Seismic attribute determination method and device based on depth domain data volume

Abstract

The invention relates to the technical field of seismic exploration data processing, in particular to a seismic attribute determination method and device based on a depth domain data volume, and the method comprises the steps: obtaining a depth domain imaging data volume after the energy balance processing and the signal-to-noise ratio improvement processing of a work area are completed; if the depth domain imaging data volume is irregular, trace header information is extracted from the depth domain imaging data volume, and regularization processing is carried out on the depth domain imaging data volume according to the trace header information; constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume, and obtaining a feature value and a feature vector corresponding to each sampling point position according to the covariance matrix; and determining a depth domain seismic attribute data volume according to the feature values and the feature vectors. The problem that the precision of subsequent seismic attribute analysis and oil and gas reservoir prediction cannot be further improved due to the fact that seismic attribute parameters can only be obtained in a time domain and cannot be directly obtained in a depth domain in the past is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration data processing, and in particular to a method and device for determining seismic attributes based on a depth domain data volume. Background Art

[0002] Seismic attribute analysis technology is one of the core technologies for oil and gas reservoir exploration and development. Its main functions are lithology, lithofacies, reservoir parameters and oil and gas prediction. The seismic data body contains rich underground geological information. Different seismic attribute combinations may have a great correlation with certain geological parameters. Therefore, reservoir prediction can be effectively carried out using seismic attribute parameters. Commonly used seismic attributes include instantaneous parameters, amplitude statistics parameters, spectrum energy spectrum statistics, correlation statistics, sequence statistics, etc. On the basis of tracking closure within the sequence interface, many new technologies such as seismic attribute analysis technology, reservoir inversion technology, coherence volume slicing technology, etc. are comprehensively applied to analysis and demonstration, which can predict favorable zones and support oil and gas reservoir exploration and development.

[0003] At present, all kinds of seismic attributes are carried out in the time domain. According to the research of domestic and foreign literature and patents, there is no relevant technology for seismic attributes in the depth domain. At the same time, depth domain imaging is the mainstream technology for supporting exploration and development of oil and gas geophysical prospecting in the future. It can directly realize the analysis of seismic attributes and accurate prediction of oil and gas reservoirs in the true depth domain, which will definitely play an important role. Summary of the invention

[0004] The present invention proposes a method and device for determining seismic attributes based on depth domain data volume to solve the problem that seismic attribute parameters can only be obtained in the time domain but not directly in the depth domain, resulting in the inability to further improve the accuracy of subsequent seismic attribute analysis and oil and gas reservoir prediction.

[0005] According to one aspect of the present invention, a method for determining seismic attributes based on a depth domain data volume is provided, comprising:

[0006] Acquire the depth domain imaging data volume after energy balance processing and signal-to-noise ratio improvement processing of the work area;

[0007] If the depth domain imaging data volume is irregular, extracting trace header information from the depth domain imaging data volume, and performing regularization processing on the depth domain imaging data volume according to the trace header information;

[0008] Constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume respectively, and obtaining an eigenvalue and an eigenvector corresponding to each sampling point position according to the covariance matrix;

[0009] A depth-domain seismic attribute data volume is determined according to the eigenvalues ​​and eigenvectors.

[0010] Preferably, the energy balancing process at least includes: dynamic balancing process or local energy adjustment or amplitude energy lighting compensation.

[0011] Preferably, the processing for improving the signal-to-noise ratio at least includes: removing outliers in the data volume or suppressing random seismic noise.

[0012] Preferably, if it is a three-dimensional depth domain imaging data volume, the trace header information includes: the maximum main survey line number, the minimum main survey line number, the maximum contact survey line number and the minimum contact survey line number of the post-stack data volume;

[0013] If it is a two-dimensional depth domain imaging data volume, the trace header information includes: the starting seismic trace number and the ending seismic trace number of the post-stack data volume.

[0014] Preferably, the regularized processing method includes:

[0015] If it is a three-dimensional depth domain imaging data volume, then each sampling value of the empty channel outside the depth domain imaging data volume and within the range of the maximum main survey line number, the minimum main survey line number, the maximum contact survey line number, and the minimum contact survey line number in the three-dimensional situation is filled with zero;

[0016] If it is a two-dimensional depth domain imaging data volume, each sampling value of the empty channel outside the depth domain imaging data volume and between the starting seismic channel number and the ending seismic channel number in the two-dimensional case is filled with zero.

