Seismic attribute-based stratum step-by-step division method, system and equipment

Abstract

The invention belongs to the field of geological modeling, particularly relates to a stratum step-by-step division method, system and equipment based on seismic attributes, and aims to solve the problem of insufficient accuracy of a determined predicted stratum. The method comprises the following steps: performing three-dimensional coordinate transformation according to three-dimensional scatter coordinates of a stratum mark point in a seismic data volume to obtain the three-dimensional scatter coordinates of the transformed mark point; in response to the fact that the three-dimensional scatter coordinates of the transformed mark points and the horizon category division results of the stratum mark points are not determined to be credible, clustering the transformed mark points based on seismic attributes; based on a clustering result, for each horizon category, judging whether a condition that the same position on the XY plane has different scatter points exists or not; and if different scatter points at the same position on the XY plane do not exist, determining that the pinched stratum is effectively identified, and obtaining a step-by-step division result of the first stratum. According to the invention, the pinched stratum can be effectively identified, and the precision of the finally obtained geologic model is improved.

Description

Background Art

[0002] In the related art, a formation horizontal layered data set is established in advance, and the seismic attribute characteristics of the horizon categories in seismic data are learned by training a neural network, and then the structure and morphology of the formation are predicted. However, since the forward algorithm in this method requires designing a horizontal layered formation in advance and then adding information such as folds and faults, it is impossible to synthesize a large amount of three-dimensional pinch-out formation information. In other words, it is impossible to determine whether there is a pinch-out formation during the stage of designing the horizontal layered formation, that is, it is difficult to create pinch-out formation data for the meeting formation, resulting in the inability to accurately identify the pinch-out formation, and further resulting in insufficient accuracy of the predicted formation determined. Summary of the Invention

[0003] To solve the above problems in the prior art, that is, unable to accurately model the geological situation including pinch-out formations, the present invention provides a method for hierarchical division of formations based on seismic attributes, and the method includes:

[0004] Perform a three-dimensional coordinate transformation according to the three-dimensional scatter coordinates of the formation marker points in the seismic data volume to obtain the three-dimensional scatter coordinates of the transformed marker points;

[0005] In response to the three-dimensional scatter coordinates of the transformed marker points and the division result of the horizon category of its formation marker points being untrusted, cluster the transformed marker points based on seismic attributes, where the trustworthiness of the division result of the horizon category indicates that the average distance between data points within the horizon category is the smallest;

[0006] Based on the clustering result, for each horizon category, determine whether there is a situation where there are different scatter points at the same position on the XY plane;

[0007] If there are no different scatter points at the same position on the XY plane, it is determined that the pinch-out formation is effectively identified, and a first hierarchical division result of the formation is obtained, where the effective identification of the pinch-out formation indicates that the three-dimensional scatter coordinates of the transformed marker points and the division result of the horizon category of its formation marker points are trusted;

[0008] If there are different scatter points at the same position on the XY plane, according to the clustering result, traverse each clustering cluster in the clustering result with different scatter points at the same position on the XY plane, re-perform a three-dimensional coordinate transformation on the formation marker points corresponding to each clustering cluster, and obtain a first hierarchical division result of each clustering cluster through adjusted clustering parameters until there are no different scatter points at the same position on the XY plane in the second clustering cluster of the first hierarchical division results of all clustering clusters.

[0009] In some embodiments, after performing a three-dimensional coordinate transformation according to the three-dimensional scatter coordinates of the formation marker points to obtain the three-dimensional scatter coordinates of the transformed marker points, the method further includes:

[0010] Divide the seismic data volume containing three-dimensional scatter coordinates into multiple seismic data blocks;

[0011] If there are no different scatter points at the same position on the XY plane, it is determined that the pinch-out formation is effectively identified, and the first hierarchical division result of the formation is obtained, including:

[0012] For each seismic data block, obtain the second hierarchical division result of the formation respectively;

[0013] Based on the second hierarchical division result corresponding to each seismic data block, search for the set of repeated classification scatter points between different second hierarchical division results;

[0014] Based on the set of repeated classification scatter points, determine the splicing data points, where the splicing data points do not belong to the same set of repeated classification scatter points but have the same horizon category;

[0015] Based on the splicing data points, combine the second hierarchical division results of the multiple seismic data blocks to obtain the first hierarchical division result of the formation.

