Deep buried hill earthquake full-scale fracture feature prediction method

Abstract

The invention relates to a deep buried hill earthquake full-scale fracture feature prediction method, which comprises the following steps: calculating texture attributes by using a gray-level co-occurrence matrix, and depicting buried hill large-scale fracture zone space distribution; depicting the spatial distribution of the mesoscale fracture zone of the buried hill by using a curvature body constrained by inclination angle and azimuth angle information; depicting the spatial distribution of the small-scale fracture zone of the buried hill by using a pre-stack amplitude azimuth anisotropy predictor; and fusing the three scale fracture zone depiction results by using a PCA body fusion technology to obtain a buried hill earthquake full-scale fracture system space depiction result.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method for predicting full-scale fracture characteristics of deep buried hill earthquakes. Background Art

[0002] The large-scale metamorphic buried-hill oil and gas reservoirs of the Archean in the Bohai Sea have become the main battlefield for increasing reserves and production in China's offshore areas. The development characteristics of fractures are the key to determining the development effect of fractured buried-hill reservoirs. Due to the deep burial depth (nearly 5,000 meters) and the influence of multiple phases of tectonic movement, the spatial characterization of the full-scale fracture system of deep buried-hill seismic data faces huge technical challenges.

[0003] Existing seismic fracture prediction technologies struggle to effectively characterize the distribution of fracture zones in deep buried hills and can only characterize fractures within a specific scale range. However, little research exists on effectively predicting fractures of varying scales and spatially characterizing the full-scale seismic fracture system. This discovery will revolutionize the design and deployment of well patterns for deep buried hill development, significantly impacting its effective development. Summary of the Invention

[0004] In response to the above problems, the present invention provides a method for predicting full-scale fracture characteristics of deep buried hill earthquakes. The method divides the buried hill scales, selects three earthquake fracture prediction technologies to effectively characterize the distribution of fracture zones at three scales, and uses attribute fusion technology to achieve the fusion of fracture prediction results at different scales, thereby realizing the spatial characterization of the full-scale fracture system of deep buried hill earthquakes.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for predicting full-scale fracture characteristics of deep buried hill earthquakes, the method comprising:

[0007] S1, obtain the original seismic data of the buried hill area;

[0008] S2, the scale of the buried hill area is divided according to the length of the fracture zone, including large scale range, medium scale range and small scale range;

[0009] S3, converting the original seismic data into a grayscale image, calculating a preset number of texture features using a gray-level co-occurrence matrix, and superimposing the preset number of texture features to obtain large-scale fracture features;

[0010] S4, calculate the curvature body constrained by the dip and azimuth information based on the structural tensor field corresponding to the original seismic data to obtain the fracture characteristics in the mesoscale range;

[0011] S5, based on the pre-stack amplitude anisotropy prediction of the original seismic data, the fracture characteristics in the small scale range are obtained;

[0012] S6, using the PCA fusion method, the fracture characteristics of large-scale, medium-scale and small-scale ranges are fused to obtain the full-scale fracture characteristics of buried hill earthquakes.

[0013] In one implementation, in S3, the gray level co-occurrence matrix is ​​calculated as follows:

[0014]

[0015] Wherein P(i, j|d, θ) represents the probability that a pixel with gray value i appears at a pixel position with gray value j at a distance of d and a direction of θ; the preset number of texture features includes energy attributes, entropy attributes, and contrast attributes.

[0016] The calculation formula for energy properties is:

[0017]

[0018] Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix.

[0019] The calculation formula of entropy property is:

[0020]

[0021] Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix.

[0022] The calculation formula of the contrast property is:

[0023]

[0024] Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix.

[0025] In S3, the energy attribute, entropy attribute, and contrast attribute are linearly superimposed in a ratio of 1:1:1 to obtain large-scale crack characteristics.

[0026] In one implementation, in S4, the curvature body calculation process includes:

[0027] The inclination and azimuth attributes are used to calculate the apparent inclination in the x and y directions. The calculation formula is:

[0028]

[0029] In the above formula, θ represents the inclination attribute value, φ represents the azimuth attribute value, and p x 、p y is the apparent inclination angle in the x and y directions;

[0030] Further on p x 、p y Calculate the first-order derivative using the formula:

[0031]

[0032] where p x '、p y ' for p x 、p y The first derivative of ;

[0033] According to the above A, B, C, D, and E, the maximum positive curvature body is calculated as follows:

[0034] K pos =A+C+[(AC) 2 +B 2 ] 1 / 2

[0035] In the above formula, K pos represents the maximum positive curvature.

[0036] In one implementation, in S5, pre-stack seismic gather data of 0°-30°, 30°-60°, 60°-120°, 120°-150° and 150°-180° azimuth angles are extracted in a 30° azimuth sector, and the 6 azimuth angle gather data are superimposed to obtain 6 azimuth stacked seismic data; the fracture sensitivity attribute body is further calculated for the azimuth stacked seismic data, and the 6 fracture sensitivity attribute bodies are elliptical fitted to form an amplitude azimuth anisotropy prediction body.

