Gradient structure tensor attribute threshold determination method, system, device and medium

Abstract

The present application belongs to the field of oil and gas seismic exploration and development interpretation, and specifically discloses a gradient structure tensor attribute threshold determination method, system, device and medium, which comprises the following steps: calculating a gradient structure tensor attribute; counting a gradient structure tensor attribute value domain distribution range; selecting sample points in the gradient structure tensor attribute value domain distribution range, performing normal fitting to obtain a non-reservoir attribute value domain distribution curve, and then subtracting the gradient structure tensor attribute value domain distribution curve from the non-reservoir attribute value domain distribution curve to obtain a reservoir attribute value domain distribution curve; and using the non-reservoir attribute value domain distribution curve and the reservoir attribute value domain distribution curve to find an intersection point to determine the gradient structure tensor attribute threshold. The present application solves the problems of low discrimination accuracy and uncertainty of the gradient structure tensor attribute threshold, and improves the precision of the fracture-vug reservoir boundary description. The present application is suitable for fine research on carbonate fracture-vug reservoirs.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas seismic exploration and development interpretation, specifically a method, system, equipment and medium for determining the threshold of gradient structure tensor attributes. Background Technology

[0002] Carbonate rocks are one of the important research targets for oil and gas exploration and development in the Tarim Basin, Sichuan Basin, and Ordos Basin. Their reservoirs are affected by faulting and karstification, forming different types of structures such as fractures, pores, and caves, exhibiting a fracture-cavity aggregate pattern with strong heterogeneous characteristics, making prediction extremely difficult.

[0003] Gradient structure tensor (GST) property is a commonly used attribute for characterizing the boundaries of heterogeneous reservoirs such as fractures and cavities, and it has played an important role in carbonate reservoir research in recent years. However, this property is dimensionless, and determining its threshold is difficult; its accuracy directly affects the accuracy of reservoir volume carving and reserve calculation. Previous studies have addressed this issue. For example, Zhang Sheng et al. used GST property to characterize the boundaries of fractures and cavities in carbonate rocks and proposed the concept of quantitative scale, using actual drilling information as a scale to give physical meaning to the GST property, and providing a threshold selection strategy, which to some extent reduced the ambiguity of fracture and cavity boundary characterization. He Zhiliang et al. used information such as drilling time, venting, and leakage to calibrate the GST property threshold and delineate the spatial morphology of fault-controlled reservoirs. These studies have promoted the application of GST property in the carbonate field, but due to the dimensionless nature of GST property, no quantitative and deterministic threshold selection method has been provided, resulting in strong ambiguity in the characterization of carbonate fracture and cavity boundaries. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and medium for determining the threshold of gradient structure tensor attributes, thereby improving the characterization accuracy of fractured-vuggy reservoir boundaries and providing strong support for carbonate reservoir calculation, trap characterization, and well location research.

[0005] To achieve the above objectives, the present invention employs the following technical methods:

[0006] A method for determining the threshold of gradient structure tensor properties includes the following steps performed sequentially:

[0007] S1. Based on three-dimensional seismic data, the gradient structure tensor properties are calculated;

[0008] S2. Based on the gradient structure tensor properties, statistically analyze the range of gradient structure tensor property values ​​and calculate the gradient structure tensor property value range distribution curve.

[0009] S3. Select sample points within the range of gradient structure tensor attribute value distribution, perform normal fitting, calculate the non-reservoir attribute value distribution curve, and then subtract the gradient structure tensor attribute value distribution curve from the non-reservoir attribute value distribution curve to obtain the reservoir attribute value distribution curve.

[0010] S4. Using the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve, find the intersection point and determine the gradient structure tensor attribute threshold.