[0017] Preferably, the method of constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume after regularization processing comprises:

[0018] If it is a three-dimensional depth domain imaging data volume, the covariance matrix is ​​constructed using formula (1);

[0019]

[0020] Where U is the three-dimensional depth domain imaging data volume, and x, y, and z represent the three dimensions of the three-dimensional data volume in the spatial direction.

[0021] Preferably, the method of constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume after regularization processing comprises:

[0022] If it is a two-dimensional depth domain imaging data volume, the covariance matrix is ​​constructed using formula (2);

[0023]

[0024] Where U is the two-dimensional depth domain imaging data volume, and x and z represent the two dimensions of the two-dimensional data volume in the spatial direction.

[0025] Preferably, the method for determining the depth domain seismic attribute data volume according to the eigenvalues ​​and eigenvectors comprises:

[0026] If it is a three-dimensional depth domain imaging data volume, each sampling point corresponds to three eigenvalues ​​and three eigenvectors, where the three eigenvalues ​​include: λ1, λ2 and λ3, and λ1>λ2>λ3;

[0027] Among them, the eigenvector azimuth corresponding to the maximum eigenvalue λ1 is the dip angle θ of the formation, and the plane angles of the two eigenvectors corresponding to the eigenvalues ​​λ2 and λ3 are the azimuths of the dip angle of the formation. λ1 is the three-dimensional stratigraphic structure attribute in the depth domain, λ2 is the three-dimensional stratigraphic heterogeneity attribute in the depth domain, λ3 is the three-dimensional stratigraphic heterogeneity secondary attribute in the depth domain, and f(λ1,λ2,λ3) is the eigenvalue combination attribute;

[0028] Each attribute constitutes its three-dimensional depth-domain seismic attribute data volume.

[0029] Preferably, the method for determining the depth domain seismic attribute data volume according to the eigenvalues ​​and eigenvectors comprises:

[0030] If it is a two-dimensional depth domain imaging data volume, then each sampling point corresponds to two eigenvalues ​​and two eigenvectors, where the two eigenvalues ​​include: λ1 and λ2, and λ1>λ2;

[0031] Among them, the eigenvector azimuth corresponding to the maximum eigenvalue λ1 is the dip angle θ of the formation, and the eigenvector corresponding to the eigenvalue λ2 is the azimuth of the dip angle of the formation λ1 is the two-dimensional stratigraphic structure attribute in the depth domain, λ2 is the two-dimensional stratigraphic heterogeneity attribute in the depth domain, and f(λ1,λ2) is the eigenvalue combination attribute;

[0032] Each attribute constitutes its two-dimensional depth-domain seismic attribute data volume.

[0033] According to one aspect of the present invention, there is provided a seismic attribute determination device based on a depth domain data volume, comprising:

[0034] An acquisition unit is used to acquire a depth domain imaging data volume after energy balance processing and signal-to-noise ratio improvement processing have been completed in the work area;

[0035] a regularization processing unit, configured to extract trace header information from the depth domain imaging data volume if the depth domain imaging data volume is irregular, and perform regularization processing on the depth domain imaging data volume according to the trace header information;

[0036] A covariance matrix construction unit, used to construct a local covariance matrix for each sampling point position of the depth domain imaging data volume, and obtain an eigenvalue and an eigenvector corresponding to each sampling point position according to the covariance matrix;

[0037] The depth domain seismic attribute data volume determining unit is used to determine the depth domain seismic attribute data volume according to the eigenvalue and the eigenvector.

[0038] The present invention has at least the following beneficial effects:

[0039] The present invention proposes a method and device for determining seismic attributes based on depth domain data volumes. The covariance matrix of a small window is constructed through the depth domain data volume to obtain the corresponding eigenvalues ​​and their eigenvectors, and then the depth domain formation dip, azimuth, eigenvalue combination attributes, formation structure attributes and formation heterogeneity attributes are obtained, providing important data guarantee for depth domain seismic reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to explain the technical solutions of the present invention.

