A method for well leakage early warning by using earthquake prediction results

Abstract

The present application relates to the field of geophysical exploration technology, and particularly relates to a method for well leakage early warning by using seismic prediction results, comprising: basic data acquisition, regional geological condition analysis, seismic data preprocessing, small fracture and fracture-vug body seismic fine delineation, and shadow relief display of the delineation result. The present application improves the basic data quality by introducing anisotropic diffusion and three-dimensional multi-level median joint filtering technology, and carries out multi-attribute fusion based on adjustable parameters by combining pre-stack multi-azimuth frequency attenuation gradient fracture prediction and post-stack structural coherent tensor, finally realizes spatial display of the multi-attribute fusion result by using shadow relief technology, improves the accuracy of small fracture and fracture-vug body delineation, and more directly shows the spatial form of the fracture and fracture-vug body, which can effectively guide well location design, drilling design and drilling trajectory optimization.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a method for well leakage early warning using earthquake prediction results. Background Technology

[0002] With the continuous development of oil and gas exploration, seismic exploration technology plays an increasingly important role in drilling engineering. Well leakage frequently occurs during oil and gas drilling, posing a serious threat to drilling operations. Traditional well leakage early warning methods mainly employ real-time drilling monitoring, integrated logging displays, drilling fluid performance monitoring, and formation pressure assessment, but most of these methods suffer from low early warning capabilities and incomplete analysis.

[0003] Existing technologies suffer from at least the following problems: Current well leakage early warning technologies based on seismic data mainly employ methods such as post-stack coherence, curvature, or structural tensor properties to characterize fractures and cavities. These methods are often limited by the quality of seismic data and the lack of anisotropic characteristics in post-stack data, making it difficult to achieve high-precision characterization of small fractures and fractures / cavities. Existing methods often do not involve seismic data preprocessing and do not consider pre-stack anisotropic characteristics. This invention improves the quality of basic data by introducing anisotropic diffusion and three-dimensional multi-level median joint filtering techniques. It also performs multi-attribute fusion based on adjustable parameters between pre-stack multi-azimuth frequency attenuation gradient fracture prediction and post-stack structural coherence tensor. Finally, the multi-attribute fusion results are spatially displayed using shadow relief technology, improving the accuracy of small fracture and fracture / cavity characterization and guiding well location design, drilling design, and drilling trajectory optimization.

[0004] It is evident that there is still room for improvement in the existing technology, and therefore, it is necessary to propose more reasonable technical solutions to address the technical problems existing in the current technology. Summary of the Invention

[0005] To overcome at least one of the aforementioned defects, this invention proposes a method for well leakage early warning using seismic prediction results. The aim is to use seismic data prediction results to make precise predictions of small-scale fracture zones or fractured cavities in the exploration and development area, and to guide well location design, drilling design and drilling trajectory optimization. This method has important practical significance and application value.

[0006] To achieve the above objectives, the early warning method disclosed in this invention can adopt the following technical solution:

[0007] A method for well leakage early warning using earthquake prediction results includes:

[0008] Basic data acquisition: Collect and organize post-stack depth-domain seismic data and pre-stack time-domain data;

[0009] Regional geological analysis: Based on regional drilling and well leakage conditions, complete regional well leakage analysis; based on the depth domain 3D seismic interpretation results, determine the type, scale, and distribution characteristics of faults in the work area;

[0010] Seismic data preprocessing: Anisotropic diffusion and three-dimensional multi-level median method combined filtering are carried out on post-stack depth domain seismic data to improve the signal-to-noise ratio of the basic data and enhance the response characteristics of faults and fractures.

[0011] Fine-scale seismic characterization of small faults and karst cavities: First, based on preprocessed post-stack depth domain seismic data, coherent structural tensor property volume calculations are performed to characterize the basic morphology of small faults and karst cavities within the region. Then, pre-stack multi-azimuth frequency attenuation gradients are performed using pre-stack seismic data to obtain fracture density results, characterizing the anisotropic differences caused by fracture zones within the region. Finally, principal component analysis (PCA) calculations under adjustable parameters are performed on the fracture density inversion results and coherent structural tensor data to characterize the morphology of small faults and karst cavities.

[0012] The shadow relief of the characterization results shows that the attribute values ​​of the detailed seismic characterization results of small faults and karst fissures are converted into shadow relief to represent the changes in convexity and depression, and to highlight the size and scale of small faults and karst fissures.

[0013] The aforementioned publicly disclosed early warning method uses seismic data prediction results to make detailed predictions of small-scale fracture zones or fractured cavities in the exploration and development area. It can detect the development degree of fracture zones and fractured cavities, and make it easier to judge the possibility of well leakage in advance based on changes in the development degree. This enables early detection of safety hazards and reduces the risk of mining.

[0014] Furthermore, in the aforementioned seismic data preprocessing step, anisotropic diffusion filtering calculations are performed according to the following method:

[0015]

[0016] In the above formula, For earthquake data, For diffusion time, For divergence calculation, For the diffusion tensor.

[0017] Furthermore, in the aforementioned seismic data preprocessing step, three-dimensional multi-level median filtering is calculated according to the following method:

[0018]

[0019] In the above formula, The amplitude of a point in 3D seismic data. for The amplitude of the vibration at 26 adjacent points in the surrounding space. This is the filtered output value. To obtain the median value, A i (s) represents the range of values ​​after median processing.

[0020] Furthermore, in the detailed seismic characterization steps for small fractures and cavities, the calculation of the structural tensor coherence attribute volume includes: calculating the directional derivative vector of each point in the three-dimensional seismic data and constructing a structural tensor; and obtaining the structural tensor coherence attribute volume by calculating the eigenvalues ​​and eigenvectors of its structural tensor matrix.

