Fracture prediction method and device based on pre-stack anisotropic seismic data

Abstract

The invention discloses a crack prediction method and device based on pre-stack anisotropic seismic data. The method comprises the following steps: based on pre-stack seismic data of a research area, through optimization of an incident angle and an azimuth angle, obtaining a plurality of stacked seismic data of different incident angles and different orientations; for the logging longitudinal wave velocity and density curve and a set orthotropic parameter curve determined according to the logging longitudinal wave velocity and density curve, respectively establishing initial models of corresponding parameters through interpolation, and according to the logging longitudinal wave velocity and density curve and the logging longitudinal wave velocity and density curve, establishing a reflection coefficient data body corresponding to each piece of superposed seismic data through deconvolution; according to the initial model and the reflection coefficient data body, utilizing a pre-established inversion equation set containing the longitudinal wave speed, the density, the set orthotropic parameter, the incident angle, the azimuth angle and the reflection coefficient to obtain a fracture characterization parameter, namely, an inversion result of the set orthotropic parameter, and then predicting a fractured reservoir. According to the method, accurate prediction of the fractured reservoir is realized.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical research, and in particular to a fracture prediction method and device based on pre-stack anisotropic seismic data. Background Art

[0002] With the continuous improvement of oil and gas exploration, oil and gas have gradually been explored in the direction of deep and complex geological conditions in recent years. Most of the relatively simple anticline and other structural oil and gas reservoirs in the basin have been almost exhausted. The main direction of oil and gas exploration in major oil fields in my country has gradually shifted to complex reservoirs such as fractured oil and gas reservoirs, and a large amount of oil and gas has been found in fractured reservoirs such as shale and carbonate rocks. Since fractures usually determine the flow direction and amount of fluids through reservoir rocks, the study of fractured reservoirs has attracted more and more attention and has become a hot topic in recent years. The distribution regularity of fractures is poor. Due to the limitations of detection means and research methods, how to solve the prediction problem of such oil and gas reservoirs and accurately and efficiently predict fractured reservoirs is still the key to current research.

[0003] The prediction methods for fractured reservoirs in seismic exploration mainly include pre-stack azimuthal anisotropy inversion based on pre-stack data and seismic attribute analysis and coherence volume technology based on post-stack data. Since post-stack seismic data has a small amount of information and lacks information such as offset and azimuth, while pre-stack seismic data contains rich information, pre-stack seismic inversion is a key technology for reservoir seismic characterization. However, the existing pre-stack seismic inversion fracture technology often approximates the underground medium as an isotropic medium, ignoring the influence of anisotropy, so the accuracy of inversion needs to be further improved. Summary of the invention

[0004] The inventors found that the degree of anisotropy is related to the degree of fracture development, so the inversion of anisotropy parameters in seismic data is a key technology for predicting fractured reservoirs.

[0005] In order to at least partially solve the technical problems existing in the prior art, the inventors have made the present invention, and through specific implementation methods, provide a fracture prediction method and device based on pre-stack anisotropic seismic data, which can achieve accurate prediction of fractured reservoirs.

[0006] In a first aspect, an embodiment of the present invention provides a fracture prediction method based on pre-stack anisotropic seismic data, comprising:

[0007] According to the pre-stack seismic data of the study area, multiple incident angles and multiple azimuths are selected to obtain stacked seismic data of multiple different azimuths corresponding to each incident angle;

[0008] Based on the P-wave velocity and density curves of the well logging in the study area and the set orthogonal anisotropy parameter curves determined according to the P-wave velocity and density curves of the well logging, initial models of the corresponding parameters are established by interpolation respectively, and according to the P-wave velocity and density curves of the well logging, a reflection coefficient data volume corresponding to each stacked seismic data is established by deconvolution, wherein the set orthogonal anisotropy parameters are reflection coefficient sensitive parameters screened out by forward simulation;

[0009] According to the initial model and the reflection coefficient data body, the inversion results of the set orthotropic parameters are obtained by using the pre-established inversion equation group, and the cracks are predicted according to the inversion results. The inversion equation group includes longitudinal wave velocity, density, set orthotropic parameters, incident angle, azimuth and reflection coefficient.