[0016] In some embodiments, the three-dimensional scatter coordinates of the formation marker points in the seismic data volume are determined by the following method:

[0017] Calculate the instantaneous phase attribute in the seismic data volume, and search for the jump points of the instantaneous phase attribute to determine the time of the jump points;

[0018] Repeatedly calculate the time of the jump points in the seismic data volume corresponding to each seismic trace, where the planar coordinates of the seismic trace have been determined;

[0019] Based on the planar coordinates of the seismic trace and the time of the jump points, determine the three-dimensional coordinates of the jump points, and determine the jump points as the marker points to obtain the three-dimensional scatter coordinates of the formation marker points.

[0020] In some embodiments, the three-dimensional coordinate transformation is performed by the PCA method.

[0021] In some embodiments, the transformed marker points are clustered by the DBSCAN algorithm.

[0022] In some embodiments, the determination of whether there are different scatter points at the same position on the XY plane includes:

[0023] Based on the horizon category vector of the data points in the clustering result, determine the number of data points belonging to the same horizon category on the same seismic trace;

[0024] If the number of data points belonging to the same horizon category on the same seismic trace is less than or equal to the set number, it is determined that there are different scatter points at the same position on the XY plane.

[0025] On the other hand, the present invention proposes a hierarchical formation division system based on seismic attributes.

[0026] A three-dimensional transformation module for performing three-dimensional coordinate transformation according to the three-dimensional scatter coordinates of formation marker points in a seismic data volume to obtain the three-dimensional scatter coordinates of the transformed marker points.

[0027] A clustering module for clustering the transformed marker points based on seismic attributes in response to the confidence of the division result of the three-dimensional scatter coordinates of the transformed marker points and the horizon category of their formation marker points being undetermined, where the confidence of the division result of the horizon category indicates that the average distance between data points within the horizon category is the smallest.

[0028] A judgment module for judging, based on the clustering result, for each horizon category, whether there are different scatter points at the same position on the XY plane.

[0029] If there are no different scatter points at the same position on the XY plane, it is determined that the pinched-out formation is effectively identified, and a first hierarchical formation division result is obtained, where the effective identification of the pinched-out formation indicates that the confidence of the division result of the three-dimensional scatter coordinates of the transformed marker points and the horizon category of their formation marker points is determined.

[0030] If there are different scatter points at the same position on the XY plane, according to the clustering result, traverse each clustering cluster in the clustering result with different scatter points at the same position on the XY plane, re-perform three-dimensional coordinate transformation on the formation marker points corresponding to each clustering cluster, and obtain the first hierarchical formation division result of each clustering cluster through the adjusted clustering parameters until there are no different scatter points at the same position on the XY plane in the second clustering cluster of the first hierarchical formation division results of all clustering clusters.

[0031] On the third aspect of the present invention, an electronic device is proposed, including:

[0032] At least one processor; and

[0033] A memory communicatively connected to at least one of the processors; where

[0034] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned hierarchical formation division method based on seismic attributes.

[0035] In a fourth aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by a computer to implement the above-mentioned method for hierarchical stratigraphic division based on seismic attributes.

[0036] Advantages of the present invention:

[0037] In this application, by checking whether there are different scatter points at the same position on the XY plane in each horizon category in the clustering result, it is determined whether the current clustering parameters can effectively identify the pinch-out strata. When there are no different scatter points at the same position on the XY plane in the same horizon category, it indicates that there is no situation where two horizons are misjudged as one horizon. When there is no misjudgment in all horizons, it means that the pinch-out strata have been effectively identified. By this method, the accuracy of the finally obtained geological model can be improved. Description of the Drawings

[0038] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives, and advantages of this application will become more apparent:

[0039] Figure 1 is a flowchart of the method for hierarchical stratigraphic division based on seismic attributes of this application.

[0040] Figure 2 is a schematic diagram of the effect of the seismic data volume in the embodiment of this application.

[0041] Figure 3 is a schematic diagram of the effect of the instantaneous phase attribute in the seismic data volume in the embodiment of this application.

[0042] Figure 4 is a perspective view of the three-dimensional coordinates of the jump points in the embodiment of this application.

[0043] Figure 5 is a schematic diagram of the three-dimensional scatter coordinates of the transformed marker points in the embodiment of this application.

