Stratum heterogeneity quantitative evaluation method
By constructing a gradient structure tensor in a three-dimensional geological grid model and performing eigenvalue decomposition, the problem that traditional methods cannot reflect the spatial distribution characteristics of heterogeneity is solved, and the accuracy and objectivity of quantitative assessment of stratigraphic heterogeneity are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ORDOS ENERGY RES INST OF PEKING UNIV
- Filing Date
- 2025-12-17
- Publication Date
- 2026-05-26
AI Technical Summary
Traditional methods for quantitatively evaluating formation heterogeneity can only describe the dispersion of parameter distribution, and cannot reflect the spatial distribution characteristics and directionality of heterogeneity, resulting in low accuracy of quantitative assessment.
The gradient structure tensor analysis method based on a three-dimensional geological grid model is adopted. By determining the permeability or porosity attribute values of each grid point, the gradient structure tensor is constructed and eigenvalue decomposition is performed to obtain quantitative indicators such as heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth angle.
It enables a comprehensive, quantitative, and structural characterization of formation heterogeneity, improves the accuracy of quantitative assessment of heterogeneity, reduces subjective human factors, and ensures the objectivity and repeatability of the results.
Smart Images
Figure CN122089976A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and in particular to a method for quantitative assessment of formation heterogeneity. Background Technology
[0002] In the fields of oil and gas development and carbon dioxide (CO2) geological storage, formation heterogeneity is a key factor affecting the fine description of reservoirs and the optimization of development plans.
[0003] Traditional quantitative evaluation methods for formation heterogeneity rely on a single parameter or statistical index. For example, permeability variation is regarded as a concentrated manifestation of heterogeneity, and permeability variation coefficient, surge coefficient, range, etc. are calculated to characterize reservoir differences.
[0004] However, traditional methods can only describe the dispersion of parameter distribution and cannot reflect the spatial distribution characteristics and directionality of heterogeneity. As a result, the accuracy of current quantitative assessments of formation heterogeneity is low. Summary of the Invention
[0005] This application aims to address at least one of the technical problems existing in the related art. To this end, this application proposes a quantitative assessment method for stratigraphic heterogeneity, which solves the problem that traditional quantitative assessment methods for stratigraphic heterogeneity can only describe the dispersion of parameter distribution and cannot reflect the spatial distribution characteristics and directionality of heterogeneity, thereby improving the accuracy of quantitative assessment of stratigraphic heterogeneity.
[0006] The method for quantitative assessment of formation heterogeneity according to the first aspect of this application includes: Determine the attribute values of each grid point in a three-dimensional geological grid model constructed based on the strata to be evaluated; the attribute values are permeability or porosity. Based on the attribute values of each grid point, construct the gradient structure tensor of each grid point; For each grid point, eigenvalue decomposition is performed based on the gradient structure tensor to obtain eigenvalue information and eigenvectors. Based on the feature value information and feature vector of each grid point, the heterogeneity quantification index of the stratum to be evaluated is determined; the heterogeneity quantification index includes heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth.
[0007] According to one embodiment of this application, determining the heterogeneity quantification index of the stratum to be evaluated based on the feature value information and feature vector of each of the grid points includes: Based on the first feature value, second feature value, and third feature value in the feature value information of each grid point, the heterogeneity intensity of the formation to be evaluated is determined; wherein, the first feature value is greater than or equal to the second feature value, and the second feature value is greater than or equal to the third feature value; Based on the first and third feature values in the feature value information of each grid point, the anisotropy intensity of the stratum to be evaluated is determined. Based on the first feature value in the feature value information of each grid point, the structural complexity of the stratum to be evaluated is determined. Based on the feature vectors of each grid point, the dominant azimuth angle of the stratum to be evaluated is determined.
[0008] According to one embodiment of this application, determining the heterogeneity intensity of the formation to be evaluated based on the first feature value, second feature value, and third feature value in the feature value information of each of the grid points includes: For each grid point, the sum of the first feature value, the second feature value, and the third feature value in the feature value information is determined respectively; The summation of the summation values of all the grid points is used to determine the heterogeneity intensity of the formation to be evaluated.
