Fracture prediction method and device, and storage medium

Abstract

The application 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: after obtaining prestack seismic data, a computer device can determine n seismic spectrums corresponding to n seismic signals at an i-th sampling time point in m sampling time points. In this way, one of the n seismic spectrums can be selected as a reference seismic spectrum, and (n-1) attenuation anisotropy characteristic values can be obtained. Since each seismic signal in the obtained prestack seismic data has the same noise at the same sampling time point, the noise at the same sampling time point can be eliminated after taking the logarithmic ratio of the two seismic spectrums corresponding to the seismic signals of the seismic waves in two different propagation directions at the same sampling time point. In this way, the influence of the noise on the calculation result can be effectively eliminated, and the accuracy of the prediction of the cracks in the stratum can be improved.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method and apparatus for predicting fractures, and a storage medium. Background Technology

[0002] Fractures are crucial spaces for the accumulation and migration of underground oil and gas. Among them, mesoscale fractures play a major role in oil and gas migration, accumulation, and production. Therefore, predicting mesoscale fractures in reservoirs is of paramount importance in modern oil and gas reservoir exploration and development.

[0003] Crack prediction refers to predicting the direction of cracks or obtaining relevant crack parameters based on the predicted crack direction. Currently, attenuated anisotropic crack prediction technology is the most effective method for mesoscale crack prediction.

[0004] However, when actual seismic data contains noise, current attenuation anisotropic fracture prediction techniques are less effective at predicting mesoscale fractures. Summary of the Invention

[0005] This application provides a method, apparatus, and storage medium for predicting cracks. It addresses the problem of poor prediction performance for mesoscale cracks using existing attenuated anisotropic crack prediction techniques. The technical solution is as follows:

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

[0007] Acquire pre-stack seismic data, which includes seismic signals of n seismic waves propagating in the strata. Each seismic signal contains signals collected at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Both n and m are integers greater than 2.

[0008] At the i-th sampling time point among the m sampling time points, n seismic spectra corresponding one-to-one with the n channels of the seismic signal are determined;

[0009] Arbitrarily select one earthquake spectrum from the n earthquake spectra as the reference earthquake spectrum, and obtain (n-1) attenuation anisotropy characteristic values ​​based on the reference earthquake spectrum and the earthquake spectra of each other except the reference earthquake spectrum at the i-th sampling time point. Each attenuation anisotropy characteristic value is used to characterize the fitting relationship between the logarithm ratio of the spectra of two earthquake waves with different propagation directions at the i-th sampling time point and the frequency of the earthquake wave.

[0010] Based on the (n-1) attenuation anisotropic feature values, the predicted fracture orientation in the formation at the i-th sampling time point is obtained.

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

[0012] Obtain the relationship between the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum among the n earthquake spectra and the reference earthquake spectrum, and the frequency variation of the earthquake wave.

[0013] Based on the aforementioned relationship, the logarithmic ratio of the spectrum is linearly fitted to the frequency to obtain the attenuation anisotropic characteristic value.

[0014] Optionally, the attenuation anisotropy characteristic value is obtained by linearly fitting the spectral logarithmic ratio to the frequency, including:

[0015] The first fitting formula is used to linearly fit the spectral logarithm ratio to the frequency to obtain the attenuation anisotropic characteristic value; the first fitting formula is:

[0016]

[0017] Where A1(f) is the reference earthquake spectrum, A2(f) is any earthquake spectrum other than the reference earthquake spectrum, f is the frequency of the seismic wave, and t i Let be the i-th sampling time point, Δ(1 / Q) be the attenuation anisotropic characteristic value, and C be a constant.

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

[0019] Arbitrarily select b seismic signals from the n seismic signals, and determine the stable frequency range 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] Specifically, linearly fitting the spectral logarithmic ratio to the frequency to obtain the attenuation anisotropic characteristic value includes:

[0021] Based on the aforementioned relationship, the stable spectrum logarithmic ratio interval corresponding to the stable frequency interval is obtained, and the spectrum logarithmic ratio in the stable spectrum logarithmic ratio interval is linearly fitted with the frequency in the stable frequency interval to obtain the attenuation anisotropy characteristic value.

[0022] Optionally, determining the stable frequency range of the seismic wave based on the b-channel seismic signal includes:

[0023] At the s-th sampling time point among the m sampling time points, determine b seismic spectra that correspond one-to-one with the b-channel seismic signals;

[0024] Arbitrarily select one of the b earthquake spectra as an auxiliary reference earthquake spectrum, and obtain (b-1) curves of the change between the logarithmic ratio of the spectrum and the frequency of the earthquake wave based on the auxiliary reference earthquake spectrum and the earthquake spectra of each other except the auxiliary reference earthquake spectrum at the s-th sampling time point.

[0025] The stable frequency range is determined based on (j-1) of the aforementioned variation curves.

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

[0027] The first acquisition module is used to acquire pre-stack seismic data, which includes seismic signals of n seismic waves propagating in the strata. Each seismic signal contains signals collected at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Both n and m are integers greater than 2.

[0028] The first determining module is used to determine n seismic spectra corresponding one-to-one with the n channels of the seismic signals at the i-th sampling time point among the m sampling time points;

[0029] The second acquisition module is used to arbitrarily select one earthquake spectrum from the n earthquake spectra as a reference earthquake spectrum, and acquire (n-1) attenuation anisotropy characteristic values ​​based on the reference earthquake spectrum and the earthquake spectra of each other except the reference earthquake spectrum at the i-th sampling time point. Each attenuation anisotropy characteristic value is used to characterize the fitting relationship between the logarithm ratio of the spectra of two earthquake waves with different propagation directions at the i-th sampling time point and the frequency of the earthquake wave.

