Method and device for constructing seismic velocity model, computing device and storage medium

Abstract

This invention discloses a method, apparatus, computing device, and storage medium for constructing a seismic velocity model. The method includes: acquiring seismic data for a target area; constructing an implicit structural model corresponding to the target area based on the seismic data; acquiring well logging data for the target area; and performing well logging velocity interpolation on the implicit structural model using the well logging data to obtain the seismic velocity model corresponding to the target area. This solution implements well logging velocity interpolation guided by an implicit structural model to obtain the seismic velocity model corresponding to the target area, thus improving the accuracy of the seismic velocity model.

Description

Technical Field

[0001] This invention relates to the field of exploration technology, specifically to a method, apparatus, computing device, and storage medium for constructing a seismic velocity model. Background Technology

[0002] With the continuous development of exploration technology, seismic migration technology is being increasingly applied in seismic exploration. The construction of seismic velocity models is a crucial step in seismic migration technology, and the accuracy of these models directly or indirectly affects the accuracy of the seismic migration processing results. Therefore, improving the accuracy of seismic velocity models is of great significance.

[0003] The commonly used method for constructing seismic velocity models is the well-to-well interpolation method guided by seismic images. However, the accuracy of existing seismic velocity model construction methods is poor and cannot meet the requirements for constructing high-precision velocity models. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method, apparatus, computing device and storage medium for constructing an earthquake velocity model that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for constructing a seismic velocity model is provided, comprising:

[0006] Acquire seismic data for the target area;

[0007] An implicit structural model corresponding to the target area is constructed based on the earthquake data;

[0008] Acquire well logging data for the target area;

[0009] The seismic velocity model corresponding to the target area is obtained by interpolating the logging velocity of the implicit structural model using the logging data.

[0010] In an optional implementation, constructing the implicit structural model corresponding to the target area based on the seismic data further includes:

[0011] Calculate the reflection dip field based on the earthquake data;

[0012] Calculate the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data.

[0013] Based on the reflection tilt field and the time offset, an implicit construction model corresponding to the target region is constructed.

[0014] In an optional implementation, calculating the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data further includes:

[0015] For any given seismic trace, the seismic trace data is arranged chronologically to form the seismic data sequence corresponding to that seismic trace.

[0016] For any two seismic traces, the time offset between the seismic data sequences corresponding to the two seismic traces is calculated using a dynamic time-rounding algorithm.

[0017] In an optional implementation, calculating the reflection dip field based on the seismic data further includes:

[0018] Calculate the structural tensor based on the earthquake data;

[0019] The reflection tilt field is calculated based on the structure tensor.

[0020] In an optional implementation, constructing the implicit construction model corresponding to the target region based on the reflection tilt field and the time offset further includes:

[0021] Construct seismic data flattening equations for the reflection dip field, the time offset, and the displacement field;

[0022] Solve the flattening equations of the seismic data to obtain the displacement field;

[0023] The implicit construction model is obtained based on the displacement field.

[0024] In an optional implementation, the step of obtaining the seismic velocity model corresponding to the target area by performing well logging velocity interpolation processing on the implicit structural model using the well logging data further includes:

[0025] Construct an interpolation grid; wherein the interpolation grid includes a first grid sampling point and a second grid sampling point, the position of the first grid sampling point matches the position corresponding to the logging velocity, and the position of the second grid sampling point does not match the position corresponding to the logging velocity;

[0026] Based on the isosurface of the implicit structural model, all logging velocity values ​​are interpolated to the first grid sampling point along the same isosurface, and the interpolation velocity of the second grid sampling point is calculated.

[0027] In an optional implementation, the step of interpolating all logging velocity values ​​along the same isosurface to the first grid sampling points and calculating the interpolation velocity at the second grid sampling points further includes:

[0028] The interpolation rate function is determined based on the radial basis function algorithm, and the interpolation rate of each grid sampling point is calculated according to the interpolation rate function.

[0029] According to another aspect of the present invention, an apparatus for constructing a seismic velocity model is provided, comprising:

[0030] The acquisition module is used to acquire seismic data and well logging data of the target area.

[0031] An implicit structural model generation module is used to construct an implicit structural model corresponding to the target area based on the seismic data.

