Method and device for calculating reservoir space volume of carbonate rock faulted dissolution body

Abstract

This invention discloses a method and apparatus for calculating the reservoir volume of carbonate rock fractured solutions. The method includes: calculating the corresponding dip and azimuth data volumes based on three-dimensional seismic data of the carbonate rock fractured solutions; performing Gaussian smoothing filtering on the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect; calculating the fractured solution boundary detection attributes based on the edge-preserving three-dimensional seismic data; comparing a preset boundary detection attribute threshold with the fractured solution boundary detection attributes to determine the effective boundary detection attributes of the fractured solution; performing time-depth conversion processing on the effective boundary detection attributes of the fractured solution to obtain the fractured solution boundary detection attribute volume in the depth domain; and performing volume integration on the fractured solution boundary detection attribute volume in the depth domain to obtain the reservoir volume of the carbonate rock fractured solution. This invention can improve the accuracy of calculating the reservoir volume of carbonate rock fractured solutions.

Description

Technical Field

[0001] This invention relates to the technical field, and in particular to a method and apparatus for calculating the reservoir volume of carbonate rock fractured solutions. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] A fractured solution is a stratigraphic fracture zone with width, length, thickness, and geometric shape; it is a tectonic geological body with a spatial and temporal concept.

[0004] Currently, based on the derivation of the horizontal trace amplitude function of lateral geological bodies using space wavelet theory, researchers have quantitatively analyzed the ability of migration imaging to determine the width of geological bodies and developed a method for estimating the width of carbonate karst fractured bodies. Furthermore, based on the carving and volume estimation of the "beaded" reflection anomalies of karst fractured bodies on seismic profiles, a volume correction coefficient is statistically derived from numerous numerical simulations for correction, allowing for the quantitative calculation of the effective volume of the fractured body.

[0005] However, the above methods for calculating the volume of fractured solutions are all based on raw 3D seismic data. Since raw 3D seismic data contains a large amount of random noise, the calculated volume of fractured solutions will be inaccurate if this random noise cannot be effectively eliminated. In addition, the above methods calculate the volume of fractured solutions in the time domain, which makes the calculated volume of fractured solutions unreliable. Summary of the Invention

[0006] This invention provides a method for calculating the reservoir volume of carbonate rock fractured solutions, thereby improving the accuracy of calculating the reservoir volume of carbonate rock fractured solutions. The method includes:

[0007] Based on the three-dimensional seismic data of carbonate rock fracture solution, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data.

[0008] Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect;

[0009] Calculate the boundary detection properties of fracture-collapse bodies based on 3D seismic data with edge preservation effect;

[0010] The preset boundary detection attribute threshold is compared with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution;

[0011] The effective boundary detection attributes of the broken solution are processed by time-depth transformation to obtain the boundary detection attribute volume of the broken solution in the depth domain;

[0012] By performing volume integration on the boundary detection property volume of the fractured solution in the depth domain, the volume of the reservoir space of the fractured solution in carbonate rock is obtained.

[0013] This invention also provides a device for calculating the reservoir space volume of carbonate rock fractured solutions, to improve the accuracy of calculating the reservoir space volume of carbonate rock fractured solutions. The device includes:

[0014] The dip and azimuth data volume calculation module is used to calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data of carbonate rock fractures and solutions.

[0015] The Gaussian smoothing filter module is used to perform Gaussian smoothing filter processing on dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect;

[0016] The fault-collapse boundary detection attribute calculation module is used to calculate the fault-collapse boundary detection attributes based on 3D seismic data with edge preservation effect;

[0017] The threshold comparison module is used to compare the preset boundary detection attribute threshold with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution.

[0018] The time-depth conversion processing module is used to perform time-depth conversion processing on the effective boundary detection attributes of the broken solution to obtain the boundary detection attribute volume of the broken solution in the depth domain.

[0019] The volume integration module is used to perform volume integration on the boundary detection property volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the fractured solution in carbonate rocks.

[0020] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for calculating the reservoir volume of the carbonate rock fracture solution described above.

[0021] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for calculating the storage space volume of the carbonate rock fracture solution described above.

[0022] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the method for calculating the reservoir volume of the carbonate rock fracture solution described above.

[0023] In this embodiment of the invention, based on the three-dimensional seismic data of carbonate rock fractured solutions, the dip and azimuth data volumes corresponding to the three-dimensional seismic data are calculated; Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect; based on the three-dimensional seismic data with edge preservation effect, the boundary detection attributes of the fractured solution are calculated; a preset boundary detection attribute threshold is compared with the boundary detection attributes of the fractured solution to determine the effective boundary detection attributes of the fractured solution; the effective boundary detection attributes of the fractured solution are subjected to time-depth conversion processing to obtain the boundary detection attribute volume of the fractured solution in the depth domain; volume integration is performed on the boundary detection attribute volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the carbonate rock fractured solution. Thus, by determining the effective boundary detection attributes of the fractured solution, the calculation of the boundary detection attributes of the fractured solution can be performed. Subsequent filtering of valid data further demonstrates the effectiveness of the calculated fault-knot boundary, achieving the goal of accurately and effectively calculating the reservoir volume of carbonate fault-knots. This also solves the problem of inaccurate fault-knot volume calculations due to random noise in 3D seismic data in existing technologies. Furthermore, by performing time-depth conversion, it ensures that the space occupied by each volume element in the fault-knot boundary detection attribute volume is the same, improving the accuracy of calculating the reservoir volume of fault-knots. This also solves the problem of unreliability in the calculated fault-knot volume due to the fact that existing technologies can only calculate the fault-knot volume in the time domain. Moreover, by performing volume integral calculation, the spatial volume of the anomaly zone caused by the presence of fault-knots in the 3D seismic data volume can be obtained, thereby achieving the effect of quantitatively calculating the total reservoir volume of fault-knots. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0025] Figure 1 This is a flowchart illustrating a method for calculating the reservoir volume of a carbonate rock fractured solution according to an embodiment of the present invention.

[0026] Figure 2 This is an example diagram illustrating the spatial distribution of the total volume of the reservoir space of a carbonate rock fractured solution obtained from calculations in an embodiment of the present invention.

[0027] Figure 3 This is a specific example diagram of a seismic profile in three-dimensional seismic data acquired in an embodiment of the present invention;

[0028] Figure 4This is a specific example diagram of the fault-dissolved body boundary of a calculated seismic profile in an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of a computer device used to calculate the reservoir volume of carbonate rock fractured solution in an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram of a device for calculating the storage space volume of a carbonate rock fracture solution according to an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0033] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0034] A fractured solution is a stratigraphic fracture zone with width, length, thickness, and geometric shape; it is a tectonic geological body with a spatial and temporal concept.

