Fracture prediction method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application relate to a crack prediction method and device, electronic equipment and storage medium, the method comprising: dividing the seismic data into multiple split azimuth seismic data according to azimuth, and stacking according to near, medium and far incidence angles; inverting the stacked seismic data of each split azimuth to obtain isotropic elastic parameters; performing Fourier series expansion on the isotropic elastic parameters to obtain first anisotropic parameters based on an isotropic background; constructing an anisotropic low-frequency model according to the first anisotropic parameters, and inverting each split azimuth seismic data based on the anisotropic low-frequency model to obtain anisotropic elastic parameters; performing Fourier series expansion on the anisotropic elastic parameters to obtain second anisotropic parameters, which are used to realize density and azimuth prediction of target formation cracks; and improving the stability and accuracy of small-scale crack prediction by using near, medium and far path data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration, and in particular to a fracture prediction method and device, an electronic device and a storage medium. BACKGROUND

[0002] For conventional dense reservoirs, fractures are one of the key elements of high yield, and for unconventional reservoirs, fractures are also one of the engineering dessert evaluation standards, and have a crucial role in the yield and pressure change of horizontal wells, therefore, high-precision fracture prediction becomes more and more important in oil and gas exploration.

[0003] In the related art, only azimuth far-angle data is usually used for single elastic impedance inversion, and a Fourier series expansion is performed on the inverted elastic impedance body to predict fractures, and because the far-angle data has a low signal-to-noise ratio and high randomness, the fracture prediction result is unstable.

[0004] In summary, the existing fracture prediction method cannot meet the needs of seismic exploration, and it is urgent to establish a more stable fracture prediction method to improve the fracture prediction accuracy and provide favorable support for subsequent reservoir comprehensive evaluation and well deployment. SUMMARY

[0005] Embodiments of the present application provide a fracture prediction method, device, electronic device and storage medium to solve the technical problem of low and unstable fracture prediction accuracy.

[0006] In a first aspect, embodiments of the present application provide a fracture prediction method, comprising: acquiring seismic data of a target formation, and dividing the seismic data into a plurality of split-azimuth seismic data according to an azimuth angle, and stacking each split-azimuth seismic data according to a near, middle and far incidence angle; performing anisotropy-free pre-stack elastic parameter inversion on each split-azimuth stacked seismic data to obtain anisotropy-free elastic parameters of each split-azimuth; performing Fourier series expansion on the anisotropy-free elastic parameters of each split-azimuth to obtain first anisotropy parameters based on an anisotropy-free background; constructing an anisotropy low-frequency model according to the first anisotropy parameters, and performing anisotropy-based pre-stack elastic parameter inversion on each split-azimuth stacked seismic data based on the anisotropy low-frequency model to obtain anisotropy elastic parameters of each split-azimuth; and performing Fourier series expansion on the anisotropy elastic parameters of each split-azimuth to obtain second anisotropy parameters, the second anisotropy parameters being used to realize density and azimuth prediction of fractures in the target formation.

[0007] In some embodiments, before the isotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth, the method further comprises: calibrating a target seismic wavelet according to the near, middle and far incidence angle seismic data of multiple split azimuths; and the isotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth comprises: performing the isotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth according to the target seismic wavelet; and the anisotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth based on the anisotropic low-frequency model comprises: performing the anisotropic prestack elastic parameter inversion on the prestack seismic data of each split azimuth based on the anisotropic low-frequency model and the target seismic wavelet.

[0008] In some embodiments, before the isotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth, the method further comprises: calibrating a target seismic wavelet according to the near, middle and far incidence angle seismic data of multiple split azimuths; and the isotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth comprises: performing the isotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth according to the target seismic wavelet; and the anisotropic prestack elastic parameter inversion on the stacked seismic data of each split azimuth based on the anisotropic low-frequency model comprises: performing the anisotropic prestack elastic parameter inversion on the prestack seismic data of each split azimuth based on the anisotropic low-frequency model and the target seismic wavelet.

[0009] In some embodiments, the calibrating a target seismic wavelet according to the near, middle and far incidence angle seismic data of multiple split azimuths comprises: determining a target split azimuth perpendicular to the principal stress direction of the target formation; extracting a candidate seismic wavelet of the seismic data corresponding to the middle incidence angle of the target split azimuth, and matching and calibrating the candidate seismic wavelet with the seismic data corresponding to the near incidence angle and the far incidence angle of the target split azimuth; and determining the candidate seismic wavelet as the target seismic wavelet when a preset condition is met.

[0010] In some embodiments, the first anisotropic parameter comprises a first anisotropic strength and a first anisotropic direction; and the formula of the anisotropic low-frequency model is as follows:

[0011]

[0012] wherein r2 represents the first anisotropic strength, ω is the central angle data of the split azimuth, φ represents the first anisotropic direction, V p represents the P-wave velocity, and V s represents the S-wave velocity.

[0013] In some embodiments, before the step of dividing the seismic data into a plurality of azimuthal seismic data according to azimuth, the method further comprises: performing an optimization process on the seismic data; wherein the optimization process comprises at least one of the following: a noise removal process, a gather flattening process, and a fidelity improvement process.