[0032] The velocity model generation module is used to perform well logging velocity interpolation processing on the implicit structural model using the well logging data to obtain the seismic velocity model corresponding to the target area.

[0033] In one alternative implementation, the implicit structural model generation module is used to: calculate the reflection dip field based on the seismic data;

[0034] Calculate the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data.

[0035] Based on the reflection tilt field and the time offset, an implicit construction model corresponding to the target region is constructed.

[0036] In one optional implementation, the implicit structural model generation module is used to: for any seismic trace corresponding to seismic trace data, arrange the seismic trace data of the seismic trace in time to form a seismic data sequence corresponding to the seismic trace.

[0037] For any two seismic traces, the time offset between the seismic data sequences corresponding to the two seismic traces is calculated using a dynamic time-rounding algorithm.

[0038] In one alternative implementation, the implicit structural model generation module is used to: calculate the structural tensor based on the seismic data;

[0039] The reflection tilt field is calculated based on the structure tensor.

[0040] In one alternative implementation, the implicit structural model generation module is used to: construct seismic data flattening equations for the reflection dip field, the time offset, and the displacement field;

[0041] Solve the flattening equations of the seismic data to obtain the displacement field;

[0042] The implicit construction model is obtained based on the displacement field.

[0043] In one alternative implementation, the velocity model generation module is used for:

[0044] Construct an interpolation grid; wherein the interpolation grid includes a first grid sampling point and a second grid sampling point, the position of the first grid sampling point matches the position corresponding to the logging velocity, and the position of the second grid sampling point does not match the position corresponding to the logging velocity;

[0045] Based on the isosurface of the implicit structural model, all logging velocity values ​​are interpolated to the first grid sampling points along the same isosurface, and the interpolation velocity at the second grid sampling points is calculated.

[0046] In one alternative implementation, the velocity model generation module is used for:

[0047] The interpolation rate function is determined based on the radial basis function algorithm, and the interpolation rate of each grid sampling point is calculated according to the interpolation rate function.

[0048] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0049] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for constructing the seismic velocity model.

[0050] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform operations corresponding to the above-described method for constructing the seismic velocity model.

[0051] The seismic velocity model construction method, apparatus, computing device, and storage medium disclosed in this invention involve: acquiring seismic data of a target area; constructing an implicit structural model corresponding to the target area based on the seismic data; acquiring well logging data of the target area; and performing well logging velocity interpolation processing on the implicit structural model using the well logging data to obtain the seismic velocity model corresponding to the target area. This scheme achieves well logging velocity interpolation guided by an implicit structural model to obtain the seismic velocity model corresponding to the target area, thereby improving the accuracy of the seismic velocity model.

[0052] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0053] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0054] Figure 1 A flowchart illustrating a method for constructing a seismic velocity model according to an embodiment of the present invention is shown.

[0055] Figure 2 The diagram illustrates a flowchart of a method for constructing an implicit construction model according to an embodiment of the present invention.

[0056] Figure 3 This diagram illustrates a structural tensor provided by an embodiment of the present invention.

[0057] Figure 4 This diagram illustrates a seismic data flattening image provided by an embodiment of the present invention.

[0058] Figure 5 A schematic diagram of a seismic image corresponding to an implicit structural model provided in an embodiment of the present invention is shown.

[0059] Figure 6 This diagram illustrates a comparison of seismic data, an implicit tectonic model, and contour lines of the implicit tectonic model provided by an embodiment of the present invention.

[0060] Figure 7 A flowchart illustrating another method for constructing a seismic velocity model provided by an embodiment of the present invention is shown;

[0061] Figure 8 This invention illustrates an interpolation speed model under different interpolation grid resolutions, as provided in an embodiment of the invention.

[0062] Figure 9 This diagram illustrates the fusion of an implicit structural model and well logging data according to an embodiment of the present invention.

[0063] Figure 10 A schematic diagram of a seismic velocity model construction device provided in an embodiment of the present invention is shown;

[0064] Figure 11 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0065] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention 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 invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0066] Figure 1 The diagram illustrates a flowchart of a method for constructing a seismic velocity model according to an embodiment of the present invention. The method for constructing the seismic velocity model provided in this embodiment can be executed by a suitable computing device.

[0067] like Figure 1 As shown, the method includes the following steps:

[0068] Step S110: Obtain seismic data for the target area.

