Multi-point geostatistics inversion method and device based on relative geologic age body

Abstract

The invention provides a multipoint geostatistics inversion method and device based on a relative geologic age body. Determining a relative geological age body corresponding to the sampling point according to the seismic data of the target area; according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated, screening the first sampling point to obtain a second sampling point; determining a preset weighting coefficient of the second sampling points according to a correlation coefficient between any two second sampling points, a correlation coefficient between the second sampling points and the sampling points to be interpolated, and a preset Lagrange multiplier; determining an elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and a preset weighting coefficient; and determining an elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated. Therefore, the prediction precision of the oil and gas reservoir in the target area is improved.

Description

Technical Field

[0001] This specification belongs to the technical field of petroleum exploration, and particularly relates to a multiple-point geostatistical inversion method and device based on relative geological chronostratigraphic units. Background Art

[0002] Currently, the traditional multiple-point geostatistical method is one of the widely used modeling techniques in prestack seismic inversion. However, due to its inherent limitations in training image acquisition and data constraint, the elastic parameters obtained by inversion are often of low accuracy and insufficient resolution, resulting in poor reservoir prediction effect and unable to meet the requirements of high-precision oil and gas exploration and development.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] This specification provides a multiple-point geostatistical inversion method and device based on relative geological chronostratigraphic units. First, by calculating the local dip angle based on the seismic data of the target area and thus deriving the relative geological chronostratigraphic unit of each sampling point, the stratigraphic time series constraint is effectively realized. Secondly, through the screening process of the first sampling point based on the relative geological chronostratigraphic unit, it is ensured that the selected sampling points for interpolation are consistent in relative sedimentary age, thereby effectively reducing the parameter inversion error caused by the traditional multiple-point geostatistics without using relative geological age constraint. Finally, using the correlation coefficient between the screened first sampling points and the sampling point to be interpolated, and the weighted coefficient determined by the preset Lagrange multiplier, weighted interpolation calculation is performed on the screened first sampling points, so as to obtain more accurate elastic parameters as the initial model, improve the inversion accuracy and resolution of the elastic parameters, and ultimately improve the accuracy of oil and gas reservoir prediction in the target area to meet the requirements of high-precision oil and gas exploration and development.

[0005] This specification provides a multiple-point geostatistical inversion method based on relative geological chronostratigraphic units, including:

[0006] Obtaining the seismic data of the target area;

[0007] Determining the local dip angle of the sampling point according to the seismic data of the target area, and determining the relative geological chronostratigraphic unit corresponding to the sampling point according to the local dip angle;

[0008] Screening the first sampling point according to the relative geological chronostratigraphic unit of the first sampling point and the relative geological chronostratigraphic unit of the sampling point to be interpolated to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters;

[0009] Obtaining the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated;

[0010] Determine the preset weighting coefficient of the second sampling points according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier;

[0011] Determine the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient;

[0012] Determine the inversion result of the elastic parameter of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; wherein, the inversion result of the elastic parameter is used for predicting the oil and gas reservoir in the target area.

[0013] In one embodiment, before obtaining the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated, it further includes:

[0014] Perform segmentation processing on the target image corresponding to the seismic data according to a preset segmentation size to obtain a plurality of sub-images;

[0015] Perform screening processing on the plurality of sub-images to obtain a filter;

[0016] Determine the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images.

[0017] In one embodiment, the determining the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images includes:

[0018] Perform equalization processing on each of the sub-images to obtain an equalized sub-image;

[0019] Determine the covariance matrix corresponding to the equalized sub-image, and determine the eigenvalues and eigenvectors corresponding to the covariance matrix;

[0020] Sort the eigenvalues, select the eigenvectors corresponding to the top preset number of eigenvalues as target vectors, and determine the target vectors as the filter;

[0021] Determine the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the target vectors and each of the sub-images. In one embodiment, the determining the local dip angle according to the seismic data of the target area includes:

[0022] Determine the decomposition residual and decomposition factor of the seismic trace data corresponding to the interpolation sampling point in the target image;

[0023] Determine the local dip angle corresponding to the target image according to the decomposition factor and the decomposition residual.

[0024] In one embodiment, the determining the relative geological chronostratigraphic body corresponding to the first sampling point according to the local dip angle includes:

[0025] Determine the vertical time shift in the space of the target image according to the local dip angle;

[0026] Determine the relative geological chronostratigraphic body corresponding to the interpolation sampling point according to the vertical time shift and the seismic wave propagation time corresponding to the interpolation sampling point.

[0027] In one embodiment, the screening the first sampling point according to the relative geological chronostratigraphic body of the first sampling point and the relative geological chronostratigraphic body of the interpolation sampling point to obtain a second sampling point includes:

[0028] Obtain the difference between the relative geological chronostratigraphic bodies of each first sampling point and the interpolation sampling point;

[0029] Screen the first sampling point according to the difference and a preset difference threshold to obtain a second sampling point. In one embodiment, the elastic parameters include P-wave velocity, S-wave velocity, and density. The determining the elastic parameters corresponding to the interpolation sampling point according to the elastic parameters corresponding to the second sampling point and the preset weighting coefficient includes:

[0030] Determine the product of the P-wave velocity corresponding to each second sampling point and the preset weighting coefficient of each second sampling point;

[0031] Determine the sum of the products as the P-wave velocity corresponding to the interpolation sampling point.

[0032] This specification provides a multi-point geostatistical inversion device based on relative geological chronostratigraphic bodies, including:

[0033] A data acquisition module, configured to acquire seismic data of a target area;

[0034] A chronostratigraphic body determination module, configured to determine the local dip angle of a sampling point according to the seismic data of the target area, and determine the relative geological chronostratigraphic body corresponding to the sampling point according to the local dip angle;

[0035] A sampling point determination module, configured to screen the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be simulated in the sampling points, so as to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters.

[0036] A correlation coefficient determination module, configured to obtain the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated.

[0037] A weighted coefficient determination module, configured to determine the preset weighted coefficient of the second sampling point according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier.

[0038] A parameter determination module, configured to determine the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighted coefficient.

