Method and device for multiple-point geostatistics inversion based on relative geologic age volume

Abstract

The present specification provides a method and device for multiple-point geostatistics inversion based on relative geologic time bodies. According to seismic data of a target region, a relative geologic time body corresponding to a sampling point is determined; according to the relative geologic time body of a first sampling point and the relative geologic time body of a sampling point to be interpolated, the first sampling point is filtered to obtain a second sampling point; according to a correlation coefficient between any two second sampling points, a correlation coefficient between the second sampling point and the sampling point to be interpolated, and a preset Lagrange multiplier, a preset weighting coefficient of the second sampling point is determined; according to the elastic parameters corresponding to the second sampling point and the preset weighting coefficient, elastic parameters corresponding to the sampling point to be interpolated are determined; and according to the elastic parameters corresponding to the sampling point to be interpolated, an elastic parameter inversion result of the target region is determined. Thus, the accuracy of oil and gas reservoir prediction of the target region is improved.

Description

Technical Field

[0001] This manual belongs to the field of petroleum exploration technology, and in particular relates to a multi-point geostatistical inversion method and apparatus based on relative geochronographs. Background Technology

[0002] Currently, traditional multi-point geostatistical methods are one of the widely used modeling techniques in pre-stack seismic inversion. However, due to inherent limitations in training image acquisition and data constraints, the elastic parameters obtained by inversion are often of low accuracy and insufficient resolution, resulting in poor reservoir prediction performance and failing to meet the requirements of high-precision oil and gas exploration and development.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This manual provides a multi-point geostatistical inversion method and apparatus based on relative geochronographs. First, local dip angles are calculated based on seismic data of the target area, and the relative geochronograph of each sampling point is derived from this, effectively achieving stratigraphic time constraints. Second, by screening the first sampling point based on the relative geochronograph, consistency in relative sedimentary age is ensured among the selected sampling points used for interpolation, thereby effectively reducing parameter inversion errors caused by the lack of relative geochronological constraints in traditional multi-point geostatistical methods. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficients determined by preset Lagrange multipliers, weighted interpolation calculations are performed on the screened first sampling point to obtain more accurate elastic parameters. These parameters serve as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately improving the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development.

[0005] This manual provides a multi-point geostatistical inversion method based on relative geochronographs, including:

[0006] Acquire seismic data for the target area;

[0007] Based on the seismic data of the target area, the local dip angle of the sampling point is determined, and based on the local dip angle, the relative geological time body corresponding to the sampling point is determined;

[0008] Based on the relative geological timescale of the first sampling point and the relative geological timescale of the sampling point to be interpolated, the first sampling point is filtered to obtain the second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters.

[0009] Obtain 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;

[0010] Based on 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, the preset weighting coefficient of the second sampling point is determined;

[0011] Based on 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.

[0012] Based on the elastic parameters corresponding to the sampling points to be interpolated, the elastic parameter inversion results of the target region are determined; wherein, the elastic parameter inversion results are used for oil and gas reservoir prediction in the target region.

[0013] In one embodiment, before 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, the method further includes:

[0014] According to the preset segmentation size, the target image corresponding to the seismic data is segmented to obtain multiple sub-images;

[0015] The multiple sub-images are filtered to obtain a filter;

[0016] Based on the filter and each of the sub-images, determine 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.

[0017] In one embodiment, determining 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, based on the filter and each of the sub-images, includes:

[0018] Each of the sub-images is averaged to obtain the averaged sub-image;

[0019] Determine the covariance matrix corresponding to the sub-image after mean normalization, and determine the eigenvalues ​​and eigenvectors corresponding to the covariance matrix;

[0020] The feature values ​​are sorted, and the feature vectors corresponding to the first preset number of feature values ​​are selected as target vectors, and the target vectors are determined as the filter;

[0021] Based on the target vector and each sub-image, determine 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. In one embodiment, determining the local dip angle based on the seismic data of the target area includes:

[0022] Based on the seismic trace data corresponding to the sampling points to be interpolated in the target image, determine the decomposition residual and decomposition factor of the seismic trace data corresponding to the sampling points to be interpolated;

[0023] The local tilt angle corresponding to the target image is determined based on the decomposition factor and the decomposition residual.

[0024] In one embodiment, determining the relative geological timescale corresponding to the first sampling point based on the local dip angle includes:

[0025] The vertical time shift in the target image space is determined based on the local tilt angle;

[0026] Based on the vertical time shift and the seismic wave propagation time corresponding to the sampling point to be interpolated, the relative geological time body corresponding to the sampling point to be interpolated is determined.

[0027] In one embodiment, the step of filtering the first sampling point based on the relative geological timescale of the first sampling point and the relative geological timescale of the sampling point to be interpolated to obtain the second sampling point includes:

[0028] Obtain the difference in relative geological timescale between each of the first sampling points and the sampling points to be interpolated;

[0029] Based on the difference and a preset difference threshold, the first sampling point is filtered to obtain a second sampling point. In one embodiment, the elastic parameters include P-wave velocity, S-wave velocity, and density. Determining the elastic parameters corresponding to the sampling point to be interpolated based on the elastic parameters corresponding to the second sampling point and the preset weighting coefficient includes:

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

[0031] The sum of the products is determined as the longitudinal wave velocity corresponding to the sampling point to be interpolated.

