A method and system for fracture prediction based on formation rotation structure tensor

Abstract

The present invention discloses a fracture prediction method and system based on a formation rotation structure tensor, comprising: acquiring a seismic profile of a target area; establishing target area structure information matching the actual formation conditions of the target area according to the seismic profile; preprocessing the target area structure information; acquiring the preprocessed target area structure information, and based on this, predicting fractures in the target area to obtain fracture possibility index characteristics for the target area. The present invention can reliably predict the spatial distribution quantization characteristic information of fractures from a seismic profile, provide a basis for oil and gas reservoir prediction and prevention of drilling engineering accidents, and meet the requirements of modern oil and gas exploration.

Description

Technical Field

[0001] The present invention relates to the field of oil exploration and development, and particularly to a fracture prediction method and system based on a formation rotation structure tensor. Background Art

[0002] Underground fractures are of great significance to oil and gas resources. Microfractures in reservoir rocks are the storage spaces for oil and gas resources; relatively large-scale fractures are the migration channels for oil and gas resources to migrate from source rocks to reservoirs, such as faults. Therefore, in oil and gas resource exploration, fracture prediction has become an important part of reservoir prediction. At the same time, the exploitation of oil and gas resources relies on drilling technology. The drilling process is to drill towards the target reservoir along the designed well trajectory, and fractures may be encountered on the well trajectory. However, fractures may cause drilling engineering accidents such as wellbore collapse, pipe sticking, drill pipe burying, and drilling fluid loss during the drilling process, and these accidents usually lead to an increase in the drilling cycle and economic losses. Therefore, the prediction technology for underground fractures is particularly important.

[0003] In the oil and gas exploration and development process, the fractures that are of concern are usually buried deep underground for thousands of meters, so their spatial positions cannot be directly detected. Using seismic exploration methods for fracture prediction is currently a more feasible approach. Fracture prediction is actually to identify fractures in seismic images.

[0004] In addition, the structure tensor method is a method widely used in image processing, which can obtain the structure information of images. In the process of implementing the present invention, the inventors found that the traditional structure tensor method is based on a rectangular coordinate system and has low adaptability to the actual formation, cannot obtain high-quality fracture prediction results, and cannot solve the problems for the actual formation conditions.

[0005] Therefore, in the prior art, there is a need to provide a fracture prediction scheme that can be applicable to the actual formation conditions. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a fracture prediction method based on a formation rotation structure tensor, including: a seismic profile input step of obtaining a seismic profile of a target area; a structure information generation step of establishing structure information of the target area that matches the actual formation conditions of the target area according to the seismic profile; a preprocessing step of preprocessing the structure information of the target area; and a fracture prediction step of obtaining the preprocessed structure information of the target area and, based on this, predicting fractures in the target area to obtain a fracture possibility index feature for the target area.

[0007] Preferably, in the step of generating the structural information, it includes: determining the difference between the actual formation dip angle of the target area and the formation dip angle in the rectangular coordinate system, as well as the difference in formation azimuth angle, according to the seismic profile; converting the rectangular coordinate system into the corresponding formation coordinate system according to the formation dip angle difference and azimuth angle difference; constructing the structural information of the target area based on the formation coordinate system and according to the seismic data corresponding to the target area.

[0008] Preferably, in the step of predicting fractures, it includes: performing eigenvalue decomposition on the preprocessed structural information of the target area; calculating a fracture indicator factor representing the possibility of developed fractures according to the decomposed multiple eigenvalues to obtain the fracture possibility index feature.

[0009] Preferably, in the preprocessing step, it includes: calibrating the rotation direction of each element of the structural information of the target area, and based on this, combining the position information of each point to be predicted in the target area, using an anisotropic Gaussian function to filter out the unstable information of each point to be predicted in the seismic profile of the target area.

[0010] Preferably, the formation coordinate system and the structural information of the target area are constructed respectively using the following expressions:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] where, θ represents the formation dip angle difference, represents the azimuth angle difference, x, y, z represent the position coordinates of each point in the rectangular coordinate system, x', y', z' represent the position coordinates of each point in the formation coordinate system, S r represents the structural information of the target area, and u represents the seismic data.