[0039] A seismic inversion module, configured to determine the elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

[0040] This specification also provides an electronic device, including a processor and a memory for storing instructions executable by the processor. When the processor executes the instructions, a multi-point geostatistical inversion method based on relative geological age bodies is implemented.

[0041] This specification also provides a computer-readable storage medium, on which computer instructions are stored. When the instructions are executed, a multi-point geostatistical inversion method based on relative geological age bodies is implemented.

[0042] Based on the multi-point geostatistical inversion method based on relative geological chronostratigraphic units provided in this specification, seismic data of the target area is obtained; based on the seismic data of the target area, the local dip angle of the sampling points is determined, and based on the local dip angle, the relative geological chronostratigraphic unit corresponding to the sampling point is determined; according to the relative geological chronostratigraphic unit of the first sampling point and the relative geological chronostratigraphic unit of the sampling point to be interpolated, the first sampling point is screened to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters; the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated are obtained; according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier, the preset weighting coefficient of the second sampling point is determined; according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient, the elastic parameter corresponding to the sampling point to be interpolated is determined; according to the elastic parameter corresponding to the sampling point to be interpolated, the elastic parameter inversion result of the target area is determined; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area. In this way, first, by calculating the local dip angle based on the seismic data of the target area and thereby deriving the relative geological chronostratigraphic unit of each sampling point, the stratigraphic time sequence constraint is effectively realized. Secondly, through the screening process of the first sampling point based on the relative geological chronostratigraphic unit, it is ensured that the selected sampling points for interpolation are consistent in sedimentary age, thereby effectively reducing the parameter inversion error caused by the traditional multi-point geostatistics without using relative geological age constraints. Finally, using the correlation coefficient between the first sampling point after screening and the sampling point to be interpolated, and the weighting coefficient determined by the preset Lagrange multiplier, weighted interpolation calculation is performed on the first sampling point after screening, so as to obtain more accurate elastic parameters as the initial model, improve the elastic parameter inversion accuracy and resolution, and ultimately improve the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 is a schematic flowchart of a multi-point geostatistical inversion method based on relative geological chronostratigraphic units provided by an embodiment of this specification;

[0045] Figure 2 is a schematic diagram of the structural composition of an electronic device provided by an embodiment of this specification;

[0046] Figure 3 It is a schematic diagram of the structural composition of a multi-point geostatistical inversion device based on relative geological age bodies provided by an embodiment of this specification;

[0047] Figures 4(a) and 4(b) are schematic diagrams of seismic data provided by an embodiment of this specification;

[0048] Figure 5 It is a schematic diagram of local dip angle provided by an embodiment of this specification;

[0049] Figure 6 It is a schematic diagram of relative geological age provided by an embodiment of this specification;

[0050] Figure 7 It is a schematic diagram of obtaining a geological pattern by a scanning panel scanning training image provided by an embodiment of this specification;

[0051] Figures 8(a), 8(b), 8(c), 8(d), 8(e), and 8(f) are schematic diagrams of filters provided by an embodiment of this specification;

[0052] Figures 9(a), 9(b), 9(c), 9(d), 9(e), and 9(f) are schematic diagrams of score profiles provided by an embodiment of this specification;

[0053] Figures 10(a), 10(b), and 10(c) are schematic diagrams of initial models of P-wave velocity, S-wave velocity, and density provided by an embodiment of this specification;

[0054] Figures 11(a), 11(b), and 11(c) are schematic diagrams of inversion results of P-wave velocity, S-wave velocity, and density provided by an embodiment of this specification. Detailed implementation manners

[0055] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without creative efforts shall fall within the protection scope of this specification.

[0056] Seismic inversion can interpret subsurface elastic parameters from observed seismic data, including acoustic impedance, P-wave velocity, S-wave velocity, and density. The obtained subsurface elastic parameters are beneficial for reservoir prediction. Using post-stack seismic inversion technology, only acoustic impedance parameters can be obtained, and reservoirs cannot be predicted with high precision. Pre-stack seismic data reflect the seismic amplitude variations at different incident angles. Compared with post-stack seismic inversion, pre-stack seismic inversion can provide multiple elastic parameters for seismic interpretation. However, seismic amplitude can only describe the interface information of horizons, such as the relative changes in elastic parameters. Seismic inversion methods relying solely on amplitude cannot well quantitatively reveal subsurface elastic parameters, while low-frequency initial models can reveal the variation trends of strata and provide initial values for seismic inversion. However, if the initial model deviates from the real situation, biased inversion results will be produced, leading to incorrect interpretation results.

[0057] Multiple-point geostatistics is one of the mainstream modeling methods and can provide an initial model for pre-stack seismic inversion. The multiple-point geostatistics method scans the training image using a defined data template to obtain the geological patterns around spatial points, and then based on a set of designed filters, the score profiles of spatial points are obtained to achieve high-resolution modeling of elastic parameters. There are many drawbacks in traditional multiple-point geostatistics methods: the accuracy of the simulation results is affected by the score profiles of spatial points, and the accuracy of the score profiles is closely related to the selection of filters. In traditional multiple-point geostatistics methods, the number of filters is fixed, and the accuracy of simulating subsurface media is limited; traditional multiple-point geostatistics methods rely on training images, and in actual work, it is difficult to obtain training images; the spatial continuity of subsurface media is not considered in the simulation process, and the accuracy of simulating complex subsurface geological structures is not high. Using the modeling results obtained based on traditional multiple-point geostatistics methods as the initial model for pre-stack seismic inversion, the accuracy and resolution of the obtained elastic parameter inversion results are not high.

[0058] When using existing methods for elastic parameter modeling, the limitation of the number of filters affects the accuracy of the simulation results. Existing methods rely on training images, and it is difficult to obtain training images. Existing methods do not consider the spatial continuity of subsurface media, and the accuracy of simulating complex subsurface geological structures is not high. In addition, if there is little logging data, high-precision elastic parameter modeling results cannot be obtained either.

