A method and apparatus for establishing a shear wave velocity model

Abstract

The application discloses a method and device for establishing a shear wave velocity model. The method comprises the following steps: determining a Poisson's ratio two-dimensional model of a study area according to micro-logging data of multiple wells in the study area; reading Poisson's ratio at a position corresponding to a surface wave dispersion curve from the Poisson's ratio two-dimensional model, and determining an optimal half-wavelength coefficient according to the read Poisson's ratio and a predetermined corresponding relationship between Poisson's ratio and the half-wavelength coefficient; establishing a surface wave average velocity three-dimensional model of the study area according to each surface wave dispersion curve and the corresponding optimal half-wavelength coefficient, and converting the surface wave average velocity three-dimensional model into an initial shear wave velocity model; and correcting the initial shear wave velocity model according to a micro-logging average shear wave velocity to obtain a final shear wave velocity model. The high-precision near-surface shear wave velocity model can be established based on shear wave micro-logging information and surface wave dispersion curve information.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and in particular to a method and apparatus for establishing a shear wave velocity model. Background Technology

[0002] Near-surface shear wave velocity models are crucial for seismic exploration data processing, and their accuracy directly affects the imaging quality of seismic profiles. In shear wave seismic exploration, establishing near-surface velocity models using first arrivals or surface waves from seismic records is the most common method. However, near-shot-receiver distances in seismic records result in significant interference from first arrivals, leading to gaps or low-quality first arrivals, resulting in lower accuracy for shear wave velocity models established using first arrival tomography. Furthermore, shear wave velocity models established using surface waves often show significant differences in velocity compared to reliable micrologging results at the same location.

[0003] When establishing an initial model using surface wave dispersion curves, a half-wavelength coefficient is needed to determine the model's depth. This coefficient is usually given based on experience and has a large error. After establishing the model using surface wave dispersion curve inversion, the model still has a large difference from the micro-logging model, so the velocity model needs to be corrected to improve its accuracy. Summary of the Invention

[0004] The inventors discovered that when establishing an initial velocity model using surface wave dispersion curves, a half-wavelength coefficient is needed to determine the depth of the model. This coefficient is generally given based on experience and has a large error. After establishing a shear wave velocity model by inversion using surface wave dispersion curves, the model still differs significantly from the micro-logging model. Therefore, the accuracy of the shear wave velocity model obtained by inversion using surface wave dispersion curves in the existing technology is low, and the accuracy of the shear wave velocity model urgently needs to be improved.

[0005] In order to at least partially solve the technical problems existing in the prior art, the inventors made this invention, which provides a method and apparatus for establishing a shear wave velocity model through specific implementation methods, which can establish a high-precision near-surface shear wave velocity model based on shear wave micrologging information and surface wave dispersion curve information.

[0006] In a first aspect, embodiments of the present invention provide a method for establishing a shear wave velocity model, comprising:

[0007] A two-dimensional model of Poisson's ratio for the study area was determined based on data from multiple micrologging wells within the study area.

[0008] The Poisson's ratio at the corresponding position of the surface wave dispersion curve is read from the two-dimensional model of Poisson's ratio, and the optimal half-wavelength coefficient is determined according to the correspondence between the read Poisson's ratio and the predetermined Poisson's ratio and the half-wavelength coefficient.

[0009] Based on the dispersion curves of each surface wave and their corresponding optimal half-wavelength coefficients, a three-dimensional model of the average surface wave velocity in the study area is established, and the three-dimensional model of the average surface wave velocity is converted into an initial shear wave velocity model.

[0010] The initial shear wave velocity model is corrected based on the average shear wave velocity from the micro-logging to obtain the final shear wave velocity model.

[0011] Secondly, embodiments of the present invention provide a transverse wave velocity model establishing device, comprising:

[0012] The Poisson's ratio two-dimensional model establishment module is used to determine the Poisson's ratio two-dimensional model of the study area based on the micro-logging data of multiple wells in the study area.

[0013] The optimal half-wavelength coefficient determination module is used to read the Poisson's ratio at the corresponding position of the surface wave dispersion curve from the two-dimensional Poisson's ratio model, and determine the optimal half-wavelength coefficient based on the read Poisson's ratio and the correspondence between the predetermined Poisson's ratio and the half-wavelength coefficient.