[0017] Preferably, the fracture indicator factor is calculated using the following expression:

[0018]

[0019] where, ε represents the error constant, f represents the fracture indicator factor, and λ1, λ2 respectively represent the multiple eigenvalues obtained after eigen-decomposition of the preprocessed structural information of the target area.

[0020] Preferably, the target area structure information is preprocessed using the following expression:

[0021]

[0022]

[0023]

[0024]

[0025] d x = x - x0

[0026] d y = y - y0

[0027] d z = z - z0

[0028] where g s represents an element in the preprocessed target area structure information, x, y, and z represent the position coordinates of each point in the rectangular coordinate system, x', y', and z' represent the position coordinates of each point in the formation coordinate system, g i represents the value of element i in the target area structure information after element calibration, x0, y0, and z0 respectively represent the position coordinates corresponding to the point to be predicted in the target area, σ x' , σ y' , σ z' respectively represent the standard variances corresponding to the point to be predicted in the x', y', and z' directions, x win , y win , z win respectively represent the preprocessing window radii corresponding to the point to be predicted in the x, y, and z directions.

[0029] On the other hand, the present invention also provides a fracture prediction system based on a formation rotation structure tensor, including: a seismic profile input module configured to obtain a seismic profile of a target area; a structure information generation module configured to establish target area structure information matching the actual formation conditions of the target area according to the seismic profile; a preprocessing module configured to preprocess the target area structure information; and a fracture prediction module configured to obtain the preprocessed target area structure information and, based on this, perform fracture prediction on the target area to obtain a fracture possibility index feature for the target area.

[0030] Preferably, the structure information generation module includes: a rotation difference generation unit configured to determine the difference between the actual formation dip angle and the formation dip angle in the rectangular coordinate system, and the difference in formation azimuth angle, based on the seismic profile; a formation coordinate system generation unit configured to convert the rectangular coordinate system into a corresponding formation coordinate system according to the formation dip angle difference and the azimuth angle difference; and a regional structure information generation unit configured to construct the target area structure information based on the formation coordinate system and according to the seismic data corresponding to the target area.

[0031] Preferably, the fracture prediction module includes: a decomposition processing unit configured to perform eigenvalue decomposition processing on the preprocessed target area structure information; and a prediction result generation unit configured to calculate a fracture indicator factor representing the likelihood of fractures developing in the target area based on the multiple eigenvalues after decomposition, so as to obtain the fracture likelihood index feature.

[0032] Compared with the prior art, one or more embodiments of the above solution may have the following advantages or beneficial effects:

[0033] The present invention discloses a fracture prediction method and system based on a formation rotation structure tensor. The method and system first utilize the formation structure information of the target area to construct a formation rotation structure tensor; then, use a rotation anisotropic Gaussian function to preprocess the formation rotation structure tensor to improve its stability; finally, perform eigenvalue decomposition on the preprocessed formation rotation structure tensor matrix, and use the multiple eigenvalues after decomposition to construct a fracture indicator factor, thereby realizing the quantification result of the fracture prediction in the target area. The seismic fracture prediction method based on the structure tensor after formation rotation processing provided by the present invention can reliably predict the quantitative characteristic information of the spatial distribution of fractures from the seismic profile, provide a basis for oil and gas reservoir prediction and prevention of drilling engineering accidents, meet the needs of modern oil and gas exploration, and play an important role in oil and gas exploration and development, thereby solving the problem of mismatch with the actual formation structure characteristics existing in the conventional means of extracting image structure information when applied to seismic profiles and being able to adapt to the changes in underground horizons.

[0034] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained through the structures specifically pointed out in the specification, claims, and drawings. Description of the Drawings

[0035] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0036] Figure 1 It is a step diagram of the fracture prediction method based on the formation rotation structure tensor according to the embodiments of the present application.

[0037] Figure 2 It is a specific example of the seismic profile of the target area in the fracture prediction method based on the formation rotation structure tensor according to the embodiments of the present application.

[0038] Figure 3 It is a schematic flow chart of the target structure information generation process in the fracture prediction method based on the formation rotation structure tensor according to the embodiments of the present application.

[0039] Figure 4 It is a specific example of the fracture prediction result distribution map of the target area in the fracture prediction method based on the formation rotation structure tensor according to the embodiments of the present application.