[0030] The third acquisition module is used to acquire the predicted location of fractures in the formation at the i-th sampling time point based on the (n-1) attenuation anisotropic feature values.

[0031] Optionally, the second acquisition module includes:

[0032] The first acquisition unit is used to acquire the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum among the n earthquake spectra and the reference earthquake spectrum, and the relationship between the frequency of the earthquake wave and the frequency of the earthquake wave.

[0033] The fitting unit is used to linearly fit the spectrum logarithm ratio to the frequency based on the change relationship, so as to obtain the attenuation anisotropic characteristic value.

[0034] Optionally, the fitting unit is configured to: linearly fit the spectrum logarithm ratio to the frequency using a first fitting formula to obtain the attenuation anisotropy characteristic value; the first fitting formula is:

[0035]

[0036] Where A1(f) is the reference earthquake spectrum, A2(f) is any earthquake spectrum other than the reference earthquake spectrum, f is the frequency of the seismic wave, and t i Let be the i-th sampling time point, Δ(1 / Q) be the attenuation anisotropic characteristic value, and C be a constant.

[0037] Optionally, the device further includes:

[0038] The second determining module is used to, after the first acquiring module acquires the pre-stack seismic data, arbitrarily select b seismic signals from the n seismic signals, and determine the stable frequency range 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] The fitting module is configured to: obtain the stable spectrum logarithm ratio interval corresponding to the stable frequency interval based on the change relationship, and linearly fit the spectrum logarithm ratio in the stable spectrum logarithm ratio interval with the frequency in the stable frequency interval to obtain the attenuation anisotropy characteristic value.

[0040] In another aspect, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein 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 crack prediction method as described above.

[0041] The beneficial effects of the technical solutions provided in this application include at least the following:

[0042] After acquiring pre-stack seismic data, computer equipment can determine n seismic spectra corresponding to n seismic signals at the i-th sampling time point out of m sampling time points. Thus, any one of the n seismic spectra can be selected as a reference seismic spectrum. Based on the reference seismic spectrum and all other seismic spectra at the i-th sampling time point out of the m sampling time points (excluding the reference spectrum), (n-1) attenuation anisotropy eigenvalues ​​are obtained. Since each of the (n-1) attenuation anisotropy eigenvalues ​​characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves from two different propagation azimuths at the same sampling time point (e.g., at the i-th sampling time point) and the frequency of the seismic wave, and since all seismic signals in the acquired pre-stack seismic data at the i-th sampling time point have the same noise, the noise at the i-th sampling time point can be eliminated by taking the logarithmic ratio of the two seismic spectra corresponding to the seismic signals from two different propagation azimuths at the same sampling time point. This effectively eliminates the impact of noise on the calculation results, thereby improving the accuracy of predicting fractures in the formation. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a crack prediction method provided in an embodiment of this application;

[0045] Figure 2 This is a flowchart of another crack prediction method provided in an embodiment of this application;

[0046] Figure 3 This is a flowchart of a method for determining the stable frequency range of seismic waves based on b-channel seismic signals, provided in an embodiment of this application.

[0047] Figure 4 This application provides an embodiment of the curve showing the change in the frequency of a seismic wave as the ratio of the spectral logarithm of six seismic spectra other than the auxiliary reference spectrum to the auxiliary reference spectrum.

[0048] Figure 5 This is a flowchart of a method for obtaining any one of the attenuation anisotropy eigenvalues ​​provided in an embodiment of this application;

[0049] Figure 6This is a prediction result diagram of an embodiment of the present application that uses an existing crack prediction method;

[0050] Figure 7 This is a prediction result diagram of a crack prediction method provided in this application embodiment;

[0051] Figure 8 This is one of the embodiments provided in this application. A schematic diagram showing how it changes over time;

[0052] Figure 9 This is a schematic diagram illustrating the change of C1 over time, provided in an embodiment of this application.

[0053] Figure 10 This is a block diagram of a crack prediction device provided in an embodiment of this application;

[0054] Figure 11 This is a structural block diagram of a second acquisition module provided in an embodiment of this application;

[0055] Figure 12 This is a block diagram of another crack prediction device provided in an embodiment of this application;

[0056] Figure 13 This is a structural block diagram of a second determining module provided in an embodiment of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0058] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a crack prediction method provided in an embodiment of this application. This crack prediction method is applied in a computer device. The crack prediction method may include:

[0059] Step 101: Obtain pre-stack seismic data.

[0060] Here, the pre-stack seismic data acquired by the computer equipment may include seismic signals of n seismic waves propagating in the strata. Each seismic signal may include signals acquired at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Here, n and m can both be integers greater than 2.

[0061] Step 102: Based on pre-stack seismic data, at the i-th sampling time point out of m sampling time points, determine the n seismic spectra that correspond one-to-one with the n-channel seismic signals.

[0062] Here, 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 n seismic spectra, 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 at the i-th sampling time point among the m sampling time points, excluding the reference spectrum.

[0064] Here, each of the (n-1) attenuation anisotropy eigenvalues ​​can be used to characterize the fitting relationship between the logarithmic ratio of the spectra of two seismic waves 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 logarithmic ratio of the spectra of a seismic spectrum other than the reference spectrum and the frequency of the seismic wave at the i-th sampling time point.