[0009] According to one embodiment of this application, determining the anisotropy intensity of the formation to be evaluated based on the first and third feature values in the feature value information of each of the grid points includes: The first feature value in the feature value information of each grid point is summed to obtain the first summation result; The third feature value in the feature value information of each grid point is summed to obtain the second summation result; Based on the first summation result and the second summation result, the anisotropy intensity of the formation to be evaluated is determined.
[0010] According to one embodiment of this application, determining the structural complexity of the stratum to be evaluated based on a first feature value in the feature value information of each of the grid points includes: Determine the mean of the first characteristic values of each grid point, and the standard deviation of the first characteristic values of each grid point; The structural complexity of the stratum to be evaluated is determined based on the mean of the first characteristic values of each grid point and the standard deviation of the first characteristic values of each grid point.
[0011] According to one embodiment of this application, determining the dominant azimuth angle of the stratum to be evaluated based on the feature vectors of each of the grid points includes: The feature vectors of each grid point are summed to obtain a composite vector. The azimuth angle of the synthesized vector is extracted to obtain the dominant azimuth angle of the stratum to be evaluated.
[0012] According to one embodiment of this application, constructing the gradient structure tensor of each of the grid points based on the attribute values of each grid point includes: The attribute values of each grid point are normalized to obtain normalized attribute values; Based on the normalized attribute values of each grid point, determine the gradient vector of each grid point; For each of the grid points, a gradient structure tensor is constructed based on the gradient vector.
[0013] An electronic device according to a second aspect of this application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for quantitative assessment of formation heterogeneity.
[0014] According to a third aspect of the present application, the storage medium is a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the quantitative assessment method for formation heterogeneity as described above.
[0015] A computer program product according to a fourth aspect of this application includes a computer program that, when executed by a processor, implements a method for quantitative assessment of formation heterogeneity as described above.
[0016] The above-described one or more technical solutions in the embodiments of this application have at least the following technical effects: A three-dimensional geological grid model is constructed based on the strata to be evaluated, and permeability or porosity is assigned as an attribute value to each grid point. This allows for the construction of a gradient structure tensor for each grid point based on its attribute value. Furthermore, eigenvalue decomposition can be performed on the gradient structure tensor for each grid point to obtain eigenvalue information and eigenvectors. Therefore, based on the eigenvalue information and eigenvectors of each grid point, the heterogeneity intensity, anisotropy intensity, structural complexity, and dominant azimuth of the strata to be evaluated can be accurately determined. By first constructing the gradient structure tensor for each grid point using permeability or porosity as an attribute value, and then combining the gradient structure tensor analysis to obtain the eigenvalue information and eigenvectors of each grid point, the heterogeneity intensity, anisotropy intensity, structural complexity, and dominant azimuth can be derived, thus comprehensively characterizing the spatial structural features of the heterogeneity of the strata to be evaluated, achieving quantitative characterization of stratum heterogeneity, and effectively improving the accuracy of quantitative assessment of stratum heterogeneity.
[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart illustrating the quantitative assessment method for formation heterogeneity provided in the embodiments of this application.
[0020] Figure 2 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0022] This application proposes a quantitative assessment method for stratigraphic heterogeneity, introducing the gradient structure tensor into the field of quantitative assessment of stratigraphic heterogeneity, using it as the mathematical foundation for describing the essential spatial variation of attributes. From the gradient structure tensor, a set of quantitative indicators with clear physical meaning are extracted, including: heterogeneity intensity I, anisotropy intensity A, structural complexity C, and dominant azimuth D, which together constitute a heterogeneity quantitative index HCI=[I,A,C,D]. This comprehensively characterizes the spatial structural features of heterogeneity, achieving a quantitative representation of stratigraphic heterogeneity.
[0023] Furthermore, the entire process is based on a defined mathematical algorithm, which quantifies the heterogeneity of the formation through quaternary indicators. This eliminates the need for human intervention, greatly reduces subjective human factors, and ensures the objectivity and repeatability of the results.
[0024] It should be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.
[0025] Figure 1This is a flowchart illustrating the quantitative assessment method for formation heterogeneity provided in this application. Figure 1 As shown, the quantitative assessment method for the heterogeneity of this formation includes: Step 110: Determine the attribute values of each grid point in the three-dimensional geological grid model constructed based on the strata to be evaluated; the attribute values are permeability or porosity.
[0026] Step 120: Construct the gradient structure tensor for each grid point based on the attribute values of each grid point.
