalues are obtained. Here, each of the obtained attenuation anisotropy eigenvalues is used to characterize the fitting relationship between the spectral logarithm ratio of seismic waves in the same propagation azimuth at two different sampling time points and the frequency of the seismic waves. Since the noise in the pre-stack seismic data obtained at different sampling time points is different. Therefore, after taking the logarithm ratio of the spectra of seismic waves in one propagation azimuth at two different sampling time points, the different noises at the two different sampling time points may be superimposed on each other. In this way, when predicting fractures, the noise will affect the calculation result and reduce the reliability of the prediction result.

[0067] In the present application, since each of the (n - 1) attenuation anisotropy eigenvalues characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves in two different propagation directions at the same sampling time point (e.g., at the i-th sampling time point), and at the same sampling time point, the seismic signals of each trace in the pre-stack seismic data obtained have the same noise. Therefore, after taking the logarithmic ratio of the two seismic spectra corresponding to the seismic signals of seismic waves in two different propagation directions at the same sampling time point, the noise at the same sampling time point can be eliminated. In this way, the influence of noise on the calculation result can be effectively eliminated, and thus the accuracy of predicting fractures in the formation can be improved.

[0068] In summary, for the fracture prediction method provided by the embodiments of the present application, after obtaining the pre-stack seismic data, the computer device can determine n seismic spectra corresponding one by one to n seismic signals at the i-th sampling time point among the m sampling time points. In this way, any one of the n seismic spectra can be selected as the reference seismic spectrum, and based on the reference seismic spectrum and the other respective seismic spectra except the reference spectrum at the i-th sampling time point among the m sampling time points, (n - 1) attenuation anisotropy eigenvalues can be obtained. Since each of the (n - 1) attenuation anisotropy eigenvalues characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves in two different propagation directions at the same sampling time point (e.g., at the i-th sampling time point), and since at the i-th sampling time point, the seismic signals of each trace in the pre-stack seismic data obtained have the same noise. Therefore, after taking the logarithmic ratio of the two seismic spectra corresponding to the seismic signals of seismic waves in two different propagation directions at the same sampling time point, the noise at the i-th sampling time point can be eliminated. In this way, the influence of noise on the calculation result can be effectively eliminated, and thus the accuracy of predicting fractures in the formation can be improved.

[0069] Please refer to Figure 2 , Figure 2 FIG. is a flowchart of another fracture prediction method provided by the embodiments of the present application. This fracture prediction method is applied to a computer device. This fracture prediction method may further include:

[0070] Step 201, obtain pre-stack seismic data.

[0071] In an embodiment of the present application, a computer device may acquire pre-stack seismic data. Among them, the pre-stack seismic data acquired by the computer device may include seismic signals of n seismic waves transmitted in a formation. Each seismic signal may include signals collected at m sampling time points, and the azimuths of the seismic waves transmitted in the formation are different for each channel. Here, both n and m may be integers greater than 2.

[0072] Step 202: Arbitrarily select b seismic signals from the n seismic signals based on the pre-stack seismic data, and determine a stable frequency range of the seismic waves based on the b seismic signals.

[0073] In the present application, the computer device may arbitrarily select b seismic signals from the n seismic signals based on the pre-stack seismic data, and determine a stable frequency range of the seismic waves based on the b seismic signals. Here, b may be an integer greater than 2 and less than or equal to n. For example, the pre-stack seismic data may include 100 seismic signals, and here 10 seismic signals may be arbitrarily selected from the 100 seismic signals to determine a stable frequency range of the seismic waves based on these 10 seismic signals.

[0074] Exemplarily, the pre-stack seismic data acquired by the computer device may include seismic signals of n seismic waves transmitted in a formation, and each seismic signal may include signals collected at m sampling time points. Therefore, any b seismic signals may be selected from the n seismic signals, and in these b seismic signals, each seismic signal also includes signals collected at m sampling time points.

[0075] In an embodiment of the present application, please refer to Figure 3 , Figure 3 is a flowchart of a method for determining a stable frequency range of seismic waves based on b seismic signals provided by an embodiment of the present application. Here, determining a stable frequency range of seismic waves based on b seismic signals may include the following steps:

[0076] Step 2021: At the s-th sampling time point among the m sampling time points, determine b seismic spectra corresponding one-to-one to the b seismic signals.

[0077] In the present application, the computer device may determine b seismic spectra corresponding one-to-one to the b seismic signals at the s-th sampling time point among the m sampling time points. Here, the s-th sampling time point among the m sampling time points refers to any one of the m sampling time points.

