A multi-scale shale gas reservoir fracture prediction method based on data preprocessing

Abstract

The application provides a multi-scale shale gas reservoir fracture prediction method based on data preprocessing, comprising: advantage direction stacking of post-stack multi-azimuth seismic data; frequency band preprocessing of the post-stack multi-azimuth seismic data after advantage direction stacking; extraction of a coherence body and a curvature body from the post-stack multi-azimuth seismic data after frequency band preprocessing, post-stack seismic fracture prediction; preprocessing of pre-stack split-azimuth seismic trace sets; calculation of a frequency attenuation gradient of the preprocessed pre-stack split-azimuth seismic trace sets, and pre-stack multi-azimuth frequency attenuation gradient fracture prediction; and joint comprehensive fracture prediction of the pre-stack fracture prediction result and the post-stack fracture prediction result. The application combines pre-stack fracture prediction and post-stack fracture prediction for analysis, the pre-stack multi-azimuth frequency attenuation gradient fracture prediction can predict micro-fissure zone distribution, the post-stack fracture prediction can predict the fine position of large and medium fractures, and unified multi-scale shale gas reservoir fracture prediction analysis is realized.

Description

Technical Field

[0001] This invention relates to the field of natural fracture prediction technology in geophysical exploration of oil and gas, and more specifically, to a multi-scale shale gas reservoir fracture prediction method based on data preprocessing. Background Technology

[0002] Existing research indicates that on seismic profiles, faults with large displacements (generally >15m) can cause significant dislocation of the seismic reflection phase axis, which can be described through tectonic interpretation or seismic attributes suitable for identifying large-scale faults. When the displacement decreases or the strata deflect, the seismic reflection phase axis exhibits slight curvature. Faults or deflection zones at this scale are in a "fuzzy zone" of identification, sometimes indistinguishable to the naked eye; in such cases, seismic attributes can be used for description. When encountering the development of micro-fracture zones at the seismic scale, there are no visible response characteristics on the seismic reflection phase axis, and they can only be characterized by seismic attributes. Traditional existing techniques use coherence attributes to describe cases of significant dislocation of the seismic reflection phase axis on seismic profiles, i.e., faults with displacements greater than 15m; and use curvature attributes to describe cases of slight curvature of the seismic reflection phase axis on seismic profiles, i.e., small faults with displacements between 5 and 15m or natural fracture zones formed by strata deflection. The main reason for this is:

[0003] Single post-stack fracture prediction techniques often fail to meet production needs. Research into small faults and natural fractures within 10m displacement is urgently needed, and the accuracy of fracture characterization requires further improvement. There is still no unified analysis for the distribution of micro-fracture zones and the prediction of large and medium-scale fractures. Fracture prediction methods place certain demands on the quality of seismic data; the raw data for common seismic fracture prediction often lacks a high signal-to-noise ratio, and existing seismic fracture prediction methods often do not include a preprocessing step for seismic data. Summary of the Invention

[0004] This invention aims to provide a multi-scale shale gas reservoir fracture prediction method based on data preprocessing. By introducing a data preprocessing step, the method improves the data quality of fracture prediction and performs RGB fusion of pre-stack multi-azimuth frequency attenuation gradient fracture prediction and post-stack fracture prediction to obtain a multi-scale fracture prediction method, thereby improving the accuracy of traditional post-stack fracture prediction methods.

[0005] This invention provides a multi-scale shale gas reservoir fracture prediction method based on data preprocessing, comprising the following steps:

[0006] Step 101: Based on the analysis of post-stack seismic data and geological structural morphology, the post-stack multi-directional seismic data are superimposed in the dominant azimuth.

[0007] Step 102: Perform frequency band preprocessing on the post-stack multi-azimuth seismic data after the dominant azimuth superposition;

[0008] Step 103: Extract coherence volume and curvature volume from the post-stack multi-azimuth seismic data after frequency band preprocessing, and predict post-stack seismic cracks based on the coherence volume and curvature volume.