[0011] As a limitation, step S1 specifically refers to:

[0012] S11. Calculate the directional derivative of the amplitude of each sample point from the three-dimensional seismic data to obtain the gradient vector of each sample point;

[0013] S12. Multiply the gradient vector by its transpose to obtain the structure tensor matrix;

[0014] S13. Perform eigenvalue decomposition on the structure tensor matrix to obtain three eigenvalues. Use the three eigenvalues ​​and their combinations to calculate the gradient structure tensor properties.

[0015] As a limitation, step S2 specifically refers to:

[0016] S21. Based on the gradient structure tensor properties, statistically analyze the distribution range of the gradient structure tensor property values ​​to obtain a statistical histogram.

[0017] S22. Using statistical histograms, calculate the envelope surface and perform normalization to obtain the distribution curve of the gradient structure tensor attribute value range.

[0018] As a limitation, the normal fitting process in step S3 is as follows:

[0019] Let the distribution of non-reservoir attribute values ​​D(x) follow a Gaussian distribution, then

[0020]

[0021] In the formula, μ is the expectation of the Gaussian distribution, and σ 2 Let x be the variance of the Gaussian distribution and x be the attribute value of the gradient structure tensor. Set the interaction parameters A1 and A2, and let A1≤x≤A2. Perform normal fitting so that the distribution curve of the non-reservoir attribute value range overlaps with the distribution curve of the gradient structure tensor attribute value range within the range of A1≤x≤A2.

[0022] The process of calculating the distribution curve of non-reservoir attribute values ​​is as follows:

[0023] Taking the logarithm of both sides of the formula for calculating the Gaussian distribution, we get...

[0024]

[0025] make Then lnD(x)=a0+a1x+a2x 2 ;

[0026] To find the coefficients a0, a1, a2, let ||lnD(x) - (a0 + a1x + a2x) 2 )|| 2 →0, the least squares algorithm is used to solve the problem, and μ and σ are calculated to determine the distribution of non-reservoir attribute values ​​D(x).

[0027] The present invention also provides a gradient structure tensor property threshold determination system, comprising:

[0028] The gradient structure tensor property calculation module calculates the gradient structure tensor properties based on 3D seismic data.

[0029] The gradient structure tensor attribute value range distribution calculation module calculates the gradient structure tensor attribute value range distribution range based on the gradient structure tensor attribute, and obtains the gradient structure tensor attribute value range distribution curve.

[0030] The module for calculating the range distribution of non-reservoir and reservoir attribute values ​​selects sample points within the range of the gradient structure tensor attribute value distribution, performs normal fitting, and obtains the range distribution curve of non-reservoir attribute values. Then, it subtracts the range distribution curve of gradient structure tensor attribute values ​​from the range distribution curve of non-reservoir attribute values ​​to obtain the range distribution curve of reservoir attribute values.

[0031] The gradient structure tensor attribute threshold determination module uses the intersection points of the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve to determine the gradient structure tensor attribute threshold.

[0032] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program in the memory to execute the above-described gradient structure tensor attribute threshold determination method.

[0033] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the above-described method for determining the threshold of gradient structure tensor attributes.

[0034] The beneficial effects achieved by this invention, due to the adoption of the above-described solution, compared with the prior art, are as follows:

[0035] A method, system, device, and medium for determining the threshold of gradient structure tensor attributes are disclosed. By statistically analyzing the value range distribution of gradient structure tensor attributes and performing normal fitting, the value range distribution of non-reservoir attributes is obtained. The value range distribution of reservoir attributes is obtained by subtracting the value range distribution of gradient structure tensor attributes from that of non-reservoir attributes. The intersection of the value range distributions of reservoir attributes and non-reservoir attributes is the threshold of the gradient structure tensor attribute. This provides a quantitative and deterministic method for threshold selection, effectively solving the problems of low accuracy and uncertainty in gradient structure tensor attribute threshold discrimination. It effectively reduces the ambiguity in characterizing the boundaries of fractured-vuggy carbonate reservoirs and improves the accuracy of using gradient structure tensor attributes to characterize the boundaries of fractured-vuggy carbonate reservoirs. This provides strong support for carbonate reservoir calculation, trap characterization, and well location research, laying the foundation for refined research on carbonate reservoirs.