[0044] Figure 6 is a schematic diagram of the clustering effect in the case of different scatter points at the same position on the XY plane in the embodiment of this application.

[0045] Figure 7 is a schematic diagram of the clustering effect in the case of no different scatter points at the same position on the XY plane in the embodiment of this application.

[0046] Figure 8 is a schematic diagram of the principle of dividing the complete seismic data volume into multiple seismic data blocks in the embodiment of this application.

[0047] Figure 9It is a schematic diagram of the principle for splicing geological models of seismic data blocks in the embodiments of the present application. Specific embodiments

[0048] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant invention and are not intended to limit the invention. Additionally, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.

[0049] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0050] Existing formation division methods can be roughly divided into three categories: (1) Image-based formation tracking method. Treat the seismic profile as a series of two-dimensional images for processing. For example, use seismic profile image edge detection methods, combine neural networks with image classification techniques, and use CNN deep convolutional neural networks to achieve synchronous automatic multi-layer tracking, and manually mark the horizon on the picture, train the neural network to learn the characteristics of seismic data, and then predict the formation. (2) Horizon tracking method based on seismic attributes. For example, use seismic phase attributes to track horizons. (3) Horizon tracing method based on seismic coherence. This method can automatically track the in-phase axis based on the similar amplitudes of adjacent channels within the same horizon. In this method, some layer points are manually selected as constraint points, and then the complete layer is obtained through interpolation.

[0051] For the above three methods, it is necessary to assume that there is no formation pinch-out at the initial stage of seismic analysis, or it is necessary to know the number and approximate location of the pinch-out formations in order to obtain the accurate situation of the pinch-out formations.

[0052] In view of this, a method is needed to accurately confirm a geological model containing pinch-out formations when it is unknown whether there are pinch-out formations and the location information of the pinch-out formations.

[0053] To more clearly illustrate the formation step-by-step division method based on seismic attributes of the present invention, the following combines Figure 1 Details of each step in the embodiments of the present invention are elaborated.

[0054] The formation step-by-step division method based on seismic attributes in the first embodiment of the present invention includes steps S10 - S30, and each step is described in detail as follows:

[0055] Step S10, perform three-dimensional coordinate transformation according to the three-dimensional scatter coordinates of formation marker points in the seismic data volume to obtain the three-dimensional scatter coordinates of the transformed marker points; for example, the three-dimensional scatter coordinates of the transformed marker points are as Figure 5 shown.

[0056] Step S20: In response to the three-dimensional scatter coordinates of the transformed fiducial points and the stratigraphic fiducial point horizon classification result being determined to be credible, clustering the transformed fiducial points based on seismic attributes, where the credible stratigraphic horizon classification result indicates that the average distance between data points within the stratigraphic horizon is minimized;

[0057] Step S30: Based on the clustering result, for each stratigraphic horizon, determine whether there are different scatter points at the same position on the XY plane;

[0058] In the embodiments of the present disclosure, the same position on the XY plane for each stratigraphic horizon represents belonging to the same seismic trace. In the same seismic trace, there should be only 1 data point for each stratigraphic horizon. Considering measurement errors, when the number of data points at the same position is less than the set number, it is considered that there are no different scatter points at the same position, that is, it indicates that the stratigraphic horizon has been effectively divided, and two or more stratigraphic horizons have not been misjudged as the same stratigraphic horizon.

[0059] If there are no different scatter points at the same position on the XY plane, it is determined that the pinch-out formation is effectively identified, and a first hierarchical stratigraphic division result is obtained, where the effective identification of the pinch-out formation indicates that the three-dimensional scatter coordinates of the transformed fiducial points and the stratigraphic fiducial point horizon classification result are credible; the clustering effect in the case of no different scatter points at the same position on the XY plane is as Figure 7 shown.

[0060] If there are different scatter points at the same position on the XY plane, according to the clustering result, traverse each clustering cluster in the clustering result with different scatter points at the same position on the XY plane, re-perform three-dimensional coordinate transformation on the stratigraphic fiducial points corresponding to each clustering cluster, and obtain the first hierarchical stratigraphic division result of each clustering cluster through the adjusted clustering parameters until there are no different scatter points at the same position on the XY plane in the second clustering cluster of the first hierarchical stratigraphic division results of all clustering clusters. The clustering effect in the case of different scatter points at the same position on the XY plane is as Figure 6 shown.