[0069] The target area is the region to be analyzed. This embodiment of the invention does not limit the scope or location of the target area. This step acquires seismic data of the target area. Specifically, this seismic data can be three-dimensional exploration data or two-dimensional seismic data. The seismic data is typically organized by seismic traces, and two-dimensional or three-dimensional seismic images can be obtained based on this data.

[0070] In one alternative implementation, in order to improve the overall construction efficiency of the seismic velocity model and save system resources, after obtaining the initial seismic data of the target area, this implementation can perform downsampling processing on the seismic data along the depth or time direction to reduce the amount of seismic data to be processed in the subsequent process. The implicit structural model is then constructed from the downsampled seismic data.

[0071] Step S120: Construct an implicit structural model corresponding to the target area based on the seismic data.

[0072] The implicit tectonic model is constructed using the seismic data in step S110. Partial differential equations are built using the implicit tectonic modeling method. By solving these partial differential equations, a scalar field that fits the data in the entire space is obtained. This scalar field can implicitly represent the subsurface structure of the target area, thus enabling the implicit tectonic model to accurately describe the subsurface structure of the target area and provide a precise data foundation for the subsequent construction of the seismic velocity model.

[0073] Step S130: Obtain well logging data for the target area.

[0074] If the target area contains one or more well logs, then for any one well, the logging data of that well is acquired. Specifically, the logging data described in this embodiment of the invention includes sonic logging data.

[0075] In one alternative implementation, in order to further reduce the amount of data processing, save system resources, and improve the efficiency of seismic velocity model construction, the well logging data can be resampled after the initial well logging data of the target area is obtained, so as to reduce the number of well logging sampling points.

[0076] Step S140: After interpolating the logging velocity of the implicit structural model using well logging data, the seismic velocity model corresponding to the target area is obtained.

[0077] The implicit structural model is used to guide the logging velocity interpolation. After interpolating the logging velocity of the implicit structural model, the seismic velocity model corresponding to the target area is obtained.

[0078] Therefore, in this embodiment of the invention, an implicit structural model of the target area is constructed based on seismic data, which can accurately describe the underground structure of the target area; and further, the implicit structural model and well logging data are integrated to realize well logging velocity interpolation guided by the implicit structural model, thereby obtaining the seismic velocity model corresponding to the target area and improving the model accuracy of the seismic velocity model.

[0079] Figure 2 The diagram illustrates a flowchart of a method for constructing an implicit structural model according to an embodiment of the present invention. This method can be applied to the construction methods of seismic velocity models in other embodiments.

[0080] like Figure 2 As shown, the method includes the following steps:

[0081] Step S210: Calculate the reflection dip field based on the seismic data.

[0082] Specifically, the structure tensor is first calculated based on the seismic data. This structure tensor can be used to estimate the underground seismic structure and stratum orientation of the target area.

[0083] If the seismic data is two-dimensional, the structural tensor can be obtained using the following formula 1:

[0084]

[0085] Where T(x) is the structure tensor, g1(x) is the gradient of the seismic data in the Z direction (vertical, depth, or vertical direction), and g2(x) is the gradient of the seismic data in the X direction (inline direction), then this structure tensor contains...<g1(x)g1(x)> ,<g1(x)g2(x)> ,<g2(x)g2(x)> The three components each constitute a two-dimensional seismic image, and the resolution of this seismic image is consistent with the resolution of the seismic image corresponding to the seismic data used to generate the structure tensor. Furthermore, the <> symbol indicates smoothing filtering applied to the corresponding component. Since this formula applies to two-dimensional seismic data, the <> here specifically represents two-dimensional smoothing filtering.

[0086] If the seismic data is three-dimensional exploration data, the structural tensor can be obtained using the following formula 2:

[0087]

[0088] Where T(x) is the structure tensor, g1(x) is the gradient of the seismic data in the Z direction, g2(x) is the gradient of the seismic data in the X direction, and g3(x) is the gradient of the seismic data in the Y direction (crossline direction), then this structure tensor contains<g1(x)g1(x)> ,<g1(x)g2(x)> ,<g1(x)g3(x)> ,<g2(x)g2(x)> ,<g2(x)g3(x)> ,<g3(x)g3(x)> Six independent components, each of which can constitute a three-dimensional seismic image, with a resolution consistent with the seismic image corresponding to the seismic data used to generate the structure tensor. The <> symbol indicates smoothing filtering of the corresponding component. Since this formula applies to three-dimensional exploration data, the <> here specifically refers to three-dimensional smoothing filtering. Figure 3 As shown, the structural tensor corresponding to the seismic data can be obtained through Formula 2.