[0010] In a second aspect, an embodiment of the present invention provides a fracture prediction device based on pre-stack anisotropic seismic data, comprising:

[0011] The stacked seismic data acquisition module is used to obtain stacked seismic data of multiple different azimuths corresponding to each incident angle, based on the pre-stack seismic data of the study area, by optimizing multiple incident angles and multiple azimuths;

[0012] An initial model building module is used to establish initial models of corresponding parameters by interpolation based on the P-wave velocity and density curves of the well logging in the study area and the set orthogonal anisotropy parameter curves determined according to the P-wave velocity and density curves of the well logging, and to establish reflection coefficient data volumes corresponding to each stacked seismic data by deconvolution according to the P-wave velocity and density curves of the well logging, wherein the set orthogonal anisotropy parameters are reflection coefficient sensitive parameters screened out by forward simulation;

[0013] The fracture inversion module is used to obtain the inversion result of the set orthotropic parameters according to the initial model and the reflection coefficient data body by using the pre-established inversion equation group, and predict the fracture according to the inversion result, wherein the inversion equation group includes the longitudinal wave velocity, density, the set orthotropic parameters, the incident angle, the azimuth angle and the reflection coefficient.

[0014] In a third aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned fracture prediction method based on pre-stack anisotropic seismic data is implemented.

[0015] In a fourth aspect, an embodiment of the present disclosure provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned fracture prediction method based on pre-stack anisotropic seismic data when executing the program.

[0016] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0017] The fracture prediction method based on pre-stack anisotropic seismic data provided by the embodiment of the present invention pre-screens out orthogonal anisotropic parameters sensitive to the reflection coefficient through forward simulation, and establishes an inversion equation group including P-wave velocity, density, set orthogonal anisotropic parameters, incident angle, azimuth and reflection coefficient based on the screening results; the initial model of P-wave velocity, density and set orthogonal anisotropic parameters obtained by interpolation of logging data is used as a constraint, and the stacked seismic data of multiple different azimuths corresponding to different incident angles are used as known data to solve the unknown quantity in the inversion equation group, and obtain fracture characterization parameters, that is, the inversion results of the set orthogonal anisotropic parameters, and predict fracture reservoirs according to the inversion results. Compared with the conventional AVA inversion method, this method can more effectively describe the development information of underground fractures, provide a more reliable means for the prediction of fracture-type reservoirs, and can be widely used in the oil and gas exploration fields related to fracture reservoir prediction, pre-drilling reservoir evaluation of closure targets, and fine reservoir prediction in oilfield development areas.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0019] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0021] Figure 1 Establishing a flow chart for the inversion equations of orthogonal anisotropy parameters in an embodiment of the present invention;

[0022] Figure 2 It is a double-layer single-interface laminar model ISO / OA horizontal interface model established in an embodiment of the present invention;

[0023] Figure 3 For the forward modeling corresponding characteristics of the embodiment of the present invention, the variation law of the longitudinal wave reflection coefficient under different azimuths and incident angles is analyzed;

[0024] Figure 4 The influence of the change of orthotropic parameters on the reflection coefficient is analyzed for the forward modeling corresponding characteristics in the embodiment of the present invention;

[0025] Figure 5 This is a flow chart of a fracture prediction method based on prestack anisotropic seismic data in an embodiment of the present invention;

[0026] Figure 6 The superimposed cross sections at different orientations corresponding to an incident angle of 20° in the embodiment of the present invention;

[0027] Figure 7 The inversion result of the fracture characterization parameters in the embodiment of the present invention;

[0028] Figure 8 It is a specific implementation flow chart of the fracture prediction method based on pre-stack anisotropic seismic data in an embodiment of the present invention;

[0029] Fig. 9 Schematic diagram of the structure of a fracture prediction device based on pre-stack anisotropic seismic data in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0031] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the present invention belongs. Although the present invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe methods and / or materials related to the documents. In the event of conflict with any incorporated document, the content of this specification shall prevail.