[0061] Through the above embodiments, by determining whether there are cases where different scatter points exist at the same position on the XY plane in the clustering results of the three-dimensional scatter points in the seismic data volume, the effectiveness of the setting of the clustering parameters is further determined, thereby ensuring that the data points of the thinned strata can be accurately distinguished. Different scatter point clusters represent different strata. Therefore, when there are no cases where different scatter points exist at the same position in the hierarchical division results of the strata in the same cluster, it indicates that the division results of this cluster have been able to accurately distinguish different horizon categories. When all the stratum clusters have been accurately distinguished, it indicates that in the stratum model obtained through the solution described in this embodiment, there is no longer a situation where multiple strata are misjudged as one horizon type, and it is considered that all the thinned strata have been distinguished.

[0062] In the embodiments of the present disclosure, by dividing the complete seismic data volume into multiple seismic data blocks and performing separate clustering and analysis on each seismic data block, the efficiency can be improved for each. Dividing the complete seismic data volume into multiple seismic data blocks can, for example, be for improving the efficiency of stratum model construction, dividing the complete seismic data volume into multiple seismic data blocks to achieve parallel computing. It can also be for reducing the computing load, dividing the large-volume complete stratum data volume into multiple small-volume seismic data blocks to achieve individual computing. The principle of dividing into multiple seismic data blocks is as Figure 8 shown. The divided seismic data blocks can be jointly subjected to three-dimensional transformation. Since the complete data volume contains a large amount of complex geological information and seismic data is a comprehensive reflection of various geological information, due to geological differences, the seismic information contained in different blocks after segmentation will also vary. Therefore, this solution allows different clustering parameters to be adopted in each seismic data volume to adapt to different geological phenomena. However, the geological models of the finally obtained seismic data blocks can be completed for splicing. The effect of splicing the geological models is as Figure 9 shown.

[0063] In an exemplary embodiment, after performing three-dimensional coordinate transformation based on the three-dimensional scatter point coordinates of the stratum marker points to obtain the three-dimensional scatter point coordinates of the transformed marker points, the method further includes:

[0064] Dividing the seismic data volume containing the three-dimensional scatter point coordinates into multiple seismic data blocks;

[0065] The three-dimensional scatter point coordinates of the data points in the seismic data volume are z ij , where i represents the scatter point serial number and j represents the dimension serial number.

[0066] Represent the seismic data volume as a set of three-dimensional scatter point coordinates {z ij , i = 1, 2, 3…shu; j = 1, 2, 3}, where shu represents the total number of scatter points.

[0067] Taking the example of evenly dividing a seismic data volume into 4 seismic data blocks, the scatter point coordinate sets of the multiple obtained seismic data blocks include:

[0068] Scatter point coordinate set of block 1: {z ij , z i1 ≤n / 2, z i2 ≤n / 2}

[0069] Scatter point coordinate set of block 2: {z ij , z i1 ≤n / 2, z i2 ≥n / 2}

[0070] Scatter point coordinate set of block 3: {z ij , z i1 ≥n / 2, z i2 ≤n / 2}

[0071] Scatter point coordinate set of block 4: {z ij , z i1 ≥n / 2, z i2 ≥n / 2}

[0072] If there are no different scatter points at the same position on the XY plane, it is determined that the pinch-out formation is effectively identified, and the first formation hierarchical division result is obtained, including:

[0073] For each seismic data block, the second formation hierarchical division result is obtained respectively;

[0074] Based on the second formation hierarchical division results corresponding to each seismic data block, search for the set of repeated classification scatter points between different second formation hierarchical division results;

[0075] Based on the set of repeated classification scatter points, determine the splicing data points, where the splicing data points do not belong to the same set of repeated classification scatter points but have the same horizon category;

[0076] Based on the splicing data points, combine the second formation hierarchical division results of the multiple seismic data blocks to obtain the first formation hierarchical division result.

[0077] It can be understood that when performing formation analysis based on directly collected seismic data, since the seismic data volume is not sensitive to the formation interface, it is easy to cause the situation of ignoring the formation interface, that is, the pinch-out formation cannot be found. Therefore, this solution is based on the formation phase attribute for analysis.