[0014] The initial shear wave velocity model establishment module is used to establish a three-dimensional model of the average surface wave velocity in the study area based on the dispersion curves of each surface wave and its corresponding optimal half-wavelength coefficient, and to convert the three-dimensional model of the average surface wave velocity into an initial shear wave velocity model.

[0015] The shear wave velocity model establishment module is used to correct the initial shear wave velocity model based on the average shear wave velocity of the micro-logging to obtain the final shear wave velocity model.

[0016] Thirdly, embodiments of the present invention provide a computer program product with a transverse wave velocity model establishment function, including a computer program / instruction, wherein the computer program / instruction implements the above-mentioned transverse wave velocity model establishment method when executed by a processor.

[0017] Fourthly, embodiments of this disclosure provide a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for establishing a transverse wave velocity model.

[0018] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0019] The shear wave velocity model establishment method provided in this invention determines a two-dimensional Poisson's ratio model of the study area based on multi-well logging data. The Poisson's ratio at the corresponding position of the surface wave dispersion curve is read from the two-dimensional model. The optimal half-wavelength coefficient is determined based on the correspondence between the read Poisson's ratio and a predetermined Poisson's ratio with the half-wavelength coefficient. This ensures a reasonable determination of the half-wavelength coefficient, avoiding the errors caused by empirically given half-wavelength coefficients in traditional methods of establishing an initial model using surface wave dispersion curves. Based on the reasonably determined half-wavelength coefficient, an initial three-dimensional model of the average surface wave velocity is established, which is then converted into an initial shear wave velocity model. The initial shear wave velocity model is corrected based on the average shear wave velocity from the micro-logging data to obtain the final shear wave velocity model, ensuring that the established shear wave velocity model matches the micro-logging data. Therefore, this invention establishes a high-precision near-surface shear wave velocity model based on shear wave micro-logging information and surface wave dispersion curve information, providing a high-quality velocity model and high-precision static correction for seismic data processing.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0023] Figure 1 This is a flowchart of the method for establishing the shear wave velocity model in Embodiment 1 of the present invention;

[0024] Figure 2 for Figure 1 A flowchart detailing the implementation of part of step S13;

[0025] Figure 3 for Figure 1 The flowchart for the specific implementation of another part of step S13;

[0026] Figure 4 for Figure 1 The detailed implementation flowchart of step S14 is shown below;

[0027] Figure 5 This is a flowchart illustrating the specific implementation of the shear wave velocity model establishment method in Embodiment 2 of the present invention.

[0028] Figure 6 This is a schematic diagram of the structure of the transverse wave velocity model establishment device in an embodiment of the present invention. Detailed Implementation

[0029] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0030] To address the problem of low accuracy in existing shear wave velocity models, this invention provides a method and apparatus for establishing a shear wave velocity model, which can establish a high-precision near-surface shear wave velocity model based on shear wave micrologging information and surface wave dispersion curve information.

[0031] Example 1

[0032] Embodiment 1 of the present invention provides a method for establishing a shear wave velocity model, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0033] Step S11: Determine the two-dimensional model of Poisson's ratio for the study area based on the micro-logging data from multiple wells within the study area.

[0034] Data from multiple micrologging wells within the study area were acquired to obtain the P-wave velocity curves and S-wave velocity curves of the micrologging wells. Based on the P-wave velocity at each sampling point in the P-wave velocity curve, the average P-wave velocity within the drilling depth of the micrologging well was determined. Based on the S-wave velocity at each sampling point in the S-wave velocity curve, the average S-wave velocity within the drilling depth of the micrologging well was determined.

[0035] After determining the average P-wave and S-wave velocities based on the micrologging data in the study area, the Poisson's ratio at the micrologging location is determined using the following formula (1) based on the average P-wave and S-wave velocities:

[0036] (1)

[0037] In formula (1), The average P-wave velocity within the drilling depth in micro-logging. This represents the average shear wave velocity within the drilling depth of the micro-logging system. This is the average Poisson's ratio of the micrologging well within the drilling depth, i.e., the Poisson's ratio at the micrologging well.