[0032] See also Figure 1 As shown, the embodiment of the present invention establishes an inversion equation group of fracture characterization parameters (orthogonal anisotropy parameters) through the following steps:

[0033] Step S11: Based on the approximate formula of the longitudinal wave reflection coefficient of the orthotropic medium, orthotropic parameters sensitive to the longitudinal wave reflection coefficient are screened through forward simulation as the set orthotropic parameters.

[0034] The fractured reservoir with high-angle fractures in the layered reservoir is regarded as an orthotropic medium. Based on the orthotropic medium, a double-layer single-interface layered model is established. Figure 2As shown, the upper medium is an isotropic medium ISO, and the lower medium is an orthotropic medium OA. The model parameters are set as follows: the longitudinal wave velocity v of the upper and lower media p , shear wave velocity v s , density ρ and orthotropic parameter ΔΓ x , ΔΓ y , Δε x , Δε y and Δδ z ,The parameter settings of the model are shown in Table 1.

[0035] Table 1

[0036]

[0037] Based on the approximate formula of longitudinal wave reflection coefficient in orthotropic media, forward simulation is performed according to the model parameters set in Table 1 to analyze the influence characteristics of different incident angles, azimuth angles and orthotropic parameters on the reflection coefficient, such as Figure 3 , Figure 4 .Depend on Figure 3 It can be seen that when a certain azimuth angle is fixed, the reflection coefficient decreases with the increase of the incident angle first to a minimum value and then increases. The difference between the reflection coefficients at different azimuth angles increases with the increase of the incident angle, indicating that when the incident angle increases, the longitudinal wave reflection coefficient is more sensitive to the change of the azimuth angle. Figure 4 It can be seen that the orthotropic anisotropy parameter ΔΓ x The change of has the most obvious effect on the reflection coefficient, followed by ΔΓ y , Δε x , Δε y and Δδ z The impact is minimal and can basically be ignored.

[0038] Step S12: Simplify the approximate formula of the longitudinal wave reflection coefficient according to the screening results of the orthogonal anisotropy parameters sensitive to the longitudinal wave reflection coefficient.

[0039] and Martins (2001) proposed a set of different anisotropic parameters to derive the kinematic and dynamic characteristics of elastic waves in arbitrary anisotropic media, and derived an approximate expression for the longitudinal wave reflection coefficient under any symmetry axis in OA media:

[0040]

[0041] The weak anisotropy parameter that appears in the formula is defined as:

[0042]

[0043]

[0044]

[0045]

[0046] Wherein, the subscript x is the orthotropic parameter in the [x, z] symmetry plane; the subscript y is the orthotropic parameter in the [y, z] symmetry plane.

[0047] and (1998) defined the longitudinal wave velocity and the shear wave velocity as:

[0048] A 33 =α 2 , A 55 =β 2 (3)

[0049] Substituting into formula (2), we can get:

[0050]

[0051] Substituting into equation (1), we can obtain the approximate equation for the longitudinal wave reflection coefficient in OA medium:

[0052]

[0053] Based on the above analysis of the seismic response characteristics of OA media, it can be known that the orthotropic parameter Δε x , Δε y and Δδ z The influence on the longitudinal wave reflection coefficient in the medium is minimal and can be basically ignored. Therefore, formula (5) can be simplified as:

[0054]

[0055] Orthotropic parameter ΔΓ x and ΔΓ y They represent the orthotropic parameters in the [x,z] symmetry plane and the orthotropic parameters in the [y,z] symmetry plane, respectively, reflecting the degree of development of cracks in different symmetry planes.

[0056] Step S13: The longitudinal wave reflection coefficient of the isotropic medium in the simplified longitudinal wave reflection coefficient approximate formula is subjected to the Shuey approximation to obtain the longitudinal wave reflection coefficient formula of the orthotropic medium including the longitudinal wave velocity, density, set orthotropic parameters, incident angle and azimuth.