[0036] This invention is applicable to the detailed study of fractured-vuggy carbonate reservoirs. Attached Figure Description

[0037] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0038] Figure 1 This is a flowchart of the gradient structure tensor attribute threshold determination method in Embodiment 1 of the present invention;

[0039] Figure 2 This is the seismic forward modeling theory model for three-dimensional seismic data in Embodiment 1 of the present invention;

[0040] Figure 3 This is a noisy seismic forward modeling model of three-dimensional seismic data from Embodiment 1 of the present invention;

[0041] Figure 4 The gradient structure tensor properties calculated in Embodiment 1 of the present invention;

[0042] Figure 5 This is the gradient structure tensor attribute value range distribution curve calculated in Embodiment 1 of the present invention;

[0043] Figure 6 The non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve are calculated in Embodiment 1 of the present invention;

[0044] Figure 7 This is a comparison chart of the non-reservoir attribute value range distribution curve calculated in Embodiment 1 of the present invention and the actual non-reservoir attribute value range distribution curve;

[0045] Figure 8 This is a comparison chart of the reservoir attribute value range distribution curve calculated in Embodiment 1 of the present invention and the actual reservoir attribute value range distribution curve;

[0046] Figure 9The intersection point of the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve calculated in Embodiment 1 of the present invention;

[0047] Figure 10 This is the intersection point of the actual non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve in Embodiment 1 of the present invention;

[0048] Figure 11 This is the synthesized TO seismic profile of the seismic forward model with a signal-to-noise ratio of 10 in Embodiment 2 of the present invention;

[0049] Figure 12 This is the gradient structure tensor property profile calculated based on a seismic forward model with a signal-to-noise ratio of 10, as shown in Embodiment 2 of the present invention.

[0050] Figure 13 The non-reservoir attribute value range distribution curves and reservoir attribute value range distribution curves are obtained from the seismic forward model with a signal-to-noise ratio of 10 in Embodiment 2 of the present invention.

[0051] Figure 14 This is the synthesized TO seismic profile of the seismic forward model with a signal-to-noise ratio of 5 in Embodiment 2 of the present invention;

[0052] Figure 15 This is the gradient structure tensor property profile calculated based on a seismic forward model with a signal-to-noise ratio of 5, as shown in Embodiment 2 of the present invention.

[0053] Figure 16 The non-reservoir attribute value range distribution curves and reservoir attribute value range distribution curves are obtained from the seismic forward model with a signal-to-noise ratio of 5, as shown in Embodiment 2 of the present invention.

[0054] Figure 17 This is the synthesized TO seismic profile of the seismic forward model with a signal-to-noise ratio of 3 in Embodiment 2 of the present invention;

[0055] Figure 18 This is the gradient structure tensor property profile calculated based on a seismic forward model with a signal-to-noise ratio of 3 in Embodiment 2 of the present invention.

[0056] Figure 19 The non-reservoir attribute value range distribution curves and reservoir attribute value range distribution curves are obtained from the seismic forward model with a signal-to-noise ratio of 3 in Embodiment 2 of the present invention.

[0057] Figure 20 This is a block diagram of the gradient structure tensor attribute threshold determination system of Embodiment 3 of the present invention;

[0058] Figure 21 This is a schematic diagram of the structure of the electronic device in Embodiment 4 of the present invention. Detailed Implementation

[0059] The present invention will be further described below with reference to the embodiments. However, those skilled in the art should understand that the present invention is not limited to the following embodiments. Any improvements and equivalent changes made based on the specific embodiments of the present invention are within the scope of protection of the claims of the present invention.