[0027] Step 130: Perform eigenvalue decomposition on each grid point based on the gradient structure tensor to obtain eigenvalue information and eigenvectors.
[0028] Step 140: Based on the eigenvalue information and eigenvector of each grid point, determine the heterogeneity quantification index of the stratum to be evaluated; the heterogeneity quantification index includes heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth.
[0029] It should be noted that the execution subject of the quantitative assessment method for formation heterogeneity provided in this application embodiment can be a computer device. Such computer device can be, for example, a mobile phone, tablet computer, laptop computer, handheld computer, vehicle electronic device, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc.
[0030] In this application, the actual strata in a designated area can be identified as the strata to be evaluated based on actual business needs (such as oil and gas development and carbon dioxide (CO2) geological storage).
[0031] Furthermore, this application can pre-construct a three-dimensional geological grid model based on relevant information of the strata to be evaluated. This three-dimensional geological grid model is constructed according to a certain scale based on the actual information of the strata to be evaluated. Specifically, this three-dimensional geological grid model can be constructed based on a three-dimensional Cartesian grid system (x,y,z), which is a fundamental mathematical framework used for accurately describing and quantifying geological space.
[0032] Furthermore, this application can divide a three-dimensional geological grid model into multiple three-dimensional grid points.
[0033] Furthermore, the true attribute values of the region where each grid point is located can be obtained, and corresponding attribute values can be set for each grid point in the three-dimensional geological grid model. Thus, the three-dimensional geological grid model can serve as a reservoir attribute field.
[0034] It should be further noted that the attribute values of each grid point in this application can be permeability or porosity, or other available attribute values, and no specific limitation is made in this application.
[0035] Among them, the information on the properties of the strata, such as permeability and porosity, can be obtained by measuring the resistivity field or the elastic parameter field (such as wave impedance) obtained by seismic inversion. That is, it can be obtained by traditional methods. In this application, it can be given information, so the specific acquisition method and acquisition process are not limited.
[0036] For each grid point in the reservoir attribute field (i.e., the three-dimensional geological grid model) obtained above, this application can determine the spatial rate of change at the grid point, i.e., the gradient vector ∇F of the grid point, by using the attribute values of multiple adjacent grid points.
[0037] It should be noted that gradient vectors describe local changes, but in order to characterize regional structural features, a gradient structure tensor needs to be constructed.
[0038] Therefore, this application further constructs a gradient structure tensor for each grid point by using the outer product of its gradient vector and itself. Specifically, for each grid point, its gradient structure tensor T is a second-order symmetric tensor.
[0039] After obtaining the gradient structure tensor T of each grid point, this application can also perform Gaussian smoothing on each component of each gradient structure tensor T to further suppress the small instabilities that may remain in the gradient calculation and preprocessing process, making the final tensor field more robust.
[0040] Specifically, this application can first construct a three-dimensional Gaussian kernel, and then independently convolve each component of the gradient structure tensor T with the Gaussian kernel to obtain a Gaussian-smoothed gradient structure tensor T. The smoothing scale (Gaussian kernel standard deviation) defines the size of the "observation window" for analyzing heterogeneity and should be set according to the specific scale of the strata to be evaluated.
[0041] Furthermore, this application can perform eigenvalue decomposition on the gradient structure tensor T of each grid point after Gaussian smoothing.
[0042] Specifically, the gradient structure tensor T is a 3x3 symmetric positive semidefinite matrix. Its eigenvalue decomposition involves finding three scalars λ (eigenvalues) and three vectors v (eigenvectors) that satisfy the following equation: T·v=λ·v; Understandably, the feature vector v is a special type of direction. The gradient structure tensor T acts on this vector, which is equivalent to scaling it by a factor of λ, without changing its direction.
[0043] For a real symmetric matrix, it can be mathematically guaranteed that there are always three real eigenvalues (satisfying λ1≥λ2≥λ3≥0) and corresponding orthogonal eigenvectors v1, v2, v3.
[0044] These eigenvalues and eigenvectors form the basis for calculating all subsequent quantitative indicators.
[0045] Furthermore, after obtaining the characteristic value information (including the first characteristic value λ1, the second characteristic value λ2 and the third characteristic value λ3) and the corresponding orthogonal characteristic vectors v1, v2 and v3 for each grid point, this application can determine the heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth of the stratum to be evaluated based on the characteristic value information of each grid point and the corresponding orthogonal characteristic vectors v1, v2 and v3.