[0078] Determine the three-dimensional scatter point coordinates of the formation marker points in the seismic data volume through the following method:

[0079] By calculating the instantaneous phase attribute in the seismic data volume and searching for jump points in the instantaneous phase attribute, the time of the jump points is determined; the jump points are used to mark the seismic reflection isochrones, and the seismic reflection isochrones reflect the distribution of the strata;

[0080] Among them, the method for confirming the instantaneous phase attribute includes:

[0081] The three-dimensional seismic data volume includes n×n traces of seismic waveform data, and each trace of seismic waveform data is an m×1 vector. Among them, the effect of the three-dimensional seismic data volume is as Figure 2 shown. For each trace of seismic waveform data seis(t), perform the Hilbert transform pair:

[0082]

[0083] where, * represents convolution, h(t) represents the Hilbert transform result, t represents time, τ represents an instantaneous time point.

[0084] Construct the analytic signal JX(t) of each trace of seismic waveform data seis(t) as:

[0085]

[0086] And:

[0087] JX(t) = AMP(t)e jθ(t) ;

[0088] The instantaneous amplitude AMP(t) is:

[0089]

[0090] The instantaneous phase θ(t) is:

[0091]

[0092] Among them, the instantaneous phase is discrete data, and the jump points TB(t) are searched for the phase attribute data through the difference algorithm:

[0093] CF(t) = θ(t) - θ(t - 1);

[0094]

[0095] CF(t) represents the phase attribute. The effect of the obtained seismic phase attribute is as Figure 3 shown.

[0096] Recalculate the jump point time in the seismic data volume corresponding to each seismic trace, where the planar coordinates of the seismic trace have been determined; the effect of the three-dimensional coordinates of the jump point obtained based on the seismic phase attribute is as Figure 4 shown.

[0097] Based on the planar coordinates of the seismic trace and the jump point time, determine the three-dimensional coordinates of the jump point, determine the jump point as a marker point, and obtain the three-dimensional scatter coordinates of the formation marker point.

[0098] Through the planar coordinates (X, Y) of the seismic trace, the three-dimensional coordinates (X, Y, TB(t)) of the jump point on the corresponding seismic trace can be determined, where X = 1, 2, 3, …, n and Y = 1, 2, 3, …, n.

[0099] Through the above embodiments, since all formation phase attributes are the sum reaction of multiple geological information, the PCA method can be used to unsupervisedly re-transform the coordinate system according to the input data, making the differences between data points belonging to different horizon types more obvious, so as to improve the accuracy of distinguishing different horizon types through the clustering algorithm.

[0100] In an exemplary embodiment, the three-dimensional coordinate transformation is performed by the PCA method.

[0101] Normalize the seismic data volume to obtain the normalized seismic data volume:

[0102]

[0103] BZ j represents the value of the j-th dimension of the scatter points in the normalized seismic data volume, and shu represents the total number of all scatter points.

[0104] Use Z to represent the matrix composed of the scatter points z ij in the seismic data volume, and calculate the eigenvalues λ and eigenvectors ξ of the 3×3 matrix of ZZ T ZZ

[0105] ZZ T ξ = λξ;

[0106] λ represents the eigenvalue matrix composed of the three eigenvalues along the diagonal, and ξ represents the eigenvector matrix composed of the arrangement of the three eigenvectors.

[0107] λ 1 ≥ λ 2 ≥ λ 3 represent the eigenvalues arranged from large to small, and ξ 1 is the eigenvector corresponding to λ 1 , ξ 2 is the eigenvector corresponding to λ 2 , ξ 3is the eigenvector corresponding to λ 3 The corresponding eigenvector.

[0108] For each scatter point coordinate z i Perform coordinate projection;

[0109] B ik = z i · ξ k ;

[0110] B ik represents the coordinate of the k-th dimension after the coordinate transformation of the i-th scatter point, z i represents the coordinate of the i-th scatter point in the original coordinate system, ξ k represents the k-th eigenvector.

[0111] In the embodiments of the present disclosure, compared with the method of clustering seismic data volumes to distinguish different strata in the related art, this solution clusters the data points that can clearly distinguish different horizon types after the PCA transformation of the formation interface marker points, which can improve the accuracy of distinguishing different horizon types.