[0040] Figure 5 It is a block diagram of the modules of the fracture prediction system based on the formation rotation structure tensor according to the embodiments of the present application. Detailed implementation manners

[0041] The following will combine the drawings and embodiments to detail the implementation manners of the present invention, so as to fully understand how the present invention uses technical means to solve technical problems and the implementation process of achieving technical effects and implement accordingly. It should be noted that as long as there is no conflict, the various embodiments in the present invention and the various features in each embodiment can be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.

[0042] In addition, the steps shown in the flow chart of the drawings can be executed in a computer system such as a set of computer executable instructions. And although the logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in a different order than here.

[0043] Underground fractures are of great significance to oil and gas resources. The microfractures in reservoir rocks are the storage spaces for oil and gas resources; the relatively large-scale fractures are the migration channels for oil and gas resources to migrate from source rocks to reservoirs, such as faults. Therefore, in oil and gas resource exploration, fracture prediction has become an important link in reservoir prediction. At the same time, the exploitation of oil and gas resources relies on drilling technology. The drilling process is to drill towards the target reservoir along the designed well trajectory, and fractures may be encountered on the well trajectory. However, fractures may cause drilling engineering accidents such as wellbore collapse, stuck pipe, buried drill, and loss of drilling fluid during the drilling process, and these accidents usually lead to an increase in the drilling cycle and economic losses. Therefore, the prediction technology for underground fractures is particularly important.

[0044] The fractures concerned in the oil and gas exploration and development processes are usually buried deep underground at a depth of several kilometers. Therefore, their spatial positions cannot be directly detected. Using seismic exploration methods for fracture prediction is currently a highly feasible approach. Fracture prediction is actually the identification of fractures in seismic images.

[0045] In addition, the structure tensor method is widely used in image processing and can obtain the structural information of images. In the process of implementing the present invention, the inventors found that the traditional structure tensor method is based on a rectangular coordinate system and has low adaptability to the actual formation, cannot obtain high-quality fracture prediction results, and cannot solve the problems for the actual formation conditions.

[0046] Therefore, to solve the above technical problems, an embodiment of the present invention proposes a fracture prediction method and system based on a formation rotation structure tensor. The method and system first construct a formation structure tensor that matches the actual formation structure of the target area based on the seismic profile of the target area; preprocess the formation structure tensor of the target area using a rotation anisotropic Gaussian function; and predict the possibility of fracture development in the target area according to the preprocessed formation structure tensor of the target area, so as to be able to quantitatively predict the fracture information at different target positions from the seismic profile.

[0047] Figure 1 It is a step diagram of the fracture prediction method based on the formation rotation structure tensor in an embodiment of the present application. The following refers to Figure 1 to illustrate the fracture prediction method described in the embodiment of the present invention.

[0048] First, in step S110, a seismic profile for the target area is obtained. Figure 2 It is a specific example of the seismic profile for the target area in the fracture prediction method based on the formation rotation structure tensor in an embodiment of the present application. Figure 2 It shows a partial seismic profile of a certain oil reservoir work area. It should be noted that in the embodiment of the present invention, the target area refers to the three-dimensional area where fracture prediction is currently required. Preferably, the three-dimensional target area has similar formation structure characteristics and can be represented by the same formation dip angle and the same formation azimuth angle.

[0049] After obtaining the seismic profile of the target area, it enters step S120. In step S120, structure information that matches the actual formation characteristics of the target area, that is, target area structure information, is established according to the seismic profile. Among them, in the embodiment of the present invention, the target area structure information is preferably the image structure tensor of the current target area.

[0050] Figure 3 It is a schematic flow chart of the target structure information generation process in the fracture prediction method based on the formation rotation structure tensor in an embodiment of the present application. The following combinesFigure 1 and Figure 3 The implementation process of step S120 will be described.

[0051] In the actual application process, in order to address the issue that the traditional structure tensor method is based on the Cartesian coordinate system and cannot solve the problem of fracture prediction for actual formation conditions. However, the actual formation structure does not conform to the Cartesian coordinate system. Therefore, in order to consider the influence of actual formation structure factors, in step S120 of the present invention embodiment, it is necessary to construct structure tensor information that conforms to the rotation characteristics of the actual formation.