[0057] The longitudinal wave reflection coefficient in the isotropic medium in equation (6) can be rewritten using the Shuey approximation as follows:

[0058]

[0059] Step S14: Obtaining an inversion equation group from the orthotropic medium longitudinal wave reflection coefficient formula.

[0060] Assume that the incident angle of the seismic wave of the nth seismic record in the pre-stack azimuth seismic data is θ n , the azimuth of the survey line is φ k , then the approximate formula of the longitudinal wave reflection coefficient of formula (7) with respect to different orthogonal anisotropy parameters of symmetry planes can be rewritten in matrix form:

[0061]

[0062] Similar to the inversion equations constructed when extracting anisotropic gradients, when inverting actual azimuth seismic data, the azimuth seismic data is represented by vector S, and the seismic wavelet extracted from the actual azimuth seismic gathers is represented by vector W. The inversion equations for the intercept term, gradient term, and orthogonal anisotropy parameters in the actual azimuth seismic data can be obtained, which can be written in matrix form:

[0063]

[0064] Formula (8) can also be abbreviated as Ax=b, specifically A n×4 x 4×1 =b n×1 , where the parameters are:

[0065]

[0066] In the above formula, θ is the incident angle, φ is the azimuth, k is the serial number of the stacked seismic data, k = 1, 2, ..., n, n is the total number of stacked seismic data, R PP (•) is the reflection coefficient, ΔΓ x and ΔΓ y To set the orthotropic parameter, ΔΓ x and ΔΓ y are the orthotropic parameters in the [x,z] symmetry plane and the orthotropic parameters in the [y,z] symmetry plane, M is the AVO intercept, G is the AVO gradient, v p is the longitudinal wave velocity, ρ is the density, △ means difference, and - means average.

[0067] By solving the inversion equation group Ax=b, the unknown quantity x can be obtained, and then the AVO intercept term, AVO gradient and orthotropic parameters can be obtained.

[0068] Example

[0069] The embodiment of the present invention provides a fracture prediction method based on prestack anisotropic seismic data, and the process thereof is as follows: Figure 5 As shown, the following steps are included:

[0070] Step S51: Based on the pre-stack seismic data of the study area, multiple incident angles and multiple azimuths are preferably selected to obtain stacked seismic data of multiple different azimuths corresponding to each incident angle.

[0071] The pre-stack azimuth seismic data is divided into azimuth angles, and the azimuth center angle is optimized according to the azimuth rosette information diagram of the omnidirectional seismic data, and the azimuth width and the number of azimuths are selected to optimize multiple azimuth angles. In addition, multiple incident angles are optimized.

[0072] The angular gather data corresponding to different azimuths are obtained, and the azimuth gather data are stacked at different angles to obtain seismic data at different incident angles and azimuths, such as Figure 6 As shown, the superimposed cross sections at different orientations corresponding to an incident angle of 20°.

[0073] Step S52: Based on the P-wave velocity and density curves of the well logging in the study area and the set orthogonal anisotropic parameter curves determined according to the P-wave velocity and density curves of the well logging, the initial models of the corresponding parameters are established by interpolation, and according to the P-wave velocity and density curves of the well logging, the reflection coefficient data body corresponding to each stacked seismic data is established by deconvolution.

[0074] The orthotropic parameter is set to be the longitudinal wave reflection coefficient sensitive parameter selected by forward modeling. Specifically, it is ΔΓ selected by the above introduction. x and ΔΓ y .

[0075] According to the P-wave and S-wave velocity and density curves of the well logging, the orthogonal anisotropy parameter ΔΓ can be determined by using the existing fracture rock physics equivalent model. x and ΔΓ y The curve is then interpolated horizontally by setting the interpolation method to obtain the P-wave velocity, density, and ΔΓ in the entire study area. x and ΔΓ y The initial model.

[0076] Furthermore, interpolation can be performed respectively according to the inverse distance interpolation method.

[0077] Furthermore, the P-wave velocity, density, ΔΓ in the entire study area are obtained. x and ΔΓ y After the initial model is obtained, it can also include low-pass filtering of each model to finally obtain the initial model constraint value x required for inversion. model .