[0038] Then, based on the determined Poisson's ratio of each micrologging well, planar interpolation is performed to obtain a two-dimensional model of Poisson's ratio for the study area.

[0039] Step S12: Read the Poisson's ratio at the corresponding position of the surface wave dispersion curve from the two-dimensional Poisson's ratio model, and determine the optimal half-wavelength coefficient based on the read Poisson's ratio and the correspondence between the predetermined Poisson's ratio and the half-wavelength coefficient.

[0040] The spatial position of the surface wave dispersion curve is the same as the spatial position of the well trajectory, which is a three-dimensional position. If the well trajectory is a vertical well, the corresponding position of the surface wave dispersion curve in the Poisson's ratio two-dimensional model can be determined directly based on the planar position of the wellhead, i.e., the x and y positions. If the well trajectory is an inclined well, well inclination correction is performed first, and the corresponding position of the surface wave dispersion curve in the Poisson's ratio two-dimensional model is determined based on the corrected planar position of the wellhead.

[0041] The relationship between Poisson's ratio and half-wavelength coefficient can be expressed as a fitted curve, i.e., a relational formula, or as a one-dimensional array.

[0042] If the relationship between Poisson's ratio and half-wavelength coefficient is in the form of a relational expression, the half-wavelength coefficient corresponding to the read Poisson's ratio can be determined by directly substituting the read Poisson's ratio into the relational expression, and the determined half-wavelength coefficient can be taken as the optimal half-wavelength coefficient.

[0043] If the correspondence between Poisson's ratio and half-wavelength coefficients is in the form of a one-dimensional array, and the correspondence includes the read Poisson's ratio, then the half-wavelength coefficient corresponding to the read Poisson's ratio in the correspondence is directly determined as the optimal half-wavelength coefficient. If the correspondence does not include the read Poisson's ratio, then the half-wavelength coefficient corresponding to the read Poisson's ratio is determined from the correspondence. two adjacent Poisson ratios and The optimal half-wavelength coefficient s is determined by the following formula (2), which is the half-wavelength coefficient s corresponding to the two adjacent Poisson ratios, s1 and s2:

[0044] (2).

[0045] The relationship between Poisson's ratio and half-wavelength coefficient can be seen in Table 1 below:

[0046] Table 1. Correspondence between Poisson's ratio and half-wavelength coefficient

[0047] Half-wavelength coefficient 0.550 0.575 0.625 0.650 0.700 0.750 0.790 0.840 0.875

[0048] For example, if the Poisson ratio read is 0.25, then the half-wavelength coefficient 0.650 corresponding to the Poisson ratio of 0.25 in Table 1 is directly determined as the optimal half-wavelength coefficient; if the Poisson ratio read is 0.24, then the two Poisson ratios 0.20 and 0.25 adjacent to 0.24 are read from Table 1, and their corresponding half-wavelength coefficients are 0.625 and 0.65, respectively. Then, the optimal half-wavelength coefficient is determined to be 0.645 by formula (2).

[0049] The optimal half-wavelength coefficient for each surface wave dispersion curve within the study area was determined using the method described above.

[0050] Step S13: Based on the dispersion curves of each surface wave and its corresponding optimal half-wavelength coefficient, establish a three-dimensional model of the average surface wave velocity in the study area, and convert the three-dimensional model of the average surface wave velocity into an initial shear wave velocity model.

[0051] In some embodiments, it may include determining the model depth of each dispersion point in the dispersion curve based on the optimal half-wavelength coefficient, and obtaining a three-dimensional model of the average surface wave velocity of the study area by interpolation based on the model depth of each dispersion point in each dispersion curve and the surface wave velocity.

[0052] Furthermore, the model depth of each dispersion point in the dispersion curve is determined based on the optimal half-wavelength coefficient; see [reference needed]. Figure 2 As shown, the following steps may be included:

[0053] Step S1311: Determine the inversion depth of each dispersion point in the dispersion curve based on the optimal half-wavelength coefficient.