[0059] Regarding the root cause of the above problems, this specification first calculates the local dip angle based on the seismic data of the target area, and thereby derives the relative geological age body of each sampling point, effectively realizing the stratigraphic time sequence constraint. Secondly, through the screening process of the first sampling point based on the relative geological age body, it ensures that the selected sampling points for interpolation are consistent in sedimentary age, thus effectively reducing the parameter simulation error caused by the traditional multiple-point geostatistics without using relative geological age constraints. Finally, using the correlation coefficient between the screened first sampling points and the sampling points to be interpolated, and the weighted coefficient determined by the preset Lagrange multiplier, weighted interpolation calculation is performed on the screened first sampling points, thereby obtaining more accurate elastic parameters as the initial model, improving the inversion accuracy and resolution of the elastic parameters, and ultimately improving the accuracy of oil and gas reservoir prediction in the target area to meet the requirements of high-precision oil and gas exploration and development.

[0060] Referring to Figure 1 , an embodiment of this specification provides a multiple-point geostatistical inversion method based on a relative geological age body, where this method is specifically applied to the server side. Specifically implemented, this method may include the following:

[0061] S101: Obtain the seismic data of the target area;

[0062] S102: Determine the local dip angle of the sampling points according to the seismic data of the target area, and determine the relative geological age body corresponding to the sampling points according to the local dip angle;

[0063] S103: Screen the first sampling points according to the relative geological age body of the first sampling points and the relative geological age body of the sampling points to be interpolated, and obtain the second sampling points; where the first sampling points are sampling points with known elastic parameters;

[0064] S104: Obtain the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling points and the sampling points to be interpolated;

[0065] S105: Determine the preset weighted coefficient of the second sampling points according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling points and the sampling points to be interpolated, and the preset Lagrange multiplier;

[0066] S106: Determine the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighted coefficient;

[0067] S107: Determine the elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; where the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

[0068] Among them, the above seismic data includes the original seismic trace records collected from the field, reflecting the original signals of the underground medium; the pre-stack and post-stack data formed after static correction, noise reduction, multiple reflection suppression, time correction and stacking processing.

[0069] The above local dip angle can refer to the degree and direction of the inclination of the formation at a certain point or in a certain area in geological or seismic data. It reflects the spatial extension trend of the underground geological body and is an important parameter for describing the geometric characteristics of the formation.

[0070] The above relative geological age body can be a geological unit divided into relative time sequences according to the geological time characteristics of the formation in seismic exploration and geological research. It is a method of stratigraphic division based on the geological time sequence, aiming to determine the relative position of the geological body in the time dimension rather than its absolute age.

[0071] The above correlation coefficient can be used to measure the similarity degree between different sampling points or data vectors, and its value ranges from -1 to 1; it can reflect the continuity and similarity of geological attributes between spatial points.

[0072] The above first sampling point is a sampling point with known elastic parameters. Specifically, it can be a sampling point with known elastic parameters in the logging data of the target area or a sampling point obtained after interpolation.

[0073] In some embodiments, determining the local dip angle of the sampling point according to the seismic data of the target area may specifically include:

[0074] In the seismic data of the target area, first select a preset spatio-temporal window near each sampling point, and assume that the reflection signals within the preset spatio-temporal window are approximately linearly distributed, and its slope is used as the local dip angle within the preset spatio-temporal window.

[0075] In some embodiments, determining the relative geological age body corresponding to the sampling point according to the local dip angle may specifically include:

[0076] After obtaining the local dip angle of each sampling point, first calculate the vertical time shift of each sampling point, that is, integrate or accumulate the time difference along the time direction according to the local dip angle to obtain the time offset of each point relative to a certain reference horizontal plane; then, use these vertical time shift values to flatten the isochrons on the seismic section and correct the inclined formation interface to an approximately horizontal state; finally, assign a relative geological age to each sampling point according to the corrected time value, and construct a relative geological age body reflecting the sedimentary sequence of the formation. The relative geological age body provides clear geological time sequence constraints for subsequent data screening and elastic parameter inversion.

[0077] In some embodiments, the preset weighting coefficient of the second sampling points is determined according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier. Specifically, the implementation may include:

[0078] According to the correlation coefficient between any two second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated, a preset Lagrange multiplier is introduced to ensure the normalization of each weight coefficient. By constructing a set of Kriging interpolation equations reflecting spatial similarity constraints, the preset weighting coefficient of each second sampling point can be solved; among them, the elements in the correlation coefficient matrix reflect the similarity degree of the geological attributes of each sampling point in space, and the Lagrange multiplier is used to force the sum of all weight coefficients to be 1, so that the solved weight coefficients have high stability while minimizing the estimation error.

[0079] Specifically, the preset Kriging interpolation theory can be used. Taking the elastic parameter as the longitudinal wave velocity as an example, the specific expression is:

[0080]

[0081] where l represents the number of second sampling points for interpolation, v pi represents the longitudinal wave velocity of the i-th second sampling point, represents the longitudinal wave velocity value obtained by interpolation, λ i is the weighting coefficient corresponding to the i-th sampling point and satisfies the following conditions:

[0082]

[0083] The final interpolation formula can be written as:

[0084]

[0085] where b xy (x ∈ [1, k], y ∈ [1, k]) represents the correlation coefficient between the x-th second sampling point and the y-th second sampling point. The parameter b x ′(x ∈ [1, k]) is the correlation coefficient between the x-th second sampling point and the sampling point to be interpolated, ω is the preset Lagrange multiplier. During the interpolation process, the relative geological age body corresponding to the above second sampling point is used to preferentially select interpolation points for interpolation, satisfying the condition where τ unkowndata represents the relative geological age of the point to be interpolated, represents the relative geological age of the x-th second sampling point, and ψ is a constant.

[0086] Based on the above embodiments, by simultaneously considering the correlation coefficient between any two second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and the preset Lagrange multiplier in the system of equations, the unbiasedness and the optimal interpolation principle are taken into account, and then the weighting coefficients of each second sampling point are accurately solved. This process not only retains the geological similarity between sampling points, but also ensures that the sum of the weighting coefficients is 1 through the Lagrange multiplier, thereby effectively avoiding the interference of cross-layer or abnormal data on the results and improving the accuracy and stability of elastic parameter interpolation.