[0046] The heterogeneity intensity of the formation to be evaluated is determined based on the first, second, and third eigenvalues of all grid points.
[0047] The anisotropy intensity of the formation to be evaluated is determined based on the first and third eigenvalues of all grid points.
[0048] The structural complexity of the strata to be evaluated is determined based on the standard deviation and mean of the largest eigenvalues at each grid point.
[0049] The dominant azimuth of the stratum to be evaluated is determined based on the eigenvector of the minimum eigenvalue of all grid points.
[0050] Furthermore, after obtaining the quantitative index of heterogeneity of the formation to be evaluated, the reservoir can be finely described and the development plan optimized based on the quantitative index of heterogeneity, so as to better carry out related business, such as oil and gas development and carbon dioxide (CO2) geological storage.
[0051] According to the method for quantitative assessment of formation heterogeneity in this application, a three-dimensional geological grid model is constructed based on the formation to be assessed, and permeability or porosity is set as an attribute value for each grid point in the three-dimensional geological grid model, so that the gradient structure tensor of each grid point can be constructed based on the attribute value of each grid point; furthermore, eigenvalue decomposition can be performed on each grid point based on the gradient structure tensor to obtain eigenvalue information and eigenvector; thus, based on the eigenvalue information and eigenvector of each grid point, the heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth of the formation to be assessed can be accurately determined. By first constructing the gradient structure tensor of each grid point using permeability or porosity as attribute values, and then combining the gradient structure tensor analysis to obtain the eigenvalue information and eigenvectors of each grid point, quantitative indicators of heterogeneity such as heterogeneity intensity, anisotropy intensity, structural complexity, and dominant azimuth can be derived. This fully characterizes the spatial structural features of the heterogeneity of the formation to be evaluated, realizes the quantitative characterization of formation heterogeneity, and effectively improves the accuracy of quantitative assessment of formation heterogeneity.
[0052] In one embodiment, the gradient structure tensor of each grid point is constructed based on the attribute values of each grid point, including: The attribute values of each grid point are normalized to obtain normalized attribute values; The gradient vector of each grid point is determined based on the normalized attribute value of each grid point. For each grid point, a gradient structure tensor is constructed based on the gradient vector.
[0053] Specifically, for any grid point (i,j,k) in the reservoir attribute field (i.e., the three-dimensional geological grid model) F(x,y,z) obtained above, to improve the stability of numerical calculation, this application can perform normalization preprocessing on the attribute values of each input grid point, linearly scaling the attribute values to the [0,1] interval to eliminate the influence of dimensions. At the same time, a Gaussian filter is used to perform convolution operation on the data volume to suppress high-frequency noise introduced by measurement or interpretation errors, while preserving the macroscopic structural characteristics of the geological body.
[0054] Furthermore, the spatial rate of change at the grid point, i.e. the gradient vector ∇F of the grid point, is determined by using the normalized attribute values of the six grid points directly adjacent to the grid point and the central difference method.
[0055] ; Where Δx, Δy, Δz are the grid spacings of two grid points in the x, y, and z directions, respectively; F(i,j,k) represents the attribute value of grid point (i,j,k); F(i+1,j,k) represents the attribute value of grid point (i+1,j,k), and so on.
[0056] After obtaining the gradient vector of each grid point, this application can construct a gradient structure tensor based on the gradient vector of each grid point.
[0057] Specifically, for any grid point, its gradient structure tensor T is a second-order symmetric tensor, consisting of the outer product of the gradient vector and itself. More specifically, it can be constructed as follows: ; Therefore, the gradient structure tensor of each grid point can be constructed.
[0058] This application calculates the gradient vector of any given reservoir property field and further constructs a gradient structure tensor. By performing eigenvalue decomposition and secondary calculation on the gradient structure tensor, four indicators with clear physical meanings can be obtained: heterogeneity intensity I, anisotropy intensity A, structural complexity C, and dominant azimuth D. These together constitute a quaternary component-based quantitative index of heterogeneity, thereby achieving a comprehensive, quantitative, and structured characterization of the heterogeneity of the formation to be evaluated, and effectively improving the accuracy of quantitative assessment of formation heterogeneity.