[0065] Step 104: Based on (n-1) attenuation anisotropic feature values, obtain the predicted fracture orientation in the formation at the i-th sampling time point.

[0066] Currently, in existing crack prediction methods, after acquiring pre-stack seismic data, m seismic spectra corresponding to m time sampling points are determined within the same seismic wave signal. Thus, one seismic spectrum can be arbitrarily selected from these m spectra as a reference seismic spectrum. Based on the selected reference seismic spectrum and all other seismic spectra under the same seismic wave signal (excluding the reference spectrum), (m-1) attenuation anisotropy eigenvalues ​​are obtained. Each attenuation anisotropy eigenvalue characterizes the fitting relationship between the logarithmic ratio of the seismic wave spectrum at two different sampling time points in the same propagation azimuth and the frequency of the seismic wave. Since the noise in the acquired pre-stack seismic data differs at different sampling time points, after taking the logarithmic ratio of the spectra of a seismic wave in the same propagation azimuth at two different sampling time points, the different noises at the two different sampling time points may overlap. Therefore, when predicting cracks, noise will affect the calculation results, reducing the reliability of the prediction results.

[0067] In this application, each of the (n-1) attenuation anisotropy eigenvalues ​​characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves from two different propagation azimuths at the same sampling time point (e.g., at the i-th sampling time point) and the frequency of the seismic waves. Furthermore, at the same sampling time point, all seismic signals in the pre-stack seismic data acquired have the same noise. Therefore, by taking the logarithmic ratio of the two seismic spectra corresponding to the seismic signals from two different propagation azimuths at the same sampling time point, the noise at the same sampling time point can be eliminated. This effectively eliminates the influence of noise on the calculation results, thereby improving the accuracy of predicting fractures in the formation.

[0068] In summary, the crack prediction method provided in this application allows a computer device to determine n seismic spectra corresponding to n seismic signals at the i-th sampling time point out of m sampling time points after acquiring pre-stack seismic data. Thus, any one of the n seismic spectra can be selected as a reference seismic spectrum, and (n-1) attenuation anisotropy eigenvalues ​​can be obtained based on the reference seismic spectrum and all other seismic spectra at the i-th sampling time point out of the m sampling time points, excluding the reference spectrum. Since each of the (n-1) attenuation anisotropy eigenvalues ​​characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves from two different propagation azimuths at the same sampling time point (e.g., at the i-th sampling time point) and the frequency of the seismic wave, and since all seismic signals in the acquired pre-stack seismic data at the i-th sampling time point have the same noise level... Therefore, by taking the logarithmic ratio of the two seismic spectra corresponding to seismic waves from two different propagation azimuths at the same sampling time point, the noise at the i-th sampling time point can be eliminated. This effectively eliminates the influence of noise on the calculation results, thereby improving the accuracy of predicting fractures in the formation.

[0069] Please refer to Figure 2 , Figure 2 This is a flowchart of another crack prediction method provided in an embodiment of this application. This crack prediction method is applied in a computer device. The crack prediction method may further include:

[0070] Step 201: Obtain pre-stack seismic data.

[0071] In this embodiment, a computer device can acquire pre-stack seismic data. The pre-stack seismic data acquired by the computer device may include seismic signals of n seismic waves propagating in the strata. Each seismic signal may include signals collected at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Here, n and m can both be integers greater than 2.

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

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

[0074] For example, pre-stack seismic data acquired by computer equipment may contain seismic signals of n seismic waves propagating in the strata, and each seismic signal may contain signals acquired at m sampling time points. Therefore, any b seismic signals can be selected from these n seismic signals, and each of these b seismic signals also contains signals acquired at m sampling time points.

[0075] In the embodiments of this application, please refer to Figure 3 , Figure 3 This is a flowchart illustrating a method for determining the stable frequency range of seismic waves based on b-channel seismic signals, as provided in an embodiment of this application. Determining the stable frequency range of seismic waves based on b-channel seismic signals may include the following steps:

[0076] Step 2021: At the s-th sampling time point out of the m sampling time points, determine the b seismic spectra that correspond one-to-one with the b-channel seismic signals.

[0077] In this application, a computer device can determine b seismic spectra corresponding one-to-one with b channels of seismic signals at the s-th sampling time point out of m sampling time points. Here, the s-th sampling time point out of the m sampling time points refers to any one of the m sampling time points.

[0078] For example, the seismic signal of the seismic wave in the pre-stack seismic data acquired by the computer equipment can be represented as a(t). After acquiring the seismic signal a(t) of the b-channel seismic wave, the Fourier transform of the seismic signal a(t) of each channel in the b-channel seismic signal can be performed according to the following formula (1):

[0079]

[0080] In this way, we can obtain b seismic spectra A(f) that correspond one-to-one with the seismic signals a(t) of each seismic wave in the b-channel seismic signal. In formula (1), j is an imaginary number, f is the frequency of the seismic wave, and t is the sampling time point of the seismic signal.

[0081] Then, by transforming the above formula (1), we can obtain the correspondence between the seismic spectrum A(f) and different sampling time points t. This correspondence can be referred to in the following formula (2):

[0082]

[0083] Here, 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 reflection coefficient and geometric diffusion, 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 properties of the formation's absorption properties.

[0084] Step 2022: Randomly select one of the b earthquake spectra as the auxiliary reference earthquake spectrum, and obtain the (b-1) spectrum logarithmic ratio versus seismic wave frequency curves based on the auxiliary reference earthquake spectrum and the earthquake spectra of each other except the auxiliary reference earthquake spectrum at the s-th sampling time point.