[0054] The inversion depth of each dispersion point in the dispersion curve can be determined according to the optimal half-wavelength coefficient using the following formula (3):

[0055] (3)

[0056] In formula (3), Let be the inversion depth of the i-th dispersion point in the dispersion curve, and s be the optimal half-wavelength coefficient. Let be the average surface wave velocity at the i-th dispersion point in the dispersion curve. Let be the surface wave frequency at the i-th dispersion point in the dispersion curve.

[0057] Step S1312: Determine the sum of the inversion depth and the measured depth of the inversion point, and determine the model depth of the inversion point as the difference between the model initial depth and the sum of the inversion depth and the measured depth.

[0058] The three-dimensional model of the surface wave mean velocity is converted into the initial shear wave velocity model. See [link / reference]. Figure 3 As shown, the following steps may be included:

[0059] Step S1321: Convert the three-dimensional model of the average surface wave velocity into a three-dimensional model of the surface wave velocity.

[0060] Specifically, the surface wave velocity here refers to the layer velocity of the surface wave. From the perspective of the model mesh, the surface wave velocity here also refers to the mesh velocity of the surface wave, that is, the average velocity of the surface wave in each mesh. The average surface wave velocity refers to the average velocity of the surface wave in the vertical range from the ground surface to the sampling point (mesh).

[0061] For each point in the three-dimensional model of surface wave average velocity, the conversion from surface wave average velocity to surface wave velocity can be divided into the following two cases, depending on the location of the point:

[0062] 1. The depth of this point is the initial depth of the model.

[0063] That is, if the point is located on the Earth's surface, the average surface wave velocity at that point can be directly determined as the surface wave velocity at that point.

[0064] Second, the depth of this point is not the starting depth of the model.

[0065] The average surface wave velocity at this point can be converted into the surface wave velocity at this point using the following formula (4):

[0066] (4)

[0067] In formula (4), Let be the surface wave velocity at point j. Let be the average surface wave velocity at point j. Let j be the depth difference between point j and the adjacent point j-1 above it. Let be the time required for the surface wave to travel the vertical distance from the depth origin (surface) to point j. Let be the depth difference between the starting point of the depth and the j-th point.

[0068] Step S1322: Based on the conversion coefficient between surface wave velocity and shear wave velocity, convert the three-dimensional surface wave velocity model into an initial shear wave velocity model.

[0069] The theoretical value of the conversion factor between surface wave velocity and shear wave velocity is 0.92. Dividing the surface wave velocity by 0.92 converts the surface wave velocity into the initial shear wave velocity.

[0070] Step S14: Correct the initial shear wave velocity model based on the average shear wave velocity of the micro-logging to obtain the final shear wave velocity model.

[0071] Further, see Figure 4 As shown, the following steps may be included:

[0072] Step S141: Establish a two-dimensional model of shear wave correction coefficients based on the average shear wave velocity and initial shear wave velocity model of the micro-logging.

[0073] The average velocity of the initial shear wave velocity model within the microlog depth at the microlog is determined. The ratio of the average shear wave velocity of the microlog to the average velocity of the determined initial shear wave velocity is determined as the shear wave correction coefficient at the microlog. Planar interpolation is performed based on the shear wave correction coefficient at each microlog to obtain a two-dimensional model of the shear wave correction coefficient.

[0074] Step S142: Correct the initial shear wave velocity model according to the two-dimensional model of the shear wave correction coefficient to obtain the final shear wave velocity model.

[0075] The final shear wave velocity model is obtained by dividing the initial shear wave velocity at each point in the initial shear wave velocity model by the shear wave correction coefficient at the corresponding point in the two-dimensional model.

[0076] The shear wave velocity model establishment method provided in Embodiment 1 of this invention determines a two-dimensional Poisson's ratio model of the study area based on multi-well logging data. The Poisson's ratio at the corresponding position of the surface wave dispersion curve is read from the two-dimensional model. The optimal half-wavelength coefficient is determined based on the correspondence between the read Poisson's ratio and a predetermined Poisson's ratio with the half-wavelength coefficient. This ensures a reasonable determination of the half-wavelength coefficient, avoiding the errors caused by empirically given half-wavelength coefficients when establishing an initial model using surface wave dispersion curves in traditional methods. Based on the reasonably determined half-wavelength coefficient, an initial three-dimensional model of the average surface wave velocity is established, which is then converted into an initial shear wave velocity model. The initial shear wave velocity model is corrected based on the average shear wave velocity from the micro-well logging data to obtain the final shear wave velocity model, ensuring that the established shear wave velocity model matches the micro-well logging data. Therefore, this embodiment of the invention establishes a high-precision near-surface shear wave velocity model based on shear wave micro-well logging information in conjunction with surface wave dispersion curve information, providing a high-quality velocity model and high-precision static correction for seismic data processing.