[0078] According to the longitudinal and transverse wave velocity and density curves of the well logging, a reflection coefficient data body corresponding to each stacked seismic data is established through deconvolution, which can include: determining the reflection coefficient curve of the well logging according to the longitudinal and transverse wave velocity and density curves of the well logging; for each set of stacked seismic data, performing a deconvolution operation on the reflection coefficient curve based on the wellside seismic trace data extracted from the stacked seismic data to obtain a seismic wavelet; based on the stacked seismic data and the obtained seismic wavelet, performing a deconvolution operation to obtain the reflection coefficient data body corresponding to the stacked seismic data.

[0079] Step S53: Based on the initial model and the reflection coefficient data volume, using the pre-established inversion equation group, obtain the inversion result of the set orthogonal anisotropy parameters, and predict the cracks based on the inversion result.

[0080] The orthotropic parameter ΔΓ is set by using the sensitive parameters of the longitudinal wave reflection coefficient selected above. x and ΔΓ y , which is also a crack characterization parameter.

[0081] The inversion equation group is specifically an inversion equation group obtained through the above steps, which includes longitudinal wave velocity, density, set orthogonal anisotropy parameters, incident angle, azimuth angle and reflection coefficient.

[0082] The damped least squares method can be used to solve the unknown quantities of the pre-established inversion equations. The solution formula is: x = x model +[A T A+ηI] -1 A T (bA x model ), where x model is the initial model of each corresponding parameter, η is the damping factor, and I is the unit matrix.

[0083] See also Figure 7 The figure shows a cross-sectional schematic diagram of the inversion results of the fracture characterization parameters.

[0084] See also Figure 8 As shown, a specific implementation flow chart of a fracture prediction method based on prestack anisotropic seismic data provided by an embodiment of the present invention includes the following steps:

[0085] Step S81: Based on the orthotropic medium, a double-layer single-interface layered model is established, and the longitudinal wave velocity v of the upper and lower layers of the model parameters is set. p , shear wave velocity v s , density ρ and orthotropic parameter ΔΓ x , ΔΓ y , Δε x , Δε y and Δδ z .

[0086] Step S82: Based on the approximate formula of the longitudinal wave reflection coefficient in the orthotropic medium, a forward response characteristic analysis is performed according to the set model parameters to obtain a simplified approximate formula of the longitudinal wave reflection coefficient in the orthotropic medium:

[0087] R PP (θ,φ)=R PP iso (θ)+R PP ani (θ,φ).

[0088] Step S83: rewrite the simplified approximate formula of the longitudinal wave reflection coefficient of the orthotropic medium into an approximate formula of the reflection coefficient facing the AVO intercept, AVO gradient and fracture parameters:

[0089]

[0090] Step S84: When the actual azimuth seismic data is inverted, the azimuth seismic data is represented by vector S, and the seismic wavelet is represented by vector W. The inversion equation group S=W·R for the intercept term, gradient term and orthogonal anisotropy parameter in the actual azimuth seismic data can be obtained. PP .

[0091] Step S85: dividing the pre-stack azimuth seismic data by azimuth and stacking them by angle to obtain seismic data with different incident angles and azimuths.

[0092] Step S86: Calculate the reflection coefficient based on the measured P- and S-wave velocities, density and anisotropy parameters of the exploration well, extract the seismic trace data near the well, perform deconvolution processing and estimate the seismic wavelet W.

[0093] Step S87: Interpolate the measured P- and S-wave velocities, density, and anisotropy parameters in the well using the inverse distance interpolation method to establish a model of underground medium elasticity and fracture characterization parameters, and obtain the initial model constraint value x required for inversion. model .

[0094] Step S88: Based on the mapping relationship between the constructed fracture characterization parameters and the pre-stack azimuth gather seismic data, the damped least squares method is used to solve the unknown quantities of the constructed inversion equations to obtain the fracture characterization parameters and predict the favorable position for the development of fracture reservoirs.