[0041] Figure 1 A flowchart showing a method for determining seismic attributes based on a depth domain data volume according to an embodiment of the present invention is shown;

[0042] Figure 2 A diagram showing the depth domain imaging result of the Marmousi theoretical model reverse time migration according to an embodiment of the present invention is shown;

[0043] Figure 3 A two-dimensional depth domain formation interface dip attribute map according to an embodiment of the present invention is shown;

[0044] Figure 4 A two-dimensional depth domain normal dip angle attribute diagram of a stratum interface according to an embodiment of the present invention is shown;

[0045] Figure 5 A two-dimensional depth domain stratigraphic grid attribute diagram according to an embodiment of the present invention is shown;

[0046] Figure 6 A two-dimensional depth domain stratigraphic heterogeneity attribute map according to an embodiment of the present invention is shown;

[0047] Figure 7 A diagram showing the depth domain imaging result of actual seismic data according to an embodiment of the present invention is shown;

[0048] Figure 8 A three-dimensional depth domain formation interface dip attribute diagram according to an embodiment of the present invention is shown;

[0049] Fig. 9 A three-dimensional depth domain normal dip angle attribute diagram of a stratum interface according to an embodiment of the present invention is shown;

[0050] Fig.10 A three-dimensional depth domain stratigraphic characteristic value combination attribute diagram according to an embodiment of the present invention is shown;

[0051] Fig.11 A three-dimensional depth domain stratigraphic grid attribute diagram according to an embodiment of the present invention is shown;

[0052] Fig.12 A three-dimensional depth domain stratigraphic heterogeneity attribute map according to an embodiment of the present invention is shown;

[0053] Fig.13 A three-dimensional depth-domain hierarchical heterogeneity attribute map according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0054] Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0055] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0056] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0057] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0058] Figure 1 A flowchart showing a method for determining seismic attributes based on a depth domain data volume according to an embodiment of the present invention is shown; Figure 2 A diagram showing the depth domain imaging result of the Marmousi theoretical model reverse time migration according to an embodiment of the present invention is shown; Figure 3A two-dimensional depth domain formation interface dip angle (formation dip angle) attribute diagram according to an embodiment of the present invention is shown; Figure 4 A two-dimensional depth domain normal dip angle (azimuth) attribute diagram of a stratum interface according to an embodiment of the present invention is shown; Figure 5 A two-dimensional depth domain stratigraphic framework attribute (structural attribute) diagram according to an embodiment of the present invention is shown; Figure 6 A two-dimensional depth domain stratigraphic heterogeneity attribute map according to an embodiment of the present invention is shown; Figure 7 A diagram showing the depth domain imaging result of actual seismic data according to an embodiment of the present invention is shown; Figure 8 A three-dimensional depth domain formation interface dip attribute diagram according to an embodiment of the present invention is shown; Fig. 9 A three-dimensional depth domain normal dip angle attribute diagram of a stratum interface according to an embodiment of the present invention is shown; Fig.10 A three-dimensional depth domain stratigraphic characteristic value combination attribute diagram according to an embodiment of the present invention is shown; Fig.11 A three-dimensional depth domain stratigraphic grid attribute diagram according to an embodiment of the present invention is shown; Fig.12 A three-dimensional depth domain stratigraphic heterogeneity attribute map according to an embodiment of the present invention is shown; Fig.13 3D depth domain hierarchical heterogeneity attribute diagram according to an embodiment of the present invention is shown. Figure 1-13 As shown, a method for determining seismic attributes based on a depth domain data volume comprises:

[0059] Step S01: Acquire the depth domain imaging data volume after energy equalization processing and signal-to-noise ratio improvement processing of the work area; Step S02: If the depth domain imaging data volume is irregular, extract the header information of the depth domain imaging data volume, and regularize the depth domain imaging data volume according to the header information; Step S03: Construct a local covariance matrix for each sampling point position of the depth domain imaging data volume, and obtain the eigenvalue and eigenvector corresponding to each sampling point position according to the covariance matrix; Step S04: Determine the depth domain seismic attribute data volume according to the eigenvalue and eigenvector.

[0060] A method for determining seismic attributes based on a depth domain data volume provided by an embodiment of the present invention specifically comprises the following steps:

[0061] Step S01: Acquire a depth domain imaging data volume after energy balancing processing and signal-to-noise ratio enhancement processing of the work area.

[0062] In the embodiment of the present invention, the depth domain imaging method of seismic data may include but is not limited to ray-based imaging methods such as Kirchhoff and wave equation-based imaging methods such as reverse time migration.

[0063] In the present invention, the energy balancing process at least includes: dynamic balancing process or local energy adjustment or amplitude energy lighting compensation.

[0064] In the present invention, the processing for improving the signal-to-noise ratio at least includes: removing outliers in the data volume or suppressing random seismic noise.