[0078] Exemplarily, the seismic signal of the seismic wave in the prestack seismic data acquired by the computer device can be expressed as a(t). After acquiring the seismic signal a(t) of the seismic wave in the b-th trace, the Fourier transform can be performed on the seismic signal a(t) of each seismic wave in the b-th trace of seismic signals according to the following formula (1):

[0079]

[0080] In this way, b seismic spectra A(f) corresponding one-to-one to the seismic signals a(t) of each seismic wave in the b-th trace of seismic signals can be obtained. Among them, in formula (1), j refers to the imaginary number, f refers to the frequency of the seismic wave, and t refers to the sampling time point of the seismic signal.

[0081] Then, the formula (1) can be transformed to obtain the corresponding relationship between the seismic spectrum A(f) and different sampling time points t, and this corresponding relationship can refer to the following formula (2):

[0082]

[0083] Among them, A0(f) refers to the spectrum of the seismic signal at the initial sampling time point; A(t) is a factor related to factors such as the reflection coefficient and geometric spreading, which is independent of the frequency of the seismic wave and only represents the influence of other factors on the absorption and attenuation of the seismic wave. The Q value refers to the attenuation quality factor of the formation, which is often used to characterize the inherent property of the absorption property of the formation.

[0084] Step 2022: Arbitrarily select one seismic spectrum from the b seismic spectra as the auxiliary reference seismic spectrum, and obtain the variation curve of (b - 1) spectral logarithmic ratios with the frequency of the seismic wave according to the auxiliary reference seismic spectrum and other seismic spectra except the auxiliary reference seismic spectrum at the s-th sampling time point.

[0085] In the present application, the computer device can arbitrarily select one seismic spectrum from the b seismic spectra as the auxiliary reference seismic spectrum, and obtain the variation curve of (b - 1) spectral logarithmic ratios with the frequency of the seismic wave according to the auxiliary reference seismic spectrum and other seismic spectra except the auxiliary reference seismic spectrum at the s-th sampling time point.

[0086] Exemplarily, after the computer device determines the auxiliary reference spectrum, according to the above formula (2), the auxiliary reference spectrum at the s-th sampling time point can be expressed as:

[0087]

[0088] At the s-th sampling time point, any one of the other seismic spectra except the auxiliary reference spectrum among the b seismic spectra can be expressed as:

[0089]

[0090] Thus, by comparing the above formula (4) with formula (3) and then taking the logarithm, the spectral logarithmic ratio between any seismic spectrum other than the auxiliary reference spectrum and the auxiliary reference spectrum at the s-th sampling time point can be obtained:

[0091]

[0092] In the above formula (5), A4(f) is any one of the b seismic spectra other than the auxiliary reference spectrum; A3(f) is the auxiliary reference spectrum. Therefore, is the spectral logarithmic ratio between any seismic spectrum other than the auxiliary reference spectrum and the auxiliary reference spectrum.

[0093] And in the above formula (5), C is a constant; f is the frequency of the seismic wave; t s represents the s-th sampling time point; Q4 represents the attenuation quality factor in the propagation direction of the formation when the seismic wave corresponding to any one of the b seismic spectra other than the auxiliary reference spectrum propagates in the formation; Q3 represents the attenuation quality factor in the propagation direction of the formation when the seismic wave corresponding to the auxiliary reference spectrum propagates in the formation.

[0094] Therefore, the computer device can obtain according to the above formula (5): the curve of the change of the spectral logarithmic ratio between any one of the b seismic spectra other than the auxiliary reference spectrum and the auxiliary reference spectrum and the frequency of the seismic wave. Exemplarily, as Figure 4 shown, Figure 4 is the curve of the change of the spectral logarithmic ratio between 6 seismic spectra other than the auxiliary reference spectrum and the auxiliary reference spectrum and the frequency of the seismic wave provided by an embodiment of the present application. Among them, Figure 4 the abscissa represents the frequency of the seismic wave, and the ordinate represents the spectral logarithmic ratio.

[0095] Step 2023: Determine the stable frequency range based on the curves of the change of (b - 1) spectral logarithmic ratios and the frequency of the seismic wave.

[0096] In the present application, the computer device can determine the stable frequency range based on the curves of the change of (b - 1) spectral logarithmic ratios and the frequency of the seismic wave.

[0097] Exemplarily, as Figure 4 shown, when the frequency of the seismic wave is between 30 Hz and 70 Hz, the trends of the 6 curves of the change of the spectral logarithmic ratio and the frequency of the seismic wave are relatively stable. Therefore, the stable frequency range can be [30, 70] Hz.