[0085] In this application, the computer device can arbitrarily select one of the b seismic spectra as an auxiliary reference seismic spectrum, and obtain (b-1) curves of the change in the logarithmic ratio of the spectrum versus the frequency of the seismic wave based on the auxiliary reference seismic spectrum and the seismic spectra of each of the other seismic spectra at the s-th sampling time point.

[0086] For example, 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 b seismic spectra, excluding the auxiliary reference spectrum, can be expressed as:

[0089]

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

[0091]

[0092] In formula (5) above, A4(f) is any one of the b earthquake spectra except for the auxiliary reference spectrum; A3(f) is the auxiliary reference spectrum. Therefore, This is the ratio of the spectral logarithm of any seismic spectrum other than the auxiliary reference spectrum to the auxiliary reference spectrum.

[0093] Furthermore, in the above formula (5), C is a constant; f is the frequency of the seismic wave; t s Q1 represents the s-th sampling time point; Q4 represents the attenuation quality factor in the stratum along its propagation direction when the seismic wave corresponding to any seismic spectrum other than the auxiliary reference spectrum propagates in the stratum; Q3 represents the attenuation quality factor in the stratum along its propagation direction when the seismic wave corresponding to the auxiliary reference spectrum propagates in the stratum.

[0094] Therefore, the computer device can obtain, according to the above formula (5), the curve of the change in the frequency of the seismic wave between the logarithm ratio of any seismic spectrum other than the auxiliary reference spectrum and the auxiliary reference spectrum. For example, such as... Figure 4 As shown, Figure 4 This application provides an embodiment of the curves showing the change in the frequency of a seismic wave as the ratio of the spectral logarithm of six seismic spectra (excluding the auxiliary reference spectrum) to the auxiliary reference spectrum. Figure 4 The horizontal axis represents the frequency of the seismic wave, and the vertical axis represents the logarithmic ratio of the spectrum.

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

[0096] In this application, a computer device can determine a stable frequency range based on (b-1) logarithmic ratios of the spectrum and the frequency variation curves of seismic waves.

[0097] For example, such as Figure 4 As shown, when the frequency of the seismic wave is between 30Hz and 70Hz, the trends of the six spectral logarithmic ratios and the frequency of the seismic wave are relatively stable. Therefore, the stable frequency range can be [30, 70]Hz.

[0098] In this scenario, computer equipment can acquire the stable frequency range of seismic waves. Subsequently, based on this stable frequency range, attenuation anisotropic characteristic values ​​can be more accurately fitted, resulting in higher accuracy in predicting fractures in the formation based on the attenuation anisotropic characteristic values.

[0099] Step 203: At the i-th sampling time point out of the m sampling time points, determine the n seismic spectra that correspond one-to-one with the n seismic signals.

[0100] In this application, a computer device can determine n seismic spectra corresponding one-to-one with n seismic signals at the i-th sampling time point out of m sampling time points. Here, the i-th sampling time point out of the m sampling time points refers to any one of the m sampling time points. It should be noted that this i-th sampling time point can be the same as or different from the s-th sampling time point in step 202. This application does not limit this aspect.

[0101] For example, the seismic signal of the seismic wave in the pre-stack seismic data acquired by the computer equipment can be represented as a(t). After acquiring the seismic signal a(t) of n seismic waves, a Fourier transform can be performed on the seismic signal a(t) of each seismic wave in the n seismic signals according to the above formula (1) to obtain n seismic spectra A(f) that correspond one-to-one with the seismic signal a(t) of each seismic wave in the n seismic signals. Then, the correspondence between the seismic spectrum A(f) and different sampling time points t can be obtained according to the above formula (2).

[0102] Step 204: Select any one of the n seismic spectra as the reference seismic spectrum, and obtain (n-1) attenuation anisotropy eigenvalues ​​based on the reference seismic spectrum and the seismic spectra of each of the other seismic spectra at the i-th sampling time point.

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

[0104] It should be noted that the propagation azimuth of the seismic wave corresponding to any reference seismic spectrum selected by the computer device from n seismic spectra can be defined as 0°. Thus, the computer device can determine the specific angle of the propagation azimuth of the seismic wave corresponding to any of the (n-1) seismic spectra based on the difference between the propagation azimuths of the seismic waves corresponding to the seismic waves in the (n-1) seismic spectra and the propagation azimuths corresponding to the reference seismic spectrum. In this case, after acquiring (n-1) attenuation anisotropy eigenvalues ​​at the i-th sampling time point, the computer device can determine the specific azimuth of the fracture in the stratum at the i-th sampling time point based on these attenuation anisotropy eigenvalues.

[0105] In this embodiment, for any attenuation anisotropy characteristic value, the computer device needs to obtain it based on the spectral logarithm ratio of the corresponding seismic spectrum to the reference seismic spectrum, combined with the frequency of the seismic wave. For an example, please refer to... Figure 5 , Figure 5 This is a flowchart illustrating a method for obtaining any one of the attenuation anisotropy characteristic values ​​provided in this application embodiment. Obtaining any one of the attenuation anisotropy characteristic values ​​using a computer device may include the following steps:

[0106] Step 2041: Obtain the relationship between the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum and the frequency of the earthquake wave.

[0107] In this application, a computer device can obtain the relationship between the spectral logarithm ratio of any seismic spectrum other than the reference seismic spectrum and the frequency of the seismic wave.