[0089] Furthermore, the reflection tilt field is calculated based on the obtained structural tensor. Specifically, the eigenvectors of the aforementioned structural tensor matrix are obtained, each having corresponding vector components in the z, x, and y directions. The corresponding reflection tilt angle is then obtained based on the ratio of these vector components.

[0090] For example, if the eigenvectors of the structure tensor matrix point downwards, the reflection tilt field in the X direction can be obtained using the following formula 3:

[0091]

[0092] Where p(x) is the reflection tilt field in the X direction, u1(x) is the component of the eigenvector u(x) of the structure tensor matrix in the Z direction, and u2(x) is the component of the eigenvector u(x) of the structure tensor matrix in the X direction.

[0093] Correspondingly, the reflection tilt field in the Y direction can also be obtained using the following formula 4:

[0094]

[0095] Where q(x) is the reflection tilt field in the Y direction, u1(x) is the component of the eigenvector u(x) of the structure tensor matrix in the Z direction, and u3(x) is the component of the eigenvector u(x) of the structure tensor matrix in the Y direction.

[0096] The reflection dip field can be accurately obtained using the aforementioned structural tensor, providing a foundation for the precise construction of subsequent implicit tectonic models.

[0097] Step S220: Calculate the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data.

[0098] Because faults may exist in the strata during actual implementation, the geological time (also known as geological age) interface is not a stable and continuous interface. Therefore, in order to achieve accurate construction of the implicit tectonic model, in addition to calculating the reflection dip field, this embodiment of the invention further determines the correlation between seismic traces in the multigrid through this step. By jointly inverting the correlation between the reflection dip field and the seismic trace, an accurate and stable implicit tectonic model is finally obtained.

[0099] The correlation between seismic traces in the multigrid is obtained as follows: Based on the seismic trace data corresponding to each seismic trace in the seismic data, the time offset between any two seismic traces is calculated. Specifically, for any seismic trace, the seismic trace data is arranged chronologically to form the seismic data sequence corresponding to that trace. For any two seismic traces, a dynamic time-correction algorithm is used to calculate the time offset between the seismic data sequences corresponding to those two traces. Thus, the dynamic time-correction algorithm aligns any two seismic data sequences on the time axis. The time displacement of a seismic data sequence during the alignment process is the time offset, thereby establishing the correlation between all sampling point pairs at all time points or depth points in any two seismic data sequences.

[0100] In this embodiment of the invention, after obtaining the reflection dip field and the time offset between any two seismic traces, an implicit structural model corresponding to the target area is constructed based on the reflection dip field and the time offset between any two seismic traces, thereby improving the accuracy and stability of the constructed implicit structural model. The specific implementation process of constructing the implicit structural model corresponding to the target area based on the reflection dip field and the time offset between any two seismic traces can be referred to in subsequent steps S230-S250.

[0101] Step S230: Construct seismic data flattening equations for reflection dip field, time offset, and displacement field.

[0102] In the process of constructing the implicit tectonic model corresponding to the target area based on the reflection dip field and the time offset between any two seismic traces, the seismic data is first flattened based on the seismic dip field and the cross-correlation of seismic traces in the multigrid. The data flattening equation is a partial differential equation that includes the reflection dip field, time offset, and displacement field.

[0103] In one alternative implementation, the three-dimensional seismic data representation equation or partial differential equation can be as shown in Equation 5 below:

[0104]

[0105] Where s is the displacement field, p(x,y,z) is the reflection tilt field in the X direction, q(x,y,z) is the reflection tilt field in the Y direction, w(x,y,z) is the weight of the reflection tilt field, and λ is a constant used to provide a trade-off between fitting the local reflection tilt field and global correlation; for example, λ can be 0.001, z j -z i is the time offset between the j-th seismic trace and the ith seismic trace. The distance between the ith and j-th seismic traces is within a preset range (e.g., 5, 10, 15, ..., 60 seismic traces, etc.), and the extracted seismic traces are approximately uniformly distributed in space; μ is another constant, which is usually small, for example, it can be 0.01.