[0077] Example 2

[0078] Embodiment 2 of the present invention provides a specific implementation flow of a method for establishing a shear wave velocity model, which includes a three-dimensional model of the average surface wave velocity, a three-dimensional model of the surface wave velocity, an initial shear wave velocity model, and a final shear wave velocity model established based on the surface wave dispersion curve. The parameters of the three-dimensional mesh model are as follows:

[0079] The origin coordinates are (x0, y0, z0), the grid size in the x, y, and z directions (i.e., the grid step size) is (Dx, Dy, Dz), the number of grids is (Nx, Ny, Nz), and the projection numbers are (1, 2, ..., Nx), (1, 2, ..., Ny), and (1, 2, ..., Nz) respectively; x0 is the starting position of the model in the x direction, and x0 + Dx × Nx is the ending position of the model in the x direction; y0 is the starting position of the model in the y direction, and y0 + Dy × Ny is the ending position of the model in the y direction; z0 is the maximum elevation of the model (measured from the ground surface, the elevation of the ground surface is the greatest), and z0 - Dz × Nz is the minimum elevation of the model; optionally, z0 can also be the minimum depth of the model (measured from the ground surface, the depth of the ground surface is the shallowest), and z0 + Dz × Nz is the maximum depth of the model.

[0080] Furthermore, the grid step size in the x-direction can be set according to the trace spacing of the seismic data in the study area. For example, if the trace spacing is 30 meters, half of the trace spacing, i.e., 15 meters, is determined as the grid step size in the x-direction. The grid step size in the y-direction can be set according to the shot distance of the seismic data. For example, if the shot distance is 20 meters, half of the shot distance, i.e., 10 meters, is determined as the grid step size in the y-direction. The grid step size in the z-direction is set according to the model accuracy requirements, and can generally be set to 5 meters, 8 meters, or 10 meters, etc.

[0081] For the specific method of establishing the shear wave velocity model, please refer to [link / reference]. Figure 5 As shown, it includes the following steps:

[0082] Step S51: Calculate Poisson's ratio using the P-wave and S-wave velocities interpreted by micro-logging and mesh the data to obtain a two-dimensional model of Poisson's ratio.

[0083] Calculate the average P-wave velocity v within the drilling depth of the micro-logging well. p and the average velocity of the transverse wave v s The Poisson's ratio of the micro-logging location is calculated according to formula (1) in Example 1.

[0084] Based on the discrete micro-logging coordinates (x, y) and corresponding Poisson's ratios within the work area, the Poisson's ratios for the entire work area are obtained by horizontal gridding. (x) m y n ), 1 m Nx, 1 n Ny is the two-dimensional model of Poisson's ratio.

[0085] Step S52: Determine the optimal half-wavelength conversion coefficient of the surface wave dispersion curve based on the two-dimensional model of Poisson's ratio.

[0086] Based on the spatial location (x, y) of the dispersion curve, the grid projection index in the horizontal direction in the Poisson's ratio two-dimensional model is determined using the following formula:

[0087] The grid number x in the x-direction is determined by the following formula (5). m The grid number y in the y direction is determined by the following formula (6). n :

[0088] x m =int[(x-x0) / Dx] (5)

[0089] y n =int[(y-y0) / Dy] (6)

[0090] Here, int represents the rounding of the country number to the nearest integer.

[0091] Once the specific grid number corresponding to the spatial location (x, y) of the dispersion curve is determined, the Poisson's ratio can be read from the two-dimensional Poisson's ratio model.

[0092] The optimal half-wavelength coefficient of the surface wave dispersion curve is determined based on the correspondence between the read Poisson's ratio and the predetermined Poisson's ratio and the half-wavelength coefficient.