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

[0109]

[0110] At the i-th sampling time point, any one of the n seismic spectra, excluding the reference seismic spectrum, can be represented as:

[0111]

[0112] Thus, by comparing formula (7) with formula (6) and then taking the logarithm, we can obtain the ratio of the spectral logarithm of any seismic spectrum other than the reference spectrum to the reference spectrum at the i-th sampling time point:

[0113]

[0114] One of the corresponding attenuation anisotropy eigenvalues ​​can be expressed as:

[0115]

[0116] Thus, substituting formula (9) into formula (8) yields the first fitting formula:

[0117]

[0118] Where A1(f) is the reference earthquake spectrum; A2(f) is any earthquake spectrum other than the reference earthquake spectrum among the n earthquake spectra. Therefore, This is the ratio of the spectral logarithm of any earthquake 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 Q1 represents the i-th sampling time point; Q2 represents the attenuation quality factor in the stratum when the seismic wave corresponding to any seismic spectrum other than the reference spectrum propagates in the stratum in its propagation direction; Q1 represents the attenuation quality factor in the stratum when the seismic wave corresponding to the reference spectrum propagates in the stratum in its propagation direction.

[0120] Therefore, the first fitting formula mentioned above can be used to reflect the relationship between the ratio of the spectral logarithm of any earthquake spectrum other than the reference earthquake spectrum and the frequency of the earthquake wave.

[0121] Step 2042: Based on the relationship between the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum and the frequency of the earthquake wave, linearly fit the spectral logarithm ratio of this earthquake spectrum and the reference earthquake spectrum with the frequency of the earthquake wave to obtain a corresponding attenuation anisotropy characteristic value.

[0122] In this embodiment of the application, the computer device can obtain a corresponding attenuation anisotropy characteristic value by linearly fitting the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum and the frequency of the earthquake wave with the relationship between the two.

[0123] For example, a computer device can use the first fitting formula described above to linearly fit the ratio of the spectral logarithm of any earthquake spectrum other than the reference earthquake spectrum to the frequency of the earthquake wave, thereby obtaining a corresponding attenuation anisotropy characteristic value.

[0124] Here, the computer equipment can use the first fitting formula mentioned above to linearly fit the logarithm ratio of any seismic spectrum (excluding the reference seismic spectrum) to the reference spectrum with the frequency of the seismic wave, thus obtaining the slope k of the linear transformation. Since the slope k of the linear transformation and the corresponding attenuation anisotropy eigenvalue Δ(1 / Q) satisfy the following relationship:

[0125]

[0126] Therefore, after obtaining the linearly changing slope k, the computer device can deduce the corresponding attenuation anisotropic eigenvalue Δ(1 / Q) using the formula (10).

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

[0128] For example, a computer device can obtain the stable spectral logarithm ratio interval within the stable frequency range of seismic waves based on the relationship between the spectral logarithm ratio of any seismic spectrum (excluding the reference seismic spectrum) and the reference spectrum, and the frequency of the seismic wave. Then, the computer device can use the aforementioned first fitting formula to linearly fit the spectral logarithm ratio within the stable spectral logarithm ratio interval to the frequency within the stable frequency range, thereby obtaining a highly accurate attenuation anisotropy characteristic value.

[0129] For example, a computer device can plot a similar graph based on the relationship between the spectral logarithm ratio of any seismic spectrum (excluding the reference seismic spectrum) to the reference spectrum and the frequency variation of the seismic waves. Figure 4 The curve in the figure. Thus, when the stable frequency range of the seismic wave is [30, 70] Hz, the range of the frequency range of the seismic wave from 30 Hz to 70 Hz can be intercepted in the plotted curve, and the range of the corresponding spectrum logarithm ratio is: the stable spectrum logarithm ratio range.

[0130] Therefore, through the above process, (n-1) attenuation anisotropic eigenvalues ​​at the i-th sampling time point can be obtained. Since the i-th sampling time point is any one of the m sampling time points, and the process of obtaining the (n-1) attenuation anisotropic eigenvalues ​​at each of the m sampling time points is the same, then through the above process, (n-1) attenuation anisotropic eigenvalues ​​at each of the m sampling time points can be obtained; that is, m×(n-1) attenuation anisotropic eigenvalues ​​can be obtained.

[0131] Step 205: Based on the (n-1) attenuation anisotropic feature values ​​obtained at the i-th sampling time point, obtain the prediction result of the fracture orientation in the strata at the i-th sampling time point.

[0132] In this application, a computer device can obtain the predicted location of fractures in the formation at the i-th sampling time point based on (n-1) attenuation anisotropic characteristic values ​​obtained at the i-th sampling time point.

[0133] For example, at the i-th sampling time point, the attenuation of the P-wave in the seismic wave undergoes cosine oscillations with the azimuth of the seismic wave propagation in the strata, as shown in the following relationship:

[0134]

[0135] Where Δ(1 / Q) is the attenuation anisotropy characteristic value obtained at the i-th sampling time point. Let represent the azimuth of the (n-1) seismic signals (excluding the 0° azimuth signal) propagating through the strata. Let C1 = Δ(1 / Q0) be the orientation of the crack at the i-th sampling time point, and let C1 = Δ(1 / Q0) represent the mean value of the all-around attenuation anisotropy change at the i-th sampling time point.