[0032] This manual provides a multi-point geostatistical inversion apparatus based on relative geochronographs, including:

[0033] The data acquisition module is used to acquire seismic data for the target area.

[0034] The geological timescale determination module is used to determine the local dip angle of a sampling point based on the seismic data of the target area, and to determine the relative geological timescale corresponding to the sampling point based on the local dip angle.

[0035] The sampling point determination module is used to filter the first sampling point based on the relative geological timescale of the first sampling point and the relative geological timescale of the sampling points to be simulated, to obtain the second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters.

[0036] The correlation coefficient determination module is used to obtain 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;

[0037] The weighting coefficient determination module is used to determine the preset weighting coefficient of the second sampling point based on 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.

[0038] The parameter determination module is used to determine the elastic parameter corresponding to the sampling point to be interpolated based on the elastic parameter corresponding to the second sampling point and the preset weighting coefficient.

[0039] The seismic inversion module is used to determine the elastic parameter inversion result of the target area based on the elastic parameters corresponding to the sampling points 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 processor-executable instructions, wherein the processor, when executing the instructions, implements a multi-point geostatistical inversion method based on relative geochronographs.

[0041] This specification also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed, implement a multi-point geostatistical inversion method based on relative geochronographs.

[0042] Based on the multi-point geostatistical inversion method based on relative geochronographs provided in this specification, seismic data of the target area is acquired; the local dip angle of the sampling point is determined according to the seismic data of the target area, and the relative geochronograph corresponding to the sampling point is determined according to the local dip angle; the first sampling point is screened according to the relative geochronograph of the first sampling point and the relative geochronograph 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; 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 are obtained; the preset weighting coefficient of the second sampling point is determined 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 the preset Lagrange multiplier; the elastic parameter corresponding to the sampling point to be interpolated is determined according to the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; the elastic parameter inversion result of the target area is determined 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. Thus, firstly, by calculating the local dip angle based on seismic data of the target area and deriving the relative geochronology of each sampling point, stratigraphic time constraints are effectively achieved. Secondly, by screening the first sampling point based on the relative geochronology, the selected sampling points used for interpolation are ensured to have consistent depositional ages, thereby effectively reducing the parameter inversion error caused by the lack of relative geochronology constraints in traditional multi-point geostatistics. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficients determined by the preset Lagrange multipliers, weighted interpolation calculations are performed on the screened first sampling point to obtain more accurate elastic parameters. These parameters serve as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately improving the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development. Attached Figure Description

[0043] To more clearly illustrate the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a multi-point geostatistical inversion method based on relative geochronographs, provided in one embodiment of this specification.

[0045] Figure 2 This is a schematic diagram of the electronic device structure provided in one embodiment of this specification;

[0046] Figure 3 This is a schematic diagram of the structural composition of a multi-point geostatistical inversion device based on relative geochronographs, provided in one embodiment of this specification.

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

[0048] Figure 5 This is a schematic diagram of a partial tilt angle provided in one embodiment of this specification;

[0049] Figure 6 This is a schematic diagram of relative geological age provided in one embodiment of this specification;

[0050] Figure 7 This is a schematic diagram of a scanning panel for acquiring geological patterns by scanning training images, provided in one embodiment of this specification;

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

[0052] Figures 9(a), 9(b), 9(c), 9(d), 9(e), and 9(f) are schematic cross-sectional views of a score provided in one embodiment of this specification.

[0053] Figures 10(a), 10(b), and 10(c) are schematic diagrams of an initial model of longitudinal wave velocity, transverse wave velocity, and density provided in one embodiment of this specification.

[0054] Figures 11(a), 11(b), and 11(c) are schematic diagrams of the inversion results of P-wave velocity, S-wave velocity, and density provided in one embodiment of this specification. Detailed Implementation

[0055] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0056] Seismic inversion can interpret subsurface elastic parameters, including acoustic impedance, P-wave velocity, S-wave velocity, and density, from observed seismic data. These obtained subsurface elastic parameters are beneficial for reservoir prediction. Post-stack seismic inversion, however, can only obtain acoustic impedance parameters and cannot predict reservoirs with high accuracy. Pre-stack seismic data reflects the variation of seismic amplitude at different incident angles, and compared to post-stack seismic inversion, it can provide a variety of elastic parameters for seismic interpretation. However, seismic amplitude can only describe interfacial information, such as the relative changes in elastic parameters. Seismic inversion methods relying solely on amplitude cannot effectively reveal subsurface elastic parameters quantitatively, while low-frequency initial models can reveal the changing trends of strata, providing initial values ​​for seismic inversion. However, if the initial model deviates from reality, it will produce biased inversion results, leading to erroneous interpretations.

[0057] Multipoint geostatistics is one of the mainstream modeling methods, providing an initial model for pre-stack seismic inversion. This method uses a predefined data template to scan training images to obtain geological patterns around spatial points. Then, based on a set of designed filters, it obtains score profiles of these spatial points, achieving high-resolution modeling of elastic parameters. However, traditional multipoint geostatistics methods have several drawbacks: the accuracy of simulation results is affected by the score profiles of the spatial points, and the accuracy of the score profiles is closely related to the choice of filters. In traditional multipoint geostatistics methods, the number of filters is fixed, limiting the accuracy of simulating the subsurface medium; traditional multipoint geostatistics methods rely on training images, which are difficult to obtain in practice; and the simulation process does not consider the spatial continuity of the subsurface medium, resulting in low accuracy in simulating complex subsurface geological structures. Therefore, using the modeling results obtained from traditional multipoint geostatistics methods as the initial model for pre-stack seismic inversion yields elastic parameter inversion results with low accuracy and resolution.