[0106] Formula 5 above is a set of four equations. The first equation in Formula 5 is used to fit the reflection dip field in the X direction. The second equation in Formula 5 is also used to fit the reflection dip field in the X direction. The third equation in Formula 5 represents the cross-correlation of multigrid seismic traces. This equation is an M×N set of equations, where M represents the number of seismic trace pairs and N represents the number of samples in the seismic traces. The fourth equation in Formula 5 is a regularization term, used to apply vertical smoothness to the calculated displacement field.

[0107] Step S240: Solve the seismic data flattening equation to obtain the displacement field.

[0108] After constructing the flattening equation for the seismic data, the equation is solved. During the solution process, multiple iterations can be performed using a combination of the conjugate gradient algorithm and the least squares method. The iteration stops when the flattened displacement calculated in the most recent iteration is close to zero. Figure 4 This illustration shows a seismic data flattening image provided by an embodiment of the present invention. Specifically, the seismic data flattening image is obtained based on the reflection dip field and the correlation of seismic traces in a multigrid.

[0109] The displacement field can be obtained by solving the seismic data flattening equation. The displacement field maps the original depth space (x,y,z) of the seismic data sampling points to the flattened space of the implicit tectonic model (the Z-axis corresponds to the relative geological age).

[0110] Step S250: Obtain the implicit construction model based on the displacement field.

[0111] The process of flattening seismic data involves mapping the seismic image from the original depth space (x,y,z) to the implicit tectonic model space (x,y,τ), where τ corresponds to the geological age.

[0112] In this model, all reflections are horizontally aligned, and τ increases monotonically in the vertical direction. Inverse interpolation can be used to convert the displacement field (calculated in the original depth space) into an implicit tectonic model space, thus mapping the seismic image onto a flattened grid. Specifically, the implicit tectonic model can be obtained using the following formula 6:

[0113] τ(x,y,z)=z+s(x,y,z) (Formula 6)

[0114] like Figure 5 As shown, Figure 5 This invention provides an embodiment of an implicit structural model, which is illustrated by a seismic image. Figure 5 The amplitude of the Z-axis corresponds to the relative geological time. This demonstrates that the implicit tectonic model fits the seismic data, implicitly representing all subsurface tectonic information. Therefore, this implicit tectonic model can be used to construct an efficient and high-precision seismic velocity model.

[0115] And, as Figure 6 As shown, Figure 6 In the image, a) is a seismic image generated based on seismic data, b) is a seismic image corresponding to the implicit structural model, and c) is an image corresponding to the contour lines of the implicit structural model. It can be seen that the contour lines in the implicit structural model in c) match the actual seismic data in a), thus the implicit structural model provided by the present invention can accurately represent the underground structure of the target area.

[0116] Therefore, the embodiments of the present invention calculate the time offset between the reflection dip field and the seismic trace based on seismic data, and construct the implicit tectonic model corresponding to the target area by combining the reflection dip field and the time offset between the seismic trace, thereby improving the accuracy of the constructed implicit tectonic model and further improving the accuracy of the seismic velocity model.

[0117] Figure 7 This diagram illustrates a flowchart of another method for constructing a seismic velocity model according to an embodiment of the present invention. The method for constructing the seismic velocity model provided in this embodiment can be executed by a suitable computing device.

[0118] like Figure 7 As shown, the method includes the following steps:

[0119] Step S710: Obtain seismic data for the target area and construct an implicit structural model for the target area based on the seismic data.

[0120] The specific implementation process of this step can be referred to the description in other embodiments, and will not be repeated here.

[0121] Step S720: Obtain logging data for the target area and resample the logging data.

[0122] After obtaining logging data for the target area, the logging data can be resampled to reduce the amount of data to be processed later.

[0123] The logging data can be expressed as follows: Formula 7 and / or Formula 8:

[0124] If a certain velocity logging data has K sampling points, then the logging data and its corresponding location can be represented as:

[0125]

[0126] Where x is the spatial location parameter and v is the logging velocity, that is, each logging velocity corresponds to a spatial location, and the logging velocity and the corresponding spatial location form a data pair. It is a set of data pairs of location and logging velocity.