[0093] Step S53: Establish a three-dimensional model of the average velocity of the surface wave based on the surface wave dispersion curve.

[0094] A surface wave dispersion curve consists of a set of dispersion points ( , When using this curve to invert (interpret) the surface wave average velocity model within a certain depth range at the location of the dispersion curve, the inversion depth of a single dispersion point is determined using formula (3) in Example 1. .

[0095] Based on the measured elevation zi of the dispersion point and the inversion depth The mesh projection index in the depth direction and the mesh number z in the z direction in the model are determined by the following formula (7). w :

[0096] z w =int[(z0-zi-hi) / Dz] (7)

[0097] The velocity v at each dispersion point in the dispersion curve i Place the values ​​at the grid positions determined by formulas (5)-(7), and obtain the values ​​at the null values ​​of the model through interpolation. Perform local smoothing on the velocity model to eliminate abnormal velocity values, and obtain the three-dimensional model of surface wave average velocity V0(x). m y n , z w ).

[0098] Step S54: Convert the three-dimensional model of the average surface wave velocity into a surface wave velocity model.

[0099] Three-dimensional model of surface wave average velocity V0(x) m y n , z w This can be viewed as the distance from the Earth's surface to the z-th point. w The average velocity of each grid depth is converted into the surface wave grid velocity by transforming the points into grids using the transformation format of formula (4) in Example 1.

[0100] The surface wave mesh velocity is smoothed to eliminate local outliers, resulting in the surface wave velocity model V1(x) derived from the surface wave inversion. m y n , z w ).

[0101] Step S55: Convert the surface wave velocity model into the initial shear wave velocity model.

[0102] Divide the surface wave velocity at each grid point in the surface wave velocity model by 0.92 to obtain the initial shear wave velocity at that grid point, thus obtaining the initial shear wave velocity model V2(x). m y n , z w ).

[0103] Step S56: Using the shear wave information from the micro-logging and the information from the initial shear wave velocity model, establish a two-dimensional model of the conversion coefficients between surface wave velocity and shear wave velocity.

[0104] Using the shear wave information from the microlobe and the information from the initial shear wave velocity model, the conversion coefficient between the surface wave velocity and the shear wave velocity at the microlobe is determined by the method in step S141 of Example 1.

[0105] Based on the discrete micro-logging coordinates (x, y) within the work area and the corresponding correction coefficients, a two-dimensional model c(x, y) of the correction coefficients for the entire work area is obtained by horizontal meshing. m y n ).

[0106] Step S57: Correct the initial shear wave velocity model based on the two-dimensional model of the conversion coefficient to obtain the final shear wave velocity model.

[0107] Based on the two-dimensional model c(x) of the transformation coefficient using the following formula (8) m y n Correcting the initial shear wave velocity model V2(x) m y n , z w The final transverse wave velocity model V3(x) is obtained. m y n , z w ):

[0108] V3(x m y n , z w )= V2(x m y n , z w ) / c(x m y n (8)

[0109] The shear wave velocity model established using the above method, compared with micrologging results at the same location, improved the accuracy of the surface wave model. Verification using actual data proved the feasibility of this method.

[0110] Based on the inventive concept of this invention, embodiments of this invention also provide a transverse wave velocity model establishing device, the structure of which is as follows: Figure 6 As shown, it includes:

[0111] The Poisson's ratio two-dimensional model establishment module 61 is used to determine the Poisson's ratio two-dimensional model of the study area based on the micro-logging data of multiple wells in the study area.

[0112] The optimal half-wavelength coefficient determination module 62 is used to read the Poisson's ratio at the corresponding position of the surface wave dispersion curve from the two-dimensional Poisson's ratio model, and determine the optimal half-wavelength coefficient based on the read Poisson's ratio and the correspondence between the predetermined Poisson's ratio and the half-wavelength coefficient.

[0113] The initial shear wave velocity model establishment module 63 is used to establish a three-dimensional model of the average surface wave velocity in the study area based on the dispersion curves of each surface wave and its corresponding optimal half-wavelength coefficient, and to convert the three-dimensional model of the average surface wave velocity into an initial shear wave velocity model.