[0112] In one exemplary embodiment, the transformed marker points are clustered by the DBSCAN algorithm.

[0113] Initialize the neighborhood ∈ = 0.1, MinPts = 10 of the DBSCAN algorithm, and initialize the core object set Initialize the number of clustering clusters k = 0, initialize the unvisited sample set Γ = D, and the cluster partition

[0114] Or execute the subsequent DBSCAN algorithm with the adjusted clustering parameters.

[0115] Input the transformed marker points B = {B k＝1 , B k＝2 , …, B k＝shu}, and execute with the current clustering parameters:

[0116] Find the core object for each marker point:

[0117] For each transformed marker point B k , find the corresponding neighborhood ∈ subsample set N ∈ (B k ):

[0118] N ∈ (B k ) = {B i ∈ D | distance(B i , B j ) ≤ ∈};

[0119] If the number of samples in the sub - sample set satisfies |N ∈ (B k )|≥MinPts, add the sample B k to the core object sample set: Ω = Ω ∪ {B k};

[0120] If the core object set then the DBSCAN algorithm ends; otherwise, randomly select a core object B k from the core object set Ω, initialize the current cluster core object queue Ω cur ={B k}, initialize the class serial number p = p + 1, initialize the current cluster sample set C p ={B k}, update the unvisited sample set Γ = Γ - {B k}.

[0121] If the current cluster core object queue then the current clustering cluster C p is generated, update the cluster partition C = {C 1 , C 2 ,…, C p}, update the core object set Ω = Ω - C p , and go to step (5). Otherwise, update the core object set Ω = Ω - C p .

[0122] Take out a core object o′ from the current cluster core object queue Ω cur , find all the ∈ - neighborhood sub - sample sets N ∈ (B k ′) through the neighborhood distance threshold ∈, let Δ = N ∈ (B k ′) ∩ Γ, update the current cluster sample set C p = C p ∪Δ, update the unvisited sample set Γ = Γ - Δ, update Ω cur = Ω cur ∪(Δ ∩ Ω)-B k ′.

[0123] Repeat the above steps. After obtaining the cluster partition C = {C 1 , C 2 ,…, C p}, obtain the class L k of the current scatter point B k , L k = C H , where B k ∈ C H .

[0124] Output layer position category vector: L.

[0125] If the process of further subdivision has just started, at this time b = 1, min(C 1 , C 2 , …, C p ) = 1; C = {C 1 , C 2 , Ω, C p} is the cluster partition

[0126] If b > 1, in the new cluster partition C_new = {C 1 , C 2 , …, C p}, min(C 1 , C 2 , …, C p ) = max(L_old) + 1; L is the category vector corresponding to the scatter points.

[0127] In the embodiments of the present disclosure, the seismic data points cannot be partitioned. This solution transforms the original seismic data into scatter points with the significance of formation boundary markers and then performs clustering partitioning. Compared with the method of first designing a formation through a convolutional network and then learning the specific geological manifestations of the formation, there is no need to pre-judge the existence and specific location of the pinch-out formation, and only all layer position categories need to be distinguished.

[0128] Since it is difficult for the clustering algorithm to obtain an accurate formation at one time, even with the processing method of this solution, it is still possible to cluster two layers into one layer. By repeatedly adjusting the clustering parameters of this layer and clustering again, different layer position categories can be accurately divided into multiple layers. This solution judges whether there is a situation of mis-clustering into one layer by the number of data points belonging to the same layer position category on the seismic trace.

[0129] The coordinates of the jump point z i (z i1 = X, z i2 = Y, z i3 = TB(t)), after coordinate transformation, it is B i (B i1 , B i2 , B i3 ), and the corresponding category is L i = C H , where B i ∈ C H .

[0130]

[0131]

[0132] In the formula, QH is the number of scatter points on the same seismic trace in the H-th category.

[0133] In an exemplary embodiment, the determination of whether there are different scatter points at the same position on the XY plane includes:

[0134] Based on the horizon category vectors of the data points in the clustering result, determine the number of data points belonging to the same horizon category on the same seismic trace;

[0135] If the number of data points belonging to the same horizon category on the same seismic trace is less than or equal to the set number, it is determined that there are different scatter points at the same position on the XY plane.

[0136] If the number of scatter points belonging to the same seismic trace in the same category of scatter points is greater than 10, continue to subdivide.