[0035] First, researchers proposed the concept of "vein-type oil and gas traps," emphasizing that these traps form on seismic stress zones and, through expansion, fracturing, and hydrothermal alteration, significantly improve the permeability of previously impermeable rocks. The formation of vein-type traps is closely related to seismic fault zones, indicating that fault-collapsed bodies can themselves form traps with oil and gas storage capabilities, and proposing the hypothesis that seismic fault zones are spatial tectonic geological bodies. Subsequently, based on research into the internal structure of strike-slip faults in the field, researchers proposed the "two-end element" structure of fault zones, consisting of a low-permeability fault core and a high-permeability fracture zone. The study of fault "core-zone" structures and "fracture facies" systems has gradually gained public attention, confirming that faults are three-dimensional geological bodies with spatial volume and internal structure. Subsequently, based on the fundamental geological conditions for trapping and accumulating oil and gas, researchers analyzed heavy oil-sealed reservoirs in the Lenghu tectonic belt on the northern margin of the Qaidam Basin. They discovered that the reservoirs roughly extend along the Hutongnuoer fault-knot body, boldly proposing the hypothesis of "fault body" traps and making theoretical predictions about the reservoir, sealing, and preservation conditions of fault body traps. Following this, based on fault zone drilling, seismic data, and production dynamics data, scholars proposed the concept of fault-knot body traps and, according to their spatial distribution morphology and controlling factors, classified them into strip-shaped, sandwich-shaped, and flat-shaped types. They analyzed the differences in development effects among different types of reservoirs and further compared the characteristics of paleokarst "stratabound" and "fault-controlled" oil and gas reservoirs in the northern Tarim Basin. Then, based on the spatial wavelet theory, researchers derived the horizontal trace amplitude function of lateral geological bodies, quantitatively analyzed the ability of migration imaging to determine the width of geological bodies, and developed a method for estimating the width of carbonate fault-knot bodies.

[0036] Furthermore, based on the carving and volume estimation of the "beaded" reflection anomalies of karst fractured bodies on seismic profiles, relevant personnel have obtained volume correction coefficients through statistical analysis of a large number of numerical simulations, and quantitatively calculated the volume of the effective fractured body.

[0037] However, the above methods for calculating the volume of fractured solutions are all based on raw 3D seismic data. Since raw 3D seismic data contains a large amount of random noise, the calculated volume of fractured solutions will be inaccurate if this random noise cannot be effectively eliminated. In addition, the above methods calculate the volume of fractured solutions in the time domain, which makes the calculated volume of fractured solutions unreliable.

[0038] To address the aforementioned problems, embodiments of the present invention provide a method for calculating the reservoir space volume of carbonate rock fractured solutions, thereby improving the accuracy of calculating the reservoir space volume of carbonate rock fractured solutions. See [link to relevant documentation]. Figure 1 The method may include:

[0039] Step 101: Based on the three-dimensional seismic data of carbonate rock fracture solution, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data;

[0040] Step 102: Perform Gaussian smoothing filtering on the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect;

[0041] Step 103: Calculate the boundary detection properties of the fracture-knot body based on the 3D seismic data with edge preservation effect;

[0042] Step 104: Compare the preset boundary detection attribute threshold with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution;

[0043] Step 105: Perform time-depth transformation on the effective boundary detection attributes of the broken solution to obtain the boundary detection attribute volume of the broken solution in the depth domain;

[0044] Step 106: Detect the attribute volume of the fractured solution boundary in the depth domain, perform volume integration, and obtain the volume of the reservoir space of the carbonate fractured solution.

[0045] In this embodiment of the invention, based on the three-dimensional seismic data of carbonate rock fractured solutions, the dip and azimuth data volumes corresponding to the three-dimensional seismic data are calculated; Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect; based on the three-dimensional seismic data with edge preservation effect, the boundary detection attributes of the fractured solution are calculated; a preset boundary detection attribute threshold is compared with the boundary detection attributes of the fractured solution to determine the effective boundary detection attributes of the fractured solution; the effective boundary detection attributes of the fractured solution are subjected to time-depth conversion processing to obtain the boundary detection attribute volume of the fractured solution in the depth domain; volume integration is performed on the boundary detection attribute volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the carbonate rock fractured solution. Thus, by determining the effective boundary detection attributes of the fractured solution, the calculation of the boundary detection attributes of the fractured solution can be performed. Subsequent filtering of valid data further demonstrates the effectiveness of the calculated fault-knot boundary, achieving the goal of accurately and effectively calculating the reservoir volume of carbonate fault-knots. This also solves the problem of inaccurate fault-knot volume calculations due to random noise in 3D seismic data in existing technologies. Furthermore, by performing time-depth conversion, it ensures that the space occupied by each volume element in the fault-knot boundary detection attribute volume is the same, improving the accuracy of calculating the reservoir volume of fault-knots. This also solves the problem of unreliability in the calculated fault-knot volume due to the fact that existing technologies can only calculate the fault-knot volume in the time domain. Moreover, by performing volume integral calculation, the spatial volume of the anomaly zone caused by the presence of fault-knots in the 3D seismic data volume can be obtained, thereby achieving the effect of quantitatively calculating the total reservoir volume of fault-knots.

[0046] In practice, the dip and azimuth data volumes corresponding to the three-dimensional seismic data of carbonate rock fractures are first calculated based on the three-dimensional seismic data of the carbonate rock fractures.

[0047] In the above embodiments, three-dimensional seismic data of carbonate rock fractures can be acquired first, and then the dip and azimuth data volumes corresponding to the three-dimensional seismic data can be calculated. This helps to perform Gaussian smoothing filtering on the dip and azimuth data volumes in subsequent steps to obtain three-dimensional seismic data with edge preservation effect.

[0048] In this embodiment, based on the three-dimensional seismic data of carbonate rock fracture solutions, the dip and azimuth data volumes corresponding to the three-dimensional seismic data are calculated, including:

[0049] Based on the three-dimensional seismic data of carbonate rock fracture solutions, a gradient structure tensor matrix is ​​constructed.

[0050] Perform matrix decomposition on the constructed gradient structure tensor matrix to obtain multiple eigenvectors corresponding to the gradient structure tensor matrix and the eigenvalues ​​corresponding to each eigenvector;

[0051] Based on multiple eigenvectors and the eigenvalues ​​corresponding to each eigenvector, determine the first eigenvector with the largest eigenvalue;

[0052] Based on the components of the first eigenvector in different directions, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data.

[0053] In one embodiment, the gradient structure tensor matrix is ​​constructed based on the three-dimensional seismic data of carbonate rock fracture solutions according to the following formula:

[0054]

[0055] Where GST(x,y,t) represents the constructed gradient structure tensor matrix; υ i and λ i (i = 1, 2, 3) represent the three eigenvectors and eigenvalues ​​corresponding to the gradient structure tensor matrix.

[0056] In one embodiment, the dip volume corresponding to the three-dimensional seismic data is calculated according to the following formula:

[0057]

[0058] Where p(x,y,t) represents the dip volume corresponding to the 3D seismic data; υ 1x (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the x-direction; υ 1t (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the t direction;

[0059]

[0060] Where q(x,y,t) represents the azimuth data volume corresponding to the three-dimensional seismic data; υ 1y (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the y direction.

[0061] In practice, after calculating the dip and azimuth data volumes corresponding to the three-dimensional seismic data based on the three-dimensional seismic data of carbonate rock fractures, the dip and azimuth data volumes are subjected to Gaussian smoothing filtering to obtain three-dimensional seismic data with edge preservation effect.

[0062] In this embodiment, Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation, including:

[0063] Using a three-dimensional Gaussian smoothing filter, the tilt and azimuth data volumes are processed using Gaussian smoothing to obtain the Gaussian smoothed tilt and azimuth data volumes:

[0064] Based on the tilt and azimuth data volumes after Gaussian smoothing, a sliding analysis window is constructed.