[0060] Example 1: Gradient Structure Tensor Attribute Threshold Determination Method

[0061] A method for determining the threshold of gradient structure tensor attributes, such as Figure 1 As shown, the steps are performed sequentially:

[0062] S1. Based on 3D seismic data, the gradient structure tensor properties are calculated; specifically:

[0063] S11. The seismic forward modeling theory for the three-dimensional seismic data in this embodiment is as follows: Figure 2 As shown, the noisy seismic forward modeling model of 3D seismic data is as follows: Figure 3 As shown, the forward model of a noisy seismic system based on 3D seismic data calculates the directional derivative of the amplitude at each sample point to obtain the gradient vector of each sample point.

[0064] S12. Multiply the gradient vector by its transpose to obtain the structure tensor matrix;

[0065] S13. Perform eigenvalue decomposition on the structure tensor matrix to obtain three eigenvalues. Use these three eigenvalues ​​and their combinations to calculate the gradient structure tensor properties, such as... Figure 4 As shown.

[0066] S2. Based on the gradient structure tensor properties, statistically analyze the distribution range of the gradient structure tensor property values, and calculate the gradient structure tensor property value distribution curve, such as... Figure 5 As shown; specifically:

[0067] S21. Based on the gradient structure tensor properties, statistically analyze the distribution range of the gradient structure tensor property values ​​to obtain a statistical histogram.

[0068] S22. Using statistical histograms, calculate the envelope surface and perform normalization to obtain the distribution curve of the gradient structure tensor attribute value range.

[0069] S3. Select sample points within the range of the gradient structure tensor attribute value distribution, perform normal fitting, and calculate the non-reservoir attribute value distribution curve. Then, subtract the gradient structure tensor attribute value distribution curve from the non-reservoir attribute value distribution curve to obtain the reservoir attribute value distribution curve. Figure 6 As shown;

[0070] The process of normal fitting is as follows:

[0071] Let the distribution of non-reservoir attribute values ​​D(x) follow a Gaussian distribution, then

[0072]

[0073] In the formula, μ is the expectation of the Gaussian distribution, and σ 2 Let x be the variance of the Gaussian distribution and x be the gradient structure tensor attribute value. Set interaction parameters A1 and A2, and let A1≤x≤A2. Perform normal fitting so that the non-reservoir attribute value range distribution curve overlaps with the gradient structure tensor attribute value range distribution curve within the range of A1≤x≤A2. The value of A1 is the gradient structure tensor attribute value corresponding to the left critical point of the gradient structure tensor attribute value range distribution curve, and the value of A2 is less than the gradient structure tensor attribute value corresponding to the peak of the gradient structure tensor attribute value range distribution curve.

[0074] The process of calculating the distribution curve of non-reservoir attribute values ​​is as follows:

[0075] Taking the logarithm of both sides of the formula for calculating the Gaussian distribution, we get...

[0076]

[0077] make Then lnD(x)=a0+a1x+a2x 2 ;

[0078] To find the coefficients a0, a1, a2, let ||lnD(x) - (a0 + a1x + a2x) 2 )|| 2 →0, the least squares algorithm is used to solve the problem, and μ and σ are calculated to determine the range distribution of non-reservoir attribute values ​​D(x);

[0079] The calculated non-reservoir attribute value range distribution curve overlaps with the actual non-reservoir attribute value range distribution curve, such as... Figure 7 As shown, the calculated reservoir attribute value range distribution curve is compared with the actual reservoir attribute value range distribution curve. Figure 8 As shown.

[0080] S4. Using the intersection points of the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve, determine the threshold of the gradient structure tensor attribute, such as... Figure 9 As shown, the calculated gradient structure tensor attribute threshold M = 37.9; the actual non-reservoir attribute value range distribution curves and reservoir attribute value range distribution curves are shown below. Figure 10 As shown, the actual gradient structure tensor attribute threshold M′=37.3, with an error rate of less than 2%.