[0014] In some embodiments, before the step of performing an isotropic pre-stack elastic parameter inversion on the stacked seismic data of each azimuth, the method further comprises: performing a forward modeling based on well logging data of the target formation to obtain a forward modeling result of whether the formation is anisotropic; performing an anisotropy analysis on the seismic data of near, middle, and far incidence angles of each azimuth, and performing the step of performing an isotropic pre-stack elastic parameter inversion on the stacked seismic data of each azimuth when the analysis result matches the forward modeling result.

[0015] In a second aspect, the application provides a fracture prediction device, comprising: an azimuthal stacking module, configured to obtain seismic data of a target formation, divide the seismic data into a plurality of azimuthal seismic data according to azimuth, and stack each azimuthal seismic data according to near, middle, and far incidence angles; a first inversion module, configured to perform an isotropic pre-stack elastic parameter inversion on the stacked seismic data of each azimuth to obtain isotropic elastic parameters of each azimuth; a first processing module, configured to perform a Fourier series expansion on the isotropic elastic parameters of each azimuth to obtain first anisotropic parameters based on an isotropic background; a second inversion module, configured to construct an anisotropic low-frequency model according to the first anisotropic parameters, and perform an anisotropic pre-stack elastic parameter inversion on the stacked seismic data of each azimuth based on the anisotropic low-frequency model to obtain anisotropic elastic parameters of each azimuth; and a second processing module, configured to perform a Fourier series expansion on the anisotropic elastic parameters of each azimuth to obtain second anisotropic parameters, wherein the second anisotropic parameters are used to realize density and azimuth prediction of fractures of the target formation.

[0016] In some embodiments, the device further comprises a data quality control module, configured to perform a forward modeling based on well logging data of the target formation to obtain a forward modeling result of whether the formation is anisotropic, perform an anisotropy analysis on the seismic data of near, middle, and far incidence angles of each azimuth, and control the first inversion module to perform the step of performing an isotropic pre-stack elastic parameter inversion on the stacked seismic data of each azimuth when the analysis result matches the forward modeling result.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory is used for storing a computer program; and the processor is used for executing the program stored on the memory to realize the steps of the fracture prediction method according to any one of the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the fracture prediction method according to any one of the first aspect.

[0019] The fracture prediction method, device, electronic device and storage medium provided by the embodiments of the present application, the method comprises: acquiring seismic data of a target formation, and dividing the seismic data into a plurality of split azimuth seismic data according to azimuth angles, and stacking each split azimuth seismic data according to near, middle and far incidence angles; performing anisotropy-free pre-stack elastic parameter inversion on each split azimuth stacked seismic data to obtain anisotropy-free elastic parameters of each split azimuth; performing Fourier series expansion on the anisotropy-free elastic parameters of each split azimuth to obtain first anisotropic parameters based on anisotropy-free background; constructing an anisotropy low-frequency model according to the first anisotropic parameters, and performing anisotropy-based pre-stack elastic parameter inversion on each split azimuth stacked seismic data based on the anisotropy low-frequency model to obtain anisotropic elastic parameters of each split azimuth; and performing Fourier series expansion on the anisotropic elastic parameters of each split azimuth to obtain second anisotropic parameters, which are used to realize density and azimuth prediction of the target formation fracture; that is, the embodiments of the present application consider anisotropy information of near, middle and far path seismic data, and use the anisotropy information to establish an anisotropy low-frequency model which can highlight anisotropy information, and on this basis, perform anisotropy-based pre-stack elastic parameter inversion to obtain anisotropy direction and strength, predict fracture density and azimuth, and improve the stability and precision of small-scale fracture prediction. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles behind the application.

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows, and obviously, other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0022] Figure 1A flowchart of a fracture prediction method provided for an embodiment of the present application is shown in the figure.

[0023] Figure 2 A flowchart of another fracture prediction method provided for an embodiment of the present application is shown in the figure.

[0024] Figure 3 A flowchart of still another fracture prediction method provided for an embodiment of the present application is shown in the figure.

[0025] Figure 4 A flowchart of yet another fracture prediction method provided for an embodiment of the present application is shown in the figure.

[0026] Figure 5 A fracture prediction profile based on anisotropy pre-stack inversion in an embodiment of the present application is shown in the figure.

[0027] Figure 6 A fracture prediction plane based on anisotropy pre-stack inversion in an embodiment of the present application is shown in the figure.

[0028] Figure 7 A structural schematic diagram of a fracture prediction device provided for an embodiment of the present application is shown in the figure.

[0029] Figure 8 A structural schematic diagram of an electronic device provided for an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0031] For conventional dense reservoirs, fractures are one of the key elements of high yield, and for unconventional reservoirs, fractures are also one of the evaluation standards of engineering dessert, which plays a crucial role in the yield and pressure change of horizontal wells, therefore, high-precision fracture prediction becomes more and more important in oil and gas exploration.