[0095] In order to predict the problem of fractured reservoirs, the fracture prediction method based on pre-stack anisotropic seismic data provided by the embodiment of the present invention systematically analyzes the seismic wave response characteristics in the equivalent medium of the fractured reservoir, derives the reflection coefficient approximate formula related to the fracture parameters, establishes the basic equation for inverting the fracture parameters from the corresponding pre-stack azimuth gather seismic data, designs and implements the damped least squares inversion algorithm, and then establishes a fractured reservoir prediction method based on pre-stack azimuth anisotropy. Compared with the conventional AVA inversion method, the present invention can more effectively describe the underground fracture development information, provide a more reliable means for predicting fractured reservoirs, and can be widely used in oil and gas exploration fields related to fractured reservoir prediction, pre-drilling reservoir evaluation of closure targets, and fine reservoir prediction in oilfield development areas.

[0096] The reflection coefficients in the embodiments of the present invention, unless otherwise specified, refer to the longitudinal wave reflection coefficients.

[0097] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a fracture prediction device based on pre-stack anisotropic seismic data. The structure of the device is as follows: Fig. 9 As shown, including:

[0098] The stacked seismic data acquisition module 91 is used to obtain stacked seismic data of multiple different azimuths corresponding to each incident angle based on the pre-stack seismic data of the study area, preferably multiple incident angles and multiple azimuths;

[0099] An initial model building module 92 is used to establish initial models of corresponding parameters by interpolation based on the P-wave velocity and density curves of the well logging in the study area and the set orthogonal anisotropy parameter curves determined according to the P-wave velocity and density curves of the well logging, and to establish reflection coefficient data volumes corresponding to each stacked seismic data by deconvolution according to the P-wave velocity and density curves of the well logging, wherein the set orthogonal anisotropy parameters are reflection coefficient sensitive parameters screened out by forward modeling;

[0100] The fracture inversion module 93 is used to obtain the inversion results of the set orthotropic parameters according to the initial model and the reflection coefficient data body using the pre-established inversion equation group, and predict the fractures according to the inversion results. The inversion equation group includes the longitudinal wave velocity, density, the set orthotropic parameters, the incident angle, the azimuth angle and the reflection coefficient.

[0101] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0102] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the above-mentioned fracture prediction method based on pre-stack anisotropic seismic data is implemented.

[0103] Based on the inventive concept of the present invention, an embodiment of the present invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned fracture prediction method based on pre-stack anisotropic seismic data is implemented.

[0104] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0105] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0106] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that an embodiment of the claimed subject matter requires more features than those set forth in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0107] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0108] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0109] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0110] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A fracture prediction method based on prestack anisotropic seismic data, characterized in that: include: According to the pre-stack seismic data of the study area, multiple incident angles and multiple azimuths are selected to obtain stacked seismic data of multiple different azimuths corresponding to each incident angle; Based on the P-wave velocity and density curves of the well logging in the study area and the set orthogonal anisotropy parameter curves determined according to the P-wave velocity and density curves of the well logging, initial models of the corresponding parameters are established by interpolation respectively, and according to the P-wave velocity and density curves of the well logging, a reflection coefficient data volume corresponding to each stacked seismic data is established by deconvolution, wherein the set orthogonal anisotropy parameters are reflection coefficient sensitive parameters screened out by forward simulation; According to the initial model and the reflection coefficient data body, the inversion results of the set orthotropic parameters are obtained by using the pre-established inversion equation group, and the cracks are predicted according to the inversion results. The inversion equation group includes longitudinal wave velocity, density, set orthotropic parameters, incident angle, azimuth and reflection coefficient.

2. The method according to claim 1, characterized in that The inversion equations are pre-established by the following steps: Based on an approximate formula for the longitudinal wave reflection coefficient of an orthotropic medium, an orthotropic parameter sensitive to the longitudinal wave reflection coefficient is screened through forward simulation as the set orthotropic parameter; According to the screening results of orthogonal anisotropy parameters sensitive to the longitudinal wave reflection coefficient, the approximate formula of the longitudinal wave reflection coefficient is simplified; The simplified approximate formula of longitudinal wave reflection coefficient of isotropic medium adopts Shuey approximation, and the formula of longitudinal wave reflection coefficient of orthotropic medium including longitudinal wave velocity, density, set orthotropic anisotropy parameter, incident angle and azimuth is obtained; The inversion equation group is obtained from the longitudinal wave reflection coefficient formula of the orthotropic medium.