[0087] In some embodiments, when determining the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient, in specific implementation, the above elastic parameters may include one or more of parameters such as longitudinal wave velocity, transverse wave velocity, density, and longitudinal wave impedance.

[0088] Based on the above embodiments, first, the local dip angle is calculated based on the seismic data of the target area, and the relative geological age body of each sampling point is derived therefrom, effectively realizing the stratigraphic time sequence constraint. Secondly, through the screening process of the first sampling points based on the relative geological age body, it is ensured that the selected sampling points for interpolation are consistent in relative sedimentary age, thereby effectively reducing the parameter inversion error caused by the traditional multi-point geostatistics without using relative geological age constraints. Finally, the weighting coefficients determined by using the correlation coefficient between the filtered first sampling points and the sampling point to be interpolated, and the preset Lagrange multiplier are used to perform weighted interpolation calculation on the filtered first sampling points, so as to obtain more accurate elastic parameters as the initial model, improve the accuracy and resolution of elastic parameter inversion, and ultimately improve the accuracy of oil and gas reservoir prediction in the target area to meet the requirements of high-precision oil and gas exploration and development.

[0089] In some embodiments, before obtaining the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated, in specific implementation of the method, the following content may further be included:

[0090] S1: According to the preset segmentation size, the target image corresponding to the seismic data is segmented to obtain a plurality of sub-images;

[0091] S2: The plurality of sub-images are screened to obtain a filter;

[0092] S3: According to the filter and each of the sub-images, the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated are determined.

[0093] Specifically, a scanning panel with a fixed size (e.g., m×m) can be preset as the segmentation size, and the sampling points on the target image are scanned point by point row by row to obtain the geological pattern x around the sampling points on the target image. n ∈R m×m , representing the pattern corresponding to the nth sampling point, and finally obtaining the set of all geological patterns. That is, multiple sub-images, where N is the number of all spatial sampling points on the section.

[0094] Based on the above embodiments, by presetting the scanning panel to finely segment and scan the target image point by point, the local geological patterns of each sampling point can be efficiently and accurately extracted, ensuring high spatial resolution and signal-to-noise ratio of the data; for example, in practical applications, by calculating the correlation coefficient of the local patterns of the sampling points, the continuity and similarity of each sampling point in the geological structure can be accurately judged, so as to use these high-quality data to achieve accurate weighted interpolation in the subsequent elastic parameter modeling and inversion processes.

[0095] In some embodiments, when determining the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images, the specific implementation of the method may further include the following:

[0096] S1: Perform mean processing on each of the sub-images to obtain the sub-images after mean processing;

[0097] S2: Determine the covariance matrix corresponding to the sub-images after mean processing, and determine the eigenvalues and eigenvectors corresponding to the covariance matrix;

[0098] S3: Sort the eigenvalues, select the eigenvectors corresponding to the top preset number of the eigenvalues as the target vectors, and determine the target vectors as the filter;

[0099] S4: Determine the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images.

[0100] Specifically, each sub-image obtained by segmenting the target image can be equalized. All pixel values within each sub-image are subtracted by the average value of the image to eliminate the brightness deviation and highlight the local structural features. Then, using the principal component analysis method, all equalized sub-images are arranged by columns to form a data matrix. The covariance matrix of this matrix is calculated and its eigenvalue decomposition or singular value decomposition is performed to obtain a set of eigenvectors and corresponding eigenvalues. These eigenvectors reflect the most important geological structure change patterns in the sub-images. Then, according to the eigenvalues sorted from large to small, a preset number (for example, the first 6) of eigenvectors are selected as target vectors, and filters are determined with these target vectors. Finally, according to the target vectors and combined with each sub-image, the correlation coefficients between any two second sampling points and between the second sampling point and the sampling point to be interpolated are calculated.

[0101] In some embodiments, for the set of all the above geological patterns That is, for all sub-images, the filters (i.e., target vectors) are obtained by using the preset principal component analysis method, and the main steps are as follows:

[0102] S1: Equalize each row of the data set X of all sub-images, which is represented by the following formula:

[0103]

[0104] Where, represents the average value obtained from all the above sub-images, and mean(·) represents the mean operation, represents the geological pattern (i.e., sub-image) of the i-th column in matrix X.

[0105] S2: Subtract the mean value μ from each column of matrix X to obtain the data which is represented by the following formula:

[0106]

[0107] S3: Calculate the covariance matrix of matrix , where T represents the transpose symbol, and it is represented by the following formula:

[0108]

[0109] Where, C is the covariance matrix calculated from all the above sub-images, is the data after de-meaning all the above sub-images, and m 2 represents the size of scanning the training image template, that is, it represents the number of rows of the matrix.

[0110] S4: Use the singular value decomposition method to find the eigenvalues and eigenvectors of matrix C, which is represented by the following formula:

[0111] C = OΣP T

[0112] Wherein, Σ represents the eigenvalue matrix, O and P T represent the left and right eigenvector matrices respectively, and T represents the transpose symbol. Among them, the principal components are determined by the right eigenvector matrix P T .

[0113] Arrange in descending order of singular values, and select the eigenvectors corresponding to the first k eigenvalues as the filter (i.e., the target vector) A j ∈R m×m (j = 1,..., k). The selection of k is variable and related to the complexity of the underground structure. The more complex the underground structure is, the larger the value of k. For example, k = 6 can be selected in this specification.

[0114] Furthermore, based on the obtained geological patterns (i.e., all sub-images) and 6 filters (i.e., target vectors), 6 score profiles can be obtained, which are represented by the following formula:

[0115]

[0116] Wherein, SC j represents the score profile corresponding to the filter A j , and x n is the geological pattern (i.e., sub-image) at the nth sampling point.

[0117] Furthermore, the score vectors at each sampling point on the target image can be represented by these 6 score profiles. If there are two sampling points c(l, q) and v(l′, q′), then the score vectors corresponding to these two points are:

[0118] E c (l, q) = [SC1(l, q), SC2(l, q), SC3(l, q), SC4(l, q), SC5(l, q), SC6(l, q)]

[0119] E v (l′, q′) = [SC1(l′, q′), SC2(l′, q′), SC3(l′, q′), SC4(l′, q′), SC5(l′, q′), SC6(l′, q′)]

[0120] Based on the score vectors corresponding to the above two points, the correlation coefficient γ = corr[E c (l, q), E v (l′, q′)] can be calculated. Among them, γ is the correlation coefficient between the sampling points c(l, q) and v(l′, q′), and corr[·] represents the correlation operation.