[0032] At present, the fracture prediction technology mainly contains two categories. The first category is a fracture detection technology based on post-stack data, mainly using coherence, curvature and other attributes to qualitatively predict large-scale fractures, and using tensor field and maximum likelihood to semi-quantitatively predict fractures. The post-stack mainly indirectly reflects the fracture development in a macroscopic manner. The second category is a fracture development degree prediction technology based on the principle of pre-stack anisotropy, mainly using amplitude, frequency and other attributes for ellipse fitting in an anisotropic medium, and predicting the strength of the fracture according to the eccentricity of the ellipse and predicting the direction of the fracture according to the long axis direction of the ellipse. However, it is found that the ellipse fitting based on the attribute reflects the comprehensive information of the two sides of the formation interface, and cannot determine the fracture information of the overlying and underlying formations, so as to cause low coincidence degree of the fracture prediction. In addition, in the pre-stack fracture prediction in the horizontal axis symmetry anisotropic (HTI) medium, the angle calculated by the Ruger formula is all positive, and there is a problem of 90° ambiguity in the direction.

[0033] In the related technical solutions, single elastic impedance inversion is generally performed on the far angle data in different directions, and the elastic impedance body obtained by the inversion is expanded by Fourier series to predict the fracture. However, in the above solutions, only the far angle data is used, the anisotropy information of the near and middle angle data is not considered, the signal-to-noise ratio of the far angle data is low, the data quality is not as good as that of the near and middle angle data, and the randomness is high, so that the fracture prediction result is unstable.

[0034] In view of the above technical problems, the technical concept of the present application is to consider the anisotropy information of the near and middle angle data, use an anisotropy low-frequency model to highlight the weight of the anisotropy information, and perform pre-stack inversion to obtain the anisotropy strength and direction, so as to improve the stability of the prediction result on the basis of accurately predicting the fracture density and strength.

[0035] Figure 1 A flowchart of a fracture prediction method provided by the embodiment of the present application is shown in the figure, and the execution subject is a fracture prediction device, which can be realized by any software / hardware. As shown in the figure, the fracture prediction method comprises the following steps. Figure 1

[0036] Step S101, acquiring seismic data of a target formation, and dividing the seismic data into a plurality of split-azimuth seismic data according to the azimuth angle, and stacking each split-azimuth seismic data according to the near, middle and far incidence angles.

[0037] Specifically, the target formation can be understood as a formation to be predicted for fracture; the seismic data can be understood as OVT gather data, which contains azimuth angle, incidence angle, offset distance and other information for division; and the near, middle and far incidence angles can be understood as the incidence angles corresponding to the near offset distance, middle offset distance and far offset distance from near to far. ​

[0038] In this embodiment, the OVT gathers can be divided into multiple partial azimuth seismic data according to the principle that the number of coverage of each partial azimuth is basically equivalent, and the seismic data in the near, medium and far incidence angle range of each partial azimuth is stacked to obtain partial azimuth stacked seismic data. For example, the seismic data is divided into 6 partial azimuth stacked seismic data according to different azimuth angles, and then each partial azimuth seismic data is stacked according to the near, medium and far incidence angles, and a total of 18 seismic data bodies are divided. In the following examples, 6 partial azimuths and 3 incidence angles are taken as examples for description.

[0039] In some embodiments, before the seismic data is divided into multiple partial azimuth seismic data according to the azimuth angle, the seismic data is further subjected to an optimization process, and the optimization process includes at least one of the following: denoising processing, gather flattening processing and fidelity improvement processing.

[0040] Specifically, after obtaining the original OVT gather data, optimization processing such as denoising, gather flattening and fidelity improvement can be carried out on the original data to effectively solve the gather moveout problem of different offsets and improve the signal-to-noise ratio and fidelity of the data. Then the subsequent seismic data division and stacking operation is carried out.

[0041] Step S102, performing anisotropy-based pre-stack elastic parameter inversion on the stacked seismic data of each partial azimuth to obtain the isotropic elastic parameters of each partial azimuth.

[0042] Specifically, the theoretical basis of the pre-stack elastic parameter inversion is the Zoeppritz equation, as shown in formula (1):

[0043]

[0044] Wherein, R pp , R ps represent the reflection coefficients of reflected P-wave and reflected S-wave, T pp , T ps represent the transmission coefficients of transmitted P-wave and transmitted S-wave, V p1 , V s1 , ρ1 represent the reflected P-wave velocity, reflected S-wave velocity and density of the overlying medium, V p2 , V s2 , ρ2 represent the transmitted P-wave velocity, transmitted S-wave velocity and density of the underlying medium. The above matrix formula reflects the relationship between the plane wave reflection coefficient and transmission coefficient represented by the reflection angle and transmission angle, and the elastic parameters.

[0045] In this step, pre-stack elastic parameter inversion is performed on the 6 partial azimuth stacked seismic data, and finally 6 isotropic elastic parameter bodies of different partial azimuths are obtained, including P-wave impedance Zp , P-wave velocity V p , S-wave velocity Vs, and density model DEN, etc.

[0046] Step S103, Fourier series expansion is performed on the isotropic elastic parameters of each azimuth to obtain first anisotropic parameters based on the isotropic background.

[0047] Specifically, Fourier series expansion is performed on the six azimuthal isotropic elastic parameters obtained by inversion to obtain anisotropic parameters based on isotropy. In the isotropic background, the detailed differences between different elastic parameter bodies represent the anisotropy information of different azimuths.