[0059] In one embodiment, based on the eigenvalue information and eigenvectors of each grid point, a quantitative index of the heterogeneity of the stratum to be evaluated is determined, including: Based on the first, second, and third characteristic values in the characteristic value information of each grid point, the heterogeneity intensity of the stratum to be evaluated is determined; wherein, the first characteristic value is greater than or equal to the second characteristic value, and the second characteristic value is greater than or equal to the third characteristic value; Based on the first and third eigenvalues in the eigenvalue information of each grid point, the anisotropy intensity of the stratum to be evaluated is determined. Based on the first feature value in the feature value information of each grid point, the structural complexity of the stratum to be evaluated is determined. Based on the feature vectors of each grid point, the dominant azimuth angle of the stratum to be evaluated is determined.
[0060] Specifically, after obtaining the eigenvalue information and eigenvector of each grid point, this application can first determine the sum of the first eigenvalue, the second eigenvalue and the third eigenvalue in the eigenvalue information of each grid point, and further determine the average value of the eigenvalues after summing of each grid point and use it as the heterogeneity intensity I of the stratum to be evaluated.
[0061] Furthermore, the sum of the first eigenvalues of each grid point and the sum of the third eigenvalues of each grid point can be determined, and the ratio of the two sums can be calculated. The result of the calculation is used as the anisotropy intensity A of the stratum to be evaluated.
[0062] In addition, the standard deviation between the largest eigenvalues of each grid point and the mean between the largest eigenvalues of each grid point.
[0063] Furthermore, based on the standard deviation between the maximum eigenvalues of each grid point and the mean between the maximum eigenvalues of each grid point, the structural complexity C of the stratum to be evaluated can be determined.
[0064] Furthermore, the eigenvector v3 of the minimum eigenvalue λ3 in each grid point can be determined, and the azimuth angle can be extracted based on the eigenvector v3 corresponding to each grid point. The extracted azimuth angle is determined as the dominant azimuth angle D of the stratum to be evaluated.
[0065] This application introduces the gradient structure tensor into the field of quantitative evaluation of formation heterogeneity based on the eigenvalues and characteristic vectors obtained by decomposing the gradient structure tensor, and performs secondary calculations based on the eigenvalues and eigenvectors. It uses the gradient structure tensor as the mathematical basis for describing the essential changes in attribute space, and thus determines four indicators with clear physical meaning: heterogeneity intensity I, anisotropy intensity A, structural complexity C, and dominant azimuth angle D. Together, they constitute a quantitative index of heterogeneity containing four components, thereby achieving a comprehensive, quantitative, and structured characterization of the heterogeneity of the formation to be evaluated.
[0066] In one embodiment, the heterogeneity intensity of the formation to be evaluated is determined based on the first, second, and third characteristic values in the characteristic value information of each grid point, including: For each grid point, the sum of the first, second, and third feature values in the feature value information is determined separately; The summation of the values of all grid points is used to determine the heterogeneity intensity of the formation to be evaluated.
[0067] Specifically, when determining the heterogeneity intensity of the stratum to be evaluated based on the first, second, and third characteristic values in the characteristic value information of each grid point, this application can calculate the sum of the first, second, and third characteristic values in the characteristic value information of each grid point.
[0068] Furthermore, the summation of the values of each grid point can be further calculated and averaged to obtain the summation average of the values of each grid point, and this summation average can be determined as the heterogeneity intensity of the stratum to be evaluated.
[0069] More specifically, the heterogeneity intensity I of the formation to be evaluated can be determined by the following formula: ; Where n represents the number of grid points in the three-dimensional geological grid model; This represents the first feature value of the i-th grid point; This represents the second eigenvalue of the i-th grid point; This represents the third eigenvalue of the i-th grid point.
[0070] It should be noted that this application can also determine the heterogeneity intensity I of each grid point separately.
[0071] Specifically, for each grid point, the sum of all eigenvalues in its eigenvalue information can be used as its heterogeneity intensity.
[0072] More specifically, the heterogeneity intensity I at the grid points can be determined using the following formula: .
[0073] This index represents a comprehensive measure of the overall drastic change in the property field around a grid point; a larger value indicates a stronger heterogeneous characteristic. Regions with high I values correspond to interfaces where lithology and physical properties undergo abrupt changes, or the interior of strongly heterogeneous bodies.