[0081] When the set threshold for the gradient structure tensor attribute is less than the calculated threshold, the distribution of non-reservoir attribute values ​​is greater than the distribution of reservoir attribute values, meaning the increase in the actual reservoir is less than the increase in noise artifacts, leading to a decrease in characterization accuracy. Conversely, when the set threshold for the gradient structure tensor attribute is greater than the calculated threshold, the distribution of non-reservoir attribute values ​​is less than the distribution of reservoir attribute values, meaning the reduction in noise artifacts is less than the reduction in the actual reservoir, also resulting in a decrease in characterization accuracy.

[0082] Example 2: The gradient structure tensor attribute threshold determination method of Example 1 is applied to the prediction of the distribution of fracture-vuggy reservoirs in carbonate rocks.

[0083] The gradient structure tensor attribute threshold determination method of Example 1 was applied to test calculations in seismic forward modeling models with signal-to-noise ratios of 10, 5, and 3, and the gradient structure tensor attributes were used to predict the distribution of fractured-vuggy carbonate reservoirs. The synthesized TO seismic profile of the seismic forward modeling model with a signal-to-noise ratio of 10 is shown below. Figure 11 As shown, the calculated gradient structure tensor property profile is as follows: Figure 12 As shown, the calculated distribution curves of non-reservoir attribute values ​​and reservoir attribute values ​​are as follows: Figure 13 As shown, Figure 13 The intersection of the distribution curve of the reservoir attribute value range and the distribution curve of the reservoir attribute value range is the threshold M of the gradient structure tensor attribute. M = 12.1, which has a high accuracy in characterizing the boundary of carbonate fracture-vuggy reservoirs. The predicted distribution of carbonate fracture-vuggy reservoirs matches the actual distribution of carbonate fracture-vuggy reservoirs by 96.2%.

[0084] The synthetic TO seismic profile of a seismic forward model with a signal-to-noise ratio of 5 is shown below. Figure 14 As shown, the calculated gradient structure tensor property profile is as follows: Figure 15 As shown, the calculated distribution curves of non-reservoir attribute values ​​and reservoir attribute values ​​are as follows: Figure 16 As shown, Figure 16 The intersection of the distribution curve of the reservoir attribute value range and the distribution curve of the reservoir attribute value range is the threshold M of the gradient structure tensor attribute. M = 37.9, which has a high accuracy in characterizing the boundary of carbonate fracture-vuggy reservoirs. The predicted distribution of carbonate fracture-vuggy reservoirs matches the actual distribution of carbonate fracture-vuggy reservoirs by 72.4%.

[0085] Synthetic TO seismic profile of a seismic forward model with a signal-to-noise ratio of 3 is shown below. Figure 17 As shown, the calculated gradient structure tensor property profile is as follows: Figure 18 As shown, the calculated distribution curves of non-reservoir attribute values ​​and reservoir attribute values ​​are as follows: Figure 19 As shown, Figure 19The intersection of the distribution curve of the reservoir attribute value range and the distribution curve of the reservoir attribute value range is the threshold M of the gradient structure tensor attribute. M = 95.8, which has a high accuracy in characterizing the boundary of carbonate fracture-vuggy reservoirs. The predicted distribution of carbonate fracture-vuggy reservoirs matches the actual distribution of carbonate fracture-vuggy reservoirs by 47.5%.

[0086] Example 3: Gradient Structure Tensor Attribute Threshold Determination System

[0087] A gradient structure tensor attribute threshold determination system, such as Figure 20 As shown, it includes:

[0088] The gradient structure tensor attribute calculation module, based on a noisy seismic forward model of 3D seismic data, calculates the directional derivative of the amplitude at each sample point to obtain the gradient vector of each sample point; multiplies the gradient vector with its transpose to obtain the structure tensor matrix; performs eigenvalue decomposition on the structure tensor matrix to obtain three eigenvalues; and uses the three eigenvalues ​​and their combinations to calculate the gradient structure tensor attributes.