[0048] Further, in the pre-stack fracture prediction, Ruger first established an approximate equation of azimuthal AVO reflection coefficient of HTI medium, which clearly shows that the reflection coefficient of AVO is mainly composed of isotropic reflection coefficient and anisotropic reflection coefficient. Then, Fourier series expansion is performed on the basis of the approximate equation of AVO reflection coefficient, as shown in formula (2):

[0049]

[0050] Wherein, r0 represents the isotropic background of V p / V s ; r2 represents the anisotropy strength of V p / V s ; r4 represents the anisotropy strength (high-order term) of V p / V s ; ω represents the central angle of the azimuth; v represents the anisotropy direction. By using the above Fourier transform, the isotropic background in the elastic parameter body can be eliminated, and the anisotropy information can be directly obtained. The second-order term r2 and the fourth-order term r4 are both anisotropy strength, but the fourth-order term r4 sometimes has obvious linear relationship, and the second-order term r2 is relatively more stable. Therefore, the second-order Fourier coefficient is used to predict the fracture in the embodiment, and the Fourier series expansion of the isotropic elastic parameters is performed to obtain φ representing the anisotropy direction and r2 representing the anisotropy strength.

[0051] Step S104, constructing an anisotropic low-frequency model according to the first anisotropic parameters, and performing pre-stack elastic parameter inversion based on anisotropy on the stacked seismic data of each azimuth based on the anisotropic low-frequency model to obtain anisotropic elastic parameters of each azimuth.

[0052] Specifically, the first anisotropic parameters are used to establish an anisotropic low-frequency model, and on this basis, pre-stack elastic parameter inversion based on anisotropy is performed on the seismic data of each azimuth to obtain anisotropic elastic parameters.

[0053] In the related art, an anisotropy parameter obtained by isotropic Fourier series expansion is used to calculate an anisotropy low-frequency model, and anisotropy parameters r2 and φ are calculated into a body, and the anisotropy low-frequency model is calculated by formula (3) as shown in the following formula (3):

[0054]

[0055] In the formula, r2 is an anisotropy strength, ω is central angle data of a bearing, and φ is an anisotropy direction.

[0056] In some embodiments, the first anisotropy parameter includes a first anisotropy strength and a first anisotropy direction, and the anisotropy low-frequency model is shown in formula (4) as shown in the following formula (4):

[0057]

[0058] In the formula, r2 represents the first anisotropy strength, ω is central angle data of a bearing, φ represents the first anisotropy direction, V p represents a P-wave velocity, and V s represents a S-wave velocity.

[0059] It should be noted that, in order to highlight the anisotropy information, the weight of the anisotropy information in the anisotropy low-frequency model is increased, that is, the weight coefficient 2 in formula (3) is changed to the weight coefficient 4, wherein the weight coefficient 4 is determined by related experiments by a person skilled in the art. The anisotropy low-frequency model constructed in this embodiment is basically consistent with the isotropic low-frequency model in the overall trend, and there is a difference in the local part. This difference is the embodiment of the anisotropy information after the isotropic background is removed. In this step, the low-frequency model with highlighted anisotropy information obtained by calculation is used to perform 6-bearings pre-stack elastic parameter inversion to obtain anisotropy-based elastic parameters.

[0060] In step S105, the anisotropy elastic parameters of each bearing are subjected to Fourier series expansion to obtain second anisotropy parameters, and the second anisotropy parameters are used to realize density and bearing prediction of a target formation fracture.

[0061] Specifically, the anisotropy elastic parameters are subjected to Fourier series expansion to obtain anisotropy parameters based on anisotropy inversion, the second-order Fourier coefficient r2 is used to represent the anisotropy strength, that is, the fracture development degree or the fracture density, and the parameter φ is used to represent the anisotropy direction, that is, the fracture bearing. In addition, the anisotropy direction obtained by Fourier series expansion has positive and negative parts, and has a 90° perpendicular relationship with the actual fracture trend, which can solve the 90° ambiguity problem of fracture prediction and effectively improve the fracture prediction accuracy.

[0062] The crack prediction method provided by the embodiment of the present application comprises the following steps: obtaining seismic data of a target formation, dividing the seismic data into a plurality of azimuthal seismic data according to azimuth angles, and stacking each azimuthal seismic data according to near, middle and far incidence angles; performing anisotropic prestack elastic parameter inversion on the stacked seismic data of each azimuthal based on the isotropic background to obtain anisotropic elastic parameters of each azimuthal; performing Fourier series expansion on the anisotropic elastic parameters of each azimuthal to obtain first anisotropic parameters based on the isotropic background; constructing an anisotropic low-frequency model according to the first anisotropic parameters, and performing anisotropic prestack elastic parameter inversion on the stacked seismic data of each azimuthal based on the anisotropic low-frequency model to obtain anisotropic elastic parameters of each azimuthal; and performing Fourier series expansion on the anisotropic elastic parameters of each azimuthal to obtain second anisotropic parameters, which are used to realize density and azimuth prediction of the target formation cracks.

[0063] On the basis of the above-mentioned embodiment, Figure 2 Another crack prediction method provided by the embodiment of the present application is shown in the flowchart as Figure 2 The crack prediction method comprises the following steps:

[0064] In step S201, seismic data of a target formation is obtained, and the seismic data is divided into a plurality of azimuthal seismic data according to azimuth angles, and each azimuthal seismic data is stacked according to near, middle and far incidence angles.