[0136] It should be noted that seismic waves can propagate through the strata at a certain speed. Therefore, based on the acquired pre-stack seismic data, computer equipment can determine the propagation distance of the seismic wave at any one of the m sampling time points, and thus the depth of the stratum where the seismic wave is located at any one of the m sampling time points. C1 = Δ(1 / Q0) can characterize the orientation of the fracture in the formation depth at the i-th sampling time point, and C1 = Δ(1 / Q0) can characterize the intensity of the fracture in the formation depth at the i-th sampling time point.

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

[0138]

[0139] Among them, let y = Δ(1 / Q). From the second fitting formula, x and y can be considered linear functions. Thus, based on the (n-1) attenuation anisotropic eigenvalues ​​Δ(1 / Q) obtained at the i-th sampling time point, and the (n-1) seismic signals (excluding the 0° azimuth signal) and the azimuth of the other (n-1) channels of seismic signal propagation in the strata... After linearly fitting x and y using the second fitting formula, the slope value of the linear function can be obtained, and thus the values ​​of a, b, and c can be obtained. Thus, at the i-th sampling time point, C1 and... They are respectively:

[0140] C1 = a;

[0141]

[0142] Therefore, based on what has been obtained... The orientation of the fracture within the formation depth at the i-th sampling time point can be obtained.

[0143] In this application, the computer device can obtain C1 and C2 at any one of the m sampling time points based on (n-1) attenuation anisotropy eigenvalues ​​at any one of the m sampling time points. Value, due to It can characterize the orientation of fractures within the formation depth at the i-th sampling time point; therefore, the computer can obtain C1 and The value reflects how it changes over time. Therefore, computer equipment can predict the location of fractures in strata at different depths.

[0144] For example, please refer to Figure 6 , Figure 6 This is a prediction result diagram provided in an embodiment of this application using an existing crack prediction method. Figure 7 This is a prediction result diagram of a crack prediction method provided in this application, according to an embodiment of the present application. Figure 6 and Figure 7 All are rose plots. The circumferential coordinates of the rose plot represent the azimuth of seismic waves propagating within the strata. The radial coordinates represent the number of fractures. For the same set of pre-stack seismic data, when processed using the fracture prediction method of this application, from... Figure 7 It is evident that the cracks are mainly distributed within the 330° to 360° range, with a higher crack density in this range. When existing crack prediction methods are used, such as... Figure 6 As shown, the main distribution area of ​​the fractures is not clearly discernible. Therefore, using the fracture prediction method of this application to analyze pre-stack data with computer equipment can greatly improve the accuracy of predicting fractures in the formation.

[0145] Please refer to Figure 8 and Figure 9 , Figure 8 This is one of the embodiments provided in this application. A diagram illustrating how it changes over time. Figure 9This is a schematic diagram illustrating the change of C1 over time, provided in an embodiment of this application. Figure 8 The six rose analysis diagrams represent the fracture prediction results at six different sampling time points. Based on this, the main distribution range of fractures at each sampling time point can be obtained at the formation depth. Figure 9 The horizontal axis represents time, and the vertical axis represents C1, where... Figure 8 The six sampling time points and Figure 9 The time axis division corresponds accordingly. In the final crack prediction results, C1 and They can verify each other's correctness, thus making the crack prediction results obtained by the computer equipment more accurate.

[0146] It should be noted that the order of steps in the crack prediction method provided in this application embodiment can be appropriately adjusted, and the steps can also be added or removed as appropriate. Any variation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application, and therefore will not be elaborated further.

[0147] In summary, the crack prediction method provided in this application allows a computer device to determine n seismic spectra corresponding to n seismic signals at the i-th sampling time point out of m sampling time points after acquiring pre-stack seismic data. Thus, any one of the n seismic spectra can be selected as a reference seismic spectrum, and (n-1) attenuation anisotropy eigenvalues ​​can be obtained based on the reference seismic spectrum and all other seismic spectra at the i-th sampling time point out of the m sampling time points, excluding the reference spectrum. Since each of the (n-1) attenuation anisotropy eigenvalues ​​characterizes the fitting relationship between the logarithmic ratio of the spectra of seismic waves from two different propagation azimuths at the same sampling time point (e.g., at the i-th sampling time point) and the frequency of the seismic wave, and since all seismic signals in the acquired pre-stack seismic data at the i-th sampling time point have the same noise level... Therefore, by taking the logarithmic ratio of the two seismic spectra corresponding to seismic waves from two different propagation directions at the same sampling time point, the noise at the i-th sampling time point can be eliminated. This effectively eliminates the influence of noise on the calculation results, thereby improving the accuracy of predicting fractures in the formation.

[0148] This application also provides a crack prediction device, please refer to... Figure 10 , Figure 10 This is a block diagram of a crack prediction device provided in an embodiment of this application. The crack prediction device 300 can be integrated into a computer device, and the crack prediction device 300 may include:

[0149] The first acquisition module 301 is used to acquire pre-stack seismic data. The pre-stack seismic data acquired by the first acquisition module 301 contains seismic signals of n seismic waves propagating in the strata. Each seismic wave signal contains signals collected at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Both n and m are integers greater than 2.

[0150] The first determining module 302 is used to: determine n seismic spectra corresponding one-to-one with the n seismic signals at the i-th sampling time point among m sampling time points.

[0151] The second acquisition module 303 is used to: arbitrarily select one of the n seismic spectra corresponding one-to-one with the n seismic signals as the reference seismic spectrum, and obtain (n-1) attenuation anisotropy characteristic values ​​based on the reference seismic spectrum and the seismic spectra of each other except the reference seismic spectrum at the i-th sampling time point. Each attenuation anisotropy characteristic value is used to characterize the fitting relationship between the logarithm ratio of the spectra of two seismic waves in two different propagation directions at the i-th sampling time point and the frequency of the seismic wave.