[0009] Step 104: Perform data quality assessment on the pre-stack azimuth seismic gathers, and preprocess the pre-stack azimuth seismic gathers based on the data quality assessment results.

[0010] Step 105: Calculate the frequency attenuation gradient for the pre-stack azimuth seismic gathers and perform pre-stack multi-azimuth frequency attenuation gradient crack prediction.

[0011] Step 106: Combine the pre-stack crack prediction results and the post-stack crack prediction results to make a comprehensive crack prediction.

[0012] Furthermore, the advantageous orientation mentioned in step 101 refers to:

[0013] Earthquake data exhibits slightly different morphological characteristics in different orientations, and the orientation that can relatively clearly reflect the tectonic morphology is the dominant orientation.

[0014] Furthermore, the frequency band preprocessing mentioned in step 102 refers to:

[0015] The post-stack multi-azimuth seismic data, after being superimposed with the dominant azimuth, are divided according to different frequency components.

[0016] Furthermore, the method for extracting the coherent volume in step 103 is as follows:

[0017] The coherence volume is obtained by calculating the eigenvalues ​​of the post-stack multi-azimuth seismic data after frequency band preprocessing; a matrix is ​​constructed from the sample vectors of the seismic data.

[0018]

[0019] Where N is the number of horizontal sampling points, a is the number of vertical sampling points, d is the number of seismic data sampling points, and D is the matrix of the seismic data. N×a The corresponding covariance matrix is ​​C a×a =D N×a T D N×a Therefore, based on intrinsic structure coherence estimation, the third-generation coherent body is defined as... n is the independent variable (ranging from 1 to a), λ max It is matrix D N×a The largest eigenvalue, λ n They are different eigenvalues, C nn It is matrix C a×a Different elements in it.

[0020] Furthermore, the method for extracting the curvature volume in step 103 is as follows:

[0021] Curvature volume is an index that characterizes the geometric features of a formation, and its calculation formula is as follows:

[0022]

[0023] Among them, K x d represents the curvature along the x-direction. x The viewing angle is the tilt angle.

[0024] Furthermore, the method for predicting post-stack seismic cracks based on coherence volume and curvature volume in step 103 is as follows:

[0025] The coherent volume and the curvature volume are fused using RGB to obtain the post-stack fracture prediction result map; different colors are used in the post-stack fracture prediction result map to reflect the location of large-scale faults and medium-scale fractures respectively.

[0026] Furthermore, the method for preprocessing the pre-stack azimuth seismic gather based on the data quality assessment results in step 104 is as follows:

[0027] Adaptive Radon transform, alpha filtering and gather flattening are performed on pre-stack azimuth seismic gathers.

[0028] Furthermore, the method for calculating the frequency attenuation gradient A of the pre-stack azimuth seismic gather in step 105 is as follows:

[0029]

[0030] In the instantaneous amplitude spectrum, the location of the maximum energy point is (f1, E1), and the location of the minimum energy point is (f2, E2). The frequency attenuation gradient A is used to describe how quickly the energy in the high-frequency band of seismic data attenuates as the frequency increases. If cracks exist, the absolute value of the frequency attenuation gradient A will increase.

[0031] Furthermore, the method for predicting pre-stack multi-directional frequency attenuation gradient cracks in step 105 is as follows:

[0032] After calculating the frequency attenuation gradient A, multi-directional anisotropic ellipse fitting is performed to obtain the pre-stack crack prediction results.

[0033] Furthermore, step 106, which combines the pre-stack crack prediction results and the post-stack crack prediction results to perform a comprehensive crack prediction, refers to:

[0034] The analysis combines pre-stack fracture prediction results with post-stack fracture prediction results. The pre-stack fracture prediction results are used to predict the distribution of micro-fracture zones, while the post-stack fracture prediction results are used to predict the precise location of large-scale faults and medium-scale fractures.