[0065] In the embodiment of the present invention, the dynamic balancing processing method is as follows: for the case where the amplitude of a single-channel record of the seismic imaging profile to be processed varies greatly, this method is applied, taking a certain amplitude energy as a reference, processing each channel to make the energy in the channel uniform, and finally making the energy of the entire seismic profile uniform. The local energy adjustment method is as follows: for the case where the horizontal and vertical amplitude energies of the seismic imaging profile to be processed are uneven, this method is applied, first obtaining the local amplitude energy based on the seismic profile data to be processed, and then dividing the seismic profile to be processed by the local amplitude energy to obtain the processing result of uniform energy of the entire seismic profile. The amplitude energy illumination compensation method is as follows: for the case where the illumination compensation amount has been synchronously obtained during imaging, the seismic imaging profile to be processed is divided by the local amplitude energy to obtain the processing result of uniform energy of the entire seismic profile.

[0066] Step S02: If the depth domain imaging data volume is irregular, extracting header information from the depth domain imaging data volume, and performing regularization processing on the depth domain imaging data volume according to the header information.

[0067] In the present invention, if it is a three-dimensional depth domain imaging data volume, the trace header information at least includes: the maximum main survey line number, the minimum main survey line number, the maximum connecting survey line number and the minimum connecting survey line number of the post-stack data volume; if it is a two-dimensional depth domain imaging data volume, the trace header information at least includes: the starting seismic trace number and the ending seismic trace number of the post-stack data volume.

[0068] In the present invention, the method of regular processing includes: if it is a three-dimensional depth domain imaging data volume, then each sampling value of the empty channel outside the depth domain imaging data volume and within the range of the maximum main survey line number, the minimum main survey line number, the maximum connecting survey line number and the minimum connecting survey line number in the three-dimensional situation is filled with zero processing; if it is a two-dimensional depth domain imaging data volume, then each sampling value of the empty channel outside the depth domain imaging data volume and between the starting seismic channel number and the ending seismic channel number in the two-dimensional situation is filled with zero processing.

[0069] In an embodiment of the present invention, the acquired post-stack three-dimensional depth domain imaging data volume header information also includes: the total number of main survey lines, the main survey line seismic trace spacing, the total number of interconnected survey lines, the interconnected survey line seismic trace spacing, the starting depth, the ending depth, the depth sampling spacing, and the total number of depth sampling points. The above header information is a description of the input post-stack three-dimensional depth domain imaging data volume data characteristics. According to the data characteristics, the data range of the three-dimensional depth domain imaging data volume can be determined, and all empty sampling points within the data range are filled with zero. Each sampling point value of the empty channel outside the three-dimensional depth domain imaging data volume acquired in step S01 and within the range of the maximum main survey line number, the minimum main survey line number, the maximum interconnected survey line number, and the minimum interconnected survey line number in the three-dimensional situation is filled with zero, thereby obtaining the regularized post-stack three-dimensional depth domain imaging data volume.

[0070] The acquired post-stack two-dimensional depth domain imaging data volume header information also includes: the total number of seismic traces, the interval between seismic traces, the starting depth, the ending depth, the depth sampling interval and the total number of depth sampling points. The above-mentioned trace header information is a description of the data characteristics of the input post-stack two-dimensional depth domain imaging data volume. According to the data characteristics, the data range of the two-dimensional depth domain imaging data volume can be determined, and all empty sampling points within the data range are filled with zero. Each sampling value of the empty trace outside the two-dimensional depth domain imaging data volume acquired in step S01 and between the starting seismic trace number and the ending seismic trace number in the two-dimensional situation is filled with zero, thereby obtaining the regularized post-stack two-dimensional depth domain imaging data volume.

[0071] It is more troublesome to process the boundaries of irregular data bodies. Regularized data bodies are easier to calculate in computer memory.

[0072] Step S03: constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume, and obtaining the eigenvalue and eigenvector corresponding to each sampling point position according to the covariance matrix.

[0073] In the present invention, the method of constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume after regularization processing comprises:

[0074] If it is a three-dimensional depth domain imaging data volume, the covariance matrix is ​​constructed using formula (1);

[0075]

[0076] Where U is the three-dimensional depth domain imaging data volume, and x, y, and z represent the three dimensions of the three-dimensional data volume in the spatial direction.

[0077] In the present invention, the method of constructing a local covariance matrix for each sampling point position of the regularized depth domain imaging data volume comprises: if it is a two-dimensional depth domain imaging data volume, constructing a covariance matrix using formula (2);

[0078]

[0079] Where U is the two-dimensional depth domain imaging data volume, and x and z represent the two dimensions of the two-dimensional data volume in the spatial direction.

[0080] In the embodiment of the present invention, a local covariance matrix is ​​constructed for each sampling point of the depth domain imaging data volume regularized in step S02; in three-dimensional case, a three-dimensional covariance matrix is ​​constructed according to formula (1), corresponding to three eigenvalues ​​and three eigenvectors; in two-dimensional case, a two-dimensional covariance matrix is ​​constructed according to formula (2), corresponding to two eigenvalues ​​and two eigenvectors.