[0037] In one implementation, in S6, the three-scale fracture zone characterization data volumes are preprocessed before fusion processing. Specifically, the means of the three-scale fracture zone characterization data volumes D1, D2, and D3 are calculated, which are recorded as M1, M2, and M3 respectively. The standard deviations of the three-scale fracture zone characterization data volumes are calculated, which are recorded as V1, V2, and V3 respectively. The upper ranges of the three-scale fracture zone characterization data volumes are recorded as follows: U1=M1+2×V1, U2=M2+2×V2, and U3=M3+2×V3 respectively. The lower ranges of the three-scale fracture zone characterization data volumes are recorded as follows: L1=M1-2×V1, L2=M2-2×V2, and L3=M3-2×V3 respectively.

[0038] The normalization calculation formula is used to normalize the three scale fracture zone characterization data volumes. The normalization formula used is:

[0039]

[0040] Where X, Y, and Z are the large-scale, medium-scale, and small-scale fracture zone characterization data volumes before normalization, respectively. 1 , Y1 , Z 1 These are the normalized data volumes for characterizing large-scale, medium-scale, and small-scale fracture zones, respectively.

[0041] The present invention provides a method for spatially characterizing full-scale fracture systems in deep buried hill earthquakes. Three earthquake fracture prediction technologies are optimized to effectively characterize the distribution of fracture zones at three scales. Attribute fusion technology is then used to integrate the prediction results of fractures at different scales, enabling spatial characterization of full-scale fracture systems in deep buried hill earthquakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of a method for spatial characterization of full-scale fracture systems in deep buried hill earthquakes provided for the implementation of the present invention;

[0043] Figure 2 This is the main survey line section of the original seismic data volume of the buried hill of Bohai A oilfield;

[0044] Figure 3 The texture attributes of the buried hill of Bohai A oilfield are calculated using the gray level co-occurrence matrix, and the main survey line profile of the large-scale fracture zone data volume of the buried hill is depicted;

[0045] Figure 4 The main survey line profile of the mesoscale fracture zone data volume of the buried hill of the Bohai A oilfield is depicted using the curvature volume constrained by the inclination and azimuth information;

[0046] Figure 5 The main survey line section of the small-scale fracture zone in the buried hill of the Bohai A oilfield is depicted using the pre-stack amplitude azimuthal anisotropy prediction volume;

[0047] Figure 6 The main survey line profile of the full-scale seismic fracture system data volume of the buried hill of Bohai A Oilfield was depicted using PCA volume fusion technology. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0049] In response to the defects and problems of the prior art, the present application provides a method for predicting full-scale fracture characteristics of deep buried hill earthquakes, the method comprising:

[0050] S1, obtain the original seismic data of the buried hill area;

[0051] S2, the scale of the buried hill area is divided according to the length of the fracture zone. The fracture zone length of more than 500 meters is the large scale range, the fracture zone length of 200-500 meters is the medium scale range, and the fracture zone length of 100-200 meters is the small scale range;

[0052] S3, converting the original seismic data into a grayscale image, calculating a preset number of texture features using a gray-level co-occurrence matrix, and superimposing the preset number of texture features to obtain large-scale fracture features;

[0053] S4, calculate the curvature body constrained by the dip and azimuth information based on the structural tensor field corresponding to the original seismic data to obtain the fracture characteristics in the mesoscale range;

[0054] S5, based on the pre-stack amplitude anisotropy prediction of the original seismic data, the fracture characteristics in the small scale range are obtained;

[0055] S6, using the PCA fusion method, the fracture characteristics of large-scale, medium-scale and small-scale ranges are fused to obtain the full-scale fracture characteristics of buried hill earthquakes.

[0056] The above method flow is described in more detail in the following embodiments in conjunction with the accompanying drawings of the present invention.

[0057] like Figure 1 As shown in the flow chart, taking the Bohai A oilfield as an example, the method of the present invention is used to achieve the spatial characterization of the deep buried hill seismic full-scale fracture system of the oilfield. The specific steps are:

[0058] (1) Using the gray-level co-occurrence matrix to calculate texture attributes, the spatial distribution of large-scale fracture zones in the buried hill of the Bohai A oilfield was characterized;

[0059] The gray-level co-occurrence matrix (GLCM) is a computational method used in image science to detect image texture features by counting the spatial relationships between pixels. Texture features are characterized by counting the number of times specific pixels in an image appear within a certain spatial relationship. The GLCM is an n-by-n matrix, where n represents the number of grayscale levels. Its size is related to the resolution of the seismic data. Seismic data is typically 8-bit, corresponding to n = 256 grayscale levels. Using the GLCM to extract the amplitude values ​​of seismic data pixels essentially calculates the probability of a specific grayscale level occurring between pixels in a given spatial relationship within a defined time window of the seismic data.