[0074] Based on the eigenvalues obtained from the gradient structure tensor decomposition, this application can accurately determine the quantified heterogeneity intensity of the formation to be evaluated, which helps to improve the accuracy of the quantitative assessment of formation heterogeneity.
[0075] In one embodiment, the anisotropy intensity of the formation to be evaluated is determined based on the first and third eigenvalues in the eigenvalue information of each grid point, including: The first feature value in the feature value information of each grid point is summed to obtain the first summation result; The third feature value in the feature value information of each grid point is summed to obtain the second summation result; Based on the first and second summation results, the anisotropy intensity of the strata to be evaluated is determined.
[0076] Specifically, when determining the anisotropy intensity of the stratum to be evaluated based on the first and third eigenvalues in the eigenvalue information of each grid point, this application can perform a summation operation on the first eigenvalues in the eigenvalue information of all grid points in the three-dimensional geological grid model, and obtain the first summation result after completing the summation operation.
[0077] Furthermore, it can perform a summation operation on the third eigenvalue of the eigenvalue information of all grid points in the three-dimensional geological grid model, and obtain the second summation result after completing the summation operation.
[0078] Then, the first summation result is compared with the second summation result, and the result of the ratio calculation is determined as the anisotropy intensity of the stratum to be evaluated.
[0079] More specifically, the anisotropy intensity of the formation to be evaluated can be calculated using the following formula: ; Where n represents the number of grid points in the three-dimensional geological grid model; This represents the first feature value of the i-th grid point; This represents the third eigenvalue of the i-th grid point.
[0080] It should be noted that this application can also determine the anisotropy intensity of each grid point separately.
[0081] Specifically, for each grid point, the ratio of its maximum eigenvalue to its minimum eigenvalue can be determined as its anisotropy intensity.
[0082] More specifically, the anisotropy intensity A at the grid points can be determined using the following formula: ; in, This represents the largest eigenvalue, also known as the first eigenvalue. This represents the smallest eigenvalue, also known as the third eigenvalue.
[0083] This indicator represents the degree of directional difference in attribute changes; the larger the value, the stronger the anisotropy.
[0084] Based on the eigenvalues obtained from the gradient structure tensor decomposition, this application can accurately determine the quantified anisotropy intensity of the formation to be evaluated, which helps to improve the accuracy of quantitative assessment of formation heterogeneity.
[0085] In one embodiment, the structural complexity of the stratum to be evaluated is determined based on the first feature value in the feature value information of each grid point, including: Determine the mean of the first eigenvalues of each grid point, and the standard deviation of the first eigenvalues of each grid point; The structural complexity of the stratum to be evaluated is determined based on the mean and standard deviation of the first characteristic values of each grid point.
[0086] Specifically, when determining the structural complexity of the stratum to be evaluated based on the first characteristic value in the characteristic value information of each grid point, this application can first calculate the mean of the first characteristic values of all grid points and the standard deviation of the first characteristic values of all grid points.
[0087] Then, the standard deviation between the first eigenvalues of all grid points is compared with the mean between the first eigenvalues of all grid points. The result of this calculation is the structural complexity of the stratum to be evaluated.
[0088] More specifically, the structural complexity of the strata to be evaluated can be calculated using the following formula: ; Where n represents the number of grid points in the three-dimensional geological grid model; This represents the first feature value of the i-th grid point; This represents the standard deviation among the first eigenvalues of all grid points.
[0089] It should be noted that this application can also calculate the structural complexity of each grid point separately.
[0090] Specifically, for each grid point, this application can determine the mean value among the first characteristic values (i.e., the maximum characteristic value) of all grid points within a pre-defined neighborhood centered on that grid point.
[0091] Furthermore, for each grid point, the standard deviation among the first eigenvalues (i.e., the largest eigenvalues) of all grid points within a pre-defined neighborhood centered on that grid point can be determined.
[0092] In one embodiment, for any grid point, a circle with a preset radius can be formed using the center point of that grid point as the center, and a preset neighborhood range can be obtained. Furthermore, the mean of the first feature values of all grid points within this preset neighborhood range is acquired. with standard deviation .
[0093] Furthermore, the standard deviation corresponding to that grid point with the mean Perform a ratio calculation to obtain the structural complexity of the grid point.
[0094] More specifically, the structural complexity C of the grid points can be determined using the following formula: .