[0081] In the three-dimensional case, the constructed three-dimensional covariance matrix is ​​discretized to determine the corresponding eigenvalues ​​and eigenvectors. The specific numerical discrete calculation formula in the three-dimensional case includes:

[0082]

[0083] Where U is the three-dimensional depth domain imaging data volume, i, j, and k represent the grid point position numbers in the three spatial directions of x, y, and z of the three-dimensional covariance matrix, respectively. The specific value range is determined according to the number of grid points and the starting number of the actual data volume, and the interval of the number is 1.

[0084] The covariance matrix formula after discretization in three-dimensional case is as follows:

[0085]

[0086] In the two-dimensional case, the constructed two-dimensional covariance matrix is ​​discretized to determine the corresponding eigenvalues ​​and eigenvectors. The specific numerical discrete calculation formula in the two-dimensional case includes:

[0087]

[0088] Where U is the two-dimensional depth domain imaging data volume, i and k represent the grid point position numbers in the three spatial directions of x and z of the two-dimensional covariance matrix, respectively. The specific value range is determined according to the number of grid points and the starting number of the actual data volume, and the interval of the number is 1.

[0089] The covariance matrix formula after discretization in the two-dimensional case is as follows:

[0090]

[0091] Among them, the methods for determining eigenvalues ​​include: power method, inverse power method, Jacobian matrix method, QR decomposition method, etc.

[0092] Step S04: Determine the depth domain seismic attribute data volume according to the eigenvalues ​​and eigenvectors.

[0093] In the embodiment of the present invention, the depth domain seismic attribute data volume includes: formation dip attributes, azimuth attributes, eigenvalue combination attributes, formation structure attributes and formation heterogeneity attributes.

[0094] In the present invention, the method for determining a depth domain seismic attribute data volume according to eigenvalues ​​and eigenvectors includes: if it is a three-dimensional depth domain imaging data volume, then each sampling point corresponds to three eigenvalues ​​and three eigenvectors, wherein the three eigenvalues ​​include: λ1, λ2 and λ3, and λ1>λ2>λ3;

[0095] Among them, the eigenvector azimuth corresponding to the maximum eigenvalue λ1 is the dip angle θ of the formation, and the plane angles of the two eigenvectors corresponding to the eigenvalues ​​λ2 and λ3 are the azimuths of the dip angle of the formation. λ1 is the three-dimensional stratum structure attribute in the depth domain, λ2 is the three-dimensional stratum heterogeneity attribute in the depth domain, λ3 is the three-dimensional stratum heterogeneity secondary attribute in the depth domain, and f(λ1, λ2, λ3) is the eigenvalue combination attribute;

[0096] Each attribute constitutes its three-dimensional depth-domain seismic attribute data volume.

[0097] In the embodiment of the present invention, the three eigenvectors are: Among them, x, y, and z are the three dimensions of each eigenvector, that is, the values ​​of the eigenvector in the three spatial coordinate directions.

[0098] The eigenvector (eigenvector) azimuth corresponding to the maximum eigenvalue λ1 is used as the dip attribute of the formation, and the plane angles of the two eigenvectors corresponding to the eigenvalues ​​λ2 and λ3 are used as the azimuth attribute of the formation dip.

[0099] f(λ1, λ2, λ3) as a characteristic value combination attribute can be extended to similar Equal combination attributes; specifically, the combination attributes can be selected according to the geological effects required by the actual application. In order to highlight a certain feature, it can be used as the numerator and other features can be combined as the denominator.

[0100] In the present invention, the method for determining a depth domain seismic attribute data volume according to eigenvalues ​​and eigenvectors includes: if it is a two-dimensional depth domain imaging data volume, then each sampling point corresponds to two eigenvalues ​​and two eigenvectors, wherein the two eigenvalues ​​include: λ1 and λ2, and λ1>λ2;

[0101] Among them, the eigenvector azimuth corresponding to the maximum eigenvalue λ1 is the dip angle θ of the formation, and the eigenvector corresponding to the eigenvalue λ2 is the azimuth of the dip angle of the formation λ1 is the two-dimensional stratigraphic structure attribute in the depth domain, λ2 is the two-dimensional stratigraphic heterogeneity attribute in the depth domain, and f(λ1, λ2) is the eigenvalue combination attribute;

[0102] Each attribute constitutes its two-dimensional depth-domain seismic attribute data volume.