[0058] Existing methods for elastic parameter modeling suffer from limitations in the number of filters, impacting simulation accuracy. These methods also rely on training images, which are difficult to obtain. Furthermore, they fail to consider the spatial continuity of the subsurface medium, resulting in low accuracy in simulating complex geological structures. Additionally, limited well logging data also hinders the acquisition of high-precision elastic parameter modeling results.

[0059] To address the root causes of the aforementioned problems, this manual first calculates the local dip angle based on seismic data of the target area, and then derives the relative geochronology of each sampling point, effectively achieving stratigraphic time constraints. Secondly, by screening the first sampling point based on the relative geochronology, it ensures that the selected sampling points used for interpolation are consistent in sedimentary age, thereby effectively reducing parameter simulation errors caused by the lack of relative geochronological constraints in traditional multi-point geostatistics. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficients determined by preset Lagrange multipliers, weighted interpolation calculations are performed on the screened first sampling point to obtain more accurate elastic parameters. These parameters serve as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately enhancing the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development.

[0060] See Figure 1 This specification provides an embodiment of a multi-point geostatistical inversion method based on relative geochronographs, specifically applied to the server side. In practical implementation, this method may include the following:

[0061] S101: Acquire seismic data for the target area;

[0062] S102: Based on the seismic data of the target area, determine the local dip angle of the sampling point, and based on the local dip angle, determine the relative geological time body corresponding to the sampling point;

[0063] S103: Based on the relative geological timescale of the first sampling point and the relative geological timescale of the sampling point to be interpolated, the first sampling point is filtered to obtain the second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters.

[0064] S104: Obtain 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;

[0065] S105: Determine the preset weighting coefficient of the second sampling point based on 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.

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

[0067] S107: Determine the elastic parameter inversion result of the target area based on the elastic parameters corresponding to the sampling points to be interpolated; wherein, the elastic parameter inversion result is used for oil and gas reservoir prediction in the target area.

[0068] The aforementioned seismic data includes raw seismic traces collected from the field, reflecting the original signals of the subsurface medium; and pre-stack and post-stack data formed after static correction, noise reduction, multiple reflection suppression, time correction, and stacking processing.

[0069] The aforementioned local dip angle refers to the degree and direction of inclination of strata at a specific point or region in geological or seismic data. It reflects the spatial extension trend of underground geological bodies and is an important parameter for describing the geometric characteristics of strata.

[0070] The aforementioned relative geochronology refers to the division of geological bodies into geological units with a relative temporal order based on the geological time characteristics of strata in seismic exploration and geological research. It is a stratigraphic division method based on geological time sequence, aiming to determine the relative position of geological bodies in the time dimension, rather than their absolute age.

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

[0072] The first sampling point mentioned above is a sampling point with known elastic parameters. Specifically, it can be a sampling point with known elastic parameters in the well 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 based on the seismic data of the target area may specifically include:

[0074] In the seismic data of the target area, a preset spatiotemporal window is first selected near each sampling point. It is assumed that the reflected signal within the preset spatiotemporal window is approximately linearly distributed, and its slope is used as the local dip angle within the preset spatiotemporal window.

[0075] In some embodiments, determining the relative geological timescale corresponding to the sampling point based on the local dip angle may specifically include:

[0076] After obtaining the local dip angle of each sampling point, the vertical time shift of each sampling point is first calculated. That is, the time offset of each point relative to a certain reference horizontal plane is obtained by integrating or accumulating the time difference along the time direction based on the local dip angle. Then, these vertical time shift values ​​are used to flatten the in-phase axis on the seismic profile, correcting the tilted stratigraphic interface to an approximately horizontal state. Finally, a relative geological age is assigned to each sampling point based on the corrected time value, constructing a relative geological timescale that reflects the chronological order of stratigraphic deposition. The relative geological timescale provides clear geological time series constraints for subsequent data screening and elastic parameter inversion.

[0077] In some embodiments, determining the preset weighting coefficient of the second sampling point based on 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 may specifically include:

[0078] Based on 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 that each weight coefficient is normalized. By constructing a set of Krijent interpolation equations that reflect spatial similarity constraints, the preset weight coefficients of each second sampling point can be solved. Among them, the elements in the correlation coefficient matrix reflect the degree of similarity of the geological attributes of each sampling point in space. The Lagrange multiplier is used to force the sum of all weight coefficients to be 1, so that the weight coefficients obtained by solving have high stability while minimizing the estimation error.