[0121] In some embodiments, when determining the local dip angle based on the seismic data of the target area, the following steps may further be included in the specific implementation of the method:

[0122] S1: Determine the decomposition residual and decomposition factor of the seismic trace data corresponding to the sampling point to be interpolated in the target image according to the seismic trace data corresponding to the sampling point to be interpolated in the target image;

[0123] S2: Determine the local dip angle corresponding to the target image according to the decomposition factor and the decomposition residual.

[0124] In some embodiments, the local dip angle corresponding to the target image is determined by using a preset plane wave decomposition technique. Specifically, the preset plane wave decomposition theory is based on the local plane wave equation and can be expressed by the following formula:

[0125]

[0126] where u(t, x) represents the wave field, σ represents the local dip angle, t represents the two-way travel time of the seismic wave, and x represents the spatial coordinate. If the local dip angle σ is a constant, the general solution form of the above formula can be expressed as:

[0127] u(t, x) = f(t - σx)

[0128] where f(t) is an arbitrary waveform. Assume S = [S1, S2,..., S j ,..., S M T is the set of all seismic traces of a seismic data, M represents the number of seismic traces, and the wave field u(t, x j ) corresponds to the seismic trace S j . The plane wave decomposition expression is:

[0129] r = H(σ)S

[0130] where r represents the decomposition residual, and H(σ) represents the decomposition factor, which can be expressed by the following formula:

[0131]

[0132] where I represents the identity matrix, and P a,b (a, b ∈ M) represents the prediction factor for predicting the b-th seismic trace from the a-th seismic trace.

[0133] Based on the preset least squares theory, it can be expressed by the following formula:

[0134]

[0135] Let the decomposition residual of r be minimized to obtain the corresponding local dip angle.​

[0136] Based on the above embodiments, by decomposing the seismic trace data corresponding to the interpolation sampling points, the decomposition residuals and decomposition factors are calculated to accurately determine the local dip angle.

[0137] In some embodiments, for determining the relative geological age body corresponding to the first sampling point among the sampling points according to the local dip angle, when the method is specifically implemented, the following content may further be included:

[0138] S1: Determine the vertical time shift in the target image space according to the local dip angle;

[0139] S2: Determine the relative geological age body corresponding to the interpolation sampling point according to the vertical time shift and the two-way travel time of the seismic wave corresponding to the interpolation sampling point.

[0140] In some embodiments, based on the local dip angle, using a preset method of flattening the isochron to obtain the corresponding relative geological age body can be expressed by the following formula:

[0141]

[0142] where s(x,t) represents the vertical time shift of the local dip angle in the target image space, w represents local similarity, t represents the propagation time of the seismic wave corresponding to the interpolation sampling point, and ε = 0.01 is a regularization factor to reduce random noise by smoothing. Then, the relative geological age body is obtained and can be expressed by the following formula:

[0143] τ(x,t) = t + s(x,t)

[0144] where the above τ(x,t) represents the relative geological age body.

[0145] Based on the above embodiments, by first determining the vertical time shift in the target image space according to the local dip angle and then calculating the corresponding geological age body in combination with the travel time of the seismic wave corresponding to the interpolation sampling point, the accurate alignment and correction of the formation time information in the seismic data are effectively realized, ensuring that only data with consistent sedimentation periods are used during the interpolation process, thereby greatly reducing error accumulation.

[0146] In some embodiments, for screening the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the interpolation sampling point to obtain the second sampling point, when the method is specifically implemented, the following content may further be included:

[0147] S1: Obtain the difference between the relative geological age bodies of each of the first sampling points and the interpolation sampling point;

[0148] S2: Screen the first sampling point according to the difference value and a preset difference threshold value to obtain a second sampling point.

[0149] For example, assume that the relative geological age of the sampling point to be interpolated is 25.1, and the relative geological age of a first sampling point is 25.0, then the difference between the two is 0.1. If the preset threshold value is 0.5, then this sampling point meets the condition; while for another sampling point with a relative geological age of 23.0, the difference is 2.1, which does not meet the threshold requirement and is discarded. Thus, only the sampling points with a relatively high matching degree with the sampling point to be interpolated in the sedimentation time sequence are retained for subsequent interpolation calculation.

[0150] Based on the above embodiments, by strictly controlling the matching degree of the geological ages of the sampling points, it can be ensured that the logging data used for interpolation is consistent in the sedimentation time sequence, effectively reducing the error accumulation caused by the mixed use of data across layers or across geological ages.

[0151] In some embodiments, the elastic parameters include the longitudinal wave velocity, the transverse wave velocity, and the density. According to the elastic parameters corresponding to the second sampling point and the preset weighting coefficient, determine the elastic parameters corresponding to the sampling point to be interpolated. When the method is specifically implemented, the following content may further be included:

[0152] S1: Determine the product between the longitudinal wave velocity corresponding to each second sampling point and the preset weighting coefficient of each second sampling point;

[0153] S2: Determine the sum value of the products as the longitudinal wave velocity corresponding to the sampling point to be interpolated.

[0154] For example, assume that there are three second sampling points around the sampling point to be interpolated, and their longitudinal wave velocities are 3200 m / s, 3300 m / s, and 3400 m / s respectively, while the preset weighting coefficients are 0.35, 0.40, and 0.25 respectively. Then the longitudinal wave velocity of the sampling point to be interpolated is 0.35×3200 + 0.40×3300 + 0.25×3400 = 1120 + 1320 + 850 = 3290 m / s.