[0137] If the number of scatter points belonging to the same seismic trace in the same category of scatter points is less than or equal to 10, it is considered that there is no need to continue subdividing, and output the category vector of the current seismic data volume or seismic data block.

[0138] Although the above steps are described in the above order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this embodiment, different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are all within the protection scope of the present invention.

[0139] The stratigraphic hierarchical division system based on seismic attributes according to the second embodiment of the present invention includes:

[0140] A three-dimensional transformation module for performing three-dimensional coordinate transformation according to the three-dimensional scatter coordinates of the stratigraphic marker points in the seismic data volume to obtain the three-dimensional scatter coordinates of the transformed marker points;

[0141] A clustering module for clustering the transformed marker points based on seismic attributes in response to the untrusted determination of the three-dimensional scatter coordinates of the transformed marker points and the stratigraphic category division results of their stratigraphic marker points, wherein the trustworthiness of the stratigraphic category division results indicates that the average distance between the data points within the stratigraphic category is the smallest;

[0142] A judgment module for judging, based on the clustering result, for each stratigraphic category, whether there are different scatter points at the same position on the XY plane;

[0143] If there are no different scatter points at the same position on the XY plane, it is determined that the pinch-out formation is effectively identified, and a first stratigraphic hierarchical division result is obtained, wherein the effective identification of the pinch-out formation indicates that the three-dimensional scatter coordinates of the transformed marker points and the stratigraphic category division results of their stratigraphic marker points are trusted;

[0144] If there are different scatter points at the same position on the XY plane, according to the clustering result, traverse each clustering cluster in the clustering result having different scatter points at the same position on the XY plane, re-perform three-dimensional coordinate transformation on the formation marker points corresponding to each clustering cluster, and obtain the first hierarchical formation division result of each clustering cluster through the adjusted clustering parameters until there are no different scatter points at the same position on the XY plane in the second clustering cluster of the first hierarchical formation division results of all clustering clusters.

[0145] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related descriptions of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0146] It should be noted that the above-described hierarchical formation division system based on seismic attributes provided in the above embodiments is only illustrated by dividing the above functional modules. In practical applications, the above functions can be allocated to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. For the names of the modules and steps involved in the embodiments of the present invention, they are only used to distinguish each module or step, and are not regarded as an improper limitation of the present invention.

[0147] An electronic device according to a third embodiment of the present invention includes:

[0148] At least one processor; and

[0149] A memory communicatively connected to at least one of the processors; wherein,

[0150] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-described hierarchical formation division method based on seismic attributes.

[0151] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-described hierarchical formation division method based on seismic attributes.

[0152] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related descriptions of the above-described storage device and processing device can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field. To clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0154] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or represent a specific order or sequence.

[0155] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, so that a process, method, article, or device / equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in these processes, methods, articles, or devices / equipment.

[0156] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for layer division based on seismic attributes, characterized in that: The method comprises: According to the three-dimensional scattered point coordinates of the stratigraphic marker points in the seismic data body, a three-dimensional coordinate transformation is performed to obtain the three-dimensional scattered point coordinates of the marker points after the transformation; In response to the three-dimensional scattered point coordinates of the transformed marker points and the stratum classification results of the stratigraphic marker points not being determined to be credible, clustering the transformed marker points based on seismic attributes, wherein the credible stratum classification result indicates that the average distance of each data point in the stratum category is the smallest; Based on the clustering results, for each layer category, determine whether there are different scattered points at the same position on the XY plane; If there are no different scattered points at the same position on the XY plane, it is determined that the pinch-out stratum is effectively identified, and a step-by-step division result of the first stratum is obtained, wherein the pinch-out stratum is effectively identified, indicating that the three-dimensional scattered point coordinates of the transformed marker point and the stratum category division result of the stratum marker point are credible; If there are different scattered points at the same position on the XY plane, according to the clustering result, each cluster cluster in each clustering result with different scattered points at the same position on the XY plane is traversed, and the stratigraphic landmark points corresponding to each cluster cluster are re-transformed into three-dimensional coordinates, and the first stratigraphic level-by-level division result of each cluster cluster is obtained through the adjusted clustering parameters until the second cluster clusters of the first stratigraphic level-by-level division results of all cluster clusters do not have different scattered points at the same position on the XY plane.