[0089] The gradient structure tensor attribute value range distribution calculation module calculates the range of gradient structure tensor attribute values ​​based on the gradient structure tensor attributes, and obtains a statistical histogram. Using the statistical histogram, the envelope surface is calculated and normalized to obtain the gradient structure tensor attribute value range distribution curve.

[0090] The module for calculating the range distribution of non-reservoir and reservoir attribute values ​​selects sample points within the range of the gradient structure tensor attribute value distribution, performs normal fitting, and obtains the non-reservoir attribute value distribution curve. Then, the gradient structure tensor attribute value distribution curve is subtracted from the non-reservoir attribute value distribution curve to obtain the reservoir attribute value distribution curve. The normal fitting process is as follows:

[0091] Let the distribution of non-reservoir attribute values ​​D(x) follow a Gaussian distribution, then

[0092]

[0093] In the formula, μ is the expectation of the Gaussian distribution, and σ 2 Let x be the variance of the Gaussian distribution and x be the gradient structure tensor attribute value. Set interaction parameters A1 and A2, and let A1≤x≤A2. Perform normal fitting so that the non-reservoir attribute value range distribution curve overlaps with the gradient structure tensor attribute value range distribution curve within the range of A1≤x≤A2. The value of A1 is the gradient structure tensor attribute value corresponding to the left critical point of the gradient structure tensor attribute value range distribution curve, and the value of A2 is less than the gradient structure tensor attribute value corresponding to the peak of the gradient structure tensor attribute value range distribution curve.

[0094] The process of calculating the distribution curve of non-reservoir attribute values ​​is as follows:

[0095] Taking the logarithm of both sides of the formula for calculating the Gaussian distribution, we get...

[0096]

[0097] make Then lnD(x)=a0+a1x+a2x 2 ;

[0098] To find the coefficients a0, a1, a2, let ||lnD(x) - (a0 + a1x + a2x) 2 )|| 2 →0, the least squares algorithm is used to solve the problem, and μ and σ are calculated to determine the distribution of non-reservoir attribute values ​​D(x).

[0099] The gradient structure tensor attribute threshold determination module uses the intersection points of the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve to determine the gradient structure tensor attribute threshold.

[0100] Example 4: An electronic device

[0101] The electronic device of this embodiment includes a memory and a processor. The memory stores a computer program, and the processor calls the computer program in the memory to execute the gradient structure tensor attribute threshold determination method of Embodiment 1. Figure 21 This is a schematic diagram of the structure of the electronic device provided in this embodiment. The electronic device can be a terminal device or a server. The terminal device can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 21 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of this embodiment.

[0102] like Figure 21As shown, electronic devices may include processing units, such as central processing units (CPUs) and graphics processors (GPUs), which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or loaded from storage devices into random access memory (RAM). RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input devices, output devices, communication devices, and storage devices are also connected to the bus via I / O interfaces.

[0103] Typically, the following devices can be connected to an I / O interface: input devices such as touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices such as liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices such as magnetic tapes, hard drives, etc.; and communication devices. Communication devices allow electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 21 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0104] Example 5: A computer-readable medium

[0105] The computer-readable storage medium of this embodiment stores a computer program, which, when executed by a processor, is used to implement the gradient structure tensor attribute threshold determination method of Embodiment 1. The computer-readable storage medium of this embodiment may be included in an electronic device; alternatively, it may exist independently and not assembled into an electronic device.