[0103] In the embodiment of the present invention, the two eigenvectors are: Among them, x

[0104] , y are the two dimensions of each eigenvector, that is, the values ​​of the eigenvector in the two spatial coordinate directions.

[0105] The eigenvector (eigenvector) azimuth corresponding to the maximum eigenvalue λ1 is used as the dip attribute of the formation, and the eigenvector corresponding to the eigenvalue λ2 is used as the azimuth attribute of the dip of the formation.

[0106] f(λ1, λ2) as a characteristic value combination attribute can be extended to similar Equal combination attributes; specifically, the combination attributes can be selected according to the geological effects required by the actual application. In order to highlight a certain feature, it is used as the numerator and other features are combined as the denominator.

[0107] Steps S03-S04 are executed for each sampling point in the regularized depth domain imaging data volume in step S02, and finally a depth domain seismic attribute data volume including formation dip, azimuth, eigenvalue combination attributes, formation structure attributes, formation heterogeneity attributes and formation heterogeneity secondary attributes (three-dimensional situation) is obtained, which is used for the fine prediction of depth domain seismic reservoirs such as fractures and heterogeneity, so as to support subsequent target optimization and well site deployment.

[0108] In the embodiment of the present invention, the application example of the depth domain imaging result of the theoretical model is as follows:

[0109] Step 1: Obtain the depth domain imaging data volume after reverse time migration of the Marmousi theoretical model. The imaging data volume has completed the amplitude energy compensation processing, such as Figure 2 shown.

[0110] Step 2: Extract the key trace header information of the depth domain imaging data volume in step 1, including: obtaining the starting seismic trace number 1, the ending seismic trace number 480, the total number of seismic traces 180, the seismic trace spacing 5m, the starting depth 0m, the ending depth 280m, the depth sampling spacing 5m, and the total number of depth sampling points 280. Due to the model data rules, the spatial range of the depth domain imaging data volume in step 1 is not regularized.

[0111] Step 3: Construct a local covariance matrix for each sampling point position of the depth domain imaging data volume in step 2 to obtain the eigenvalue and eigenvector of the sampling point position. Among them, a two-dimensional covariance matrix is ​​constructed, corresponding to two eigenvalues ​​and two eigenvectors. The specific covariance matrix calculation formula is as shown in formula (2).

[0112] Step 4: Determine the dip angle, azimuth angle, eigenvalue combination attribute, structural attribute and heterogeneous attribute of the 2D depth domain according to the eigenvalues ​​and eigenvectors of the sampling point positions in step 3. Obtain two eigenvalues ​​[λ1λ2], λ1>λ2, and two eigenvectors [ν1ν2]. The azimuth of the eigenvector ν1 corresponding to the maximum eigenvalue λ1 is taken as the dip angle θ of the formation, and the eigenvector ν2 corresponding to the eigenvalue λ2 is taken as the azimuth angle of the dip angle of the formation. Eigenvalue combination attributes as the coherence volume value, λ1 as the structural attribute, and λ2 as the inhomogeneous attribute.

[0113] After the calculation of all sampling points, the formation dip angle (such as Figure 3 As shown), azimuth (as shown Figure 4 ), structural attributes (such as Figure 5 ) and heterogeneous properties (such as Figure 6 The two-dimensional depth-domain seismic attribute data bodies (as shown in the figure) have realized the fine characterization of the stratigraphic details of the depth-domain theoretical model, which is consistent with the structural characteristics of the theoretical model and verifies the accuracy and reliability of the method.

[0114] In the embodiment of the present invention, the application example of the depth domain imaging result of the actual data of Songliao Basin is as follows:

[0115] Step 1: Obtain the depth domain imaging data volume after prestack reverse time migration of actual data (such as Figure 7 As shown), the imaging data volume has completed amplitude energy illumination compensation and dynamic balancing processing.

[0116] Step 2: Extract the key trace information of the depth domain imaging data volume in step 1, including: obtaining the maximum main survey line number of the post-stack data volume as 811, the minimum main survey line number as 100, the total number of main survey lines as 712, the main survey line seismic trace spacing as 20m, the maximum connecting survey line number as 1131, the minimum connecting survey line number as 100, the total number of connecting survey lines as 1032, the connecting survey line seismic trace spacing as 20m, the starting depth as 0m, the ending depth as 6000m, the depth sampling spacing as 10m, and the total number of depth sampling points as 601.