[0079] Specifically, we can use the pre-defined Kriging interpolation theory, taking the elastic parameter as the longitudinal wave velocity as an example, the specific expression is as follows:

[0080]

[0081] Where l represents the number of second sampling points used for interpolation, v pi Represents the longitudinal wave velocity at the i-th second sampling point. λ represents the interpolated P-wave velocity value. i It is the weighting coefficient corresponding to the i-th sampling point, and satisfies the following condition:

[0082]

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

[0084]

[0085] Among them, 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. Parameter b x ′(x∈[1,k]) is the correlation coefficient between the x-th second sampling point and the sampling point to be interpolated, and ω is a preset Lagrange multiplier. During the interpolation process, the relative geological timescale corresponding to the aforementioned second sampling point is used to optimize the interpolation points, satisfying the condition... Where, τ unkowndata Indicates the relative geological age of the point to be interpolated. Let ψ represent the relative geological age of the x-th second sampling point, where 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 multipliers in the equation system, the unbiasedness and optimal interpolation principles are taken into account, thereby accurately solving for the weighting coefficient of each second sampling point. This process preserves the geological similarity between sampling points and ensures that the sum of the weighting coefficients is 1 through Lagrange multipliers, thus effectively avoiding interference from cross-layer or anomalous data on the results and improving the accuracy and stability of elastic parameter interpolation.

[0087] In some embodiments, the elastic parameters corresponding to the sampling point to be interpolated are determined based on the elastic parameters corresponding to the second sampling point and the preset weighting coefficients. In specific implementation, the 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, firstly, the local dip angle is calculated based on seismic data of the target area, and the relative geochronology of each sampling point is derived from this, effectively realizing stratigraphic time constraints. Secondly, by screening the first sampling point based on the relative geochronology, the selected sampling points used for interpolation are ensured to be consistent in relative sedimentary age, thereby effectively reducing the parameter inversion error caused by the lack of relative geochronology constraints in traditional multi-point geostatistics. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficient determined by the preset Lagrange multipliers, weighted interpolation calculation is performed on the screened first sampling point to obtain more accurate elastic parameters as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately improving the accuracy of oil and gas reservoir prediction in the target area, meeting the requirements of high-precision oil and gas exploration and development.

[0089] In some embodiments, before 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, the method may further include the following:

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

[0091] S2: Perform filtering processing on the multiple sub-images to obtain a filter;

[0092] S3: Based on the filter and each of the sub-images, determine 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.

[0093] Specifically, a fixed-size scanning panel (e.g., m×m) can be preset as the segmentation size, and sampling points on the target image can be scanned 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, ultimately yields the set of all geological patterns. That is, multiple sub-images, where N is the number of all spatial sampling points on the profile.

[0094] Based on the above embodiments, by performing fine segmentation and point-by-point scanning of the target image through a preset scanning panel, the local geological pattern of each sampling point can be extracted efficiently and accurately, ensuring that the data has high spatial resolution and signal-to-noise ratio. For example, in practical applications, by calculating the correlation coefficient of the local pattern of the sampling point, the continuity and similarity of each sampling point in the geological structure can be accurately determined, thereby using these high-quality data to achieve accurate weighted interpolation in the subsequent elastic parameter modeling and inversion process.

[0095] In some embodiments, the method for determining 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, based on the filter and each sub-image, may further include the following:

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

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

[0098] S3: Sort the feature values, select the feature vectors corresponding to the first preset number of feature values ​​as target vectors, and determine the target vectors as the filter;

[0099] S4: Based on the filter and each of the sub-images, determine 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.

[0100] Specifically, the sub-images obtained from segmenting the target image can be averaged by subtracting the average value of the image from all pixel values ​​in each sub-image to eliminate brightness deviations and highlight local structural features. Next, using principal component analysis, all averaged sub-images are arranged column-wise to form a data matrix. The covariance matrix of this matrix is ​​calculated, and eigenvalue decomposition or singular value decomposition is performed to obtain a set of eigenvectors and corresponding eigenvalues. These eigenvectors reflect the most significant geological structural change patterns in the sub-images. Then, based on the eigenvalues ​​sorted from largest to smallest, a predetermined number (e.g., the first 6) of eigenvectors are selected as target vectors, and these target vectors are used as filters. Finally, based on the target vectors and in conjunction 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 models That is, for all sub-images, the filter (i.e., the target vector) is obtained using a preset principal component analysis method, which mainly involves the following steps:

[0102] S1: Average each row of the dataset X for all sub-images, expressed by the following formula:

[0103]

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

[0105] S2: Subtract the mean μ from each column of matrix X to obtain the data. This can be expressed by the following formula:

[0106]

[0107] S3: Find the matrix The covariance matrix, where T denotes the transpose, is expressed by the following formula:

[0108]

[0109] Where C is the covariance matrix calculated from all the sub-images. For the mean-free data of all the sub-images, m 2 This represents the size of the scanned training image template, which is equivalent to the number of rows in the matrix.

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

[0111] C=OΣP T

[0112] Where Σ represents the eigenvalue matrix, O and P T Let P and T represent the left and right eigenvector matrices, respectively, and T denote the transpose. The principal components are represented by the right eigenvector matrix P. T Sure.

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

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

[0115]

[0116] Among them, SC j Represents the relationship between filter A and filter A. j The corresponding score profile, x n It is the geological pattern (i.e., sub-image) at the nth sampling point.

[0117] Furthermore, the score vector at each sampling point in 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 two points mentioned above, the correlation coefficient γ = corr[E] can be calculated. c (l,q),E v (l′,q′)]. Where γ is the correlation coefficient between sampling points c(l,q) and v(l′,q′), and corr[·] represents the correlation operation.

[0121] In some embodiments, the method for determining the local dip angle based on seismic data of the target area may further include the following:

[0122] S1: Based on the seismic trace data corresponding to the sampling points to be interpolated in the target image, determine the decomposition residual and decomposition factor of the seismic trace data corresponding to the sampling points to be interpolated;

[0123] S2: Determine the local tilt angle corresponding to the target image based on the decomposition factor and the decomposition residual.