[0065] An energy gradient weighted average algorithm is used to perform dip- and azimuth-guided Gaussian smoothing filtering on the 3D seismic data based on the constructed sliding analysis time window, resulting in 3D seismic data with edge preservation effect.

[0066] In the above embodiments, the dip and azimuth data volumes were preprocessed with smoothing filters to ensure their noise resistance. Then, the 3D seismic data was subjected to Gaussian smoothing filters. During the filtering process, an energy gradient weighted average algorithm was used to effectively control the edge preservation effect of the Gaussian smoothing filters, enabling edge calculation to obtain the abnormal change areas in the 3D seismic data volume caused by the presence of fault-dissolved bodies.

[0067] Furthermore, this embodiment of the invention constructs a sliding analysis window based on the dip and azimuth data volumes after Gaussian smoothing filtering. This allows for the rapid and sequential calculation of the dip and azimuth values ​​for each data point in the 3D seismic data, preventing any data points from being missed. Additionally, the sliding analysis window constructed in this paper is a dip-guided analysis window, capable of adapting to changing structural and fault information in the data, and exhibiting adaptability to abrupt seismic changes.

[0068] In one embodiment, the tilt data volume and azimuth data volume are subjected to Gaussian smoothing filtering using a three-dimensional Gaussian smoothing filter according to the following formula, to obtain the Gaussian smoothed tilt data volume and azimuth data volume:

[0069] p′(x,y,t)=p(x,y,t)*G(x,y,t,σ g );

[0070] q′(x,y,t)=q(x,y,t)*G(x,y,t,σ g );

[0071]

[0072] Where p′(x,y,t) and q′(x,y,t) represent the tilt angle data volume and azimuth angle data volume after Gaussian smoothing filtering, respectively; G(x,y,t,σ g ) represents a three-dimensional Gaussian smoothing filter; σ g is the noise scale parameter; p(x,y,t) represents the dip volume corresponding to the 3D seismic data; q(x,y,t) represents the azimuth volume corresponding to the 3D seismic data.

[0073] In one embodiment, an energy gradient weighted average algorithm is used to apply dip- and azimuth-guided Gaussian smoothing filtering to the 3D seismic data according to the constructed sliding analysis window, based on the following formula, to obtain 3D seismic data with edge preservation effect:

[0074] S(t,p′,q′)=S(tp′x j -q′y j );

[0075] S′(x,y,t)=S(t,p′,q′)*G(x,y,t,σ g );

[0076]

[0077] Where S(t,p′,q′) represents the constructed sliding analysis window; S′(x,y,t) represents the sliding analysis window after Gaussian smoothing filtering; the subscript j indicates that the j-th seismic trace falls within the sliding analysis window; x j and y j σj represents the spatial distance of the j-th seismic data along the x and y directions at the center point of the analysis window; p′ and q′ represent the dip and azimuth values ​​along the x and y directions, respectively; S″(x,y,t) represents the 3D seismic data with edge preservation after Gaussian smoothing filtering; G(x,y,t,σj) ... G(x,y,t,σjg ) represents a three-dimensional Gaussian smoothing filter; σ gThis is a noise scale parameter.

[0078] In practice, after Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain 3D seismic data with edge preservation effect, the boundary detection attributes of the fracture-dissolved body are calculated based on the 3D seismic data with edge preservation effect.

[0079] In one embodiment, the boundary detection properties of the fracture-collapse body are calculated based on 3D seismic data with edge preservation effect according to the following formula:

[0080]

[0081]

[0082]

[0083]

[0084] τ x =tan(p′)sinq′;

[0085] τ y =tan(p′)cosq′;

[0086] Where p′(x,y,t) and q′(x,y,t) represent the dip and azimuth data volumes after Gaussian smoothing in 3D seismic data with edge preservation, respectively; S″′(x,y,t) represents the fault-collapse boundary detection attribute; K is the total number of all seismic data points within the sliding analysis window, which is constructed based on the dip and azimuth data volumes after Gaussian smoothing; M x and M y These are the operators for the boundary detection attributes in two orthogonal directions; S″(x,y,t) represents all seismic data points within the sliding analysis window; Δt is the sampling rate. and These represent the delay times at the same strata as the longitudinal and transverse survey lines corresponding to the center point of the sliding analysis time window (i.e., the local window of the sliding analysis time window); n is the total number of sample points in the t direction; i is the time shift in the x direction; j is the time shift in the y direction; and k is the time shift in the t direction.

[0087] In practice, after calculating the boundary detection attributes of the fault-dissolved body based on the 3D seismic data with edge preservation effect, the preset boundary detection attribute threshold is compared with the boundary detection attributes of the fault-dissolved body to determine the effective boundary detection attributes of the fault-dissolved body.

[0088] In one embodiment, a preset boundary detection attribute threshold is compared with the boundary detection attributes of the broken-down body to determine the effective boundary detection attributes of the broken-down body, including:

[0089] The boundary detection attributes of the broken solution that are greater than the preset boundary detection attribute threshold are determined as the valid boundary detection attributes of the broken solution; and the attribute values ​​of the valid boundary detection attributes of the broken solution are assigned to the first value.

[0090] Boundary detection attributes of broken solutions that are less than or equal to the preset boundary detection attribute threshold are identified as invalid boundary detection attributes of broken solutions; and the attribute values ​​of invalid boundary detection attributes of broken solutions are assigned to the second value.

[0091] In the above embodiments, after calculating the detection attributes of the fractured solution boundary, effective data was screened, which further demonstrates the effectiveness of the calculated fractured solution boundary and can achieve the purpose of accurately and effectively calculating the reservoir volume of carbonate rock fractured solution.

[0092] In practice, after comparing the preset boundary detection attribute threshold with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution, the effective boundary detection attribute of the broken solution is subjected to time-depth conversion processing to obtain the boundary detection attribute volume of the broken solution in the depth domain.

[0093] In the above embodiments, by performing time-depth conversion on the selected effective data, it can be ensured that the space occupied by each voxel is the same, so as to improve the accuracy of subsequent calculation of the storage space volume of the broken solution.

[0094] In practice, after performing time-depth conversion processing on the effective boundary detection attributes of the fractured solution body to obtain the boundary detection attribute volume of the fractured solution body in the depth domain, the volume of the storage space of the carbonate rock fractured solution body is obtained by volume integration on the boundary detection attribute volume of the fractured solution body in the depth domain.

[0095] In the above embodiments, by using volume integral calculation, the spatial volume of the abnormal zone caused by the presence of fault-dissolved bodies in the three-dimensional seismic data volume can be obtained, thereby achieving the effect of quantitatively calculating the total volume of the fault-dissolved body storage space.

[0096] In one embodiment, the volumetric integral of the boundary detection property volume of the fractured solution in the depth domain is performed to obtain the volume of the reservoir space of the fractured solution in carbonate rocks, including:

[0097] Calculate the volume of each volume element in the depth domain's broken-dissolve boundary detection attribute volume and the product of the corresponding depth domain's broken-dissolve boundary detection attribute.