[0127] If the target area contains I well logs, and each well log has L... i Includes K i If there are 1 sampling point, it can be represented as:

[0128]

[0129] Among them, (x k,i ,v k,i ) is located at x k,i At position L, the range of i is from 1 to I. iLet be the set of data pairs for the i-th well logging.

[0130] Step S730: Construct the interpolation grid.

[0131] A uniform sampling grid, or interpolation grid, is constructed, in which each grid cell has the same size, corresponding to one sampling point. The resolution of this interpolation grid can be the same as or lower than that of the well logging data. In this embodiment of the invention, the resolution of the interpolation grid can be dynamically selected according to the application scenario of the seismic velocity model. For example, the resolution of the interpolation grid can be consistent with the resolution of the resampled seismic data, or twice the resolution of the seismic data, etc. The resolution of the interpolation grid directly affects the resolution of the generated seismic velocity model, and the resolution of the seismic velocity model can be adjusted by adjusting the resolution of the interpolation grid, thus achieving adjustable seismic velocity model resolution and improving the flexibility of the seismic velocity model's application scenarios. Figure 8 As shown, in a), the interpolation grid uses the same resolution as the seismic data; in b), the resolution of the interpolation grid is twice that of the seismic data; and in c), the resolution of the interpolation grid is four times that of the seismic data. Finally, the resolution of the seismic velocity model in a), b), and c) increases sequentially.

[0132] The interpolation grid includes a first grid sampling point and a second grid sampling point. The location of the first grid sampling point matches the location corresponding to the logging velocity, while the location of the second grid sampling point does not match the location corresponding to the logging velocity. Specifically, as shown in formulas 7 and 8 above, each logging velocity corresponds to a physical location. If this physical location is within a certain grid sampling point, then that grid sampling point is determined as the first grid sampling point. If the physical locations of all logging velocities do not fall within a certain grid sampling point, then that grid sampling point is the second grid sampling point.

[0133] In an alternative implementation, to improve subsequent interpolation accuracy, after constructing the interpolation grid, the data pairs of spatial location and logging velocity are converted into data pairs of grid and logging velocity, that is, the logging velocity is mapped to the grid of the interpolation network, which can be identified as shown in Equation 9:

[0134]

[0135] in, For located At the grid level, i ranges from 1 to I, thus each logging velocity corresponds to one grid, forming a data pair between logging velocity and grid. This is a set of data pairs representing logging velocity and grid.

[0136] Step S740: Based on the isosurface of the implicit structural model, interpolate all logging velocity values ​​along the same isosurface to the first grid sampling point, and calculate the interpolation velocity of the second grid sampling point.

[0137] Overlaying implicit structural models with well logging data, such as... Figure 9 As shown, A is the implicit structural model, and B is the logging data from multiple wells.

[0138] For the first grid sampling point, which has phase-mapped well logging data, the velocity interpolation process guided by the implicit structural model involves inserting the well logging velocity from the well logging data into the corresponding first grid sampling point along the same isosurface. This same isosurface of the implicit structural model corresponds to the same geological time, ensuring that the interpolation process can be performed along stratigraphic intervals of the same geological age. Before interpolation, the implicit structural model needs to be resampled to ensure that the resolution of the resampled implicit structural model is consistent with that of the interpolation grid.

[0139] Specifically, the implicit structural model space is (x, y, τ). Mapping it to an interpolation network yields grid-to-geological-time data pairs. Matching these grid-to-geological-time data pairs with the grid-to-logging-velocity data pairs from step S630 above, the correspondence between geological time and logging velocity can be obtained. For example, this can be expressed using Equation 10:

[0140]

[0141] in, To correspond to the geological time and logging rate of the same grid, Let be the set of data pairs of geological time and logging rate for the i-th well.

[0142] For the second grid sampling point, which lacks phase-mapped logging data, an interpolation algorithm (such as linear interpolation) can be used to establish the relationship function between geological events and logging velocities in the implicit structural model. This function can determine the interpolation rate corresponding to the sampling point of the second grid.

[0143] In an optional implementation, to improve the accuracy of the interpolation speed, this method combines radial basis functions to obtain an interpolation speed function that can uniformly determine the interpolation speed of each grid sampling point. This interpolation speed function can be expressed as the following formula 11:

[0144]

[0145] Where q(x,y,τ) is the interpolation rate, and w i(x,y) are the weighting coefficients obtained based on the radial basis function, h i (τ) takes a value of 1 at the first grid sampling point and a value of 0 at the second grid sampling point; p i (τ) represents the logging rate mapped to the implicit structural model.