[0124] In some embodiments, a preset plane wave decomposition technique is used to determine the local tilt angle corresponding to the target image. Specifically, the preset plane wave decomposition theory is based on the local plane wave equation, which 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 coordinates. If the local dip angle σ is a constant, the general solution 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 It is a collection of all seismic traces of a seismic dataset, where M represents the number of seismic traces, and the wavefield u(t,x) is... j (Corresponds to seismic trace S) j The plane wave decomposition expression is as follows:

[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, 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 pre-defined least squares theory, it can be expressed by the following formula:

[0134]

[0135] Minimize the decomposition residual of r to obtain the corresponding local tilt angle.

[0136] Based on the above embodiments, by decomposing the seismic trace data corresponding to the sampling point to be interpolated, the decomposition residual and decomposition factor are calculated, thereby achieving accurate determination of the local dip angle.

[0137] In some embodiments, the method for determining the relative geological timescale corresponding to the first sampling point among the sampling points based on the local dip angle may further include the following:

[0138] S1: Determine the vertical time shift in the target image space based on the local tilt angle;

[0139] S2: Determine the relative geological time body corresponding to the sampling point to be interpolated based on the vertical time shift and the two-way travel time of the seismic wave corresponding to the sampling point to be interpolated.

[0140] In some embodiments, based on the local dip angle, the corresponding relative geological timescale is obtained using a preset in-phase axis flattening method, which can be expressed by the following formula:

[0141]

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

[0143] τ(x,t)=t+s(x,t)

[0144] Here, τ(x,t) represents the relative geological timescale.

[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 time volume by combining the seismic wave travel time corresponding to the sampling point to be interpolated, the accurate alignment and correction of stratigraphic time information in seismic data is effectively achieved, ensuring that only data with consistent depositional periods are used in the interpolation process, thereby significantly reducing error accumulation.

[0146] In some embodiments, the method of filtering the first sampling point based on the relative geological timescale of the first sampling point and the relative geological timescale of the sampling point to be interpolated to obtain the second sampling point may further include the following:

[0147] S1: Obtain the difference in relative geological timescale between each of the first sampling points and the sampling points to be interpolated;

[0148] S2: Based on the difference and the preset difference threshold, the first sampling point is filtered to obtain the second sampling point.

[0149] For example, suppose the relative geological age of the sampling point to be interpolated is 25.1, while the relative geological age of a certain first sampling point is 25.0. The difference between the two is 0.1. If the preset threshold is 0.5, then the sampling point meets the condition. However, the relative geological age of another sampling point is 23.0, so the difference is 2.1, which does not meet the threshold requirement and is discarded. Thus, only the sampling points with a high degree of matching with the sampling point to be interpolated in terms of sedimentary time are retained for subsequent interpolation calculations.

[0150] Based on the above embodiments, by strictly controlling the degree of geological age matching of sampling points, it is possible to ensure that the logging data used for interpolation has consistency in sedimentary time sequence, effectively reducing the accumulation of errors caused by the mixing of cross-layer or cross-geological age data.

[0151] In some embodiments, the elastic parameters include P-wave velocity, S-wave velocity, and density. The method for determining the elastic parameters corresponding to the sampling point to be interpolated based on the elastic parameters corresponding to the second sampling point and the preset weighting coefficients may further include the following:

[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: The sum of the products is determined as the longitudinal wave velocity corresponding to the sampling point to be interpolated.

[0154] For example, assuming there are three second sampling points around the sampling point to be interpolated, with P-wave velocities of 3200 m / s, 3300 m / s, and 3400 m / s respectively, and preset weighting coefficients of 0.35, 0.40, and 0.25 respectively, then the P-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 geochronographs provided in this specification acquires seismic data of a target area; determines the local dip angle of a sampling point based on the seismic data of the target area, and determines the relative geochronograph corresponding to the sampling point based on the local dip angle; filters the first sampling point based on the relative geochronograph of the first sampling point and the relative geochronograph of the sampling point to be interpolated, obtaining a second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters; obtains 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; determines the preset weighting coefficient of the second sampling point based on 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; determines the elastic parameter corresponding to the sampling point to be interpolated based on the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; determines the elastic parameter inversion result of the target area based on 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. Thus, firstly, by calculating the local dip angle based on seismic data of the target area and deriving the relative geochronology of each sampling point, stratigraphic time constraints are effectively achieved. Secondly, by screening the first sampling point based on the relative geochronology, the selected sampling points used for interpolation are ensured to have consistent depositional ages, thereby effectively reducing the parameter inversion error caused by the lack of relative geochronology constraints in traditional multi-point geostatistics. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficients determined by the preset Lagrange multipliers, weighted interpolation calculations are performed on the screened first sampling point to obtain more accurate elastic parameters. These parameters serve as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately improving 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] See Figure 2 The embodiments of this specification also provide a specific electronic device, wherein the electronic device includes a network communication port 201, a processor 202 and a memory 203, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

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

[0158] The processor 202 can specifically be used to: determine the local dip angle of sampling points based on seismic data of the target area; determine the relative geochronology corresponding to the sampling points based on the local dip angle; filter the first sampling points based on the relative geochronology of the first sampling points and the relative geochronology of the sampling points to be interpolated to obtain second sampling points; wherein the first sampling points are sampling points with known elastic parameters; obtain the correlation coefficient between any two second sampling points and the correlation coefficient between the second sampling points and the sampling points to be interpolated; determine the preset weighting coefficient of the second sampling points based on the correlation coefficient between any two second sampling points, the correlation coefficient between the second sampling points and the sampling points to be interpolated, and a preset Lagrange multiplier; determine the elastic parameter corresponding to the sampling points to be interpolated based on the elastic parameter corresponding to the second sampling points and the preset weighting coefficient; determine the elastic parameter inversion result of the target area based on the elastic parameter corresponding to the sampling points 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 used to store the corresponding instruction program.