[0155] As can be seen from the above, the multi-point geostatistical inversion method based on relative geological age bodies provided in the embodiments of this specification acquires seismic data of a target area; determines the local dip angle of a sampling point according to the seismic data of the target area, and determines the relative geological age body corresponding to the sampling point according to the local dip angle; screens the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters; obtains the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated; determines the preset weighting coefficient of the second sampling point according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier; determines the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; determines the elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area. In this way, firstly, by calculating the local dip angle based on the seismic data of the target area and thereby deriving the relative geological age body of each sampling point, the stratigraphic time sequence constraint is effectively realized. Secondly, by screening the first sampling point based on the relative geological age body, it is ensured that the selected sampling points for interpolation are consistent in sedimentary age, thereby effectively reducing the parameter inversion error caused by the traditional multi-point geostatistics without using relative geological age constraints. Finally, using the correlation coefficient between the first sampling point after screening and the sampling point to be interpolated, and the weighting coefficient determined by the preset Lagrange multiplier, weighted interpolation calculation is performed on the first sampling point after screening, so as to obtain more accurate elastic parameters as the initial model, improve the elastic parameter inversion accuracy and resolution, and ultimately improve the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development.

[0156] Referring to Figure 2 , the embodiments of this specification also provide a specific electronic device. Among them, the electronic device includes a network communication port 201, a processor 202, and a memory 203. The above structures are connected by internal cables so that each structure can perform specific data interaction.

[0157] Among them, the network communication port 201 can specifically be used to acquire seismic data of a target area.

[0158] The processor 202 can be specifically configured to determine the local dip angle of a sampling point according to the seismic data of the target area, and determine the relative geological age body corresponding to the sampling point according to the local dip angle; screen the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated, so as to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters; obtain the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated; determine the preset weighting coefficient of the second sampling point according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier; determine the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; determine the elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

[0159] The memory 203 can be specifically configured to store corresponding instruction programs.

[0160] Based on the above method, the relevant structural performance of the electronic device can be effectively utilized, the data processing speed of the electronic device can be improved, and the multi-point geostatistical inversion method based on the relative geological age body can be efficiently realized.

[0161] In this embodiment, the network communication port 201 can be bound to different communication protocols, so as to send or receive different data. For example, the network communication port can be a port responsible for web data communication, or a port responsible for FTP data communication, or a port responsible for email data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0162] In this embodiment, the processor 202 can be implemented in any suitable manner. For example, the processor can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not make any limitations.

[0163] In this embodiment, the memory 203 may include levels. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with a storage function without a physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory module, a TF card, etc.

[0164] The embodiments of this specification also provide a computer-readable storage medium based on the above multi-point geostatistical inversion method based on relative geological age bodies. Obtain seismic data of a target area; determine the local dip angle of sampling points according to the seismic data of the target area, and determine the relative geological age body corresponding to the sampling points according to the local dip angle; perform screening processing on the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated to obtain a second sampling point; where the first sampling point is a sampling point with known elastic parameters, including logging data; obtain the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated; determine the preset weighting coefficient of the second sampling point according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier; determine the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; determine the elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; where the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

[0165] In this embodiment, the above storage medium includes but is not limited to Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions. The network communication unit can be set according to the standards specified by the communication protocol and is used for the interface of network connection communication.

[0166] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be explained in contrast with other embodiments and will not be elaborated here.

[0167] Refer to Figure 3 , at the software level, the embodiments of this specification also provide a multi-point geostatistical inversion device based on relative geological age bodies. This device may specifically include the following structural modules:

[0168] A data acquisition module 301, configured to acquire seismic data of a target area;

[0169] A chronostratigraphic unit determination module 302, configured to determine a local dip angle of a sampling point according to the seismic data of the target area, and determine a relative geochronologic unit corresponding to the sampling point according to the local dip angle;

[0170] A sampling point determination module 303, configured to perform screening processing on the first sampling point according to the relative geochronologic unit of the first sampling point and the relative geochronologic unit of the sampling point to be interpolated, so as to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters;

[0171] A correlation coefficient determination module 304, configured to obtain a correlation coefficient between any two of the second sampling points, and a correlation coefficient between the second sampling point and the sampling point to be interpolated;

[0172] A weighted coefficient determination module 305, configured to determine a preset weighted coefficient of the second sampling point according to a correlation coefficient between any two of the second sampling points, a correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier;

[0173] A parameter determination module 306, configured to determine an elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighted coefficient;

[0174] A seismic inversion module 307, configured to determine an elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

[0175] In some embodiments, before the above-mentioned correlation coefficient determination module 304, specifically in implementation, according to a preset segmentation size, perform segmentation processing on a target image corresponding to the seismic data to obtain a plurality of sub-images; perform screening processing on the plurality of sub-images to obtain a filter; a coefficient determination module, configured to determine a correlation coefficient between any two of the second sampling points, and a correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images.

[0176] In some embodiments, when the above coefficient determination module is specifically implemented, each of the sub-images is subjected to a homogenization process to obtain a homogenized sub-image; a covariance matrix corresponding to the homogenized sub-image is determined, and eigenvalues and eigenvectors corresponding to the covariance matrix are determined; the eigenvalues are sorted, and the eigenvectors corresponding to the top preset number of the eigenvalues are selected as target vectors, and the target vectors are determined as the filters; according to the filters and each of the sub-images, the correlation coefficient between any two of the second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated are determined.

[0177] In some embodiments, when the above age body determination module 302 is specifically implemented, according to the seismic trace data corresponding to the sampling point to be interpolated in the target image, the decomposition residual and the decomposition factor of the seismic trace data corresponding to the sampling point to be interpolated are determined; according to the decomposition factor and the decomposition residual, the local dip angle corresponding to the target image is determined.

[0178] In some embodiments, when the above age body determination module 302 is specifically implemented, the vertical time shift in the space of the target image is determined according to the local dip angle; according to the vertical time shift and the seismic propagation time corresponding to the sampling point to be interpolated, the relative geological age body corresponding to the sampling point to be interpolated is determined.

[0179] In some embodiments, when the above sampling point determination module 303 is specifically implemented, the difference in the relative geological age body between each of the first sampling points and the sampling point to be interpolated is obtained; according to the difference and a preset difference threshold, the first sampling points are screened to obtain second sampling points.

[0180] In some embodiments, when the above parameter determination module 306 is specifically implemented, the product of the longitudinal wave velocity corresponding to each of the second sampling points and the preset weighting coefficient of each of the second sampling points is determined; the sum of the products is determined as the longitudinal wave velocity corresponding to the sampling point to be interpolated.