[0114] The shear wave velocity model establishment module 64 is used to correct the initial shear wave velocity model based on the average shear wave velocity of the micro-logging well to obtain the final shear wave velocity model.

[0115] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0116] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer program product with a transverse wave velocity model establishment function, including a computer program / instruction, wherein the computer program / instruction implements the above-mentioned transverse wave velocity model establishment method when executed by a processor.

[0117] Based on the inventive concept of this invention, this embodiment of the invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for establishing a transverse wave velocity model.

[0118] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can 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 can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0119] 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 may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0120] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0121] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0122] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0123] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0124] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for establishing a shear wave velocity model, characterized in that, include: A two-dimensional model of Poisson's ratio for the study area was determined based on data from multiple micrologging wells within the study area. The Poisson's ratio at the corresponding position on the surface wave dispersion curve is read from the two-dimensional Poisson's ratio model. If the read Poisson's ratio is included in the predefined one-dimensional array format of the correspondence between Poisson's ratio and half-wavelength coefficients, the half-wavelength coefficient corresponding to the read Poisson's ratio in the correspondence is determined as the optimal half-wavelength coefficient; if the correspondence does not include the read Poisson's ratio, the half-wavelength coefficient corresponding to the read Poisson's ratio is determined from the correspondence. two adjacent Poisson ratios and The optimal half-wavelength coefficient s is determined by the following formula (1), and the half-wavelength coefficients s1 and s2 corresponding to the two adjacent Poisson ratios are: （1）； Based on the dispersion curves of each surface wave and their corresponding optimal half-wavelength coefficients, a three-dimensional model of the average surface wave velocity in the study area is established, and the three-dimensional model of the average surface wave velocity is converted into an initial shear wave velocity model. The initial shear wave velocity model is corrected based on the average shear wave velocity from the micro-logging to obtain the final shear wave velocity model.

2. The method as described in claim 1, characterized in that, The step of correcting the initial shear wave velocity model based on the average shear wave velocity from the micro-logging to obtain the final shear wave velocity model specifically includes: A two-dimensional model of shear wave correction coefficients is established based on the average shear wave velocity of the micro-logging and the initial shear wave velocity model. The initial shear wave velocity model is corrected according to the two-dimensional model of the shear wave correction coefficient to obtain the final shear wave velocity model.

3. The method as described in claim 2, characterized in that, The establishment of a two-dimensional model for shear wave correction coefficients based on the average shear wave velocity of the micro-logging and the initial shear wave velocity model specifically includes: The average velocity of the initial shear wave velocity model within the micro-logging depth at the micro-logging well is determined, and the ratio of the average shear wave velocity of the micro-logging well to the determined average velocity is determined as the shear wave correction coefficient at the micro-logging well. Based on the shear wave correction coefficients at each micrologging point, a two-dimensional model of the shear wave correction coefficients is obtained by planar interpolation.

4. The method as described in claim 2, characterized in that, The step of correcting the initial shear wave velocity model according to the two-dimensional model of the shear wave correction coefficient to obtain the final shear wave velocity model specifically includes: The initial shear wave velocity at each point in the initial shear wave velocity model is divided by the shear wave correction coefficient at the corresponding point in the two-dimensional shear wave correction coefficient model to obtain the final shear wave velocity model.

5. The method as described in claim 1, characterized in that, The determination of the two-dimensional Poisson's ratio model of the study area based on data from multiple micro-logging wells within the study area specifically includes: The average P-wave and S-wave velocities are determined based on the micrologging data in the study area, and the Poisson's ratio of the micrologging is determined based on the average P-wave and S-wave velocities. Based on the determined Poisson's ratio of each micrologging well, a two-dimensional model of Poisson's ratio for the study area is obtained by planar interpolation.

6. The method as described in claim 5, characterized in that, The determination of Poisson's ratio based on the average velocities of the longitudinal and transverse waves specifically includes: Based on the average velocities of the longitudinal and transverse waves, the Poisson's ratio is determined using the following formula (2): （2）； In formula (2), The average P-wave velocity within the drilling depth in micro-logging. This represents the average shear wave velocity within the drilling depth of the micro-logging system. It represents the Poisson's ratio within the drilling depth of the micro-logging system.