[0052] As Figure 3 shown, step S1201 will respectively determine the difference between the actual formation dip angle and the formation dip angle in the Cartesian coordinate system (formation dip angle difference), and the difference between the actual formation azimuth angle and the formation azimuth angle in the Cartesian coordinate system (azimuth angle difference) based on the seismic profile of the target area obtained in step S110, and then enter step S1202.

[0053] Step S1202 converts the Cartesian coordinate system into the corresponding formation coordinate system according to the formation dip angle difference and azimuth angle difference obtained in step S1201. In this way, the coordinate conversion operation from the Cartesian coordinate system to the formation coordinate system that conforms to the actual formation structure characteristics is completed, and then enter step S1203. In step S1202, the conversion from the Cartesian coordinate system to the formation coordinate system is realized by using the following expression:

[0054]

[0055] where θ represents the formation dip angle difference, represents the azimuth angle difference, x, y, z represent the position coordinates of each point in the Cartesian coordinate system, and x', y', z' represent the position coordinates of each point in the formation coordinate system.

[0056] Step S1203 constructs the target area structure information based on the formation coordinate system constructed in step S1202 and according to the seismic data corresponding to the target area. Among them, the target area structure information is the formation structure tensor obtained after the coordinate rotation process for the target area. In step S1203, by using the following expression, in the formation coordinate system, the target area structure formation structure tensor matrix is constructed:

[0057]

[0058]

[0059]

[0060]

[0061] Among them, S r represents the target area structure information, and u represents the seismic data corresponding to the target area (obtained directly from the seismic profile according to the position of the target area).

[0062] In the traditional structure tensor algorithm, each element in the obtained structure tensor matrix is constructed directly from the gradient data in the rectangular coordinate system: without considering the influencing factors of the actual formation structure characteristics. In the embodiments of the present invention, in the formation rotation structure tensor matrix, each element is constructed from the gradient data after considering coordinate rotation to consider the actual formation structure characteristic information. In this way, the embodiments of the present invention obtain a formation structure tensor matrix that conforms to the actual formation structure characteristics of the target area through the above steps S1201 to S1203.

[0063] Continue to refer to Figure 1 , after completing the construction of the target area structure information, enter step S130. Step S130 preprocesses the target area structure information (the formation structure tensor matrix regarding the target area) constructed in step S120.

[0064] In the actual application process, the data read from the seismic profile usually has abnormal factors such as noise, which affects the spatial stability of each element in the formation rotation structure tensor matrix. Therefore, in order to improve the stability and further consider the formation structure characteristic factors, the embodiments of the present invention need to perform a preprocessing operation based on the rotation anisotropic Gaussian function on each element in the formation rotation structure tensor matrix in step S130.

[0065] As Figure 1 shown, step S130 calibrates the rotation direction of each element of the target area structure information. Based on this, the anisotropic Gaussian function is used to filter out the unstable information of each point to be predicted in the target area seismic profile. Since there are points to be predicted (points that need to predict the crack possibility state) distributed at different positions in the target area, the embodiments of the present invention will execute step S130 for each point to be predicted, so as to filter out the unstable information of all points to be predicted in the target area. It should be noted that since the filtering method for the unstable information of each point to be predicted is the same, in the embodiments of the present invention, only the filtering method for one point is taken as an example for illustration. Specifically, step S130 first calibrates the rotation direction of each element in the target area structure information according to the target area structure information obtained in step S120.

[0066] Specifically, each element in the formation structure tensor matrix after coordinate rotation regarding the target area is calibrated in the following manner: g1 = g x' gx' , g2 = g y' g y' , g3 = g z' g z' , g4 = g x' g y' , g5 = g x' g z' , g6 = g y' g z' .