[0035] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0036] This invention combines pre-stack and post-stack fracture prediction for analysis. Pre-stack multi-azimuth frequency attenuation gradient fracture prediction can predict the distribution of micro-fracture zones, while post-stack fracture prediction can predict the precise location of large and medium-sized faults, achieving unified multi-scale shale gas reservoir fracture prediction analysis. The seismic data preprocessing step for fracture prediction improves the data quality used for fracture prediction, thereby enhancing the accuracy of multi-scale shale gas reservoir fracture prediction analysis to a certain extent. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 The flowchart illustrates a multi-scale shale gas reservoir fracture prediction method based on data preprocessing, provided by this invention.

[0039] Figure 2 This is a diagram showing the predicted crack after stacking in an embodiment of the present invention.

[0040] Figure 3 This is a diagram showing the pre-stack crack prediction results in an embodiment of the present invention.

[0041] Figure 4 This is an RGB fusion result image of the combination of pre-stack crack prediction results and post-stack crack prediction results in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0044] Example

[0045] The Ordovician Wufeng Formation in the Baozang syncline of the Yang 101 well area in the Southwest Oil and Gas Field's Chuannan Shale Gas Block was selected, and the reservoir is a marine shale reservoir. For example... Figure 1 As shown in the figure, this embodiment proposes a multi-scale shale gas reservoir fracture prediction method based on data preprocessing, including the following steps:

[0046] Step 101: Based on the analysis of post-stack seismic data and geological structural morphology, the post-stack multi-directional seismic data in the area are superimposed in the dominant directional position.

[0047] The advantageous orientation refers to:

[0048] Earthquake data exhibits slightly different morphological characteristics in different orientations, and the orientation that can relatively clearly reflect the tectonic morphology is the dominant orientation.

[0049] Step 102: Perform frequency band preprocessing on the post-stack multi-azimuth seismic data after the dominant azimuth superposition;

[0050] The frequency band preprocessing refers to:

[0051] The post-stack multi-azimuth seismic data, after being superimposed with the dominant azimuth, are divided according to different frequency components.

[0052] Step 103: Extract coherence volume and curvature volume from the post-stack multi-azimuth seismic data after frequency band preprocessing, and predict post-stack seismic cracks based on the coherence volume and curvature volume.

[0053] The method for extracting coherence is as follows:

[0054] The coherence volume is obtained by calculating the eigenvalues ​​of the post-stack multi-azimuth seismic data after frequency band preprocessing; the seismic data sample vectors form a matrix, and the seismic data can be represented as D. N×a :

[0055]

[0056] In the above formula, N is the number of horizontal sampling points, a is the number of vertical sampling points, d is the number of seismic data sampling points, and D is the matrix of the seismic data. N×a The corresponding covariance matrix is ​​C a×a =D N×a T D N×a Therefore, based on intrinsic structure coherence estimation, the third-generation coherent body is defined as... n is the independent variable (ranging from 1 to a), λ max It is matrix D N×a The largest eigenvalue, λ n They are different eigenvalues, C nnIt is matrix C a×a Different elements in it.

[0057] The method for extracting the curvature body is as follows:

[0058] Curvature volume is an index that characterizes the geometric features of a formation, and its calculation formula is as follows:

[0059]

[0060] Among them, K x d represents the curvature along the x-direction. x The x-direction represents the horizontal direction along the ground, and the y-direction is the vertical downward direction perpendicular to the ground.

[0061] The method for predicting post-stack seismic fractures based on coherence volume and curvature volume is as follows:

[0062] The post-stack fracture prediction result image is obtained by RGB fusion of the coherence volume and the curvature volume; different colors are used in the post-stack fracture prediction result image to reflect the location of large-scale faults and medium-scale fractures respectively. The post-stack fracture prediction result image obtained in this embodiment is as follows. Figure 2 As shown in the figure, the post-stack crack prediction results are as follows:

[0063] Black is used to represent coherent volumes, which are used to reflect the location of large-scale faults;

[0064] Color was used (due to drafting requirements). Figure 2 The curvature volume (displayed as different shades of gray) is used to represent the location of medium-scale cracks.