[0160] Based on the above method, the relevant structural performance of electronic equipment can be effectively utilized to improve the data processing speed of electronic equipment and efficiently realize the multi-point geostatistical inversion method based on relative geological time bodies.

[0161] In this embodiment, the network communication port 201 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, 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 or CDMA; it can also be a Wi-Fi chip; or it can 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 a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0163] In this embodiment, the memory 203 may include a hierarchy. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no 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 stick, TF card, etc.

[0164] This specification also provides a computer-readable storage medium based on the above-described multi-point geostatistical inversion method based on relative geochronology, which acquires seismic data of a target area; determines the local dip angle of a sampling point based on the seismic data of the target area, and determines the relative geochronology corresponding to the sampling point based on the local dip angle; filters the first sampling point based on the relative geochronology of the first sampling point and the relative geochronology 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, including well logging data; acquires 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; determines the preset weighting coefficient of the second sampling point based on 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; determines the elastic parameter corresponding to the sampling point to be interpolated based on the elastic parameter corresponding to the second sampling point and the preset weighting coefficient; determines the elastic parameter inversion result of the target area based on 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.

[0165] In this embodiment, the 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 an interface configured according to standards specified in the communication protocol for network connection communication.

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

[0167] See Figure 3 At the software level, this specification also provides a multi-point geostatistical inversion device based on relative geochronographs, which may specifically include the following structural modules:

[0168] Data acquisition module 301 is used to acquire seismic data of the target area;

[0169] The geological timescale determination module 302 is used to determine the local dip angle of the sampling point based on the seismic data of the target area, and to determine the relative geological timescale corresponding to the sampling point based on the local dip angle.

[0170] The sampling point determination module 303 is used to filter the first sampling point according to the relative geological timescale of the first sampling point and the relative geological timescale of the sampling point to be interpolated, so as to obtain the second sampling point; wherein, the first sampling point is a sampling point with known elastic parameters.

[0171] The correlation coefficient determination module 304 is used to obtain 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;

[0172] The weighting coefficient determination module 305 is used to determine the preset weighting coefficient of the second sampling point based on 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.

[0173] The parameter determination module 306 is used to determine the elastic parameter corresponding to the sampling point to be interpolated based on the elastic parameter corresponding to the second sampling point and the preset weighting coefficient.

[0174] The seismic inversion module 307 is used to determine the elastic parameter inversion result of the target area based on the elastic parameters corresponding to the sampling points 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 correlation coefficient determination module 304 described above, in specific implementation, the target image corresponding to the seismic data is segmented according to a preset segmentation size to obtain multiple sub-images; the multiple sub-images are filtered to obtain a filter; the coefficient determination module is used to determine 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, based on the filter and each sub-image.

[0176] In some embodiments, the coefficient determination module performs mean-normalization on each sub-image to obtain a mean-normalized sub-image; determines the covariance matrix corresponding to the mean-normalized sub-image, and determines the eigenvalues ​​and eigenvectors corresponding to the covariance matrix; sorts the eigenvalues, selects the eigenvectors corresponding to the first preset number of eigenvalues ​​as target vectors, and determines the target vectors as the filter; and determines 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 based on the filter and each sub-image.

[0177] In some embodiments, the above-mentioned age determination module 302, in specific implementation, determines the decomposition residual and decomposition factor of the seismic trace data corresponding to the sampling point to be interpolated in the target image based on the seismic trace data corresponding to the sampling point to be interpolated; and determines the local dip angle corresponding to the target image based on the decomposition factor and the decomposition residual.

[0178] In some embodiments, the aforementioned chronosphere determination module 302, in specific implementation, determines the vertical time shift in the target image space based on the local tilt angle; and determines the relative geological chronosphere corresponding to the sampling point to be interpolated based on the vertical time shift and the seismic propagation time corresponding to the sampling point to be interpolated.

[0179] In some embodiments, the sampling point determination module 303 specifically acquires the difference between the relative geological timescales between each first sampling point and the sampling point to be interpolated; and filters the first sampling points according to the difference and a preset difference threshold to obtain the second sampling point.

[0180] In some embodiments, the parameter determination module 306, in a specific implementation, determines the product between the longitudinal wave velocity corresponding to each second sampling point and the preset weighting coefficient of each second sampling point; and determines the sum of the products as the longitudinal wave velocity corresponding to the sampling point to be interpolated.