[0060] The gray level co-occurrence matrix calculation formula is:

[0061]

[0062] Where P(i, j|d, θ) represents the probability that a pixel with grayscale value i appears at a pixel position with grayscale value j at a distance d and a direction θ.

[0063] After calculating the gray-level co-occurrence matrix, three commonly used texture feature parameters, namely energy, entropy, and contrast, are calculated based on it.

[0064] ① Energy attribute refers to the balanced characteristics of data structure calculated based on the gray-level co-occurrence matrix. The energy value of coarse texture is larger and the energy value of fine texture is smaller. The energy attribute calculation formula is:

[0065]

[0066] Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix, and the same applies below.

[0067] ② Entropy attribute refers to the disorder and complexity characteristics of the image calculated based on the gray-level co-occurrence matrix. The entropy attribute calculation formula is:

[0068]

[0069] If the image does not contain regular textures, the entropy attribute value is close to zero; if the image has more fine textures, the entropy attribute value of the image is the largest; if there are fewer textures distributed in the image, the entropy value of the image is relatively small.

[0070] ③ Contrast attribute refers to the description of image correlation or local change characteristics calculated based on the gray level co-occurrence matrix. The contrast attribute calculation formula is:

[0071]

[0072] The contrast attribute reflects the clarity of texture features. The more obvious the main features of the texture are, the greater the contrast attribute is, and the overall visual clarity of the image is better.

[0073] Using the above three texture feature attributes, according to the 1:1:1 linear weighted fusion, we get the fusion attribute that comprehensively describes the image texture features. We use this data volume to realize the large-scale fracture characterization of the buried hill. The results are as follows: Figure 3 shown.

[0074] (2) Using the curvature volume constrained by the inclination and azimuth information, the spatial distribution of the mesoscale fracture zone in the buried hill is characterized;

[0075] The inclination information and azimuth information refer to the inclination body attributes and azimuth body attributes obtained by the traditional structural tensor field calculation method.

[0076] The inclination and azimuth attributes are used to calculate the apparent inclination in the x and y directions. The calculation formula is:

[0077]

[0078] In the above formula, θ represents the inclination attribute value, φ represents the azimuth attribute value, and p x 、p y is the apparent inclination angle in the x-direction and the y-direction, which can be solved by the above formula.

[0079] Further on p x 、p y Calculate the first-order derivative using the formula:

[0080]

[0081] where p x '、p y ' is the first-order derivative of px and py. The maximum positive curvature body is calculated based on the above A, B, C, D, and E. The calculation formula is:

[0082] K pos =A+C+[(AC) 2 +B 2 ] 1 / 2

[0083] In the above formula, Kpos represents the maximum positive curvature. The data volume is used to characterize the mesoscale fractures in the buried hill. The results are as follows: Figure 4 shown.

[0084] (3) Using the pre-stack amplitude azimuthal anisotropy prediction volume, the spatial distribution of small-scale fracture zones in the buried hill is characterized;

[0085] The pre-stack amplitude azimuth anisotropy prediction volume refers to: for the pre-stack wide azimuth seismic data of oil field A, the 30° azimuth angle sector is used to extract the 0°-30°, 30°-60°, 60°-120°, 120°-150° and 150°-180° pre-stack seismic data, and the 6 azimuth angle data are superimposed to obtain 6 azimuth stacked seismic data. The fracture sensitivity attribute volume is further calculated for the azimuth stacked seismic data, and the 6 fracture sensitivity attribute volumes are elliptical fitted to form the amplitude azimuth anisotropy prediction volume. The data volume is used to realize the small-scale fracture characterization of the buried hill. The results are as follows: Figure 5 shown.

[0086] (4) Using PCA volume fusion technology, the above three scale fracture zone characterization results are fused to obtain the spatial characterization results of the full-scale fracture system of the buried hill earthquake.

[0087] The PCA body fusion technology refers to: a traditional method of mathematical statistical analysis, feature extraction, and dimensionality reduction. It mainly uses the method of replacing multiple original variables with fewer new variables based on the correlation between each variable or feature. The fewer new variables can retain as much information as possible reflected by the original variables. Denote the multi-variable sample as X = [x1, x2, x3,..., x n , perform a linear transformation to obtain the new variable Y = [y1, y2, y3,..., y m (m < n), and the used linear transformation formula is Y = a T X, and the linear transformation vector a satisfies a T a = 1, a i T a j≠i = 0. In this case, make the variance var(y i ) reach the maximum. This y i with the maximum variance is the first principal component of the multi-variable sample X, the second largest is the second principal component, and so on. i

[0088] The fusion of the characterization results of three-scale fracture zones refers to: using the large-scale fracture characterization body of the buried hill ( Figure 3 ), the medium-scale fracture characterization body of the buried hill ( Figure 4 ), and the small-scale fracture characterization body of the buried hill ( Figure 5 ) to establish a multi-dimensional variable sample Z = [Z1, Z2,..., Z n , using the PCA body fusion technology to perform linear transformation and dimensionality reduction on the multi-dimensional sample, and obtaining a new multi-dimensional variable sample T = [T1, T2,..., T m (m < n), and taking the first principal component of the sample T as the characterization result of the full-scale fracture system of the buried hill earthquake, as Figure 6 shown.