[0065] In step S202, a target seismic wavelet is calibrated according to the near, middle and far incidence angle seismic data of a plurality of azimuthals.

[0066] In step S203, anisotropic prestack elastic parameter inversion is performed on the stacked seismic data of each azimuthal based on the target seismic wavelet to obtain isotropic elastic parameters of each azimuthal.

[0067] In step S204, Fourier series expansion is performed on the isotropic elastic parameters of each azimuthal to obtain first anisotropic parameters based on the isotropic background.

[0068] Step S205, constructing an anisotropy low-frequency model according to the first anisotropy parameter, and performing anisotropy-based pre-stack elastic parameter inversion on the pre-stack seismic data of each azimuth based on the anisotropy low-frequency model and the target seismic wavelet, to obtain anisotropy elastic parameters of each azimuth.

[0069] Step S206, performing Fourier series expansion on the anisotropy elastic parameters of each azimuth to obtain a second anisotropy parameter, which is used to realize the density and azimuth prediction of the target formation fracture.

[0070] The steps S201, S204 and S206 in the embodiment of the present application are similar to the implementation manners of the steps S101, S103 and S105 in the foregoing embodiment, and will not be described here again.

[0071] The difference from the foregoing embodiment is that, in the embodiment of the present application, the target seismic wavelet is calibrated according to the seismic data of near, middle and far incidence angles of multiple azimuths; the pre-stack elastic parameter inversion based on isotropy is performed on the stacked seismic data of each azimuth according to the target seismic wavelet; and the pre-stack elastic parameter inversion based on anisotropy is performed on the pre-stack seismic data of each azimuth based on the anisotropy low-frequency model and the target seismic wavelet.

[0072] Specifically, first, 18 seismic data bodies of near, middle and far incidence angles of 6 azimuths are used to select the seismic wavelet with the best performance, i.e., the target seismic wavelet, through multi-round fine calibration; then the isotropic inversion is performed by using the target seismic wavelet to obtain the anisotropy parameter with isotropic background; then the anisotropic inversion is performed by using the same target seismic wavelet and parameter, i.e., the same target seismic wavelet is used in both rounds of inversion, which improves the reliability of the inversion result; finally, the elastic parameters obtained through the inversion directly reflect the anisotropy information of the formation.

[0073] In some embodiments, the step S202 includes: determining a target azimuth perpendicular to the principal stress direction of the target formation; extracting a candidate seismic wavelet of the seismic data corresponding to the middle incidence angle of the target azimuth, and performing matching calibration on the candidate seismic wavelet and the seismic data corresponding to the near incidence angle and the far incidence angle of the target azimuth; and in the case of meeting a preset condition, determining the candidate seismic wavelet as the target seismic wavelet.

[0074] Specifically, first, a target azimuth is selected which is perpendicular to the principal stress direction of the target formation; then a seismic wavelet is calibrated from the mid-azimuth seismic data corresponding to the target azimuth, and is matched and calibrated with the near-azimuth data and the far-azimuth data of the target azimuth, and the optimal wavelet with the best effect is selected by continuously adjusting the signal-to-noise ratio, curve correlation, anti-correlation, curve normalization standard deviation, sparsity and merging difference value and other parameters.

[0075] On the basis of the foregoing embodiment, the isotropic elastic parameters are obtained by performing isotropic pre-stack inversion on each azimuthal seismic data by using the optimal wavelet, and then the anisotropic information based on the isotropy is calculated; when performing anisotropic pre-stack inversion by using the anisotropic information, the same optimal wavelet is used, and the reliability of the inversion result is improved, and then the anisotropic elastic parameters obtained by inversion directly reflect the anisotropic information of the formation.

[0076] On the basis of the foregoing embodiment, Figure 3 Another fracture prediction method provided by the embodiment of the present application is shown in the flowchart as shown in Figure 3 The fracture prediction method comprises the following steps.

[0077] In step S301, seismic data of a target formation is obtained, and the seismic data is divided into multiple azimuthal seismic data according to the azimuth angle, and each azimuthal seismic data is stacked according to the near, mid and far incidence angles.

[0078] In step S302, forward modeling is performed based on the logging data of the target formation, and the forward modeling result of whether the formation has anisotropy is obtained.

[0079] In step S303, anisotropy analysis is performed on the seismic data of the near, mid and far incidence angles of each azimuth, and it is determined whether the analysis result matches the forward modeling result.

[0080] If the analysis result matches the forward modeling result, step S304 is performed; otherwise, the embodiment is ended.

[0081] In step S304, if the analysis result matches the forward modeling result, pre-stack elastic parameter inversion based on isotropy is performed on the stacked seismic data of each azimuth, and the isotropic elastic parameters of each azimuth are obtained.

[0082] In step S305, Fourier series expansion is performed on the isotropic elastic parameters of each azimuth, and the first anisotropic parameter based on the isotropic background is obtained.