[0098] In this case, the computer device can obtain the stable frequency range of the seismic wave. Subsequently, based on this stable frequency range of the seismic wave, the attenuation anisotropy eigenvalue can be more accurately fitted, so that the accuracy of predicting the fractures in the formation based on the attenuation anisotropy eigenvalue is higher in the subsequent process.

[0099] Step 203: At the i-th sampling time point among the m sampling time points, determine n seismic spectra corresponding one-to-one to n seismic signals.

[0100] In this application, the computer device can determine n seismic spectra corresponding one-to-one to n seismic signals at the i-th sampling time point among the m sampling time points. Herein, the i-th sampling time point among the m sampling time points refers to any one of the m sampling time points. It should be noted that the i-th sampling time point may be the same as the s-th sampling time point in step 202 or different from the s-th sampling time point in step 202. The embodiments of this application do not make any limitations in this regard.

[0101] Exemplarily, the seismic signal of the seismic wave in the pre-stack seismic data obtained by the computer device can be expressed as a(t). After obtaining the seismic signals a(t) of n seismic waves, the Fourier transform can be performed on the seismic signals a(t) of each seismic wave in the n seismic signals according to the above formula (1) to obtain n seismic spectra A(f) corresponding one-to-one to the seismic signals a(t) of each seismic wave in the n seismic signals, and then the corresponding relationship between the seismic spectrum A(f) and different sampling time points t can be obtained according to the above formula (2).

[0102] Step 204: Arbitrarily select one seismic spectrum from the n seismic spectra as the reference seismic spectrum, and obtain (n - 1) attenuation anisotropy eigenvalues based on the reference seismic spectrum and the other seismic spectra except the reference seismic spectrum at the i-th sampling time point.

[0103] In this application, the computer device can arbitrarily select one seismic spectrum from the n seismic spectra as the reference seismic spectrum, and obtain (n - 1) attenuation anisotropy eigenvalues based on the reference seismic spectrum and the other seismic spectra except the reference seismic spectrum at the i-th sampling time point. Among them, each attenuation anisotropy eigenvalue can be used to characterize the fitting relationship between the spectral logarithm ratio of seismic waves in two different propagation directions at the i-th sampling time point and the frequency of the seismic wave, that is, each attenuation anisotropy eigenvalue can be used to characterize the fitting relationship between the spectral logarithm ratio of a corresponding seismic spectrum other than the reference spectrum and the reference spectrum and the frequency of the seismic wave at the i-th sampling time point.

[0104] It should be noted that the azimuth of the seismic wave corresponding to the reference seismic spectrum arbitrarily selected by the computer device from n seismic spectra in the stratum can be defined as 0°. In this way, the computer device can determine the specific angles of the azimuths of the seismic waves corresponding to the seismic spectra in the (n - 1) seismic spectra in the stratum according to the differences between the azimuths of the seismic waves corresponding to the seismic spectra in the (n - 1) seismic spectra and the azimuth of the seismic wave corresponding to the reference seismic spectrum in the stratum. In this case, after the computer device obtains (n - 1) attenuation anisotropy eigenvalues at the i-th sampling time point, based on these attenuation anisotropy eigenvalues, the computer device can obtain the specific azimuth of the fractures in the stratum at the i-th sampling time point.

[0105] In the embodiments of the present application, for any one of the attenuation anisotropy eigenvalues, the computer device needs to obtain it according to the spectral logarithmic ratio of the corresponding seismic spectrum and the reference seismic spectrum, and in combination with the frequency of the seismic wave. Exemplarily, please refer to Figure 5 , Figure 5 which is a flowchart of a method for obtaining any one of the attenuation anisotropy eigenvalues provided by the embodiments of the present application. The computer device obtaining any one of the attenuation anisotropy eigenvalues may include the following steps:

[0106] Step 2041: Obtain the variation relationship between the spectral logarithmic ratio of any one of the n seismic spectra except the reference seismic spectrum and the reference spectrum, and the frequency of the seismic wave.

[0107] In the present application, the computer device can obtain the variation relationship between the spectral logarithmic ratio of any one of the n seismic spectra except the reference seismic spectrum and the reference spectrum, and the frequency of the seismic wave.

[0108] Exemplarily, according to the above formula (2), the reference spectrum at the i-th sampling time point can be expressed as:

[0109]

[0110] Any one of the n seismic spectra except the reference seismic spectrum at the i-th sampling time point can be expressed as:

[0111]

[0112] In this way, after comparing the above formula (7) with formula (6) and then taking the logarithm, the spectral logarithmic ratio of any one of the seismic spectra except the reference spectrum and the reference spectrum at the i-th sampling time point can be:

[0113]

[0114] Among them, a corresponding attenuation anisotropy eigenvalue can be expressed as:

[0115]

[0116] In this way, substituting the above formula (9) into formula (8), the first fitting formula can be obtained as:

[0117]

[0118] Among them, A1(f) is the reference seismic spectrum; A2(f) is any one of the n seismic spectra other than the reference seismic spectrum. Therefore, is the spectral logarithmic ratio of any one seismic spectrum other than the reference spectrum to the reference spectrum.