[0181] It should be noted that the units, devices, modules, etc. illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, when describing the above devices, they are divided into various modules according to functions and described separately. Of course, when implementing this specification, the functions of each module can be implemented in the same software and / or hardware, or the modules implementing the same function can be realized by a combination of sub-modules or sub-units. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, the units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0182] As can be seen from the above, based on the multi-point geostatistical inversion device based on the relative geological age body provided in the embodiments of this specification, first, by calculating the local dip angle based on the seismic data of the target area and thereby deriving the relative geological age body of each sampling point, the stratigraphic time series constraint is effectively realized. Secondly, through the screening process of the first sampling point based on the relative geological age body, it is ensured that the selected sampling points for interpolation are consistent in sedimentary age, thus effectively reducing the parameter inversion error caused by the traditional multi-point geostatistics without using the relative geological age constraint. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, and the weighted coefficient determined by the preset Lagrange multiplier, weighted interpolation calculation is performed on the screened first sampling point, so as to obtain more accurate elastic parameters as the initial model, improve the inversion accuracy and resolution of the elastic parameters, and ultimately improve the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development.

[0183] In a specific scenario example, the multi-point geostatistical inversion method and device provided in this specification can be applied. First, by calculating the local dip angle based on the seismic data of the target area, the relative geological age body of each sampling point is deduced, effectively realizing the stratigraphic time series constraint. Second, through the screening process of the first sampling point based on the relative geological age body, it is ensured that the selected sampling points for interpolation are consistent in relative sedimentary age, thus effectively reducing the parameter inversion error caused by the traditional multi-point geostatistics without using relative geological age constraints. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, and the weighted coefficient determined by the preset Lagrange multiplier, weighted interpolation calculation is performed on the screened first sampling point, so as to obtain more accurate elastic parameters as the initial model, improve the inversion accuracy and resolution of the elastic parameters, and ultimately improve the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development. The specific implementation process may include the following content.

[0184] S1: Determine the target image and the local dip angle according to the obtained seismic data.

[0185] Among them, the seismic data shown in Figure 4(a) and the logging data shown in Figure 4(b) are data characterizing the underground geological structure characteristics.

[0186] In some embodiments, determining the target image and the local dip angle according to the obtained seismic data includes:

[0187] Determine the target image based on the seismic data;

[0188] Based on the target image, use the plane wave decomposition method to determine the local dip angle.

[0189] It can be understood that traditional multi-point geostatistics relies on the training image. To solve the problem that it is difficult to obtain the training image, this specification uses the seismic data as the target image to replace the training image in multi-point geostatistics, cleverly solving the problem of difficult acquisition of the training image.

[0190] S2: Determine the relative geological age body based on the local dip angle.

[0191] Specifically, based on the obtained local dip angle, use the preset in-phase axis flattening method to obtain the relative geological age body. Among them, the schematic diagram corresponding to the above local dip angle can be referred to Figure 5 , and the schematic diagram corresponding to the above relative geological age body can be referred to Figure 6 .

[0192] S3: Based on the target image, determine the geological patterns (i.e., multiple sub-images) around the elastic parameter to be interpolated on the section to be interpolated.

[0193] Specifically, refer to Figure 7 , which from left to right respectively represent the schematic diagram of the scanning panel, the schematic diagram of the training image, and the geological pattern of the spatial points obtained by scanning. Based on the target image, use the scanning panel to scan each spatial point on the training image to determine the geological pattern around the elastic parameter to be interpolated on the interpolation section.

[0194] S4: Determine the filter based on the geological pattern.

[0195] Specifically, based on the geological pattern, use the method of preset principal component analysis to determine the filter. Arrange according to the eigenvalues from large to small. Refer to Figure 8(a), Figure 8(b), Figure 8(c), Figure 8(d), Figure 8(e), Figure 8(f), which are the eigenvectors corresponding to the first 6 eigenvalues respectively, that is, the filters.

[0196] S5: Determine the score profile based on the filter and the geological pattern.

[0197] Specifically, based on the obtained geological pattern and 6 filters, 6 score profiles can be obtained. Refer to Figure 9(a), Figure 9(b), Figure 9(c), Figure 9(d), Figure 9(e), Figure 9(f), which are the schematic diagrams of the obtained 6 score profiles respectively.

[0198] S6: Based on the score profile, determine the correlation coefficient between any two of the second sampling points on the interpolation section, and the correlation coefficient between the second sampling point and the interpolation sampling point.

[0199] S7: Based on the relative geological age body, the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the interpolation sampling point, determine the initial model of the elastic parameter to be interpolated.

[0200] Specifically, refer to Figure 10(a), Figure 10(b), Figure 10(c), which are the schematic diagrams of the initial model of the P-wave velocity, the initial model of the S-wave velocity, and the initial model of the density respectively.

[0201] S8: Based on the initial model of the elastic parameter to be interpolated and the seismic data, determine the target inversion result.

[0202] Specifically, based on the initial model of the elastic parameter to be interpolated and the seismic data, under the Bayesian theory framework, determine the target inversion result.

[0203] Furthermore, under the Bayesian theory framework, according to the obtained initial model and seismic data, perform pre-stack seismic inversion to obtain the target inversion result with high precision and high resolution. Refer to Figure 11(a), Figure 11(b), Figure 11(c), which are the schematic diagrams of the P-wave velocity model, the S-wave velocity model, and the density model obtained by inversion respectively.

[0204] In some embodiments, according to the obtained seismic data, a target image and a local dip angle are determined, and based on the local dip angle, a relative geological chronostratigraphic body is determined. Then, based on the target image, geological patterns (i.e., multiple sub-images) around the to-be-interpolated elastic parameters on the to-be-interpolated section are determined. Based on the geological patterns, a filter (i.e., a target vector) is determined. Based on the filter and the geological patterns, a score section is determined. Based on the score section, the correlation coefficient between any two of the second sampling points on the to-be-interpolated section and the correlation coefficient between the second sampling point and the to-be-interpolated sampling point are determined. Secondly, based on the relative geological chronostratigraphy, the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the to-be-interpolated sampling point, an initial model of the to-be-interpolated elastic parameter is determined. Finally, based on the initial model of the to-be-interpolated elastic parameter and the seismic data, a target inversion result is determined.