[0146] w in Formula 11 i (x, y) are weight coefficients obtained based on the radial basis function. These weights are distance-related, such that the magnitude of the interpolation rate depends on its horizontal Euclidean distance from the sampling points of the interpolation grid. That is, the closer the sampling point is to the first grid sampling point, the greater the weight of the second grid sampling point. For 3D seismic data, this can be represented by Equation 12:

[0147]

[0148] Among them, K i denoted as the number of sampling points for the i-th well logging, ∈ , which is a distance-dependent radial parameter used to balance the well logging data and is set to 1 based on previous experiments.

[0149] Once the velocities of the first and second grid sampling points are determined, the seismic velocity model can be obtained.

[0150] Therefore, the embodiments of the present invention construct an interpolation grid, and based on the isosurface of the implicit structural model, interpolate all logging velocity values ​​to the first grid sampling point along the same isosurface, and calculate the interpolation velocity of the second grid sampling point. This ensures that the interpolation process can be performed along the stratigraphic interval of the same geological age, thereby improving the accuracy of the seismic velocity model.

[0151] Figure 10 A schematic diagram of a seismic velocity model construction device provided in an embodiment of the present invention is shown. Figure 10 As shown, the device 1000 includes: an acquisition module 1010, an implicit construction model generation module 1020, and a velocity model generation module 1030.

[0152] The acquisition module 1010 is used to acquire seismic data and well logging data of the target area.

[0153] The implicit structural model generation module 1020 is used to construct an implicit structural model corresponding to the target area based on the seismic data.

[0154] The velocity model generation module 1030 is used to perform well logging velocity interpolation processing on the implicit structural model using the well logging data to obtain the seismic velocity model corresponding to the target area.

[0155] In one alternative implementation, the implicit structural model generation module is used to: calculate the reflection dip field based on the seismic data;

[0156] Calculate the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data.

[0157] Based on the reflection tilt field and the time offset, an implicit construction model corresponding to the target region is constructed.

[0158] In one optional implementation, the implicit structural model generation module is used to: for any seismic trace corresponding to seismic trace data, arrange the seismic trace data of the seismic trace in time to form a seismic data sequence corresponding to the seismic trace.

[0159] For any two seismic traces, the time offset between the seismic data sequences corresponding to the two seismic traces is calculated using a dynamic time-rounding algorithm.

[0160] In one alternative implementation, the implicit structural model generation module is used to: calculate the structural tensor based on the seismic data;

[0161] The reflection tilt field is calculated based on the structure tensor.

[0162] In one alternative implementation, the implicit structural model generation module is used to: construct seismic data flattening equations for the reflection dip field, the time offset, and the displacement field;

[0163] Solve the flattening equations of the seismic data to obtain the displacement field;

[0164] The implicit construction model is obtained based on the displacement field.

[0165] In one alternative implementation, the velocity model generation module is used for:

[0166] Construct an interpolation grid; wherein the interpolation grid includes a first grid sampling point and a second grid sampling point, the position of the first grid sampling point matches the position corresponding to the logging velocity, and the position of the second grid sampling point does not match the position corresponding to the logging velocity;

[0167] Based on the isosurface of the implicit structural model, all logging velocity values ​​are interpolated to the first grid sampling point along the same isosurface, and the interpolation velocity of the second grid sampling point is calculated.

[0168] In one alternative implementation, the velocity model generation module is used for:

[0169] The interpolation rate function is determined based on the radial basis function algorithm, and the interpolation rate of each grid sampling point is calculated according to the interpolation rate function.

[0170] Therefore, in this embodiment of the invention, an implicit structural model of the target area is constructed based on seismic data, which can accurately describe the underground structure of the target area; and further, the implicit structural model and well logging data are integrated to realize well logging velocity interpolation guided by the implicit structural model, thereby obtaining the seismic velocity model corresponding to the target area and improving the model accuracy of the seismic velocity model.

[0171] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the implicit construction model construction method in any of the above method embodiments.