[0152] The third acquisition module 304 is used to: based on (n-1) attenuation anisotropic feature values, obtain the predicted result of the fracture orientation in the stratum at the i-th sampling time point.

[0153] In summary, the fracture prediction device provided in this application, after acquiring pre-stack seismic data, obtains the fitting relationship between the spectral logarithm ratio of each seismic spectrum (excluding the reference seismic spectrum) and the frequency of the seismic wave at the i-th sampling time point out of m sampling time points. This allows for the acquisition of (n-1) attenuation anisotropy eigenvalues ​​at the i-th sampling time point, thereby eliminating the common noise in the pre-stack seismic data acquired by the computer at the i-th sampling time point. This effectively eliminates the influence of noise on the calculation results, thereby improving the accuracy of predicting fractures in the formation.

[0154] Optional, please refer to Figure 11 , Figure 11 This is a structural block diagram of a second acquisition module provided in an embodiment of this application. The second acquisition module 303 may include:

[0155] The first acquisition unit 3031 is used to: acquire the relationship between the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum and the frequency of the earthquake wave among the n earthquake spectra.

[0156] The fitting unit 3032 is used to linearly fit the frequency of the seismic wave to the logarithmic ratio of any seismic spectrum other than the reference seismic spectrum among the n seismic spectra, in order to obtain the corresponding attenuation anisotropy characteristic value.

[0157] Optionally, the fitting unit 3032 is used to: employ a first fitting formula to linearly fit the logarithm ratio of the spectrum of any seismic spectrum other than the reference seismic spectrum to the frequency of the seismic wave, thereby obtaining the corresponding attenuation anisotropy characteristic value. The first fitting formula is:

[0158]

[0159] Where A1(f) is the reference earthquake spectrum, A2(f) is any earthquake spectrum other than the reference earthquake spectrum, f is the frequency of the seismic wave, and t i Let be the i-th sampling time point, Δ(1 / Q) be the attenuation anisotropic characteristic value, and C be a constant.

[0160] Optional, please refer to Figure 12 , Figure 12 This is a block diagram of another crack prediction device provided in an embodiment of this application. The crack prediction device 300 may further include:

[0161] The second determining module 305 is used to: after the first acquiring module acquires pre-stack seismic data, select any b seismic signals from n seismic signals, and determine the stable frequency range of seismic waves based on the b seismic signals. Here, b is an integer greater than 2 and less than or equal to n.

[0162] The fitting unit 3032 is used to: obtain the stable spectrum logarithm ratio interval corresponding to the stable frequency interval based on the change relationship, and linearly fit the spectrum logarithm ratio in the stable spectrum logarithm ratio interval with the frequency of the seismic wave in the stable frequency interval to obtain the attenuation anisotropic characteristic value.

[0163] It should be noted that, please refer to Figure 13 , Figure 13 This is a structural block diagram of a second determining module provided in an embodiment of this application. The second determining module 305 may include:

[0164] The first determining unit 3051 is used to: determine b seismic spectra that correspond one-to-one with the b-channel seismic signals at the s-th sampling time point among m sampling time points;

[0165] The second acquisition unit 3052 is used to: arbitrarily select one earthquake spectrum from b earthquake spectra as an auxiliary reference earthquake spectrum, and based on the auxiliary reference earthquake spectrum and the earthquake spectra of each other except the auxiliary reference earthquake spectrum at the s-th sampling time point, acquire (b-1) curves showing the change in the logarithmic ratio of the spectrum versus the frequency of the earthquake wave.

[0166] The second determining unit 3053 is used to: determine the stable frequency range based on (j-1) change curves.

[0167] In summary, the fracture prediction device provided in this application, after acquiring pre-stack seismic data, obtains the fitting relationship between the spectral logarithm ratio of each seismic spectrum (excluding the reference seismic spectrum) and the frequency of the seismic wave at the i-th sampling time point out of m sampling time points. This allows for the acquisition of (n-1) attenuation anisotropy eigenvalues ​​at the i-th sampling time point, thereby eliminating the common noise in the pre-stack seismic data acquired by the computer at the i-th sampling time point. This effectively eliminates the influence of noise on the calculation results, thereby improving the accuracy of predicting fractures in the formation.

[0168] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, modules, units, and sub-units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] This application also provides a computer device. The computer device may include a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set, which is loaded and executed by the processor to implement [the desired functionality]. Figure 1 or Figure 2 The crack prediction method is shown.

[0170] This application also provides a computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a processing component, cause the processing component to perform... Figure 1 or Figure 2 The crack prediction method is shown.

[0171] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "multiple" refers to two or more unless otherwise expressly defined.

[0172] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0173] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting cracks, characterized in that, The method includes: Acquire pre-stack seismic data, which includes seismic signals of n seismic waves propagating in the strata. Each seismic signal contains signals collected at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Both n and m are integers greater than 2. At the i-th sampling time point among the m sampling time points, n seismic spectra corresponding one-to-one with the n channels of the seismic signal are determined, where the i-th sampling time point refers to any one of the m sampling time points; Arbitrarily select one earthquake spectrum from the n earthquake spectra as the reference earthquake spectrum, and obtain n-1 attenuation anisotropy characteristic values ​​based on the reference earthquake spectrum and the earthquake spectra of each other except the reference earthquake spectrum at the i-th sampling time point. Each attenuation anisotropy characteristic value is used to characterize the fitting relationship between the logarithm ratio of the spectra of two earthquake waves with different propagation directions at the i-th sampling time point and the frequency of the earthquake wave. Based on the n-1 attenuation anisotropic characteristic values, the predicted location of the fracture in the stratum at the i-th sampling time point is obtained, wherein the azimuth of the seismic wave corresponding to the reference seismic spectrum propagating in the stratum is defined as 0°.