[0095] This index is used to quantify the complexity of heterogeneity across multiple scales. A low C value indicates that the heterogeneity pattern is consistent and the structure is simple when observed at different scales. A high C value indicates that the dominant heterogeneity directional pattern changes with the scale of observation, revealing the complex structure of the geological body (such as composite sand bodies or multiple superpositions).
[0096] Based on the eigenvalues obtained from the gradient structure tensor decomposition, this application can accurately determine the quantified structural complexity of the formation to be evaluated, which helps to improve the accuracy of quantitative assessment of formation heterogeneity.
[0097] In one embodiment, determining the dominant azimuth angle of the stratum to be evaluated based on the feature vectors of each grid point includes: The feature vectors of each grid point are summed to obtain the composite vector; The azimuth angle of the synthesized vector is extracted to obtain the dominant azimuth angle of the stratum to be evaluated.
[0098] Specifically, this application can sum the feature vectors of each grid point and use the summed vector as a composite vector.
[0099] Furthermore, an azimuth angle is extracted from the synthesized vector using a function for "taking the azimuth angle", and the extracted azimuth angle is used as the dominant azimuth angle of the stratum to be evaluated.
[0100] More specifically, the dominant azimuth of the stratum to be evaluated can be determined using the following formula: ; Where θ() is a function used to calculate and extract the azimuth angle of a three-dimensional vector on the horizontal plane; v3 represents the feature vector of the i-th grid point; n represents the number of grid points in the three-dimensional geological grid model.
[0101] It should be noted that this application can also calculate the dominant azimuth angle of each grid point separately.
[0102] Specifically, for each grid point, the eigenvector v3 with the smallest eigenvalue λ3 at that grid point can be determined.
[0103] Furthermore, an azimuth angle is extracted from the feature vector v3 using a function for "taking the azimuth angle", and the extracted azimuth angle is used as the dominant azimuth angle of the grid point.
[0104] More specifically, the dominant azimuth angle of a grid point can be determined using the following formula: ; in, The feature vector v3 represents the grid points.
[0105] This index indicates the direction in which the property field changes most gently within a local area. In sedimentary strata, it can correspond to the direction of channel sand body extension or the orientation of dominant seepage channels.
[0106] Based on the eigenvectors obtained from the gradient structure tensor decomposition, this application can accurately determine the quantified dominant azimuth of the formation to be evaluated, which helps to improve the accuracy of quantitative assessment of formation heterogeneity.
[0107] Figure 2 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 2 As shown, the electronic device may include a processor 210, a communications interface 220, a memory 230, and a communication bus 240, wherein the processor 210, communications interface 220, and memory 230 communicate with each other via the communication bus 240. The processor 210 can call logical instructions in the memory 230 to execute the following method: determining the attribute values of each grid point in a three-dimensional geological grid model constructed based on the strata to be evaluated; the attribute values are permeability or porosity. Based on the attribute values of each grid point, construct the gradient structure tensor of each grid point; For each grid point, eigenvalue decomposition is performed based on the gradient structure tensor to obtain eigenvalue information and eigenvectors. Based on the feature value information and feature vector of each grid point, the heterogeneity quantification index of the stratum to be evaluated is determined; the heterogeneity quantification index includes heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth.
[0108] Furthermore, the logical instructions in the aforementioned memory 230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0109] In another aspect, embodiments of this application also provide a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program is implemented to perform the methods provided in the above embodiments, such as: determining the attribute values of each grid point in a three-dimensional geological grid model constructed based on the strata to be evaluated; the attribute values being permeability or porosity. Based on the attribute values of each grid point, construct the gradient structure tensor of each grid point; For each grid point, eigenvalue decomposition is performed based on the gradient structure tensor to obtain eigenvalue information and eigenvectors. Based on the feature value information and feature vector of each grid point, the heterogeneity quantification index of the stratum to be evaluated is determined; the heterogeneity quantification index includes heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth.
[0110] In another aspect, embodiments of this application also provide a computer program product having a computer program stored thereon, which, when executed by a processor, implements the methods provided in the above embodiments, such as: determining the attribute values of each grid point in a three-dimensional geological grid model constructed based on the strata to be evaluated; the attribute values being permeability or porosity; Based on the attribute values of each grid point, construct the gradient structure tensor of each grid point; For each grid point, eigenvalue decomposition is performed based on the gradient structure tensor to obtain eigenvalue information and eigenvectors. Based on the feature value information and feature vector of each grid point, the heterogeneity quantification index of the stratum to be evaluated is determined; the heterogeneity quantification index includes heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth.