[0098] The above products of each volume element are summed to obtain the volume of the storage space of the carbonate rock fracture solution.

[0099] In one embodiment, the volume of the storage space of the carbonate rock fractured solution is obtained by performing volume integration on the boundary detection attribute volume of the fractured solution in the depth domain according to the following formula:

[0100]

[0101] Where V is the volume of the reservoir space of the carbonate rock fractured solution; m, n, and l are the boundary detection attribute volumes S″′ of the fractured solution in the transformed depth domain, respectively. D (x,y,d) represents the number of grid points in the x, y, and t directions; S″′ D (x i ,y j ,d k ) is the boundary detection attribute of the fractured body in the depth domain at point (x i ,y j ,d k The attribute value of ).

[0102] For example, Figure 2 As shown, Figure 2 This is a schematic diagram of the total volumetric space distribution of the carbonate rock fractured solution reservoir calculated in this invention.

[0103] The following is a specific embodiment to illustrate the application of the method of the present invention. This embodiment may include the following steps:

[0104] S1. Acquire 3D seismic data S(x,y,t), and calculate the dip data volume p(x,y,t) and azimuth data volume q(x,y,t) corresponding to the 3D seismic data S(x,y,t);

[0105] S2. Gaussian smoothing filter is applied to the dip data volume p(x,y,t) and azimuth data volume q(x,y,t) to obtain three-dimensional seismic data S″(x,y,t) with edge preservation effect;

[0106] S3. Calculate the boundary detection attributes S″′(x,y,t) of the broken solution body;

[0107] S4. Valid data is filtered using the property S″′(x,y,t) for detecting the boundary of the broken solution.

[0108] S5. Perform time-depth transformation on the selected valid data to obtain the depth domain boundary detection attribute volume S″′. D (x,y,d);

[0109] S6. Depth-domain dissolve boundary detection attribute S″′ D By performing volume integration on (x,y,d), the total volume of the carbonate rock fractured solution reservoir is calculated.

[0110] The following is a detailed explanation of each of the above steps:

[0111] 1. Step S1 above specifically includes:

[0112] S 11 Acquire 3D seismic data S(x,y,t); for example, Figure 3 As shown, Figure 3 This is a seismic profile of the three-dimensional seismic data collected in this invention;

[0113] S 12 Construct the gradient structure tensor matrix GST(x,y,t), and perform matrix decomposition based on the matrix eigenvalues:

[0114]

[0115] Among them, υ i and λ i (i = 1, 2, 3) represent the three eigenvectors and eigenvalues ​​corresponding to the gradient structure tensor matrix, respectively.

[0116] S 13 Sort these three eigenvalues ​​so that λ1 > λ2 > λ3, and obtain the largest eigenvalue λ1.

[0117] S 14 Using the calculated eigenvector υ1(x,y,t) of the largest eigenvalue λ1, the vector υ in the x-direction is... 1x (x, y, t), the vector υ in the y direction 1y (x,y,t) and the vector υ in the direction of t. 1t (x,y,t), calculate the tilt data volume p(x,y,t) and azimuth data volume q(x,y,t):

[0118]

[0119]

[0120] 2. Step S2 above specifically includes:

[0121] S 21 Using a three-dimensional Gaussian smoothing filter G(x,y,t,σ) g The tilt data volume p(x,y,t) and azimuth data volume q(x,y,t) are preprocessed with a smoothing filter to obtain the Gaussian smoothed tilt data volume p′(x,y,t) and azimuth data volume q′(x,y,t):

[0122] p′(x,y,t)=p(x,y,t)*G(x,y,t,σg );

[0123] q′(x,y,t)=q(x,y,t)*G(x,y,t,σ g );

[0124]

[0125] Wherein G(x,y,t,σ) g ) is a three-dimensional Gaussian smoothing filter, σ g For noise scale parameters;

[0126] S 22 Based on the dip angle data volume p′(x,y,t) and azimuth angle data volume q′(x,y,t) after Gaussian smoothing filtering, a sliding analysis time window S(t,p′,q′) is constructed. Then, the 3D seismic data is subjected to dip and azimuth-guided Gaussian smoothing filtering according to the constructed sliding analysis time window S(t,p′,q′) to obtain 3D seismic data S″(x,y,t) with edge preservation effect.

[0127] The above step S 22 In the Gaussian smoothing filtering process, the energy gradient weighted averaging algorithm is used to obtain 3D seismic data S″(x,y,t) with edge preservation effect:

[0128] S(t,p′,q′)=S(tp′x j -q′y j );

[0129] S′(x,y,t)=S(t,p′,q′)*G(x,y,t,σ g );

[0130]

[0131] Where the subscript j indicates that the j-th seismic data falls within the sliding analysis window, x j and y j , , and , respectively, represent the spatial distances of the j-th seismic data along the x and y directions at the center point of the analysis window; p′ and q′ represent the dip and azimuth values ​​along the x and y directions, respectively; S″(x,y,t) represents the 3D seismic data with edge preservation effect after Gaussian smoothing filtering.

[0132] 3. Step S3 above specifically refers to:

[0133] Based on the dip angle information p′(x,y,t) and azimuth angle information q′(x,y,t) in the 3D seismic data, the boundary detection attribute S″′(x,y,t) of the fractured karst body is calculated:

[0134]

[0135]

[0136] Where K is the total number of all seismic data points within the analysis window; M x and M y These are the operators for the boundary detection attributes in two orthogonal directions, respectively; S″(x,y,t) represents all seismic data points within the analysis window; Δt is the sampling rate. and These represent the delay times for the longitudinal and transverse survey lines corresponding to the center point of the local window, respectively, and their specific meanings are shown in the following formulas:

[0137]

[0138]

[0139] τ x =tan(p′)sinq′;τ y =tan(p′)cosq′

[0140] 4. Step S4 above specifically refers to:

[0141] When the boundary detection attribute S″′(x,y,t) of the broken solution is less than or equal to 0.5, the attribute value is assigned to zero; when the attribute value is greater than 0.5, the attribute value is assigned to 1.

[0142]

[0143] 5. In step S5 above, after the time-depth conversion, the boundary detection attribute volume S″′ of the dissolution body in the depth domain is... D Each volume element in (x,y,d) is a three-dimensional volume, i.e., a depth domain sample volume element, with a base area of ​​25 meters by 25 meters and a height of 10 meters.

[0144] For example, such as Figure 4 As shown, Figure 4 This is a schematic diagram of the fault-dissolved body boundary of a certain seismic profile calculated in an embodiment of the present invention.

[0145] 6. The volume integration in step S6 above, calculating the total volume of the carbonate rock fractured solution reservoir, specifically refers to multiplying the volume of all depth domain sample elements by the depth domain boundary detection attribute value, and then summing them up:

[0146]

[0147] Where V is the total volume of the carbonate rock fractured-dissolved reservoir space, and m, n, and l are the transformed depth domain fractured-dissolved boundary detection attributes S″′, respectively.D (x,y,d) represents the number of grid points in the x, y, and t directions, and S″′ represents the number of grid points in those directions. D (x i ,y j ,d k ) represents the depth domain boundary detection attribute value of 0 or 1 at any point.

[0148] Of course, it is understood that there may be other variations of the above detailed process, and all such variations should fall within the protection scope of this invention.