[0119] In the above formula (8) and the above first fitting formula, C is a constant; f is the frequency of the seismic wave; t i represents the i-th sampling time point; Q2 represents the attenuation quality factor of the formation in the propagation direction of the seismic wave corresponding to any one of the n seismic spectra other than the reference spectrum when propagating in the formation; Q1 represents the attenuation quality factor of the formation in the propagation direction of the seismic wave corresponding to the reference spectrum when propagating in the formation.

[0120] Therefore, the above first fitting formula can be used to reflect the variation relationship between the spectral logarithmic ratio of any one of the n seismic spectra other than the reference seismic spectrum to the reference spectrum and the frequency of the seismic wave.

[0121] Step 2042: Based on the variation relationship between the spectral logarithmic ratio of any one of the n seismic spectra other than the reference seismic spectrum to the reference spectrum and the frequency of the seismic wave, linearly fit the spectral logarithmic ratio of this seismic spectrum to the reference spectrum and the frequency of the seismic wave to obtain a corresponding attenuation anisotropy eigenvalue.

[0122] In the embodiment of the present application, the computer device can linearly fit the spectral logarithmic ratio of any one of the n seismic spectra other than the reference seismic spectrum to the reference spectrum and the frequency of the seismic wave based on the variation relationship therebetween, and then a corresponding attenuation anisotropy eigenvalue can be obtained.

[0123] Exemplarily, the computer device can adopt the above first fitting formula to linearly fit the spectral logarithmic ratio of any one of the n seismic spectra other than the reference seismic spectrum to the reference spectrum and the frequency of the seismic wave, and then a corresponding attenuation anisotropy eigenvalue can be obtained.

[0124] Here, the computer device can use the above first fitting formula to linearly fit the spectral logarithmic ratio between any one of the n seismic spectra except the reference seismic spectrum and the reference spectrum, and the frequency of the seismic wave, and obtain the slope k of the linear change. Since the slope k of the linear transformation satisfies the following relationship with the corresponding attenuation anisotropy eigenvalue Δ(1 / Q):

[0125]

[0126] Therefore, after the computer device obtains the slope k of the linear change, it can inversely deduce the corresponding attenuation anisotropy eigenvalue Δ(1 / Q) through this formula (10).

[0127] Optionally, in order to further improve the accuracy of the obtained attenuation anisotropy eigenvalue, when performing linear fitting using the above first fitting formula, the fitting can be performed based on the stable frequency range of the seismic wave determined in step 202.

[0128] Exemplarily, the computer device can obtain the corresponding stable spectral logarithmic ratio range within the stable frequency range of the seismic wave based on the change relationship between the spectral logarithmic ratio between any one of the n seismic spectra except the reference seismic spectrum and the reference spectrum, and the frequency of the seismic wave. Then, the computer device can use the above first fitting formula to linearly fit the spectral logarithmic ratio in the stable spectral logarithmic ratio range and the frequency in the stable frequency range, and thus can obtain the attenuation anisotropy eigenvalue with relatively high accuracy.

[0129] For example, the computer device can draw a curve similar to Figure 4 based on the change relationship between the spectral logarithmic ratio between any one of the n seismic spectra except the reference seismic spectrum and the reference spectrum, and the frequency of the seismic wave. In this way, when the stable frequency range of the seismic wave is [30, 70] Hz, the frequency range of the seismic wave from 30 Hz to 70 Hz can be intercepted in the drawn curve, and the corresponding range of the spectral logarithmic ratio, and this range of the spectral logarithmic ratio is the stable spectral logarithmic ratio range.

[0130] For this reason, (n - 1) attenuation anisotropy eigenvalues at the i-th sampling time point can be obtained through the above process. Also, since the i-th sampling time point is any one of the m sampling time points, and the acquisition process of the (n - 1) attenuation anisotropy eigenvalues at each sampling time point among the m sampling time points is the same. Therefore, through the above process, the (n - 1) attenuation anisotropy eigenvalues at each sampling time point among the m sampling time points can be obtained, that is, m×(n - 1) attenuation anisotropy eigenvalues can be obtained.