[0205] Based on the above embodiments, using the post-stack seismic data as the target image to replace the training image in multi-point geostatistics overcomes the problem of difficult acquisition of the training image. The principal component analysis method is used to obtain the filter (i.e., the target vector), breaking the limitation of the number of filters in conventional multi-point geostatistics and enabling the adaptive selection of the number of filters. In addition, the interpolation modeling of well logging data is constrained by the relative geological chronostratigraphic body to determine the initial model of the elastic parameters, increasing the accuracy and spatial continuity of the simulation results and solving the problem of low modeling accuracy in the case of less well logging data. Finally, based on the obtained initial model of the elastic parameters and the seismic data, within the framework of Bayesian theory, a high-precision and high-resolution elastic parameter inversion result is obtained, which is more conducive to reservoir prediction and more in line with the actual production requirements.

[0206] Although this specification provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The step sequences listed in the embodiments are only one way among many step execution sequences and do not represent the only execution sequence. When the actual device or client product executes, it can be executed in the method sequence shown in the embodiments or the drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment). The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, product or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, product or device. Without further limitation, there is no exclusion of additional identical or equivalent elements in the process, method, product or device comprising the said elements. The words such as first and second are used to denote names and do not represent any specific order.

[0207] Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to implement the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0208] From the description of the above embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this specification can essentially be embodied in the form of a software product, which can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this specification.

[0209] Although this specification is depicted through embodiments, those of ordinary skill in the art know that this specification has many variations and changes without departing from the spirit of this specification. It is hoped that the appended claims will cover these variations and changes without departing from the spirit of this specification.

Claims

1. A multi-point geostatistical inversion method based on relative geological age bodies, characterized in that, Including: Obtaining seismic data of a target area; Determining the local dip angle of a sampling point according to the seismic data of the target area, and determining a relative geological age body corresponding to the sampling point according to the local dip angle; Performing a screening process on the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated, to obtain a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters; Obtaining the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated; Determining a preset weighting coefficient of the second sampling point according to the correlation coefficient between any two of the second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier; Determining the elastic parameter corresponding to the sampling point to be interpolated according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; Determining an elastic parameter inversion result of the target area according to the elastic parameter corresponding to the sampling point to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

2. The method according to claim 1, wherein Before obtaining the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated, including: Performing a segmentation process on a target image corresponding to the seismic data according to a preset segmentation size to obtain a plurality of sub-images; Performing a screening process on the plurality of sub-images to obtain a filter; Determining the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images.

3. The method according to claim 2, wherein The determining the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images includes: Performing an equalization process on each of the sub-images to obtain an equalized sub-image; Determining a covariance matrix corresponding to the equalized sub-image, and determining eigenvalues and eigenvectors corresponding to the covariance matrix; Sorting the eigenvalues, selecting eigenvectors corresponding to the first preset number of the eigenvalues as target vectors, and determining the target vectors as the filter; Determining the correlation coefficient between any two of the second sampling points, and the correlation coefficient between the second sampling point and the sampling point to be interpolated according to the filter and each of the sub-images.

4. The method according to claim 3, characterized in that, The determining the local dip angle according to the seismic data of the target area includes: Determining a decomposition residual and a decomposition factor of seismic trace data corresponding to a sampling point to be interpolated in the target image; Determining the local dip angle corresponding to the target image according to the decomposition factor and the decomposition residual.

5. The method according to claim 4, characterized in that The determining the relative geological age body corresponding to the first sampling point among the sampling points according to the local dip angle includes: Determining a vertical time shift in the spatial domain of the target image according to the local dip angle; Determine the relative geological age body corresponding to the sampling point to be interpolated according to the vertical time shift and the seismic wave propagation time corresponding to the sampling point to be interpolated.

6. The method according to claim 5, characterized in that The screening process of the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated in the sampling points to obtain the second sampling point includes: Obtain the difference in the relative geological age body between each first sampling point and the sampling point to be interpolated; Perform a screening process on the first sampling point according to the difference and a preset difference threshold to obtain the second sampling point.

7. The method according to claim 6, wherein The elastic parameters include the longitudinal wave velocity, the transverse wave velocity, and the density. The determination of the elastic parameters corresponding to the sampling point to be interpolated according to the elastic parameters corresponding to the second sampling point and the preset weighting coefficient includes: Determine the product between the longitudinal wave velocity corresponding to each second sampling point and the preset weighting coefficient of each second sampling point; Determine the sum value of the products as the longitudinal wave velocity corresponding to the sampling point to be interpolated.

8. A multi-point geostatistical inversion device based on relative geological age bodies, characterized in that, Includes: A data acquisition module for acquiring seismic data of a target area; An age body determination module for determining the local dip angle of the sampling point according to the seismic data of the target area and determining the relative geological age body corresponding to the sampling point according to the local dip angle; A sampling point determination module for performing a screening process on the first sampling point according to the relative geological age body of the first sampling point and the relative geological age body of the sampling point to be interpolated to obtain the second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters; A correlation coefficient determination module for obtaining the correlation coefficient between any two second sampling points and the correlation coefficient between the second sampling point and the sampling point to be interpolated; A weighting coefficient determination module for determining the preset weighting coefficient of the second sampling point according to the correlation coefficient between any two second sampling points, the correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier; A parameter determination module for determining the elastic parameters corresponding to the sampling point to be interpolated according to the elastic parameters corresponding to the second sampling point and the preset weighting coefficient; A seismic inversion module for determining the elastic parameter inversion result of the target area according to the elastic parameters corresponding to the sampling point to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

9. An electronic device, characterized in that, Includes a processor and a memory for storing processor-executable instructions. When the processor executes the instructions, it implements the steps of the multi-point geostatistical inversion method based on relative geological age body according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Stored thereon are computer instructions that, when executed by a processor, implement the steps of the multi-point geostatistical inversion method based on relative geological age body according to any one of claims 1 to 7.

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