[0172] Figure 11 A schematic diagram of a computing device according to an embodiment of the present invention is shown. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0173] like Figure 11 As shown, the computing device may include: a processor 1102, a communications interface 1104, a memory 1106, and a communications bus 1108.

[0174] The processor 1102, communication interface 1104, and memory 1106 communicate with each other via communication bus 1108. Communication interface 1104 is used to communicate with other network elements, such as clients or other servers. The processor 1102 executes program 1110, specifically performing the relevant steps described in the embodiment of the implicit model construction method.

[0175] Specifically, program 1110 may include program code that includes computer operation instructions.

[0176] Processor 1102 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0177] Memory 1106 is used to store program 1110. Memory 1106 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device. Program 1110 can specifically be used to cause processor 1102 to perform the operations described in the method embodiments above.

[0178] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0179] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0180] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0181] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0182] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0183] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0184] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for constructing a seismic velocity model, characterized in that, include: Acquire seismic data for the target area; The reflection dip field is calculated based on the earthquake data; the time offset between any two earthquake traces is calculated based on the earthquake trace data corresponding to each earthquake trace in the earthquake data; and an implicit tectonic model corresponding to the target area is constructed based on the reflection dip field and the time offset. Acquire well logging data for the target area; The seismic velocity model corresponding to the target area is obtained by interpolating the logging velocity of the implicit structural model using the logging data. An interpolation grid is constructed, comprising a first grid sampling point and a second grid sampling point. The positions of the first grid sampling points match the corresponding positions of the logging velocities, while the positions of the second grid sampling points do not match the corresponding positions of the logging velocities. For the first grid sampling point, all logging velocity values ​​are interpolated to the first grid sampling point along the same isosurface. For the second grid sampling point, an interpolation algorithm is used to establish a relationship function between geological time and logging velocity in the implicit structural model, and the interpolated velocity of the second grid sampling point is calculated based on this relationship function.

2. The method according to claim 1, characterized in that, The step of calculating the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data further includes: For any given seismic trace, the seismic trace data is arranged chronologically to form the seismic data sequence corresponding to that seismic trace. For any two seismic traces, the time offset between the seismic data sequences corresponding to the two seismic traces is calculated using a dynamic time-rounding algorithm.

3. The method according to claim 1, characterized in that, The step of calculating the reflection dip field based on the seismic data further includes: Calculate the structural tensor based on the earthquake data; The reflection tilt field is calculated based on the structure tensor.

4. The method according to any one of claims 1-3, characterized in that, The step of constructing the implicit construction model corresponding to the target region based on the reflection tilt field and the time offset further includes: Construct seismic data flattening equations for the reflection dip field, the time offset, and the displacement field; Solve the flattening equations of the seismic data to obtain the displacement field; The implicit construction model is obtained based on the displacement field.

5. The method according to claim 1, characterized in that, Interpolating all logging velocity values ​​along the same isosurface to the first grid sampling points, and calculating the interpolation velocity at the second grid sampling points further includes: The interpolation rate function is determined based on the radial basis function algorithm, and the interpolation rate of each grid sampling point is calculated according to the interpolation rate function.

6. A device for constructing a seismic velocity model, characterized in that, include: The acquisition module is used to acquire seismic data and well logging data of the target area. An implicit structural model generation module is used to calculate the reflection dip field based on the seismic data, calculate the time offset between any two seismic traces based on the seismic trace data corresponding to each seismic trace in the seismic data, and construct an implicit structural model corresponding to the target area based on the reflection dip field and the time offset. A velocity model generation module is used to obtain a seismic velocity model corresponding to the target area by interpolating the logging velocity of the implicit structural model using the logging data. This includes constructing an interpolation grid, which comprises a first grid sampling point and a second grid sampling point. The positions of the first grid sampling points match the corresponding positions of the logging velocities, while the positions of the second grid sampling points do not match the corresponding positions of the logging velocities. For the first grid sampling point, all logging velocity values ​​are interpolated to the first grid sampling point along the same isosurface. For the second grid sampling point, an interpolation algorithm is used to establish a relationship function between geological time and logging velocity in the implicit structural model, and the interpolated velocity of the second grid sampling point is calculated based on this relationship function.

7. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the method for constructing the seismic velocity model as described in any one of claims 1-5.

8. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the method for constructing the seismic velocity model as described in any one of claims 1-5.

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