[0089] In several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0090] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some of the steps of the above-mentioned methods of various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0091] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting full-scale fracture characteristics of deep buried hill earthquakes, characterized in that: The method comprises: S1, obtain the original seismic data of the buried hill area; S2, the scale of the buried hill area is divided according to the length of the fracture zone, including large scale range, medium scale range and small scale range; S3, converting the original seismic data into a grayscale image, calculating a preset number of texture features using a grayscale co-occurrence matrix, and superimposing the preset number of texture features to obtain fracture features in a large scale range; S4, calculate the curvature body constrained by the dip and azimuth information according to the structural tensor field corresponding to the original seismic data, and obtain the fracture characteristics in the mesoscale range; S5, based on the prediction of prestack amplitude directional anisotropy of the original seismic data, the fracture characteristics in the small scale range are obtained; S6, using the PCA fusion method, the fracture characteristics of large-scale, medium-scale and small-scale ranges are fused to obtain the full-scale fracture characteristics of buried hill earthquakes.

2. The method for predicting full-scale fracture characteristics of deep buried hill earthquakes according to claim 1 is characterized in that: In S3, the calculation formula of the gray level co-occurrence matrix is: Where P(i, j|d, θ) represents the probability that a pixel with gray value i appears at a pixel with gray value j at a distance d and direction θ from it. In S3, the preset number of texture features includes energy attribute, entropy attribute, and contrast attribute; The calculation formula of energy attribute is: Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix; The calculation formula of entropy property is: Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix; The contrast property is calculated as: Where P(i, j) represents the element in the i-th row and j-th column of the gray-level co-occurrence matrix; In S3, the energy attribute, entropy attribute, and contrast attribute are linearly superimposed in a ratio of 1:1:1 to obtain the crack characteristics in a large scale range.

3. The method for predicting full-scale fracture characteristics of deep buried hill earthquakes according to claim 1, characterized in that: In S4, the curvature body calculation process includes: The inclination angle and azimuth angle attributes are used to calculate the apparent inclination angle in the x and y directions. The calculation formula is: In the above formula, θ represents the inclination attribute value, φ represents the azimuth attribute value, px and py are the apparent inclination angles in the x direction and y direction; Further calculate the first-order derivative of px and py, the calculation formula is: Where px' and py' are the first-order derivatives of px and py; According to the above A, B, C, D, E, the maximum positive curvature body is calculated using the following formula: K pos =A+C+[(A-C) 2 +B 2 ] 1 / 2 In the above formula, Kpos represents the maximum positive curvature.

4. The method for predicting full-scale fracture characteristics of deep buried hill earthquakes according to claim 1, characterized in that: In S5, the pre-stack seismic gather data of 0°-30°, 30°-60°, 60°-120°, 120°-150° and 150°-180° azimuth angles are extracted in the 30° azimuth sector, and the six azimuth angle gather data are superimposed to obtain six azimuth stacked seismic data. The fracture sensitive attribute body is further calculated for the azimuth stacked seismic data, and the six fracture sensitive attribute bodies are elliptical fitted to form an amplitude azimuth anisotropy prediction body.

5. The method for predicting full-scale fracture characteristics of deep buried hill earthquakes according to claim 1, characterized in that: In S6, the three-scale fracture belt characterization data volumes are preprocessed before fusion processing. The specific method is to calculate the mean values ​​of the three-scale fracture belt characterization data volumes D1, D2, and D3, which are recorded as M1, M2, and M3 respectively, and calculate the standard deviations of the three data volumes, which are recorded as V1, V2, and V3 respectively. The upper ranges of the three-scale fracture belt characterization data volumes are: U1=M1+2×V1, U2=M2+2×V2, U3=M3+2×V3, the lower ranges of the three scale fracture zone characterization data volumes are: L1=M1-2×V1, L2=M2-2×V2, L3=M3-2×V3; The normalized calculation formula is used to normalize the three scale fracture zone characterization data volumes. The normalized formula used is: Where X, Y, and Z are the large, medium, and small scale fracture zone characterization data volumes before normalization, respectively. 1 , Y 1 , Z 1 They are the normalized data volumes for characterizing large, medium and small scale fracture zones respectively.

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