7. The method as described in claim 1, characterized in that, The step of establishing a three-dimensional model of the average surface wave velocity in the study area based on the dispersion curves of each surface wave and its corresponding optimal half-wavelength coefficient specifically includes: The model depth of each dispersion point in the dispersion curve is determined based on the optimal half-wavelength coefficient. Based on the model depth of each dispersion point in each dispersion curve and the surface wave velocity, a three-dimensional model of the average surface wave velocity of the study area is obtained by interpolation.

8. The method as described in claim 7, characterized in that, The step of determining the model depth of each dispersion point in the dispersion curve based on the optimal half-wavelength coefficient specifically includes: The inversion depth of each dispersion point in the dispersion curve is determined based on the optimal half-wavelength coefficient. The sum of the inversion depth and the measured depth of the inversion point is determined, and the difference between the model's initial depth and the sum of the inversion depth and the measured depth is determined as the model depth of the inversion point.

9. The method as described in claim 8, characterized in that, The step of determining the inversion depth of each dispersion point in the dispersion curve based on the optimal half-wavelength coefficient specifically includes: The inversion depth of each dispersion point in the dispersion curve is determined according to the optimal half-wavelength coefficient using the following formula (3): （3）； In formula (3), The inversion depth of the i-th dispersion point in the dispersion curve is given by s, where s is the optimal half-wavelength coefficient. The surface wave average velocity at the i-th dispersion point in the dispersion curve is... Let be the surface wave frequency at the i-th dispersion point in the dispersion curve.

10. The method as described in claim 1, characterized in that, The process of converting the three-dimensional model of the average surface wave velocity into an initial shear wave velocity model specifically includes: The three-dimensional model of average surface wave velocity is converted into a three-dimensional model of surface wave velocity. Based on the conversion coefficient between surface wave velocity and shear wave velocity, the three-dimensional surface wave velocity model is converted into an initial shear wave velocity model.

11. The method as described in claim 10, characterized in that, The process of converting the three-dimensional model of the average surface wave velocity into a three-dimensional model of the surface wave velocity specifically includes: For each point in the three-dimensional model of the average surface wave velocity, if the depth of the point is the starting depth of the model, the average surface wave velocity of the point is determined as the surface wave velocity of the point. If the depth of this point is not the initial depth of the model, the average surface wave velocity at this point is converted into the surface wave velocity at this point using the following formula (4): （4）； In formula (4), Let be the surface wave velocity at point j. Let be the average surface wave velocity at point j. Let j be the depth difference between point j and the adjacent point j-1 above it. Let j be the time required for the surface wave to travel from the initial depth point to the j-th point. Let be the depth difference between the starting point of the depth and the j-th point.

12. The method according to any one of claims 1 to 11, characterized in that, The final shear wave velocity model has a grid step size in the x-direction based on the trace spacing of the seismic data in the study area, and a grid step size in the y-direction based on the shot distance of the seismic data.

13. A device for establishing a transverse wave velocity model, characterized in that, The apparatus is used to perform the shear wave velocity model establishment method according to claim 1, and the apparatus includes: The Poisson's ratio two-dimensional model establishment module is used to determine the Poisson's ratio two-dimensional model of the study area based on the micro-logging data of multiple wells in the study area. The optimal half-wavelength coefficient determination module is used to read the Poisson's ratio at the corresponding position of the surface wave dispersion curve from the two-dimensional Poisson's ratio model, and determine the optimal half-wavelength coefficient based on the read Poisson's ratio and the correspondence between the predetermined Poisson's ratio and the half-wavelength coefficient. The initial shear wave velocity model establishment module is used to establish a three-dimensional model of the average surface wave velocity in the study area based on the dispersion curves of each surface wave and its corresponding optimal half-wavelength coefficient, and to convert the three-dimensional model of the average surface wave velocity into an initial shear wave velocity model. The shear wave velocity model establishment module is used to correct the initial shear wave velocity model based on the average shear wave velocity of the micro-logging to obtain the final shear wave velocity model.

14. A computer program product with shear wave velocity model establishment function, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the transverse wave velocity model establishment method according to any one of claims 1 to 12.

15. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the transverse wave velocity model establishment method according to any one of claims 1 to 12.

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