[0106] The computer-readable storage medium of this embodiment may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for determining the threshold of gradient structure tensor attributes, characterized in that, This includes the following steps performed sequentially: S1. Based on three-dimensional seismic data, the gradient structure tensor properties are calculated; S2. Based on the gradient structure tensor properties, statistically analyze the range of gradient structure tensor property values ​​and calculate the gradient structure tensor property value range distribution curve. S3. Select sample points A1 and A2 within the range of gradient structure tensor attribute value distribution. The value of A1 is the gradient structure tensor attribute value corresponding to the left critical point of the gradient structure tensor attribute value distribution curve. The value of A2 is less than the gradient structure tensor attribute value corresponding to the peak of the gradient structure tensor attribute value distribution curve. Perform normal fitting to calculate the non-reservoir attribute value distribution curve. Then subtract the gradient structure tensor attribute value distribution curve from the non-reservoir attribute value distribution curve to obtain the reservoir attribute value distribution curve. S4. Using the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve, find the intersection point and determine the gradient structure tensor attribute threshold.

2. The method for determining the threshold of gradient structure tensor attributes according to claim 1, characterized in that, Step S1 is as follows: S11. Calculate the directional derivative of the amplitude of each sample point from the three-dimensional seismic data to obtain the gradient vector of each sample point; S12. Multiply the gradient vector by its transpose to obtain the structure tensor matrix; S13. Perform eigenvalue decomposition on the structure tensor matrix to obtain three eigenvalues. Use the three eigenvalues ​​and their combinations to calculate the gradient structure tensor properties.

3. The method for determining the threshold of gradient structure tensor attributes according to claim 1, characterized in that, Step S2 is as follows: S21. Based on the gradient structure tensor properties, statistically analyze the distribution range of the gradient structure tensor property values ​​to obtain a statistical histogram. S22. Using statistical histograms, calculate the envelope surface and perform normalization to obtain the distribution curve of the gradient structure tensor attribute value range.

4. The method for determining the threshold of gradient structure tensor attributes according to claim 1, characterized in that, The normal fitting process in step S3 is as follows: Let the range distribution of non-reservoir attribute values If it follows a Gaussian distribution, then ， In the formula, Let the expected value be a Gaussian distribution. Let x be the variance of the Gaussian distribution, x be the gradient structure tensor attribute value, and set the interaction parameters A1 and A2, let... Perform a normal fit so that in The distribution curves of non-reservoir attribute values ​​within the range overlap with the distribution curves of gradient structure tensor attribute values. The process of calculating the distribution curve of non-reservoir attribute values ​​is as follows: Taking the logarithm of both sides of the formula for calculating the Gaussian distribution, we get... ， make , , ,but ; To obtain the coefficient term , , ,make The least squares algorithm is used to solve the problem, and the calculation is performed accordingly. and Determine the range distribution of non-reservoir attribute values. .

5. A system for determining the threshold of gradient structure tensor attributes, characterized in that, include: The gradient structure tensor property calculation module calculates the gradient structure tensor properties based on 3D seismic data. The gradient structure tensor attribute value range distribution calculation module calculates the gradient structure tensor attribute value range distribution range based on the gradient structure tensor attribute, and obtains the gradient structure tensor attribute value range distribution curve. The module for calculating the range distribution of non-reservoir and reservoir attribute values ​​selects sample points A1 and A2 within the range of the gradient structure tensor attribute value distribution. The value of A1 is the gradient structure tensor attribute value corresponding to the left critical point of the gradient structure tensor attribute value distribution curve, and the value of A2 is less than the gradient structure tensor attribute value corresponding to the peak of the gradient structure tensor attribute value distribution curve. Normal fitting is performed to obtain the non-reservoir attribute value distribution curve. Then, the gradient structure tensor attribute value distribution curve is subtracted from the non-reservoir attribute value distribution curve to obtain the reservoir attribute value distribution curve. The gradient structure tensor attribute threshold determination module uses the intersection points of the non-reservoir attribute value range distribution curve and the reservoir attribute value range distribution curve to determine the gradient structure tensor attribute threshold.

6. An electronic device, characterized in that, It includes a memory and a processor. The memory stores a computer program, and the processor calls the computer program in the memory to execute the gradient structure tensor attribute threshold determination method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, is used to implement the gradient structure tensor attribute threshold determination method according to any one of claims 1-4.

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