[0067] Then, step S130 also preprocesses the target area structure information according to the target area structure information with completed element calibration, combines the position information of the current point to be predicted in the target area, and uses the anisotropic Gaussian function to filter the seismic profile noise of the current point to be predicted, so as to filter the unstable information of each element in the matrix (so as to filter the unstable information of the current point to be predicted). Specifically, the preprocessing operation of the target area structure information is performed according to the following expression:

[0068]

[0069]

[0070]

[0071]

[0072] d x = x - x0 (10)

[0073] d y = y - y0 (11)

[0074] d z = z - z0 (12)

[0075] where g s represents the element in the preprocessed target area structure information, g i represents the value of element i in the target area structure information after element calibration (i = 1, 2,..., 6), x0, y0, and z0 respectively represent the position coordinates corresponding to the point to be predicted in the current target area, σ x' , σ y' , σ z' represent the standard variances corresponding to the current point to be predicted along the x', y', and z' directions respectively, x win , y win , z win represent the radii of the preprocessing windows corresponding to the current point to be predicted along the x, y, and z directions respectively. For example: σ x' = 5, σ y' = 1, σz' = 1, x win = 10, y win = 2, z win = 2.

[0076] Thus, in the embodiment of the present invention, through the above step S130, the preprocessing task of the target area structure information is completed for each prediction point to be predicted in the target area, and further, the corresponding preprocessing results are obtained for each prediction point to be predicted. Thus, on the basis of considering the actual formation structure characteristics, the unstable factors of each element in the target area structure information are filtered out, and the spatial stability of the target area structure information is improved. Then, it enters step S140.

[0077] Step S140 obtains the preprocessed target area structure information (corresponding to each prediction point to be predicted). Based on this, fracture prediction is performed on the target area to obtain the fracture possibility index characteristics for the target area (that is, the corresponding fracture possibility index characteristics are obtained for each prediction point to be predicted in the target area). In step S140, first, according to the preprocessing results of each element in the target area structure information corresponding to each prediction point obtained in step S130, the corresponding preprocessed target area structure information is obtained for each prediction point to be predicted. Among them, the preprocessed target area structure information is represented by the following expression:

[0078]

[0079] Among them, S rp represents the preprocessed target area structure information. Then, step S140 performs eigenvalue decomposition processing on the preprocessed target area structure information corresponding to each prediction point to be predicted to obtain multiple decomposed eigenvalues (corresponding to each prediction point to be predicted). Among them, in the process of eigenvalue decomposition processing, the preprocessed target area structure information is converted from matrix form to eigenvalue expression form, so that the preprocessed target area structure information represented in eigenvalue expression form is represented by the following expression:

[0080]

[0081] Among them, λ1, λ2, and λ3 respectively represent the multiple eigenvalues obtained after eigenvalue decomposition processing, and v1, v2, and v3 respectively represent the multiple eigenvectors obtained after eigenvalue decomposition processing.

[0082] Next, after obtaining multiple eigenvalue, step S140 further calculates a fracture indication factor that characterizes the possibility of developed fractures in the target area (calculates the fracture indication factor for each prediction point in each target area) based on the multiple decomposed eigenvalues (corresponding to each prediction point to be predicted), so as to obtain corresponding fracture possibility index feature information for each prediction point to be predicted. Wherein, in step S140, the above-mentioned fracture indication factor is calculated using the following expression:

[0083]

[0084] Wherein, ε represents an error constant, and f represents a fracture indication factor. It should be noted that, first, in expression (15), the error constant is a positive number close to zero, so as to ensure the stability of the calculation result. Second, in expression (15), the value of the fracture indication factor f ranges from 0 to 1, and is used to characterize the possibility degree of developed fractures in the current prediction point area in the target area. Further, the smaller the value of the fracture indication factor, the smaller the possibility of developed fractures at the current prediction point to be predicted; the larger the value of the fracture indication factor, the greater the possibility of developed fractures at the current prediction point to be predicted.

[0085] In this way, the embodiment of the present invention uses the above-mentioned steps S110 to S140 to reliably quantify the possibility of fracture development in the target area. Further, after continuously implementing the fracture prediction method described in the above steps S110 to S140, the fracture prediction result information corresponding to different target area positions can be predicted from the seismic profiles of the entire work area, see Figure 4 , so as to effectively predict the spatial distribution of fractures from the seismic profiles. Thereby, it provides corresponding quantitative data basis for oil and gas reservoir prediction and prevention of drilling engineering accidents, meeting the requirements of modern oil and gas exploration and drilling.

[0086] Figure 4 is a specific example of the fracture prediction result distribution map for the target area in the fracture prediction method based on the formation rotation structure tensor of the embodiment of the present application. Figure 4 shows the corresponding spatial distribution after fracture prediction of a certain oil reservoir work area using the fracture prediction method described in the embodiment of the present invention.