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

[0182] As can be seen from the above, the multi-point geostatistical inversion device based on relative geochronology provided in the embodiments of this specification firstly calculates the local dip angle based on seismic data of the target area and derives the relative geochronology of each sampling point, effectively realizing stratigraphic time constraints. Secondly, by screening the first sampling point based on the relative geochronology, it ensures that the selected sampling points used for interpolation are consistent in sedimentary age, thereby effectively reducing the parameter inversion error caused by the lack of relative geochronology constraints in traditional multi-point geostatistical methods. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficient determined by the preset Lagrange multipliers, weighted interpolation calculation is performed on the screened first sampling point to obtain more accurate elastic parameters as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately improving 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 apparatus based on relative geochronographs provided in this manual can be applied. First, the local dip angle is calculated based on seismic data of the target area, and the relative geochronograph of each sampling point is derived from this, effectively achieving stratigraphic time constraints. Second, by screening the first sampling point based on the relative geochronograph, the selected sampling points used for interpolation are ensured to be consistent in relative sedimentary age, thereby effectively reducing the parameter inversion error caused by the lack of relative geochronological constraints in traditional multi-point geostatistical methods. Finally, using the correlation coefficient between the screened first sampling point and the sampling point to be interpolated, as well as the weighting coefficients determined by preset Lagrange multipliers, weighted interpolation calculations are performed on the screened first sampling point to obtain more accurate elastic parameters as the initial model, improving the accuracy and resolution of elastic parameter inversion, and ultimately improving 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:

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

[0185] Among them, the seismic data shown in Figure 4(a) and the well logging data shown in Figure 4(b) are data that characterize the features of the underground geological structure.

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

[0187] Based on seismic data, the target image is determined;

[0188] Based on the target image, the local tilt angle is determined using the plane wave decomposition method.

[0189] It is understandable that traditional multi-point geostatistics relies on training images. In order to solve the problem of the difficulty in obtaining training images, this manual uses seismic data as the target image to replace the training images in multi-point geostatistics, which cleverly solves the problem of the difficulty in obtaining training images.

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

[0191] Specifically, based on the obtained local dip angle, a pre-defined phase axis flattening method is used to obtain the relative geological timescale. A schematic diagram corresponding to the aforementioned local dip angle can be found in [reference needed]. Figure 5 The above diagrams corresponding to relative geological timescales can be found in [reference needed]. Figure 6 .

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

[0193] For details, please refer to Figure 7 From left to right, these represent a schematic diagram of the scanning panel, a schematic diagram of the training image, and the geological pattern of the spatial points obtained from the scan. Based on the target image, the scanning panel is used to scan each spatial point on the training image to determine the geological pattern around the elastic parameter to be interpolated on the profile to be interpolated.

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

[0195] Specifically, based on the geological model, the filter is determined using a pre-defined principal component analysis method. The eigenvectors corresponding to the first six eigenvalues, arranged from largest to smallest, are shown in Figures 8(a), 8(b), 8(c), 8(d), 8(e), and 8(f), representing the filter.

[0196] S5: Determine the scoring profile based on filters and geological models.

[0197] Specifically, based on the obtained geological model and 6 filters, 6 scoring profiles can be obtained. See Figures 9(a), 9(b), 9(c), 9(d), 9(e), and 9(f) for schematic diagrams of the 6 obtained scoring profiles.

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

[0199] S7: Determine the initial model of the elastic parameters to be interpolated based on the relative geological timescale, 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.

[0200] Specifically, see Figures 10(a), 10(b), and 10(c), which are schematic diagrams of the initial models for P-wave velocity, S-wave velocity, and density, respectively.

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

[0202] Specifically, based on the initial model and seismic data of the elastic parameters to be interpolated, the target inversion result is determined within the framework of Bayesian theory.

[0203] Furthermore, within the framework of Bayesian theory, pre-stack seismic inversion is performed based on the obtained initial model and seismic data to obtain high-precision and high-resolution inversion results for the target. Figures 11(a), 11(b), and 11(c) are schematic diagrams of the inverted P-wave velocity model, S-wave velocity model, and density model, respectively.

[0204] In some embodiments, based on the acquired seismic data, a target image and local dip angle are determined, and a relative geological timescale is determined based on the local dip angle. Then, based on the target image, a geological model (i.e., multiple sub-images) surrounding the elastic parameter to be interpolated on the interpolation profile is determined. Based on the geological model, a filter (i.e., a target vector) is determined. Based on the filter and the geological model, a score profile is determined. Based on the score profile, the correlation coefficient between any two second sampling points on the interpolation profile, and the correlation coefficient between the second sampling point and the interpolation sampling point are determined. Next, based on the relative geological timescale, the correlation coefficient between any two second sampling points, and the correlation coefficient between the second sampling point and the interpolation sampling point, an initial model for the elastic parameter to be interpolated is determined. Finally, based on the initial model for the elastic parameter to be interpolated and the seismic data, the target inversion result is determined.

[0205] Based on the above embodiments, post-stack seismic data is used as the target image to replace the training image in multi-point geostatistics, overcoming the problem of difficulty in obtaining training images. Principal component analysis is used to obtain filters (i.e., target vectors), breaking the limitation on the number of filters in conventional multi-point geostatistics and enabling adaptive selection of the number of filters. Furthermore, interpolation modeling with well logging data constrained by relative geological timescales determines the initial model of elastic parameters, increasing the accuracy and spatial continuity of the simulation results and solving the problem of low modeling accuracy when well logging data is limited. Finally, based on the obtained initial model of elastic parameters and seismic data, high-precision, high-resolution elastic parameter inversion results are obtained within the Bayesian theoretical framework, which is more conducive to reservoir prediction and better meets actual production needs.