[0083] Step S306, constructing an anisotropy low-frequency model according to the first anisotropy parameter, and performing anisotropy-based pre-stack elastic parameter inversion on the stacked seismic data of each azimuth based on the anisotropy low-frequency model to obtain anisotropy elastic parameters of each azimuth.

[0084] Step S307, performing Fourier series expansion on the anisotropy elastic parameters of each azimuth to obtain a second anisotropy parameter, which is used to realize the density and azimuth prediction of the target formation fracture.

[0085] Steps S301, S304-S307 in the embodiment are similar to the implementation manners of steps S101-S105 in the foregoing embodiment, and will not be described herein.

[0086] The difference from the foregoing embodiment is that, in the embodiment, forward modeling is performed based on the logging data of the target formation to obtain a forward modeling result of whether the formation has anisotropy; anisotropy analysis is performed on the seismic data of each azimuth at near, medium and far incidence angles, and subsequent pre-stack elastic parameter inversion is performed when the analysis result matches the forward modeling result.

[0087] Specifically, after obtaining the seismic gather data, the gather data is quality controlled to lay a foundation for subsequent pre-stack inversion. First, forward modeling is performed, that is, based on the logging data of the target formation, the data of the six azimuths is simulated according to the actual filling medium of the fracture. The forward modeling result shows that, according to the change of the azimuth, the amplitude of the fracture changes with the change of the offset, which confirms that there is obvious anisotropy characteristic in different azimuths; then, the gather data is analyzed and quality controlled, that is, by analyzing the moveout of the near, medium and far gathers, the frequency, amplitude of the target interval have good consistency, the phase relationship is reasonable, and from the stacked data of different azimuths, the anisotropy characteristic of the medium and far gathers is more obvious when the incidence angle is greater than 16°, which is consistent with the forward modeling result, and the analysis confirms that the data is real and reliable, laying a foundation for subsequent pre-stack inversion.

[0088] On the basis of the foregoing embodiment, the anisotropy characteristic is determined through forward modeling of the logging data, and the anisotropy analysis is further performed on the actual seismic data body collected, and when the simulation result and the actual result are consistent, the analysis confirms that the gather data is real and reliable, laying a foundation for subsequent pre-stack inversion.

[0089] In order to further understand the embodiments of the present application, Figure 4 Another fracture prediction method provided by the embodiments of the present application is shown in the flowchart as Figure 4 The fracture prediction method specifically includes the following steps:

[0090] (1) Azimuth data stacking

[0091] Specifically, the OVT domain five-dimensional gather data is optimized, and then the gather data is divided into 6 azimuthal seismic data (such as azimuth 1 seismic data, azimuth 2 seismic data, …, azimuth 6 seismic data) according to the principle that the number of coverages of each partial azimuth is basically equivalent; then, for each azimuthal seismic data, near, medium and far three partial incidence angle stacking is carried out.

[0092] (2) Quality control of gather data

[0093] Specifically, forward modeling is carried out by using logging data, it is clear that the data has anisotropy characteristics, and near, medium and far angle data quality control analysis is carried out, and it is confirmed that the frequency, amplitude and phase relationship of the target layer are reasonable, which lays a foundation for prestack inversion.

[0094] (3) Anisotropy prestack elastic parameter inversion based on isotropy

[0095] Specifically, the prestack elastic parameter inversion based on isotropy is carried out by using 6 partial azimuth stacking data, and the elastic parameters of the inversion are expanded by Fourier series to obtain anisotropy parameters based on isotropy.

[0096] (4) Anisotropy prestack elastic parameter inversion.

[0097] Specifically, an anisotropy low-frequency model is established by using the anisotropy parameters based on isotropy, and on this basis, the anisotropy prestack elastic parameter inversion is carried out.

[0098] (5) Fourier series expansion.

[0099] Specifically, the elastic parameters based on anisotropy inversion are expanded by Fourier series, and finally the anisotropy parameters are used to represent the fracture density and azimuth.

[0100] Figure 5 It is a fracture prediction profile effect diagram based on anisotropy prestack inversion in the embodiment of the application; Figure 6 It is a fracture prediction plane effect diagram based on anisotropy prestack inversion in the embodiment of the application. The shale oil fracture prediction in a certain area is carried out by using the embodiment, and the anisotropy attribute plane is extracted along the layer, and is verified with the well with imaging logging data, wherein the fracture of J7035 well is developed, the imaging logging measured fracture direction is 110°-170°, from the prediction result, the anisotropy of the fracture development area is accurately predicted, and the prediction fracture direction is 30°-80°, which is perpendicular to the measured direction by 90°, the prediction result is highly consistent, which shows that the prediction accuracy of the embodiment is high.

[0101] In summary, the embodiment of the present application fully utilizes the rich characteristics of drilling and logging data and five-dimensional seismic data in the research area, fully mines the information of the seismic data, and applies a fracture prediction method based on anisotropic pre-stack inversion, and the coincidence rate is higher than 90% in verification with 5 wells with imaging logging data, which can effectively guide the fracturing scheme deployment of the horizontal well and provide a favorable basis for increasing reserves and production in the shale oil area.