[0131] Step 205: Based on the (n - 1) attenuation anisotropy eigenvalues obtained at the i-th sampling time point, obtain the prediction result of the fracture orientation in the formation at the i-th sampling time point.

[0132] In this application, the computer device can obtain the prediction result of the fracture orientation in the formation at the i-th sampling time point based on the (n - 1) attenuation anisotropy eigenvalues obtained at the i-th sampling time point.

[0133] Exemplarily, at the i-th sampling time point, the attenuation of the P-wave in the seismic wave undergoes a cosine oscillation as the azimuth in which the seismic wave can propagate in the formation changes. The specific relationship is as follows:

[0134]

[0135] where Δ(1 / Q) is the attenuation anisotropy eigenvalue obtained at the i-th sampling time point, is the azimuth in which the other (n - 1) seismic signals among the n seismic signals propagate in the formation except for the seismic signal at the 0° azimuth, is the azimuth of the fracture at the i-th sampling time point, and C1 = Δ(1 / Q0) represents the mean value of the attenuation anisotropy change in all directions at the i-th sampling time point.

[0136] It should be noted that the seismic wave can propagate in the formation at a certain speed. Therefore, based on the acquired pre-stack seismic data, the computer device can obtain the propagation distance of the seismic wave at any one of the m sampling time points, and further obtain the formation depth where the seismic wave is located at any one of the m sampling time points. Therefore, can represent: the azimuth of the fracture in the formation depth at the i-th sampling time point, and C1 = Δ(1 / Q0) can represent: the intensity of the fracture in the formation depth at the i-th sampling time point.

[0137] In this application, by transforming the above formula (11), a second fitting formula can be obtained:

[0138]

[0139] where, let y = Δ(1 / Q). From the second fitting formula, it can be seen that x and y can have a linear function relationship. Thus, according to the (n - 1) attenuation anisotropy eigenvalues Δ(1 / Q) obtained at the i-th sampling time point as described above, and the (n - 1) azimuths in which the other (n - 1) seismic signals among the n seismic signals propagate in the formation except for the seismic signal at the 0° azimuth After linearly fitting x and y using the second fitting formula, the slope value in the linear function relationship can be obtained, and then the values of a, b, and c can be obtained. Thus, at the i-th sampling time point, C1 and are respectively:

[0140] C1 = a;

[0141]

[0142] Therefore, based on what is obtained, the azimuth of the fractures in the formation depth at the i-th sampling time point can be obtained.

[0143] In this application, the computer device can obtain C1 and values at any one of the m sampling time points based on the (n - 1) attenuation anisotropy eigenvalue at any one of the m sampling time points. Since can represent: the azimuth of the fractures in the formation depth at the i-th sampling time point, therefore, the computer can obtain how the C1 and values change with time. Therefore, the computer device can obtain the prediction results of the fracture azimuths in the formations at different formation depths.

[0144] For example, please refer to Figure 6 , Figure 6 which is the prediction result diagram of a prediction method for existing fractures provided by an embodiment of this application, Figure 7 and Figure 6 and Figure 7 are both rose analysis diagrams. The circumferential coordinate of the rose analysis diagram represents the azimuth in which the seismic wave propagates in the formation. The radial coordinate represents the number of fractures. For the same set of pre-stack seismic data, when processed using the fracture prediction method of this application, it can be clearly seen from Figure 7 that the fractures are mainly distributed in the interval from 330° to 360°, and the fracture density is relatively large in the interval from 330° to 360°. When processed using the existing fracture prediction method, as shown in Figure 6 , the main distribution interval of the fractures cannot be clearly seen. Therefore, the computer device using the fracture prediction method of this application to analyze the pre-stack data can greatly improve the accuracy of predicting the fractures in the formation.

[0145] Please refer to Figure 8 and Figure 9 , Figure 8 which is a schematic diagram of changing with time provided by an embodiment of this application, Figure 9It is a schematic diagram showing the variation of C1 over time provided by an embodiment of the present application. Figure 8 The six rose diagrams in [reference] represent the crack prediction result diagrams at six different sampling time points. From this, it can be obtained that in the formation depth at each sampling time point, the main distribution azimuth interval of the cracks. Figure 9 In [reference], the abscissa is time and the ordinate is C1, where Figure 8 the six sampling time points in [reference] and Figure 9 the division of the time axis in [reference] correspond. In the final crack prediction result, C1 and can verify each other's correctness, so that the crack prediction result obtained by the computer device is relatively accurate.

[0146] It should be noted that for the crack prediction method provided by the embodiments of the present application, the order of the steps can be appropriately adjusted, and the steps can also be increased or decreased accordingly according to the situation. Any person skilled in the art in the technical field disclosed in the present application can easily think of the changed methods, which should all be covered within the protection scope of the present application, so details are not described herein again.