2. The method according to claim 1, characterized in that, Obtaining any one of the attenuation anisotropy eigenvalues ​​includes: Obtain the relationship between the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum among the n earthquake spectra and the reference earthquake spectrum, and the frequency variation of the earthquake wave. Based on the aforementioned relationship, the logarithmic ratio of the spectrum is linearly fitted to the frequency to obtain the attenuation anisotropic characteristic value.

3. The method according to claim 2, characterized in that, The attenuation anisotropy characteristic value is obtained by linearly fitting the spectral logarithmic ratio to the frequency, including: The first fitting formula is used to linearly fit the spectral logarithm ratio to the frequency to obtain the attenuation anisotropic characteristic value; the first fitting formula is: ； in, The reference earthquake spectrum, For any earthquake spectrum other than the reference earthquake spectrum, The frequency of the seismic wave is... For the i-th sampling time point, Let C be the attenuation anisotropic characteristic value, and C be a constant.

4. The method according to claim 2, characterized in that, After acquiring the pre-stack seismic data, the method further includes: Arbitrarily select b seismic signals from the n seismic signals, and determine the stable frequency range 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; Specifically, linearly fitting the spectral logarithmic ratio to the frequency to obtain the attenuation anisotropic characteristic value includes: Based on the aforementioned relationship, the stable spectrum logarithmic ratio interval corresponding to the stable frequency interval is obtained, and the spectrum logarithmic ratio in the stable spectrum logarithmic ratio interval is linearly fitted with the frequency in the stable frequency interval to obtain the attenuation anisotropy characteristic value.

5. The method according to claim 4, characterized in that, Determining the stable frequency range of the seismic wave based on the b-channel seismic signal includes: At the s-th sampling time point among the m sampling time points, determine b seismic spectra that correspond one-to-one with the b-channel seismic signals; Arbitrarily select one of the b earthquake spectra as an auxiliary reference earthquake spectrum, and obtain b-1 curves of the change between the logarithmic ratio of the spectrum and the frequency of the earthquake wave based on the auxiliary reference earthquake spectrum and the earthquake spectra of each other except the auxiliary reference earthquake spectrum at the s-th sampling time point. Based on b-1 of the aforementioned variation curves, the stable frequency range is determined.

6. A crack prediction device, characterized in that, The device includes: The first acquisition module is used to acquire pre-stack seismic data, which includes seismic signals of n seismic waves propagating in the strata. Each seismic signal contains signals collected at m sampling time points, and the azimuth of each seismic wave propagating in the strata is different. Both n and m are integers greater than 2. The first determining module is used to determine n seismic spectra corresponding one-to-one with the n channels of the seismic signals at the i-th sampling time point among the m sampling time points, wherein the i-th sampling time point refers to any one of the m sampling time points; The second acquisition module is used to arbitrarily select one earthquake spectrum from the n earthquake spectra as a reference earthquake spectrum, and based on the reference earthquake spectrum and the earthquake spectra of each other except the reference earthquake spectrum at the i-th sampling time point, acquire n-1 attenuation anisotropy characteristic values. Each attenuation anisotropy characteristic value is used to characterize the fitting relationship between the logarithm ratio of the spectra of two earthquake waves with different propagation directions at the i-th sampling time point and the frequency of the earthquake wave. The third acquisition module is used to acquire the predicted location of the fracture in the stratum at the i-th sampling time point based on the n-1 attenuation anisotropic characteristic values, wherein the azimuth of the seismic wave corresponding to the reference seismic spectrum propagating in the stratum is defined as 0°.

7. The apparatus according to claim 6, characterized in that, The second acquisition module includes: The first acquisition unit is used to acquire the spectral logarithm ratio of any earthquake spectrum other than the reference earthquake spectrum among the n earthquake spectra and the reference earthquake spectrum, and the relationship between the frequency of the earthquake wave and the frequency of the earthquake wave. The fitting unit is used to linearly fit the spectrum logarithm ratio to the frequency based on the change relationship, so as to obtain the attenuation anisotropic characteristic value.

8. The apparatus according to claim 7, characterized in that, The fitting unit is configured to: linearly fit the spectrum logarithm ratio to the frequency using a first fitting formula to obtain the attenuation anisotropy characteristic value; the first fitting formula is: ； in, The reference earthquake spectrum, For any earthquake spectrum other than the reference earthquake spectrum, The frequency of the seismic wave is... For the i-th sampling time point, Let C be the attenuation anisotropic characteristic value, and C be a constant.

9. The apparatus according to claim 7, characterized in that, The device further includes: The second determining module is used to, after the first acquiring module acquires the pre-stack seismic data, arbitrarily select b seismic signals from the n seismic signals, and determine the stable frequency range 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. The fitting unit is configured to: obtain the stable spectral logarithmic ratio interval corresponding to the stable frequency interval based on the change relationship, and linearly fit the spectral logarithmic ratio in the stable spectral logarithmic ratio interval with the frequency in the stable frequency interval to obtain the attenuation anisotropy characteristic value.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement the crack prediction method as described in any one of claims 1 to 5.

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