[0065] Step 104: Perform data quality assessment on the pre-stack azimuth seismic gathers, and preprocess the pre-stack azimuth seismic gathers based on the data quality assessment results.

[0066] The preprocessing methods include:

[0067] Adaptive Radon transform, alpha filtering and gather flattening are performed on pre-stack azimuth seismic gathers.

[0068] (1) The formula for the adaptive Radon transform is as follows:

[0069] R(ρ,θ)=∫∫f(x,y)δ(ρ-x cosθ-y sinθ)dxdy

[0070] Where R is the value in the transform domain, ρ is the distance from the line to the origin, θ is the angle between the normal to the line and the horizontal line, δ is the Dirac function, and f is the seismic data;

[0071] (2) The alpha filtering joint denoising refers to:

[0072] Seismic data is filtered in units of surface cells. The parameter alpha represents the degree of noise removal in the surface cells. The larger the value, the greater the degree of noise removal, and the smaller the value, the weaker the degree of noise removal.

[0073] (3) The gather flattening process refers to:

[0074] The velocity-independent residual time difference (AVO) correction method uses near / mid-channel stacked traces as templates to maintain the signal amplitude before and after processing, preserving AVO characteristics. For details, see "Seismic Data Analysis: Seismic Data Processing, Inversion, and Interpretation" by W. Yılmaz.

[0075] Step 105: Calculate the frequency attenuation gradient for the pre-stack azimuth seismic gathers and perform pre-stack multi-azimuth frequency attenuation gradient crack prediction.

[0076] The method for calculating the frequency attenuation gradient A of the pre-stack azimuth seismic gathers is as follows:

[0077]

[0078] In the instantaneous amplitude spectrum, the location of the maximum energy point is (f1, E1), and the location of the minimum energy point is (f2, E2). The frequency attenuation gradient A is used to describe how quickly the energy in the high-frequency band of seismic data attenuates as the frequency increases. If cracks exist, the absolute value of the frequency attenuation gradient A will increase.

[0079] The method for pre-stack multi-directional frequency attenuation gradient crack prediction is as follows: After calculating the frequency attenuation gradient A, multi-directional anisotropic ellipse fitting is performed to obtain the pre-stack crack prediction result. The pre-stack crack prediction result obtained in this embodiment is as follows: Figure 3 As shown in the figure, the distribution location and direction of the micro-cracks can be seen. The depth of color represents the intensity of the crack distribution zone; the darker the color, the greater the intensity, and vice versa.

[0080] Step 106: Combine the pre-stack fracture prediction results and the post-stack fracture prediction results to perform a comprehensive fracture prediction. That is, analyze the combined pre-stack and post-stack fracture prediction results. The pre-stack fracture prediction results are used to predict the distribution of micro-fracture zones, while the post-stack fracture prediction results are used to predict the fine locations of large-scale faults and medium-scale fractures. In this embodiment, the combined pre-stack and post-stack fracture prediction results yield the following result: Figure 4 The RGB fusion result shown in the image ( Figure 4 (Including wells YANG101H56-1 and YANG101H53-3).