[0147] In summary, for the crack prediction method provided by the embodiments of the present application, after the computer device obtains the pre-stack seismic data, it can determine, at the i-th sampling time point among the m sampling time points, n seismic spectra corresponding to n seismic signals one by one. In this way, any one of the n seismic spectra can be selected as the reference seismic spectrum, and based on the reference seismic spectrum and the other seismic spectra except the reference spectrum at the i-th sampling time point among the m sampling time points, (n - 1) attenuation anisotropy eigenvalues can be obtained. Since each of the (n - 1) attenuation anisotropy eigenvalues characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves at the same sampling time point (for example, at the i-th sampling time point) in two different propagation azimuths and the frequency of the seismic waves, and since at the i-th sampling time point, the seismic signals of each channel in the obtained pre-stack seismic data have the same noise. Therefore, after taking the logarithmic ratio of the two seismic spectra corresponding to the seismic signals of seismic waves in two different propagation azimuths at the same sampling time point, the noise at the i-th sampling time point can be eliminated. In this way, the influence of noise on the calculation result can be effectively eliminated, and further the accuracy of predicting cracks in the formation can be improved.

[0148] The embodiments of the present application also provide a crack prediction device. Please refer to Figure 10 , Figure 10 which is a block diagram of a crack prediction device provided by an embodiment of the present application. The crack prediction device 300 can be integrated in a computer device. The crack prediction device 300 may include:

[0149] The first acquisition module 301 is configured to: acquire pre-stack seismic data. The pre-stack seismic data acquired by the first acquisition module 301 includes seismic signals of n seismic waves transmitted in a formation. The seismic signal of each seismic wave includes signals collected at m sampling time points, and the propagation azimuths of the seismic waves in the formation are different. Both n and m are integers greater than 2.

[0150] The first determination module 302 is configured to: at the i-th sampling time point among the m sampling time points, determine n seismic frequency spectra corresponding one by one to the n seismic signals.

[0151] The second acquisition module 303 is configured to: arbitrarily select one seismic frequency spectrum from the n seismic frequency spectra corresponding one by one to the n seismic signals as a reference seismic frequency spectrum, and based on the reference seismic frequency spectrum and the other seismic frequency spectra except the reference seismic frequency spectrum at the i-th sampling time point, acquire (n - 1) attenuation anisotropy eigenvalues. Each attenuation anisotropy eigenvalue is used to characterize the fitting relationship between the spectral logarithm ratio of seismic waves in two different propagation azimuths and the frequency of the seismic waves at the i-th sampling time point.

[0152] The third acquisition module 304 is configured to: based on the (n - 1) attenuation anisotropy eigenvalues, acquire a prediction result of the fracture azimuth in the formation at the i-th sampling time point.

[0153] In summary, a fracture prediction device provided by an embodiment of the present application, after acquiring pre-stack seismic data, by acquiring the fitting relationship between the spectral logarithm ratio of each of the other seismic frequency spectra except the reference seismic frequency spectrum and the reference frequency spectrum and the frequency of the seismic wave at the i-th sampling time point among the m sampling time points, and then acquiring the (n - 1) attenuation anisotropy eigenvalues at the i-th sampling time point, can eliminate the same noise in the pre-stack seismic data acquired by the computer at the i-th sampling time point. In this way, the influence of noise on the calculation result can be effectively eliminated, and thus the accuracy of predicting fractures in the formation can be improved.

[0154] Optionally, please refer to Figure 11 , Figure 11 is a structural block diagram of a second acquisition module provided by an embodiment of the present application. The second acquisition module 303 may include:

[0155] The first acquisition unit 3031 is configured to: acquire the variation relationship between the spectral logarithm ratio of any one seismic frequency spectrum except the reference seismic frequency spectrum and the reference seismic frequency spectrum among the n seismic frequency spectra and the frequency of the seismic wave.

[0156] The fitting unit 3032 is configured to perform a linear fit between the spectral logarithm ratio of any one seismic spectrum except the reference seismic spectrum to the reference seismic spectrum and the frequency of the seismic wave based on the variation relationship therebetween, so as to obtain the corresponding attenuation anisotropy eigenvalue.