[0117] The irregularity in the spatial range of the depth domain imaging data volume in step 1 is regularized according to the maximum data range, and each sampling value of the empty channel outside the depth domain imaging data volume in step 1 and within the range of the maximum main survey line number, the minimum main survey line number, the maximum connecting survey line number, and the minimum connecting survey line number in the three-dimensional situation is filled with zero.

[0118] Step 3: Construct a local covariance matrix for each sampling point of the depth domain imaging data volume in step 2 to obtain the eigenvalue and eigenvector of the sampling point. A three-dimensional covariance matrix is ​​constructed, corresponding to three eigenvalues ​​and three eigenvectors. The specific covariance matrix calculation formula is as shown in formula (1).

[0119] Step 4: Determine the formation dip, azimuth, eigenvalue combination attributes, structural attributes, and heterogeneous attributes in the three-dimensional depth domain based on the eigenvalues ​​and eigenvectors of the sampling point positions in step 3. Obtain three eigenvalues, [λ1λ2λ3], λ1>λ2>λ3, and three eigenvectors [ν1ν2ν3]. The azimuth of the eigenvector ν1 corresponding to the maximum eigenvalue λ1 is taken as the formation dip θ, and the plane angles of the two eigenvectors ν2 and ν3 corresponding to the eigenvalues ​​λ2 and λ3 are taken as the formation dip azimuth. Eigenvalue combination attributes As the coherent volume value, λ1 as the structural attribute, λ2 as the inhomogeneous attribute, and λ3 as the inhomogeneous secondary attribute.

[0120] After the calculation of all sampling points, the formation dip angle (such as Figure 8 As shown), azimuth (as shown Fig. 9 As shown), coherent bodies (such as Fig.10 ), structural attributes (such as Fig.11 ), heterogeneous properties (such as Fig.12 ), heterogeneous secondary attributes (such as Fig.13 A total of 6 types of 3D depth-domain seismic attribute data volumes were constructed (as shown in the figure), which achieved a fine depiction of the details of complex structural strata in the depth domain of actual data, which was consistent with the structural characteristics of the imaging results and supported the fine exploration and development work in the seismic area.

[0121] It can be understood that the above-mentioned various method embodiments mentioned in the present invention can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present invention will not go into details.

[0122] The execution subject of the method for determining seismic attributes based on the depth domain data volume may be a seismic attribute determination device based on the depth domain data volume. For example, the method for determining seismic attributes based on the depth domain data volume may be executed by a terminal device or a server or other processing device, wherein the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the method for determining seismic attributes based on the depth domain data volume may be implemented by a processor calling computer-readable instructions stored in a memory. "

[0123] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0124] The present invention also provides a seismic attribute determination device based on a depth domain data volume, comprising: an acquisition unit, used to acquire a depth domain imaging data volume after energy balance processing and signal-to-noise ratio improvement processing have been completed in a work area; a regularization processing unit, used to determine if the depth domain imaging data volume is irregular, extract header information from the depth domain imaging data volume, and perform regularization processing on the depth domain imaging data volume based on the header information; a covariance matrix construction unit, used to respectively construct a local covariance matrix for each sampling point position of the depth domain imaging data volume, and obtain the eigenvalues ​​and eigenvectors corresponding to each sampling point position based on the covariance matrix; a depth domain seismic attribute data volume determination unit, used to determine the depth domain seismic attribute data volume based on the eigenvalues ​​and eigenvectors.

[0125] In some embodiments, the functions or modules and units included in the device provided by the embodiment of the present invention can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0126] The present invention discloses a method and device for determining seismic attributes based on a depth domain data volume. This is the first time in the art that seismic attributes are directly extracted in the depth domain, achieving a breakthrough from 0 to 1. Multiple depth domain seismic attributes such as formation dip, azimuth, combined attributes, structural attributes, and heterogeneous attributes can be obtained, providing important data guarantee for the precise prediction of various seismic reservoirs in the depth domain.

[0127] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining seismic attributes based on a depth domain data volume, characterized in that: include: Acquire the depth domain imaging data volume after energy balance processing and signal-to-noise ratio improvement processing of the work area; If the depth domain imaging data volume is irregular, extracting trace header information from the depth domain imaging data volume, and performing regularization processing on the depth domain imaging data volume according to the trace header information; Constructing a local covariance matrix for each sampling point position of the depth domain imaging data volume respectively, and obtaining an eigenvalue and an eigenvector corresponding to each sampling point position according to the covariance matrix; A depth-domain seismic attribute data volume is determined according to the eigenvalues ​​and eigenvectors.

2. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: The energy balancing process at least includes: dynamic balancing process or local energy adjustment or amplitude energy lighting compensation.

3. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: The signal-to-noise ratio improvement process at least includes: removing outliers in the data volume or suppressing random seismic noise.

4. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: If it is a three-dimensional depth domain imaging data volume, the trace header information includes: the maximum main survey line number, the minimum main survey line number, the maximum contact survey line number and the minimum contact survey line number of the post-stack data volume; If it is a two-dimensional depth domain imaging data volume, the trace header information includes: the starting seismic trace number and the ending seismic trace number of the post-stack data volume.

5. The method for determining seismic attributes based on depth domain data volume according to claim 4, characterized in that: The regularized processing method includes: If it is a three-dimensional depth domain imaging data volume, then each sampling value of the empty channel outside the depth domain imaging data volume and within the range of the maximum main survey line number, the minimum main survey line number, the maximum contact survey line number, and the minimum contact survey line number in the three-dimensional situation is filled with zero; If it is a two-dimensional depth domain imaging data volume, each sampling value of the empty channel outside the depth domain imaging data volume and between the starting seismic channel number and the ending seismic channel number in the two-dimensional case is filled with zero.

6. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: The method of constructing a local covariance matrix for each sampling point position of the regularized depth domain imaging data volume comprises: If it is a three-dimensional depth domain imaging data volume, the covariance matrix is ​​constructed using formula (1); Where U is the three-dimensional depth domain imaging data volume, and x, y, and z represent the three dimensions of the three-dimensional data volume in the spatial direction.

7. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: The method of constructing a local covariance matrix for each sampling point position of the regularized depth domain imaging data volume comprises: If it is a two-dimensional depth domain imaging data volume, the covariance matrix is ​​constructed using formula (2); Where U is the two-dimensional depth domain imaging data volume, and x and z represent the two dimensions of the two-dimensional data volume in the spatial direction.

8. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: The method for determining a depth domain seismic attribute data volume based on eigenvalues ​​and eigenvectors comprises: If it is a three-dimensional depth domain imaging data volume, each sampling point corresponds to three eigenvalues ​​and three eigenvectors, where the three eigenvalues ​​include: λ1, λ2 and λ3, and λ1>λ2>λ3; Among them, the eigenvector azimuth corresponding to the maximum eigenvalue λ1 is the dip angle θ of the formation, and the plane angles of the two eigenvectors corresponding to the eigenvalues ​​λ2 and λ3 are the azimuths of the dip angle of the formation. λ1 is the three-dimensional stratigraphic structure attribute in the depth domain, λ2 is the three-dimensional stratigraphic heterogeneity attribute in the depth domain, λ3 is the three-dimensional stratigraphic heterogeneity secondary attribute in the depth domain, and f(λ1,λ2,λ3) is the eigenvalue combination attribute; Each attribute constitutes its three-dimensional depth-domain seismic attribute data volume.

9. The method for determining seismic attributes based on depth domain data volume according to claim 1, characterized in that: The method for determining a depth domain seismic attribute data volume based on eigenvalues ​​and eigenvectors comprises: If it is a two-dimensional depth domain imaging data volume, then each sampling point corresponds to two eigenvalues ​​and two eigenvectors, where the two eigenvalues ​​include: λ1 and λ2, and λ1>λ2; Among them, the eigenvector azimuth corresponding to the maximum eigenvalue λ1 is the dip angle θ of the formation, and the eigenvector corresponding to the eigenvalue λ2 is the azimuth of the dip angle of the formation λ1 is the two-dimensional stratigraphic structure attribute in the depth domain, λ2 is the two-dimensional stratigraphic heterogeneity attribute in the depth domain, and f(λ1,λ2) is the eigenvalue combination attribute; Each attribute constitutes its two-dimensional depth-domain seismic attribute data volume.

10. A seismic attribute determination device based on a depth domain data volume, characterized in that: include: An acquisition unit is used to acquire a depth domain imaging data volume after energy balance processing and signal-to-noise ratio improvement processing have been completed in the work area; a regularization processing unit, configured to extract trace header information from the depth domain imaging data volume if the depth domain imaging data volume is irregular, and perform regularization processing on the depth domain imaging data volume according to the trace header information; A covariance matrix construction unit, used to construct a local covariance matrix for each sampling point position of the depth domain imaging data volume, and obtain an eigenvalue and an eigenvector corresponding to each sampling point position according to the covariance matrix; The depth domain seismic attribute data volume determining unit is used to determine the depth domain seismic attribute data volume according to the eigenvalue and the eigenvector.

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