[0206] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0207] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

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

[0209] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations and modifications are possible without departing from the spirit of this specification, and it is intended that the appended claims cover such variations and modifications without departing from the spirit of this specification.

Claims

1. A method of multiple-point geostatistics inversion based on relative geochronological volumes, characterized in that, The method comprises the following steps: acquiring seismic data of a target region; determining local dip angles of sampling points according to the seismic data of the target region, and determining relative geologic age bodies corresponding to the sampling points according to the local dip angles; performing screening processing on a first sampling point according to a relative geologic age body of the first sampling point and a relative geologic age body of a sampling point to be interpolated, to obtain a second sampling point; wherein the first sampling point is a sampling point whose elastic parameters are known; acquiring correlation coefficients between any two second sampling points and correlation coefficients between the second sampling points and the sampling point to be interpolated; determining preset weighting coefficients of the second sampling points according to the correlation coefficients between any two second sampling points, the correlation coefficients between the second sampling points and the sampling point to be interpolated, and a preset Lagrange multiplier; determining elastic parameters corresponding to the sampling point to be interpolated according to elastic parameters corresponding to the second sampling points and the preset weighting coefficients; determining an elastic parameter inversion result of the target region 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 of the target region.

2. The method of claim 1, wherein, Before the step of acquiring the correlation coefficients between any two second sampling points and the correlation coefficients between the second sampling points and the sampling point to be interpolated, the method comprises the following steps: performing cutting processing on a target image corresponding to the seismic data according to a preset cutting size, to obtain a plurality of sub-images; performing screening processing on the plurality of sub-images, to obtain a filter; determining the correlation coefficients between any two second sampling points and the correlation coefficients between the second sampling points and the sampling point to be interpolated according to the filter and each sub-image.

3. The method of claim 2, wherein, The step of determining the correlation coefficients between any two second sampling points and the correlation coefficients between the second sampling points and the sampling point to be interpolated according to the filter and each sub-image comprises the following steps: performing mean value processing on each sub-image, to obtain a mean value processed sub-image; determining a covariance matrix corresponding to the mean value processed sub-image, and determining eigenvalues and eigenvectors corresponding to the covariance matrix; sorting the eigenvalues, selecting the eigenvectors corresponding to a preset number of the eigenvalues as target vectors, and determining the target vectors as the filter; determining the correlation coefficients between any two second sampling points and the correlation coefficients between the second sampling points and the sampling point to be interpolated according to the filter and each sub-image.

4. The method of claim 3, wherein, The step of determining local dip angles according to the seismic data of the target region comprises the following steps: determining decomposition residuals and decomposition factors of seismic trace data corresponding to a sampling point to be interpolated according to the seismic trace data corresponding to the sampling point to be interpolated in the target image; determining local dip angles corresponding to the target image according to the decomposition factors and the decomposition residuals.

5. The method of claim 4, wherein, The step of determining relative geologic age bodies corresponding to first sampling points in the sampling points according to the local dip angles comprises the following steps: determining a vertical time shift on a space of the target image according to the local dip angles. According to the vertical time shift and the seismic wave propagation time corresponding to the sampling point to be interpolated, a relative geologic age body corresponding to the sampling point to be interpolated is determined.

6. The method of claim 5, wherein, The screening processing of the first sampling point according to the relative geologic age body of the first sampling point and the relative geologic age body of the sampling point to be interpolated is performed to obtain a second sampling point, including: The difference of the relative geologic age body between each first sampling point and the sampling point to be interpolated is obtained; The first sampling point is screened according to the difference and a preset difference threshold to obtain a second sampling point.

7. The method of claim 6, wherein, The elastic parameters include P-wave velocity, S-wave velocity and density, and the elastic parameters corresponding to the sampling point to be interpolated are determined according to the elastic parameters corresponding to the second sampling point and the preset weighting coefficient, including: The product between the P-wave velocity corresponding to each second sampling point and the preset weighting coefficient of each second sampling point is determined; The sum of the products is determined as the P-wave velocity corresponding to the sampling point to be interpolated.

8. Apparatus for multiple-point geostatistical inversion based on relative geologic age volumes, characterized in that, It includes: A data acquisition module is configured to acquire seismic data of a target region; An age body determination module is configured to determine a local dip angle of a sampling point according to the seismic data of the target region, and determine a relative geologic age body corresponding to the sampling point according to the local dip angle; A sampling point determination module is configured to screen the first sampling point according to the relative geologic age body of the first sampling point and the relative geologic 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; A correlation coefficient determination module is configured to obtain a correlation coefficient between any two second sampling points and a correlation coefficient between the second sampling points and the sampling point to be interpolated; A weighting coefficient determination module is configured to determine a 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 points and the sampling point to be interpolated, and a preset Lagrange multiplier; A parameter determination module is configured to determine 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 is configured to determine an elastic parameter inversion result of the target region 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 of the target region.

9. An electronic device, comprising: The processor executes the instructions to implement the steps of the multi-point geostatistics inversion method based on the relative geologic age body according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer instructions are stored thereon, and the instructions are executed by the processor to implement the steps of the multi-point geostatistics inversion method based on the relative geologic age body according to any one of claims 1 to 7.

Images