[0157] Optionally, the fitting unit 3032 is configured to: perform a linear fit between the spectral logarithm ratio of any one seismic spectrum except the reference seismic spectrum to the reference seismic spectrum and the frequency of the seismic wave by using a first fitting formula, so as to obtain the corresponding attenuation anisotropy eigenvalue. The first fitting formula is:

[0158]

[0159] wherein, A1(f) is the reference seismic spectrum, A2(f) is any one seismic spectrum except the reference seismic spectrum, f is the frequency of the seismic wave, t i is the i-th sampling time point, Δ(1 / Q) is the attenuation anisotropy eigenvalue, and C is a constant.

[0160] Optionally, please refer to Figure 12 Figure 12 which is a block diagram of another crack prediction device provided by an embodiment of the present application. The crack prediction device 300 may further include:

[0161] The second determination module 305 is configured to: after the first acquisition module acquires the prestack seismic data, select any b seismic signals from the n seismic signals, and determine the stable frequency range of the seismic wave based on the b seismic signals. Wherein, b is an integer greater than 2 and less than or equal to n.

[0162] The fitting unit 3032 is configured to: based on the variation relationship, obtain the corresponding stable spectral logarithm ratio range within the stable frequency range, and perform a linear fit between the spectral logarithm ratio in the stable spectral logarithm ratio range and the frequency of the seismic wave in the stable frequency range, so as to obtain the attenuation anisotropy eigenvalue.

[0163] It should be noted that, please refer to Figure 13 Figure 13 which is a block diagram of a structure of a second determination module provided by an embodiment of the present application. The second determination module 305 may include:

[0164] The first determination unit 3051 is configured to: at the s-th sampling time point among the m sampling time points, determine b seismic spectra corresponding one-to-one to the b seismic signals;

[0165] ​​The second acquisition unit 3052 is configured to: arbitrarily select one seismic spectrum from b seismic spectra as an auxiliary reference seismic spectrum, and obtain a variation curve of (b - 1) spectral log ratios and the frequency of the seismic wave according to the auxiliary reference seismic spectrum and each of the other seismic spectra except the auxiliary reference seismic spectrum at the s-th sampling time point;

[0166] The second determination unit 3053 is configured to: determine a stable frequency interval based on (j - 1) variation curves.

[0167] In summary, an apparatus for predicting fractures provided in an embodiment of the present application, after acquiring prestack seismic data, by obtaining a fitting relationship between the spectral log ratio of each of the other seismic spectra except the reference seismic spectrum and the reference spectrum and the frequency of the seismic wave at the i-th sampling time point among m sampling time points, and then obtaining (n - 1) attenuation anisotropy eigenvalues at the i-th sampling time point, can eliminate the same noise in the prestack seismic data acquired by the computer at the i-th sampling time point. In this way, the influence of noise on the calculation result can be effectively eliminated, and thus the accuracy of predicting fractures in the formation can be improved.

[0168] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described apparatus, module, unit, and subunit can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0169] An embodiment of the present application also provides a computer device. The computer device may include: a processor and a memory. Among them, at least one instruction, at least one program, a code set, or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement Figure 1 or Figure 2 the fracture prediction method shown.

[0170] An embodiment of the present application also provides a computer-readable storage medium. Instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium runs on a processing component, the processing component is caused to execute Figure 1 or Figure 2 the fracture prediction method shown.

[0171] In the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. The term "plurality" means two or more, unless otherwise clearly defined.

[0172] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, an optical disc, etc.

[0173] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting cracks, characterized in that, The method includes: Obtaining pre-stack seismic data, where the pre-stack seismic data contains seismic signals of n seismic waves transmitted in a formation. Each seismic signal contains signals collected at m sampling time points, and the azimuths of propagation of the seismic waves in each channel are different. Both n and m are integers greater than 2; At the i-th sampling time point among the m sampling time points, determining n seismic spectra corresponding one-to-one to the n seismic signals; Arbitrarily selecting one seismic spectrum from the n seismic spectra as a reference seismic spectrum, and based on the reference seismic spectrum and the other seismic spectra except the reference seismic spectrum at the i-th sampling time point, obtaining (n - 1) attenuation anisotropy eigenvalues. Each attenuation anisotropy eigenvalue is used to characterize the fitting relationship between the spectral logarithmic ratio of seismic waves in two different propagation azimuths and the frequency of the seismic waves at the i-th sampling time point; Based on the (n - 1) attenuation anisotropy eigenvalues, obtaining a prediction result of the fracture azimuth in the formation at the i-th sampling time point.

2. The method according to claim 1, characterized in that, Obtaining any one of the attenuation anisotropy eigenvalues includes: Obtaining the variation relationship between the spectral logarithmic ratio of any one of the n seismic spectra except the reference seismic spectrum and the reference seismic spectrum and the frequency of the seismic wave; Based on the variation relationship, linearly fitting the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue.