[0087] On the other hand, based on the above-mentioned fracture prediction method, the present invention also proposes a fracture prediction system based on the formation rotation structure tensor. Figure 5 is the module block diagram of the fracture prediction system based on the formation rotation structure tensor of the embodiment of the present application. As Figure 5 shown, the fracture prediction system based on the formation rotation structure tensor (hereinafter referred to as "fracture prediction system") described in the embodiment of the present invention includes: a seismic profile input module 51, a structure information generation module 52, a preprocessing module 53, and a fracture prediction module 54.

[0088] Among them, the seismic profile input module 51 is implemented according to the method described in the above step S110, and is configured to obtain the seismic profile of the target area. The structure information generation module 52 is implemented according to the method described in the above step S120, and is configured to establish the structure information of the target area that matches the actual formation situation of the target area based on the seismic profile. The preprocessing module 53 is implemented according to the method described in the above step S130, and is configured to preprocess the above-mentioned structure information of the target area. The fracture prediction module 54 is implemented according to the method described in the above step S140, and is configured to obtain the preprocessed structure information of the target area, and based on this, perform fracture prediction on the target area to obtain the fracture possibility index feature for the target area.

[0089] Furthermore, the above-mentioned structure information generation module 52 includes: a rotation difference generation unit 521, a formation coordinate system generation unit 522, and a regional structure information generation unit 523. Specifically, the rotation difference generation unit 521 is configured to determine the difference between the actual formation dip angle of the target area and the formation dip angle in the rectangular coordinate system, and the difference in formation azimuth angle according to the seismic profile of the target area; the formation coordinate system generation unit 522 is configured to convert the rectangular coordinate system into the corresponding formation coordinate system according to the formation dip angle difference and the azimuth angle difference; the regional structure information generation unit 523 is configured to construct the structure information of the target area based on the formation coordinate system output by the formation coordinate system generation unit 522 and according to the seismic data corresponding to the target area.

[0090] Furthermore, the above-mentioned fracture prediction module 54 includes: a decomposition processing unit 541 and a prediction result generation unit 542. Specifically, the decomposition processing unit 541 is configured to perform eigenvalue decomposition processing on the preprocessed structure information of the target area; the prediction result generation unit 542 is configured to calculate a fracture indication factor representing the possibility of fractures developed in the target area according to the decomposed multiple eigenvalues to obtain the above-mentioned fracture possibility index feature.

[0091] The present invention discloses a fracture prediction method and system based on a formation rotation structure tensor. The method and system first utilize the formation structure information of a target area to construct a formation rotation structure tensor; then, use a rotation anisotropic Gaussian function to preprocess the formation rotation structure tensor to improve its stability; finally, perform eigenvalue decomposition on the preprocessed formation rotation structure tensor matrix, and use multiple eigenvalues after decomposition to construct a fracture indication factor, thereby realizing the quantification result of fracture prediction in the target area. The seismic fracture prediction method based on the structure tensor processed by formation rotation provided by the present invention can reliably predict the spatial distribution quantification characteristic information of fractures from a seismic profile, provide a basis for oil and gas reservoir prediction and prevention of drilling engineering accidents, meet the requirements of modern oil and gas exploration, and play an important role in oil and gas exploration and development, thus solving the problem of mismatch with the actual formation structure characteristics existing in the means of conventional extraction of image structure information when applied to a seismic profile and being able to adapt to the changes of underground horizons.

[0092] As described above, only the specific preferred embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0093] It should be understood that the embodiments disclosed by the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should extend to equivalent alternatives of these features understood by those of ordinary skill in the relevant art. It should also be understood that the terms used herein are only for the purpose of describing specific embodiments and do not mean to limit.

[0094] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in connection with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" that appear throughout the specification do not necessarily all refer to the same embodiment.

[0095] Although the disclosed embodiments of the present invention are as above, the content described is only an embodiment adopted for the convenience of understanding the present invention and is not used to limit the present invention. Any person skilled in the technical field to which the present invention pertains can make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by the present invention. However, the patent protection scope of the present invention still needs to be subject to the scope defined by the appended claims.