3. The method according to claim 2, characterized in that The formula for the longitudinal wave reflection coefficient of the orthotropic medium is: The inversion equation system is Ax=b, where: In the above formula, θ is the incident angle, φ is the azimuth, k is the serial number of the stacked seismic data, k = 1, 2, ..., n, n is the total number of stacked seismic data, R PP (·) is the reflection coefficient, ΔΓ x and ΔΓ y To set the orthotropic parameter, ΔΓ x and ΔΓ y are the orthotropic parameters in the [x,z] symmetry plane and the orthotropic parameters in the [y,z] symmetry plane, M is the AVO intercept, G is the AVO gradient, v p is the longitudinal wave velocity, ρ is the density, △ means difference, and - means average.

4. The method according to claim 3, characterized in that The inversion result of setting the orthotropic parameters by using the pre-established inversion equation group is obtained, including: The damped least squares method is used to solve the unknowns of the pre-established inversion equations and obtain the inversion results with set orthogonal anisotropy parameters.

5. The method according to claim 4, characterized in that The damped least square method is used to solve the unknown quantities of the pre-established inversion equations, including: The damped least squares method is used to solve the unknown quantities of the pre-established inversion equations. The solution formula is: x = x model +[A T A+ηI] -1 A T (b-Ax model ), where x model is the initial model of the corresponding parameters, η is the damping factor, and I is the unit matrix.

6. The method according to claim 1, characterized in that The interpolation is performed respectively, including: Interpolation is performed separately according to the inverse distance interpolation method.

7. The method according to claim 1, characterized in that After the initial model of each corresponding parameter is established, the method further includes: Perform low-pass filtering on the initial model of each corresponding parameter.

8. The method according to claim 1, characterized in that The method of establishing a reflection coefficient data volume corresponding to each stacked seismic data by deconvolution according to the longitudinal and transverse wave velocity and density curves of the well logging includes: Determining the reflection coefficient curve of the well logging according to the longitudinal and transverse wave velocities and density curves of the well logging; Deconvolution operation is performed on the wellside seismic trace data extracted from the stacked seismic data and the reflection coefficient curve to obtain a seismic wavelet. Based on the stacked seismic data and the obtained seismic wavelet, a deconvolution operation is performed to obtain a reflection coefficient data volume corresponding to the stacked seismic data.

9. A fracture prediction device based on pre-stack anisotropic seismic data, characterized in that: The device comprises: The stacked seismic data acquisition module is used to obtain stacked seismic data of multiple different azimuths corresponding to each incident angle, based on the pre-stack seismic data of the study area, by optimizing multiple incident angles and multiple azimuths; An initial model building module is used to establish initial models of corresponding parameters by interpolation based on the P-wave velocity and density curves of the well logging in the study area and the set orthogonal anisotropy parameter curves determined according to the P-wave velocity and density curves of the well logging, and to establish reflection coefficient data volumes corresponding to each stacked seismic data by deconvolution according to the P-wave velocity and density curves of the well logging, wherein the set orthogonal anisotropy parameters are reflection coefficient sensitive parameters screened out by forward simulation; The fracture inversion module is used to obtain the inversion result of the set orthotropic parameters according to the initial model and the reflection coefficient data body by using the pre-established inversion equation group, and predict the fracture according to the inversion result, wherein the inversion equation group includes the longitudinal wave velocity, density, the set orthotropic parameters, the incident angle, the azimuth angle and the reflection coefficient.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the fracture prediction method based on pre-stack anisotropic seismic data according to any one of claims 1 to 8 is implemented.

11. A server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting fractures based on prestack anisotropic seismic data as claimed in any one of claims 1 to 8 is implemented.

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