3. The method according to claim 2, characterized in that, Linearly fitting the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue includes: Using a first fitting formula to linearly fit the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue; the first fitting formula is: where A1(f) is the reference seismic spectrum, A2(f) is any one seismic spectrum other than the reference seismic spectrum, f is the frequency of the seismic wave, t i is the i-th sampling time point, Δ(1 / Q) is the attenuation anisotropy eigenvalue, and C is a constant.

4. The method according to claim 2, characterized in that, After obtaining the pre-stack seismic data, the method further includes: Arbitrarily selecting b seismic signals from the n seismic signals, and based on the b seismic signals, determining the stable frequency interval of the seismic wave. b is an integer greater than 2 and less than or equal to n; Wherein, linearly fitting the spectral logarithmic ratio and the frequency to obtain the attenuation anisotropy eigenvalue includes: Based on the variation relationship, obtaining a corresponding stable spectral logarithmic ratio interval within the stable frequency interval, and linearly fitting the spectral logarithmic ratio in the stable spectral logarithmic ratio interval and the frequency in the stable frequency interval to obtain the attenuation anisotropy eigenvalue.

5. The method according to claim 4, characterized in that, Determining the stable frequency interval of the seismic wave based on the b seismic signals includes: At the s-th sampling time point among the m sampling time points, determining b seismic spectra corresponding one-to-one to the b seismic signals; Arbitrarily selecting one seismic spectrum from the b seismic spectra as an auxiliary reference seismic spectrum, and based on the auxiliary reference seismic spectrum and the other seismic spectra except the auxiliary reference seismic spectrum at the s-th sampling time point, obtaining (b - 1) variation curves of the spectral logarithmic ratio and the frequency of the seismic wave; Determine the stable frequency interval based on the (j - 1) change curves.

6. A device for predicting cracks, characterized in that, The device includes: A first acquisition module, configured to acquire prestack seismic data, where the prestack seismic data contains seismic signals of n seismic waves transmitted in a formation, each seismic signal contains signals collected at m sampling time points, and the azimuths of propagation of the n seismic waves in the formation are different, and both n and m are integers greater than 2; A first determination module, configured to determine, at the i-th sampling time point among the m sampling time points, n seismic spectra corresponding one-to-one to the n seismic signals; A second acquisition module, configured to arbitrarily select one seismic spectrum from the n seismic spectra as a reference seismic spectrum, and based on the reference seismic spectrum and the other seismic spectra except the reference seismic spectrum at the i-th sampling time point, acquire (n - 1) attenuation anisotropy eigenvalues, and each attenuation anisotropy eigenvalue is used to characterize the fitting relationship between the spectral logarithmic ratio of seismic waves in two different propagation azimuths and the frequency of the seismic waves at the i-th sampling time point; A third acquisition module, configured to obtain a prediction result of the fracture azimuth in the formation at the i-th sampling time point based on the (n - 1) attenuation anisotropy eigenvalues.

7. The device according to claim 6, characterized in that, The second acquisition module includes: A first acquisition unit, configured to obtain the change relationship between the spectral logarithmic ratio of any one seismic spectrum except the reference seismic spectrum in the n seismic spectra and the reference seismic spectrum and the frequency of the seismic waves; A fitting unit, configured to perform linear fitting on the spectral logarithmic ratio and the frequency based on the change relationship to obtain the attenuation anisotropy eigenvalue.

8. The device according to claim 7, characterized in that, The fitting unit is configured to: perform linear fitting on the spectral logarithmic ratio and the frequency using a first fitting formula to obtain the attenuation anisotropy eigenvalue; the first fitting formula is: where, A1(f) is the reference seismic spectrum, A2(f) is any one seismic spectrum other than the reference seismic spectrum, f is the frequency of the seismic wave, t i is the i-th sampling time point, Δ(1 / Q) is the attenuation anisotropy eigenvalue, and C is a constant.

9. The device according to claim 7, characterized in that,The device further includes: A second determination module, configured to, after the first acquisition module acquires the prestack seismic data, arbitrarily select b seismic signals from the n seismic signals, and determine the stable frequency interval of the seismic waves based on the b seismic signals, where b is an integer greater than 2 and less than or equal to n; Wherein, the fitting module is configured to: based on the change relationship, obtain a corresponding stable spectral logarithmic ratio interval within the stable frequency interval, and perform linear fitting on the spectral logarithmic ratio in the stable spectral logarithmic ratio interval and the frequency in the stable frequency interval to obtain the attenuation anisotropy eigenvalue.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the fracture prediction method according to any one of claims 1 to 5.

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