Claims

1. A fracture prediction method based on formation rotation structure tensor, comprising: Seismic profile input step: Obtain the seismic profile of the target area; Structure information generation step: Based on the seismic profile, establish the structure information of the target area that matches the actual formation situation of the target area; Preprocessing step: Preprocess the structure information of the target area; Fracture prediction step: Obtain the preprocessed structure information of the target area, and based on this, perform fracture prediction on the target area to obtain the fracture possibility index feature for the target area. Wherein, in the structure information generation step, it includes: Determine the difference between the actual formation dip angle of the target area and the formation dip angle in the rectangular coordinate system, and the difference in formation azimuth angle according to the seismic profile; Convert the rectangular coordinate system to the corresponding formation coordinate system according to the formation dip angle difference and azimuth angle difference; Based on the formation coordinate system, and according to the seismic data corresponding to the target area, construct the structure information of the target area.

2. The crack prediction method according to claim 1, characterized in that In the fracture prediction step, it includes: Perform eigenvalue decomposition processing on the preprocessed structure information of the target area; Calculate the fracture indication factor representing the possibility of developed fractures according to the decomposed multiple eigenvalues to obtain the fracture possibility index feature.

3. The crack prediction method according to claim 2, wherein In the preprocessing step, it includes: Calibrate the rotation direction of each element of the structure information of the target area. Based on this, combined with the position information of each point to be predicted in the target area, use the anisotropic Gaussian function to filter out the unstable information of each point to be predicted in the seismic profile of the target area.

4. The crack prediction method according to claim 1, wherein Construct the formation coordinate system and the structure information of the target area respectively using the following expressions: where θ represents the formation dip difference, represents the azimuth difference, x, y, z represent the position coordinates of each point in the rectangular coordinate system, x', y', z' represent the position coordinates of each point in the formation coordinate system, S r represents the target area structure information, and u represents the seismic data.

5. The crack prediction method according to claim 2, wherein Calculate the fracture indication factor using the following expression: Wherein, ε represents the error constant, f represents the fracture indication factor, and λ1 and λ2 respectively represent the multiple eigenvalues obtained after eigenvalue decomposition of the preprocessed structure information of the target area.

6. The crack prediction method according to claim 3, characterized in that Preprocess the structure information of the target area using the following expression: d x = x - x0 d y = y - y0 d z = z - z0 where θ represents the formation dip angle difference, represents the azimuth difference, g s represents an element in the preprocessed target area structure information, x, y, and z represent the position coordinates of each point in the rectangular coordinate system, and x', y', and z' represent the position coordinates of each point in the formation coordinate system, g i represents the value of element i in the target area structure information after element calibration, and x0, y0, and z0 respectively represent the position coordinates corresponding to the point to be predicted in the target area, σ x' σ y' σ z' respectively represent the standard deviations corresponding to the point to be predicted in the x', y', and z' directions, x win y win z win respectively represent the preprocessing window radii corresponding to the point to be predicted in the x, y, and z directions.

7. A fracture prediction system based on formation rotation structure tensor, comprising: A seismic profile input module configured to obtain the seismic profile of the target area; A structure information generation module configured to establish the structure information of the target area that matches the actual formation situation of the target area according to the seismic profile; A preprocessing module configured to preprocess the structure information of the target area; A fracture prediction module configured to obtain the preprocessed structure information of the target area, and based on this, perform fracture prediction on the target area to obtain the fracture possibility index feature for the target area. Among them, the structure information generation module includes: A rotation difference generation unit configured to determine the difference between the actual formation dip angle of the target area and the formation dip angle in the rectangular coordinate system, and the difference in formation azimuth angle according to the seismic profile; A formation coordinate system generation unit configured to convert the rectangular coordinate system to the corresponding formation coordinate system according to the formation dip angle difference and azimuth angle difference; A regional structure information generation unit configured to construct the structure information of the target area based on the formation coordinate system and according to the seismic data corresponding to the target area.

8. The crack prediction system according to claim 7, wherein The crack prediction module includes: A decomposition processing unit configured to perform eigenvalue decomposition processing on the preprocessed target area structure information; A prediction result generation unit configured to calculate a crack indication factor representing the likelihood of a developed crack in the target area based on a plurality of decomposed eigenvalues to obtain the crack likelihood index feature.

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