[0149] In this embodiment of the invention, based on the three-dimensional seismic data of carbonate rock fractured solutions, the dip and azimuth data volumes corresponding to the three-dimensional seismic data are calculated; Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect; based on the three-dimensional seismic data with edge preservation effect, the boundary detection attributes of the fractured solution are calculated; a preset boundary detection attribute threshold is compared with the boundary detection attributes of the fractured solution to determine the effective boundary detection attributes of the fractured solution; the effective boundary detection attributes of the fractured solution are subjected to time-depth conversion processing to obtain the boundary detection attribute volume of the fractured solution in the depth domain; volume integration is performed on the boundary detection attribute volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the carbonate rock fractured solution. Thus, by determining the effective boundary detection attributes of the fractured solution, the calculation of the boundary detection attributes of the fractured solution can be performed. Subsequent filtering of valid data further demonstrates the effectiveness of the calculated fault-knot boundary, achieving the goal of accurately and effectively calculating the reservoir volume of carbonate fault-knots. This also solves the problem of inaccurate fault-knot volume calculations due to random noise in 3D seismic data in existing technologies. Furthermore, by performing time-depth conversion, it ensures that the space occupied by each volume element in the fault-knot boundary detection attribute volume is the same, improving the accuracy of calculating the reservoir volume of fault-knots. This also solves the problem of unreliability in the calculated fault-knot volume due to the fact that existing technologies can only calculate the fault-knot volume in the time domain. Moreover, by performing volume integral calculation, the spatial volume of the anomaly zone caused by the presence of fault-knots in the 3D seismic data volume can be obtained, thereby achieving the effect of quantitatively calculating the total reservoir volume of fault-knots.

[0150] As described above, the embodiments of the present invention have the following advantages:

[0151] 1. After calculating the boundary detection attributes of the fractured solution body, effective data was screened, further demonstrating the validity of the calculated boundary and achieving the goal of accurately and effectively calculating the reservoir volume of carbonate fractured solution bodies. This invention also performs time-depth conversion on the screened effective data to ensure that each volume element occupies the same space, thereby improving the accuracy of subsequent calculations of the reservoir volume of fractured solution bodies.

[0152] 2. Smoothing filtering preprocessing was performed on the dip and azimuth data volumes to ensure their noise resistance. Then, Gaussian smoothing filtering was applied to the 3D seismic data. During the filtering process, the energy gradient weighted average algorithm was used, which effectively controlled the edge preservation effect of Gaussian smoothing filtering, enabling edge calculation to obtain the anomaly areas in the 3D seismic data volume caused by the presence of fault-dissolved bodies.

[0153] 3. Based on the dip and azimuth data volumes after Gaussian smoothing, a sliding analysis window is constructed. This window can quickly and sequentially calculate the dip and azimuth values ​​for each data point in the 3D seismic data, avoiding any omissions. Furthermore, the sliding analysis window constructed in this paper is dip-guided, adaptable to changing structural and fault information in the data, and possesses adaptability to abrupt seismic changes.

[0154] 4. By using volumetric integral calculations, the spatial volume of the abnormal change zone can be obtained, thus achieving the effect of quantitatively calculating the total volume of the discontinuous solution reservoir.

[0155] This invention also provides a device for calculating the reservoir space volume of carbonate rock fractured solutions, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the method for calculating the reservoir space volume of carbonate rock fractured solutions, the implementation of this device can refer to the implementation of the method for calculating the reservoir space volume of carbonate rock fractured solutions; repeated details will not be elaborated further.

[0156] This invention also provides a device for calculating the reservoir space volume of carbonate rock fractured solutions, thereby improving the accuracy of calculating the reservoir space volume of carbonate rock fractured solutions, such as... Figure 6 As shown, the device includes:

[0157] The dip and azimuth data volume calculation module 601 is used to calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data based on the three-dimensional seismic data of carbonate rock fracture solution.

[0158] The Gaussian smoothing filter module 602 is used to perform Gaussian smoothing filter processing on the dip angle data volume and the azimuth angle data volume to obtain three-dimensional seismic data with edge preservation effect;

[0159] The fault-collapse boundary detection attribute calculation module 603 is used to calculate the fault-collapse boundary detection attributes based on three-dimensional seismic data with edge preservation effect;

[0160] The threshold comparison module 604 is used to compare the preset boundary detection attribute threshold with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution.

[0161] The time-depth conversion processing module 605 is used to perform time-depth conversion processing on the effective boundary detection attributes of the broken solution to obtain the boundary detection attribute volume of the broken solution in the depth domain.

[0162] The volume integration module 606 is used to perform volume integration on the boundary detection attribute volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the carbonate fractured solution.

[0163] In one embodiment, the tilt angle data volume and azimuth angle data volume calculation module is specifically used for:

[0164] Based on the three-dimensional seismic data of carbonate rock fracture solutions, a gradient structure tensor matrix is ​​constructed.

[0165] Perform matrix decomposition on the constructed gradient structure tensor matrix to obtain multiple eigenvectors corresponding to the gradient structure tensor matrix and the eigenvalues ​​corresponding to each eigenvector;

[0166] Based on multiple eigenvectors and the eigenvalues ​​corresponding to each eigenvector, determine the first eigenvector with the largest eigenvalue;

[0167] Based on the components of the first eigenvector in different directions, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data.

[0168] In one embodiment, the tilt angle data volume and azimuth angle data volume calculation module is specifically used for:

[0169] Construct the gradient structure tensor matrix based on the 3D seismic data of carbonate rock fracture solutions using the following formula:

[0170]

[0171] Where GST(x,y,t) represents the constructed gradient structure tensor matrix; υ i and λ i (i = 1, 2, 3) represent the three eigenvectors and eigenvalues ​​corresponding to the gradient structure tensor matrix.

[0172] In one embodiment, the tilt angle data volume and azimuth angle data volume calculation module is specifically used for:

[0173] The dip volume corresponding to the 3D seismic data is calculated using the following formula:

[0174]

[0175] Where p(x,y,t) represents the dip volume corresponding to the 3D seismic data; υ 1x (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the x-direction; υ 1t(x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the t direction;

[0176]

[0177] Where q(x,y,t) represents the azimuth data volume corresponding to the three-dimensional seismic data; υ 1y (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the y direction.

[0178] In one embodiment, the Gaussian smoothing filter processing module is specifically used for:

[0179] Using a three-dimensional Gaussian smoothing filter, the tilt and azimuth data volumes are processed using Gaussian smoothing to obtain the Gaussian smoothed tilt and azimuth data volumes:

[0180] Based on the tilt and azimuth data volumes after Gaussian smoothing, a sliding analysis window is constructed.

[0181] An energy gradient weighted average algorithm is used to perform dip- and azimuth-guided Gaussian smoothing filtering on the 3D seismic data based on the constructed sliding analysis time window, resulting in 3D seismic data with edge preservation effect.