[0102] Figure 7 A structural schematic diagram of a fracture prediction device provided by the embodiment of the present application is shown in FIG. 1, which comprises: Figure 7

[0103] The azimuth stacking module 701 is configured to obtain seismic data of a target formation, divide the seismic data into a plurality of azimuth seismic data according to azimuth angles, and stack each azimuth seismic data according to near, medium and far incident angles; the first inversion module 702 is configured to perform isotropic pre-stack elastic parameter inversion on each azimuth stacked seismic data to obtain isotropic elastic parameters of each azimuth; the first processing module 703 is configured to perform Fourier series expansion on the isotropic elastic parameters of each azimuth to obtain first anisotropic parameters based on an isotropic background; the second inversion module 704 is configured to construct an anisotropic low-frequency model according to the first anisotropic parameters, and perform anisotropic pre-stack elastic parameter inversion on each azimuth stacked seismic data based on the anisotropic low-frequency model to obtain anisotropic elastic parameters of each azimuth; and the second processing module 705 is configured to perform Fourier series expansion on the anisotropic elastic parameters of each azimuth to obtain second anisotropic parameters, which are used to realize density and azimuth prediction of fractures in the target formation.

[0104] In some embodiments, the first inversion module 702 is specifically configured to calibrate a target seismic wavelet according to seismic data of near, medium and far incident angles of a plurality of azimuths, and perform isotropic pre-stack elastic parameter inversion on each azimuth stacked seismic data according to the target seismic wavelet; and the second inversion module 704 is specifically configured to perform anisotropic pre-stack elastic parameter inversion on each azimuth pre-stack seismic data based on the anisotropic low-frequency model and the target seismic wavelet.

[0105] In some embodiments, the first inversion module 703 is specifically configured to determine a target azimuth perpendicular to the principal stress direction of the target formation, extract a candidate seismic wavelet of seismic data corresponding to the medium incident angle of the target azimuth, and match and calibrate the candidate seismic wavelet with seismic data corresponding to the near incident angle and the far incident angle of the target azimuth; and in the case of meeting a preset condition, determine the candidate seismic wavelet as the target seismic wavelet.​

[0106] In some embodiments, the first anisotropy parameter comprises a first anisotropy strength and a first anisotropy direction; and the formula of the anisotropy low-frequency model is as follows:

[0107]

[0108] wherein r2 represents the first anisotropy strength, ω is central angle data of the azimuth, φ represents the first anisotropy direction, V p represents the P-wave velocity, V s represents the S-wave velocity.

[0109] In some embodiments, the azimuth stack module 701 is further configured to perform optimization processing on the seismic data; and the optimization processing comprises at least one of the following: noise removal processing, gather flattening processing and fidelity improvement processing.

[0110] In some embodiments, the method further comprises a data quality control module 706, which is specifically configured to perform forward modeling based on well logging data of a target formation to obtain a forward modeling result of whether the formation is anisotropic; perform anisotropy analysis on seismic data of each azimuth at near, medium and far incidence angles, and control the first inversion module 702 to perform the step of performing anisotropy-based pre-stack elastic parameter inversion on the stacked seismic data of each azimuth in the case that the analysis result matches the forward modeling result.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the fracture prediction device described above and the corresponding benefits can refer to the corresponding process in the foregoing method examples, and will not be described here.

[0112] Figure 8 A hardware structure schematic diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 8. Figure 8 As shown in FIG. 8, the electronic device comprises a processor 801, a communication interface 802, a memory 803 and a communication bus 804, wherein the processor 801, the communication interface 802 and the memory 803 complete mutual communication through the communication bus 804,

[0113] The memory 803 is configured to store a computer program.

[0114] In an embodiment of the present application, the processor 801 is configured to execute the program stored in the memory 803 to implement the steps of the fracture prediction method provided by any one of the foregoing method embodiments.

[0115] The electronic device provided by the embodiment of the present application has similar implementation principles and technical effects to the above-described embodiments, and will not be described here.

[0116] The memory 803 can be an electronic storage such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. The memory 803 has a storage space for program codes for executing any of the steps of the above-described methods. For example, the storage space for program codes can include individual program codes for implementing individual steps of the above-described methods, respectively. The program codes can be read from or written to one or more computer program products. The computer program products include program code carriers such as a hard disk, a compact disk (CD), a memory card, or a floppy disk. Such computer program products are typically portable or stationary storage units. The storage unit can have a storage section or a storage space, etc., which is arranged similarly to the memory 803 in the above-described electronic device. The program codes can be compressed in an appropriate form, for example. Typically, the storage unit includes programs for executing the steps of the methods according to the embodiments of the present application, i.e., codes that can be read by a processor such as 801, which, when executed by the electronic device, cause the electronic device to perform the individual steps of the above-described methods.

[0117] The embodiments of the present application also provide a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the steps of the crack prediction method as described above.

[0118] The computer readable storage medium can be included in the device / apparatus described in the above-described embodiments; or can exist separately from the device / apparatus and not be assembled into the device / apparatus. The computer readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present application.

[0119] According to the embodiments of the present application, the computer readable storage medium can be a non-volatile computer readable storage medium, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device.

[0120] It has to be noted that, in the present document, relational terms are intended only to convey a possible relationship between elements or

[0121] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, which modifications and changes are to be understood as intended to be encompassed by the general scope of the application. Accordingly, the application is not to be limited to the above described or illustrated embodiments, but is intended to encompass all embodiments consistent with the principles of the application.