2. The method for layer division based on seismic attributes according to claim 1, characterized in that: After performing three-dimensional coordinate transformation according to the three-dimensional scattered point coordinates of the stratum marker points to obtain the transformed three-dimensional scattered point coordinates of the marker points, the method further includes: Dividing a seismic data volume containing three-dimensional scattered point coordinates into a plurality of seismic data blocks; If there are no different scattered points at the same position on the XY plane, it is determined that the pinch-out stratum is effectively identified, and the step-by-step division result of the first stratum is obtained, including: For each seismic data block, a step-by-step division result of the second stratum is obtained respectively; Based on the second stratum level-by-level division result corresponding to each seismic data block, searching for repeated classification scattered point sets between different second stratum level-by-level division results; Determining a spliced ​​data point based on the repeated classification scattered point set, wherein the spliced ​​data point does not belong to the same repeated classification scattered point set but has the same layer category; Based on the spliced ​​data points, the second stratum level-by-level division results of the plurality of seismic data blocks are combined to obtain the first stratum level-by-level division results.

3. The method for layer division based on seismic attributes according to claim 1 or 2, characterized in that: The three-dimensional scattered coordinates of the stratigraphic markers in the seismic data volume are determined as follows: By calculating the instantaneous phase attribute in the seismic data volume and searching for a jump point for the instantaneous phase attribute, the time of the jump point is determined; Repeatedly calculating the time of the jump point in the seismic data volume corresponding to each seismic trace, wherein the plane coordinates of the seismic trace have been determined; Based on the plane coordinates of the seismic trace and the time of the jump point, the three-dimensional coordinates of the jump point are determined, the jump point is determined as a marker point, and the three-dimensional scattered point coordinates of the stratum marker point are obtained.

4. The method for layer division based on seismic attributes according to claim 1, characterized in that: The three-dimensional coordinate transformation is performed by the PCA method.

5. The method for layer division based on seismic attributes according to claim 1, characterized in that: The transformed landmarks are clustered using the DBSCAN algorithm.

6. The method for layer division based on seismic attributes according to claim 1, characterized in that: The determining whether there are different scattered points at the same position on the XY plane includes: Based on the horizon category vectors of the data points in the clustering results, the number of data points belonging to the same horizon category on the same seismic trace is determined; If the number of data points belonging to the same layer category on the same seismic trace is less than or equal to the set number, it is determined that there are different scattered points at the same position on the XY plane.

7. A system for layer division based on seismic attributes, characterized in that: include: A three-dimensional transformation module is used to perform three-dimensional coordinate transformation according to the three-dimensional scattered point coordinates of the stratum marker points in the seismic data volume to obtain the three-dimensional scattered point coordinates of the marker points after transformation; A clustering module, for clustering the transformed marker points based on seismic attributes in response to the three-dimensional scattered point coordinates of the transformed marker points and the stratum marker points thereof being not determined to be credible, wherein the credible stratum marker points classification result indicates that the average distance of each data point in the stratum category is the smallest; A judgment module is used to judge whether there are different scattered points at the same position on the XY plane for each layer category based on the clustering results; If there are no different scattered points at the same position on the XY plane, it is determined that the pinch-out stratum is effectively identified, and a step-by-step division result of the first stratum is obtained, wherein the pinch-out stratum is effectively identified, indicating that the three-dimensional scattered point coordinates of the transformed marker point and the stratum category division result of the stratum marker point are credible; If there are different scattered points at the same position on the XY plane, according to the clustering result, each cluster cluster in each clustering result with different scattered points at the same position on the XY plane is traversed, and the stratigraphic landmark points corresponding to each cluster cluster are re-transformed into three-dimensional coordinates, and the first stratigraphic level-by-level division result of each cluster cluster is obtained through the adjusted clustering parameters until the second cluster clusters of the first stratigraphic level-by-level division results of all cluster clusters do not have different scattered points at the same position on the XY plane.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method for hierarchical division of strata based on seismic attributes as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the method for hierarchical division of strata based on seismic attributes as described in any one of claims 1-6.

10. A computer program product, characterized in that The computer program product is used to enable an electronic device to execute the method for hierarchical division of strata based on seismic attributes as described in any one of claims 1 to 6.

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