[0182] In one embodiment, the Gaussian smoothing filter processing module is specifically used for:

[0183] The tilt and azimuth data volumes are processed using a three-dimensional Gaussian smoothing filter according to the following formula, resulting in Gaussian smoothed tilt and azimuth data volumes:

[0184] p′(x,y,t)=p(x,y,t)*G(x,y,t,σ g );

[0185] q′(x,y,t)=q(x,y,t)*G(x,y,t,σ g );

[0186]

[0187] Where p′(x,y,t) and q′(x,y,t) represent the tilt angle data volume and azimuth angle data volume after Gaussian smoothing filtering, respectively; G(x,y,t,σ g ) represents a three-dimensional Gaussian smoothing filter; σ g is the noise scale parameter; p(x,y,t) represents the dip volume corresponding to the 3D seismic data; q(x,y,t) represents the azimuth volume corresponding to the 3D seismic data.

[0188] In one embodiment, the Gaussian smoothing filter processing module is specifically used for:

[0189] Using the energy gradient weighted average algorithm according to the following formula, and based on the constructed sliding analysis window, the 3D seismic data is subjected to dip- and azimuth-guided Gaussian smoothing filtering to obtain 3D seismic data with edge preservation effect:

[0190] S(t,p′,q′)=S(tp′x j -q′y j );

[0191] S′(x,y,t)=S(t,p′,q′)*G(x,y,t,σ g );

[0192]

[0193] Where S(t,p′,q′) represents the constructed sliding analysis window; S′(x,y,t) represents the sliding analysis window after Gaussian smoothing filtering; the subscript j indicates that the j-th seismic trace falls within the sliding analysis window; x j and y j σj represents the spatial distance of the j-th seismic data along the x and y directions at the center point of the analysis window; p′ and q′ represent the dip and azimuth values ​​along the x and y directions, respectively; S″(x,y,t) represents the 3D seismic data with edge preservation after Gaussian smoothing filtering; G(x,y,t,σj) ... G(x,y,t,σjg ) represents a three-dimensional Gaussian smoothing filter; σ g This is a noise scale parameter.

[0194] In one embodiment, the melt boundary detection attribute calculation module is specifically used for:

[0195] The boundary detection properties of fracture-collapse bodies are calculated using the following formula based on 3D seismic data with edge preservation:

[0196]

[0197]

[0198]

[0199]

[0200] τ x =tan(p′)sinq′;

[0201] τ y =tan(p′)cosq′;

[0202] Where p′(x,y,t) and q′(x,y,t) represent the dip and azimuth data volumes after Gaussian smoothing in 3D seismic data with edge preservation, respectively; S″′(x,y,t) represents the fault-collapse boundary detection attribute; K is the total number of all seismic data points within the sliding analysis window, which is constructed based on the dip and azimuth data volumes after Gaussian smoothing; M x and M y These are the operators for the boundary detection attributes in two orthogonal directions; S″(x,y,t) represents all seismic data points within the sliding analysis window; Δt is the sampling rate. and , respectively, represent the delay time of the same layer as the longitudinal and transverse survey lines corresponding to the center point of the sliding analysis time window; n is the total number of sample points in the t direction; i is the time shift in the x direction; j is the time shift in the y direction.

[0203] In one embodiment, the threshold comparison module is specifically used for:

[0204] The boundary detection attributes of the broken solution that are greater than the preset boundary detection attribute threshold are determined as the valid boundary detection attributes of the broken solution; and the attribute values ​​of the valid boundary detection attributes of the broken solution are assigned to the first value.

[0205] Boundary detection attributes of broken solutions that are less than or equal to the preset boundary detection attribute threshold are identified as invalid boundary detection attributes of broken solutions; and the attribute values ​​of invalid boundary detection attributes of broken solutions are assigned to the second value.

[0206] In one embodiment, the volumetric integration module is specifically used for:

[0207] Calculate the volume of each volume element in the depth domain's broken-dissolve boundary detection attribute volume and the product of the corresponding depth domain's broken-dissolve boundary detection attribute.

[0208] The above products of each volume element are summed to obtain the volume of the storage space of the carbonate rock fracture solution.

[0209] In one embodiment, the volumetric integration module is specifically used for:

[0210] The volume of the reservoir space of the carbonate rock fractured solution is obtained by performing volume integration on the boundary detection property volume of the fractured solution in the depth domain according to the following formula:

[0211]

[0212] Where V is the volume of the reservoir space of the carbonate rock fractured solution; m, n, and l are the boundary detection attribute volumes S″′ of the fractured solution in the transformed depth domain, respectively. D(x,y,d) represents the number of grid points in the x, y, and t directions; S″′ D (x i ,y j ,d k ) is the boundary detection attribute of the fractured body in the depth domain at point (x i ,y j ,d k The attribute value of ).

[0213] Based on the above inventive concept, such as Figure 5 As shown, the present invention also proposes a computer device 500, including a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 530, it implements a method for calculating the storage space volume of the carbonate rock fracture solution.

[0214] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for calculating the storage space volume of the carbonate rock fracture solution.

[0215] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements a method for calculating the reservoir volume of the carbonate rock fracture solution.

[0216] In this embodiment of the invention, based on the three-dimensional seismic data of carbonate rock fractured solutions, the dip and azimuth data volumes corresponding to the three-dimensional seismic data are calculated; Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect; based on the three-dimensional seismic data with edge preservation effect, the boundary detection attributes of the fractured solution are calculated; a preset boundary detection attribute threshold is compared with the boundary detection attributes of the fractured solution to determine the effective boundary detection attributes of the fractured solution; the effective boundary detection attributes of the fractured solution are subjected to time-depth conversion processing to obtain the boundary detection attribute volume of the fractured solution in the depth domain; volume integration is performed on the boundary detection attribute volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the carbonate rock fractured solution. Thus, by determining the effective boundary detection attributes of the fractured solution, the calculation of the boundary detection attributes of the fractured solution can be performed. Subsequent filtering of valid data further demonstrates the effectiveness of the calculated fault-knot boundary, achieving the goal of accurately and effectively calculating the reservoir volume of carbonate fault-knots. This also solves the problem of inaccurate fault-knot volume calculations due to random noise in 3D seismic data in existing technologies. Furthermore, by performing time-depth conversion, it ensures that the space occupied by each volume element in the fault-knot boundary detection attribute volume is the same, improving the accuracy of calculating the reservoir volume of fault-knots. This also solves the problem of unreliability in the calculated fault-knot volume due to the fact that existing technologies can only calculate the fault-knot volume in the time domain. Moreover, by performing volume integral calculation, the spatial volume of the anomaly zone caused by the presence of fault-knots in the 3D seismic data volume can be obtained, thereby achieving the effect of quantitatively calculating the total reservoir volume of fault-knots.

[0217] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0218] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0219] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0220] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0221] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the reservoir space volume of a carbonate rock fractured solution, characterized in that, include: Based on the three-dimensional seismic data of carbonate rock fracture solution, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data. Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect; Calculate the boundary detection properties of fracture-collapse bodies based on 3D seismic data with edge preservation effect; The preset boundary detection attribute threshold is compared with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution; The effective boundary detection attributes of the broken solution are processed by time-depth transformation to obtain the boundary detection attribute volume of the broken solution in the depth domain; By performing volume integration on the boundary detection property volume of the fractured solution in the depth domain, the volume of the reservoir space of the fractured solution in carbonate rock is obtained.