Claims

1. A method of fracture prediction, characterized by, The method comprises the following steps: acquiring seismic data of a target formation, and dividing the seismic data into a plurality of azimuthal seismic data according to azimuth, and stacking each azimuthal seismic data according to near, medium and far incident angles; performing isotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data to obtain isotropic elastic parameters of each azimuthal direction; performing Fourier series expansion on the isotropic elastic parameters of each azimuthal direction to obtain first anisotropic parameters based on an isotropic background; constructing an anisotropic low-frequency model according to the first anisotropic parameters, and performing anisotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data based on the anisotropic low-frequency model to obtain anisotropic elastic parameters of each azimuthal direction; performing Fourier series expansion on the anisotropic elastic parameters of each azimuthal direction to obtain second anisotropic parameters, which are used to realize density and azimuth prediction of fractures in the target formation; Before the isotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data, the method further comprises the following steps: calibrating a target seismic wavelet according to seismic data of near, medium and far incident angles of a plurality of azimuthal directions; the isotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data comprises the following steps: performing isotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data according to the target seismic wavelet; the anisotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data based on the anisotropic low-frequency model comprises the following steps: performing anisotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data based on the anisotropic low-frequency model and the target seismic wavelet.

2. The method of claim 1, wherein, The calibration of the target seismic wavelet according to seismic data of near, medium and far incident angles of a plurality of azimuthal directions comprises the following steps: determining a target azimuthal direction perpendicular to the principal stress direction of the target formation; extracting a candidate seismic wavelet of seismic data corresponding to the medium incident angle of the target azimuthal direction, and matching and calibrating the candidate seismic wavelet with seismic data corresponding to the near and far incident angles of the target azimuthal direction; in the case of meeting a preset condition, determining the candidate seismic wavelet as the target seismic wavelet.

3. The method according to claim 1 or 2, characterized in that, The first anisotropic parameters comprise first anisotropic strength and first anisotropic direction; and a formula of the anisotropic low-frequency model is as follows: = wherein, denotes the first anisotropy strength, is azimuthal central angle data, denotes the first anisotropy direction, denotes the P-wave velocity, denotes the S-wave velocity.

4. The method according to claim 1 or 2, characterized in that, Before the division of the seismic data into a plurality of azimuthal seismic data according to azimuth, the method further comprises the following step: performing optimization processing on the seismic data; The optimization processing comprises at least one of the following: noise removal processing, gather flattening processing and fidelity improvement processing.

5. The method according to claim 1 or 2, characterized in that, Before the isotropic pre-stack elastic parameter inversion on each azimuthal stacked seismic data, the method further comprises the following steps: performing forward modeling based on logging data of the target formation to obtain a forward modeling result of whether the formation is anisotropic; The anisotropy analysis is performed on the near, middle and far incidence angle seismic data of each azimuth, and the step of performing the isotropic prestack elastic parameter inversion on the stacked seismic data of each azimuth is executed under the condition that the analysis result matches the forward simulation result.

6. A fracture prediction apparatus characterized by comprising: The method comprises the following steps: The azimuth stacking module is used to acquire seismic data of a target formation, and divide the seismic data into multiple azimuth seismic data according to azimuth angles, and stack each azimuth seismic data according to near, middle and far incidence angles. The first inversion module is used to perform isotropic prestack elastic parameter inversion on the stacked seismic data of each azimuth to obtain isotropic elastic parameters of each azimuth. The first processing module is used to perform Fourier series expansion on the isotropic elastic parameters of each azimuth to obtain first anisotropic parameters based on the isotropic background. The second inversion module is used to construct an anisotropic low-frequency model according to the first anisotropic parameters, and perform anisotropic prestack elastic parameter inversion on the stacked seismic data of each azimuth based on the anisotropic low-frequency model to obtain anisotropic elastic parameters of each azimuth. The second processing module is used to perform Fourier series expansion on the anisotropic elastic parameters of each azimuth to obtain second anisotropic parameters, which are used to realize the density and azimuth prediction of the fractures in the target formation. The first inversion module is specifically used to: Calibrate a target seismic wavelet according to the near, middle and far incidence angle seismic data of multiple azimuths; Perform isotropic prestack elastic parameter inversion on the stacked seismic data of each azimuth according to the target seismic wavelet. The second inversion module is specifically used to: Perform anisotropic prestack elastic parameter inversion on the prestack seismic data of each azimuth based on the anisotropic low-frequency model and the target seismic wavelet.

7. The apparatus of claim 6, wherein, The method further comprises a data quality control module. The data quality control module is used to perform forward simulation based on the logging data of the target formation to obtain a forward simulation result of whether the formation is anisotropic. The anisotropy analysis is performed on the near, middle and far incidence angle seismic data of each azimuth, and the step of performing the isotropic prestack elastic parameter inversion on the stacked seismic data of each azimuth is executed under the condition that the analysis result matches the forward simulation result.

8. An electronic device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus. The memory is used to store a computer program. The processor is used to execute the program stored on the memory to realize the steps of the fracture prediction method in any one of claims 1-5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the fracture prediction method in any one of claims 1-5.

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