[0111] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable 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, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0113] Finally, it should be noted that the above embodiments are only used to illustrate this application and are not intended to limit this application. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that various combinations, modifications, or equivalent substitutions of the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application.
Claims
1. A method for quantitative assessment of formation heterogeneity, characterized in that, include: Determine the attribute values of each grid point in the three-dimensional geological grid model constructed based on the strata to be evaluated; The attribute value is permeability or porosity; Based on the attribute values of each grid point, construct the gradient structure tensor of each grid point; For each grid point, eigenvalue decomposition is performed based on the gradient structure tensor to obtain eigenvalue information and eigenvectors. Based on the feature value information and feature vector of each grid point, the heterogeneity quantification index of the stratum to be evaluated is determined; the heterogeneity quantification index includes heterogeneity intensity, anisotropy intensity, structural complexity and dominant azimuth.
2. The method for quantitative assessment of formation heterogeneity according to claim 1, characterized in that, The process of determining the heterogeneity quantification index of the stratum to be evaluated based on the feature value information and feature vector of each grid point includes: Based on the first feature value, second feature value, and third feature value in the feature value information of each grid point, the heterogeneity intensity of the formation to be evaluated is determined; wherein, the first feature value is greater than or equal to the second feature value, and the second feature value is greater than or equal to the third feature value; Based on the first and third feature values in the feature value information of each grid point, the anisotropy intensity of the stratum to be evaluated is determined. Based on the first feature value in the feature value information of each grid point, the structural complexity of the stratum to be evaluated is determined. Based on the feature vectors of each grid point, the dominant azimuth angle of the stratum to be evaluated is determined.
3. The method for quantitative assessment of formation heterogeneity according to claim 2, characterized in that, The determination of the heterogeneity intensity of the formation to be evaluated based on the first, second, and third feature values from the feature value information of each grid point includes: For each grid point, the sum of the first feature value, the second feature value, and the third feature value in the feature value information is determined respectively; The summation of the summation values of all the grid points is used to determine the heterogeneity intensity of the formation to be evaluated.
4. The method for quantitative assessment of formation heterogeneity according to claim 2, characterized in that, The determination of the anisotropy intensity of the formation to be evaluated based on the first and third feature values in the feature value information of each grid point includes: The first feature value in the feature value information of each grid point is summed to obtain the first summation result; The third feature value in the feature value information of each grid point is summed to obtain the second summation result; Based on the first summation result and the second summation result, the anisotropy intensity of the formation to be evaluated is determined.
5. The method for quantitative assessment of formation heterogeneity according to claim 2, characterized in that, The determination of the structural complexity of the stratum to be evaluated based on the first feature value in the feature value information of each of the grid points includes: Determine the mean of the first characteristic values of each grid point, and the standard deviation of the first characteristic values of each grid point; The structural complexity of the stratum to be evaluated is determined based on the mean of the first characteristic values of each grid point and the standard deviation of the first characteristic values of each grid point.
6. The method for quantitative assessment of formation heterogeneity according to claim 2, characterized in that, Determining the dominant azimuth angle of the stratum to be evaluated based on the feature vectors of each grid point includes: The feature vectors of each grid point are summed to obtain a composite vector. The azimuth angle of the synthesized vector is extracted to obtain the dominant azimuth angle of the stratum to be evaluated.
7. The method for quantitative assessment of formation heterogeneity according to claim 1, characterized in that, The step of constructing the gradient structure tensor of each grid point based on the attribute values of each grid point includes: The attribute values of each grid point are normalized to obtain normalized attribute values; Based on the normalized attribute values of each grid point, determine the gradient vector of each grid point; For each of the grid points, a gradient structure tensor is constructed based on the gradient vector.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for quantitative assessment of formation heterogeneity as described in any one of claims 1 to 7.
9. A storage medium, said storage medium being a non-transitory computer-readable storage medium, wherein a computer program is stored thereon, characterized in that, When executed by a processor, the computer program implements the method for quantitative assessment of formation heterogeneity as described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for quantitative assessment of formation heterogeneity as described in any one of claims 1 to 7.