2. The method as described in claim 1, characterized in that, Based on the 3D seismic data of carbonate rock fractured karst bodies, calculate the dip and azimuth data volumes corresponding to the 3D seismic data, including: Based on the three-dimensional seismic data of carbonate rock fractures, a gradient structure tensor matrix is ​​constructed. Perform matrix decomposition on the constructed gradient structure tensor matrix to obtain multiple eigenvectors corresponding to the gradient structure tensor matrix and the eigenvalues ​​corresponding to each eigenvector; Based on multiple eigenvectors and the eigenvalues ​​corresponding to each eigenvector, determine the first eigenvector with the largest eigenvalue; Based on the components of the first eigenvector in different directions, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data.

3. The method as described in claim 2, characterized in that, Construct the gradient structure tensor matrix based on the 3D seismic data of carbonate rock fracture solutions using the following formula: Where GST(x,y,t) represents the constructed gradient structure tensor matrix; υ i and λ i (i = 1, 2, 3) represent the three eigenvectors and eigenvalues ​​corresponding to the gradient structure tensor matrix.

4. The method as described in claim 2, characterized in that, The dip volume corresponding to the 3D seismic data is calculated using the following formula: Where p(x,y,t) represents the dip volume corresponding to the 3D seismic data; υ 1x (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the x-direction; υ 1t (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the t direction; Where q(x,y,t) represents the azimuth data volume corresponding to the three-dimensional seismic data; υ 1y (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the y direction.

5. The method as described in claim 1, characterized in that, Gaussian smoothing filtering is applied to the dip and azimuth data volumes to obtain 3D seismic data with edge preservation, including: Using a three-dimensional Gaussian smoothing filter, the tilt and azimuth data volumes are processed using Gaussian smoothing to obtain the Gaussian smoothed tilt and azimuth data volumes: Based on the tilt and azimuth data volumes after Gaussian smoothing, a sliding analysis window is constructed. An energy gradient weighted average algorithm is used to perform dip- and azimuth-guided Gaussian smoothing filtering on the 3D seismic data based on the constructed sliding analysis time window, resulting in 3D seismic data with edge preservation effect.

6. The method as described in claim 5, characterized in that, The tilt and azimuth data volumes are processed using a three-dimensional Gaussian smoothing filter according to the following formula, resulting in Gaussian smoothed tilt and azimuth data volumes: p′(x,y,t)=p(x,y,t)*G(x,y,t,σ g ); q′(x,y,t)=q(x,y,t)*G(x,y,t,σ g ); Where p′(x,y,t) and q′(x,y,t) represent the tilt angle data volume and azimuth angle data volume after Gaussian smoothing filtering, respectively; G(x,y,t,σ g ) represents a three-dimensional Gaussian smoothing filter; σ g is the noise scale parameter; p(x,y,t) represents the dip volume corresponding to the 3D seismic data; q(x,y,t) represents the azimuth volume corresponding to the 3D seismic data.

7. The method as described in claim 5, characterized in that, Using the energy gradient weighted average algorithm according to the following formula, and based on the constructed sliding analysis window, the 3D seismic data is subjected to dip- and azimuth-guided Gaussian smoothing filtering to obtain 3D seismic data with edge preservation effect: S(t,p′,q′)=S(t-p′x j -q′y j ); S′(x,y,t)=S(t,p′,q′)*G(x,y,t,σ g ); Where S(t,p′,q′) represents the constructed sliding analysis window; S′(x,y,t) represents the sliding analysis window after Gaussian smoothing filtering; the subscript j indicates that the j-th seismic trace falls within the sliding analysis window; x j and y j σj represents the spatial distance of the j-th seismic data along the x and y directions at the center point of the analysis window; p′ and q′ represent the dip and azimuth values ​​along the x and y directions, respectively; S″(x,y,t) represents the 3D seismic data with edge preservation after Gaussian smoothing filtering; G(x,y,t,σj) ... G(x,y,t,σjg ) represents a three-dimensional Gaussian smoothing filter; σ g This is a noise scale parameter.

8. The method as described in claim 1, characterized in that, The boundary detection properties of fracture-collapse bodies are calculated using the following formula based on 3D seismic data with edge preservation: τ x =tan(p′)sinq′; τ y =tan(p′)cosq′; Where p′(x,y,t) and q′(x,y,t) represent the dip and azimuth data volumes after Gaussian smoothing in 3D seismic data with edge preservation, respectively; S″′(x,y,t) represents the fault-collapse boundary detection attribute; K is the total number of all seismic data points within the sliding analysis window, which is constructed based on the dip and azimuth data volumes after Gaussian smoothing; M x and M y These are the operators for the boundary detection attributes in two orthogonal directions; S″(x,y,t) represents all seismic data points within the sliding analysis window; Δt is the sampling rate. and , respectively, represent the delay time of the same layer as the longitudinal and transverse survey lines corresponding to the center point of the sliding analysis time window; n is the total number of sample points in the t direction; i is the time shift in the x direction; j is the time shift in the y direction.

9. The method as described in claim 1, characterized in that, The preset boundary detection attribute thresholds are compared with the boundary detection attributes of the broken solution to determine the effective boundary detection attributes of the broken solution, including: The boundary detection attributes of the broken solution that are greater than the preset boundary detection attribute threshold are determined as the valid boundary detection attributes of the broken solution; and the attribute values ​​of the valid boundary detection attributes of the broken solution are assigned to the first value. Boundary detection attributes of broken solutions that are less than or equal to the preset boundary detection attribute threshold are identified as invalid boundary detection attributes of broken solutions; and the attribute values ​​of invalid boundary detection attributes of broken solutions are assigned to the second value.

10. The method as described in claim 1, characterized in that, By performing volume integration on the boundary detection property volume of the fractured solution in the depth domain, the volume of the reservoir space of the carbonate rock fractured solution is obtained, including: Calculate the volume of each volume element in the depth domain's broken-dissolve boundary detection attribute volume and the product of the corresponding depth domain's broken-dissolve boundary detection attribute. The product of each volume element is summed to obtain the volume of the storage space of the carbonate rock fracture solution.

11. The method as described in claim 1, characterized in that, The volume of the reservoir space of the carbonate rock fractured solution is obtained by performing volume integration on the boundary detection property volume of the fractured solution in the depth domain according to the following formula: Where V is the volume of the reservoir space of the carbonate rock fractured solution; m, n, and l are the boundary detection attribute volumes S′″ of the fractured solution in the transformed depth domain, respectively. D (x,y,d) represents the number of grid points in the x, y, and t directions; S′″ D (x i ,y j ,d k ) is the boundary detection attribute of the fractured body in the depth domain at point (x i ,y j ,d k The attribute value of ).

12. A device for calculating the reservoir space volume of a carbonate rock fractured solution, characterized in that, include: The dip and azimuth data volume calculation module is used to calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data of carbonate rock fractures and solutions. The Gaussian smoothing filter module is used to perform Gaussian smoothing filter processing on dip and azimuth data volumes to obtain three-dimensional seismic data with edge preservation effect. The fault-collapse boundary detection attribute calculation module is used to calculate the fault-collapse boundary detection attributes based on 3D seismic data with edge preservation effect; The threshold comparison module is used to compare the preset boundary detection attribute threshold with the boundary detection attribute of the broken solution to determine the effective boundary detection attribute of the broken solution. The time-depth conversion processing module is used to perform time-depth conversion processing on the effective boundary detection attributes of the broken solution to obtain the boundary detection attribute volume of the broken solution in the depth domain. The volume integration module is used to perform volume integration on the boundary detection property volume of the fractured solution in the depth domain to obtain the volume of the reservoir space of the fractured solution in carbonate rocks.