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-scale shale gas reservoir fracture prediction method based on data preprocessing, characterized in that, Includes the following steps: Step 101: Based on the analysis of post-stack seismic data and geological structural morphology, the post-stack multi-directional seismic data are superimposed in the dominant azimuth. Step 102: Perform frequency band preprocessing on the post-stack multi-azimuth seismic data after the dominant azimuth superposition; Step 103: Extract coherence volume and curvature volume from the post-stack multi-azimuth seismic data after frequency band preprocessing, and predict post-stack seismic cracks based on the coherence volume and curvature volume. Step 104: Perform data quality assessment on the pre-stack azimuth seismic gathers, and preprocess the pre-stack azimuth seismic gathers based on the data quality assessment results. Step 105: Calculate the frequency attenuation gradient for the pre-stack azimuth seismic gathers and perform pre-stack multi-azimuth frequency attenuation gradient crack prediction. Step 106: Combine the pre-stack crack prediction results and the post-stack crack prediction results to make a comprehensive crack prediction. The method for predicting post-stack seismic cracks based on coherence volume and curvature volume in step 103 is as follows: The coherent volume and the curvature volume are RGB fused to obtain the post-stack fracture prediction result map; different colors are used in the post-stack fracture prediction result map to reflect the location of large-scale faults and medium-scale fractures respectively. The method for preprocessing prestack azimuth seismic gathers based on data quality assessment results in step 104 is as follows: Adaptive Radon transform, alpha filtering combined denoising, and gather flattening are performed on pre-stack azimuth seismic gathers. The method for predicting pre-stack multi-directional frequency attenuation gradient cracks in step 105 is as follows: The frequency attenuation gradient is calculated. A Then, multi-directional anisotropic ellipse fitting was performed to obtain the pre-stack crack prediction results; Step 106, which combines the pre-stack crack prediction results and the post-stack crack prediction results to make a comprehensive crack prediction, refers to: The analysis combines pre-stack fracture prediction results with post-stack fracture prediction results. The pre-stack fracture prediction results are used to predict the distribution of micro-fracture zones, while the post-stack fracture prediction results are used to predict the precise location of large-scale faults and medium-scale fractures.

2. The multi-scale shale gas reservoir fracture prediction method based on data preprocessing according to claim 1, characterized in that, The advantageous orientation mentioned in step 101 refers to: Earthquake data exhibits slightly different morphological characteristics in different orientations, and the orientation that can relatively clearly reflect the tectonic morphology is the dominant orientation.

3. The multi-scale shale gas reservoir fracture prediction method based on data preprocessing according to claim 1, characterized in that, The frequency band preprocessing mentioned in step 102 refers to: The post-stack multi-azimuth seismic data, after being superimposed with the dominant azimuth, are divided according to different frequency components.

4. The multi-scale shale gas reservoir fracture prediction method based on data preprocessing according to claim 1, characterized in that, The method for extracting coherent volumes in step 103 is as follows: The coherence volume is obtained by calculating the eigenvalues ​​of the post-stack multi-azimuth seismic data after frequency band preprocessing; a matrix is ​​constructed from the sample vectors of the seismic data. in, This represents the number of horizontal sampling points. This represents the number of vertical sampling points. These are seismic data sampling points, and the matrix of this seismic data. The corresponding covariance matrix is Therefore, based on intrinsic structure coherence estimation, the third-generation coherent body is defined as... , It is the independent variable, ranging from 1 to... , It is a matrix The largest eigenvalue, They are different eigenvalues. It is a matrix Different elements in it.

5. The multi-scale shale gas reservoir fracture prediction method based on data preprocessing according to claim 1, characterized in that, The method for extracting the curvature volume in step 103 is as follows: Curvature volume is an index that characterizes the geometric features of a formation, and its calculation formula is as follows: in, Indicates along Curvature of direction, The viewing angle is the tilt angle.

6. The multi-scale shale gas reservoir fracture prediction method based on data preprocessing according to claim 1, characterized in that, In step 105, the frequency attenuation gradient is calculated for the pre-stack azimuth seismic gathers. A The method is as follows: The location of the maximum energy point in the instantaneous amplitude spectrum is ( , The location of the minimum energy point is ( , ); and frequency attenuation gradient A This describes the rate at which energy in the high-frequency band of seismic data decays with increasing frequency. If cracks exist, the frequency attenuation gradient is also considered. A The absolute value will increase.

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