13. The apparatus as claimed in claim 12, characterized in that, The tilt and azimuth data volume calculation module is specifically used for: Based on the three-dimensional seismic data of carbonate rock fractures, a gradient structure tensor matrix is ​​constructed. Perform matrix decomposition on the constructed gradient structure tensor matrix to obtain multiple eigenvectors corresponding to the gradient structure tensor matrix and the eigenvalues ​​corresponding to each eigenvector; Based on multiple eigenvectors and the eigenvalues ​​corresponding to each eigenvector, determine the first eigenvector with the largest eigenvalue; Based on the components of the first eigenvector in different directions, calculate the dip and azimuth data volumes corresponding to the three-dimensional seismic data.

14. The apparatus as claimed in claim 13, characterized in that, The tilt and azimuth data volume calculation module is specifically used for: Construct the gradient structure tensor matrix based on the 3D seismic data of carbonate rock fracture solutions using the following formula: Where GST(x,y,t) represents the constructed gradient structure tensor matrix; υ i and λ i (i = 1, 2, 3) represent the three eigenvectors and eigenvalues ​​corresponding to the gradient structure tensor matrix.

15. The apparatus as claimed in claim 13, characterized in that, The tilt and azimuth data volume calculation module is specifically used for: The dip volume corresponding to the 3D seismic data is calculated using the following formula: Where p(x,y,t) represents the dip volume corresponding to the 3D seismic data; υ 1x (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the x-direction; υ 1t (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the t direction; Where q(x,y,t) represents the azimuth data volume corresponding to the three-dimensional seismic data; υ 1y (x,y,t) represents the vector of the first eigenvector υ1(x,y,t) in the y direction.

16. The apparatus as claimed in claim 12, characterized in that, The Gaussian smoothing filter module is specifically used for: Using a three-dimensional Gaussian smoothing filter, the tilt and azimuth data volumes are processed using Gaussian smoothing to obtain the Gaussian smoothed tilt and azimuth data volumes: Based on the tilt and azimuth data volumes after Gaussian smoothing, a sliding analysis window is constructed. An energy gradient weighted average algorithm is used to perform dip- and azimuth-guided Gaussian smoothing filtering on the 3D seismic data based on the constructed sliding analysis time window, resulting in 3D seismic data with edge preservation effect.

17. The apparatus as claimed in claim 16, characterized in that, The Gaussian smoothing filter module is specifically used for: The tilt and azimuth data volumes are processed using a three-dimensional Gaussian smoothing filter according to the following formula, resulting in Gaussian smoothed tilt and azimuth data volumes: p′(x,y,t)=p(x,y,t)*G(x,y,t,σ g ); q′(x,y,t)=q(x,y,t)*G(x,y,t,σ g ); Where p′(x,y,t) and q′(x,y,t) represent the tilt angle data volume and azimuth angle data volume after Gaussian smoothing filtering, respectively; G(x,y,t,σ g ) represents a three-dimensional Gaussian smoothing filter; σ g is the noise scale parameter; p(x,y,t) represents the dip volume corresponding to the 3D seismic data; q(x,y,t) represents the azimuth volume corresponding to the 3D seismic data.

18. The apparatus as claimed in claim 16, characterized in that, The Gaussian smoothing filter module is specifically used for: Using the energy gradient weighted average algorithm according to the following formula, and based on the constructed sliding analysis window, the 3D seismic data is subjected to dip- and azimuth-guided Gaussian smoothing filtering to obtain 3D seismic data with edge preservation effect: S(t,p′,q′)=S(t-p′x j -q′y j ); S′(x,y,t)=S(t,p′,q′)*G(x,y,t,σ g ); Where S(t,p′,q′) represents the constructed sliding analysis window; S′(x,y,t) represents the sliding analysis window after Gaussian smoothing filtering; the subscript j indicates that the j-th seismic trace falls within the sliding analysis window; x j and y j σj represents the spatial distance of the j-th seismic data along the x and y directions at the center point of the analysis window; p′ and q′ represent the dip and azimuth values ​​along the x and y directions, respectively; S″(x,y,t) represents the 3D seismic data with edge preservation after Gaussian smoothing filtering; G(x,y,t,σj) ... G(x,y,t,σjg ) represents a three-dimensional Gaussian smoothing filter; σ g This is a noise scale parameter.

19. The apparatus as claimed in claim 12, characterized in that, The broken-body boundary detection attribute calculation module is specifically used for: The boundary detection properties of fracture-collapse bodies are calculated using the following formula based on 3D seismic data with edge preservation: τ x =tan(p′)sin q′; τ y =tan(p′)cos q′; Where p′(x,y,t) and q′(x,y,t) represent the dip and azimuth data volumes after Gaussian smoothing in 3D seismic data with edge preservation, respectively; S″′(x,y,t) represents the fault-collapse boundary detection attribute; K is the total number of all seismic data points within the sliding analysis window, which is constructed based on the dip and azimuth data volumes after Gaussian smoothing; M x and M y These are the operators for the boundary detection attributes in two orthogonal directions; S″(x,y,t) represents all seismic data points within the sliding analysis window; Δt is the sampling rate. and , respectively, represent the delay time of the same layer as the longitudinal and transverse survey lines corresponding to the center point of the sliding analysis time window; n is the total number of sample points in the t direction; i is the time shift in the x direction; j is the time shift in the y direction.

20. The apparatus as claimed in claim 12, characterized in that, The threshold comparison module is specifically used for: The boundary detection attributes of the broken solution that are greater than the preset boundary detection attribute threshold are determined as the valid boundary detection attributes of the broken solution; and the attribute values ​​of the valid boundary detection attributes of the broken solution are assigned to the first value. Boundary detection attributes of broken solutions that are less than or equal to the preset boundary detection attribute threshold are identified as invalid boundary detection attributes of broken solutions; and the attribute values ​​of invalid boundary detection attributes of broken solutions are assigned to the second value.

21. The apparatus as claimed in claim 12, characterized in that, The volumetric integral module is specifically used for: Calculate the volume of each volume element in the depth domain's broken-dissolve boundary detection attribute volume and the product of the corresponding depth domain's broken-dissolve boundary detection attribute. The product of each volume element is summed to obtain the volume of the storage space of the carbonate rock fracture solution.

22. The apparatus as claimed in claim 12, characterized in that, The volumetric integral module is specifically used for: The volume of the reservoir space of the carbonate rock fractured solution is obtained by performing volume integration on the boundary detection property volume of the fractured solution in the depth domain according to the following formula: Where V is the volume of the reservoir space of the carbonate rock fractured solution; m, n, and l are the boundary detection attribute volumes S′″ of the fractured solution in the transformed depth domain, respectively. D (x,y,d) represents the number of grid points in the x, y, and t directions; S′″ D (x i ,y j ,d k ) is the boundary detection attribute of the fractured body in the depth domain at point (x i ,y j ,d k The attribute value of ).

23. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 11.

24. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 11.

25. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 11.

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