[0021] Furthermore, in the detailed seismic characterization step of small fractures and cavities, the multi-azimuth frequency attenuation gradient is determined according to the following method:

[0022]

[0023] The location of the maximum energy point in the instantaneous amplitude spectrum is ( , The location of the minimum point is ( , ), For frequency attenuation gradient, E For amplitude energy, , These represent the frequency and amplitude energy of the maximum energy point, respectively. , These represent the frequency and amplitude energy of the minimum energy point, respectively.

[0024] Furthermore, in the detailed seismic characterization steps for small fractures and cavities, the attribute fusion calculation under adjustable parameters includes:

[0025] Establish a matrix from the two attribute bodies. ,in The main data consists of the attribute value of the i-th attribute and the j-th sample point, where a is the number of attribute volumes and b is the number of sample points for each attribute. Standardization is performed to eliminate the influence between different units.

[0026] Calculate the correlation coefficients of the standardized attribute matrices;

[0027] By finding the non-negative eigenvalues ​​of the correlation matrix R, the eigenvector matrix E is obtained, and the principal component components are calculated. F=HD ;in, D A matrix created for the attribute body;

[0028] Set a threshold factor k. If the variance of the first i principal components accounts for a proportion of the total variance that is greater than or equal to the threshold factor k, then the original attribute sample can be represented by the first i principal components.

[0029] Each principal component is fused, and the threshold factor k is adjusted based on a comprehensive evaluation of the fusion effect and geological conditions.

[0030] Furthermore, the correlation coefficients of the standardized attribute matrices are calculated as follows:

[0031]

[0032] In the above formula, Let be the correlation coefficient, and R be the correlation coefficient matrix. The matrix established for the attribute body, For the attribute values ​​of the sample points, The number of attribute bodies.

[0033] Furthermore, the principal components are fused using the following method:

[0034] The first i principal component components are rearranged into a new eigenvector matrix W, resulting in a new attribute matrix. , here A matrix created for the attribute body.

[0035] Furthermore, the threshold factor k is adjusted as follows:

[0036] Attribute body If the spatial correlation with the number of lost wells is greater than 80%, no adjustment is needed; if it is less than 80%, the k-factor should be increased, generally based on experience.

[0037] Compared with the prior art, some of the beneficial effects of the technical solution disclosed in this invention include:

[0038] This invention improves the quality of basic data by introducing anisotropic diffusion and three-dimensional multi-level median joint filtering technology. It also performs multi-attribute fusion based on adjustable parameters for pre-stack multi-directional frequency attenuation gradient fracture prediction and post-stack structural coherence tensor. Finally, the multi-attribute fusion results are displayed spatially using shadow relief technology, which improves the accuracy of small fractures and fracture cavities and more intuitively shows the spatial morphology of fractures and fracture cavities. This can effectively guide well location design, drilling design and drilling trajectory optimization. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced 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.

[0040] Figure 1This is a flowchart illustrating a method for well leakage early warning using earthquake prediction results.

[0041] Figure 2 This is a detailed seismic characterization result of small fractures and fissures provided in the embodiments of the present invention.

[0042] Figure 3 The image shows a shadow relief display of the detailed seismic characterization results of small fractures and cavities provided in the embodiments of the present invention.

[0043] Figure 4 This is a schematic diagram of azimuth anisotropy of different attributes in the same CMP gather. Detailed Implementation

[0044] The following description, in conjunction with the accompanying drawings and specific embodiments, further illustrates this embodiment.

[0045] To address the shortcomings of existing technologies in predicting and warning of well leakage, which suffer from insufficient accuracy and poor effectiveness, the following embodiments are optimized to overcome these deficiencies.

[0046] Example

[0047] like Figure 1 As shown in the embodiment, a method for well leakage early warning using earthquake prediction results is provided, as detailed below:

[0048] Step 1: Basic Data Acquisition. Collect and organize post-stack depth-domain seismic data and pre-stack time-domain data.

[0049] Step Two: Regional Geological Analysis. Based on regional drilling and well leakage conditions, a regional well leakage analysis is completed; based on the results of depth-domain 3D seismic interpretation, the fault types, scales, and distribution characteristics within the work area are further determined.

[0050] Step 3: Seismic data preprocessing for fault and fracture-cavity prediction. Anisotropic diffusion and three-dimensional multi-level median filtering are combined for post-stack depth-domain seismic data to improve the signal-to-noise ratio of the base data and enhance the response characteristics of faults and fractures. To further ensure the anisotropic characteristics of pre-stack time-domain data, no further data preprocessing is performed on the pre-stack time-domain data.

[0051] Step 4: Fine-grained seismic characterization of small faults and karst cavities. First, based on preprocessed post-stack depth-domain seismic data, coherent structural tensor attribute volume calculations are performed to characterize the basic morphology of small faults and karst cavities within the region. Then, pre-stack multi-azimuth frequency attenuation gradients are performed using pre-stack seismic data to obtain fracture density results, finely characterizing the anisotropic differences caused by fracture zones within the region. Finally, the fracture density inversion results are combined with coherent structural tensor data to perform attribute fusion calculations under adjustable parameters, further integrating the characteristics of both to ultimately achieve a high-precision characterization of small faults and karst cavities. Specific results can be seen... Figure 2 The darker the color, the more developed the small fractures and crevices; the lighter the color, the less developed they are.

[0052] Step 5: Shading and Relief Display of the Characterization Results. The attribute values ​​of the detailed seismic characterization results for small faults and karst fissures are converted into shading and relief to represent variations in convexity and concavity, further highlighting the size and scale of small faults and karst fissures. Specific results can be seen... Figure 3 Its convex and concave variations better reflect its scale and size, and need to be combined with Figure 2 The degree of development, extension scale and size of small fractures and cavities are comprehensively identified.

[0053] The anisotropic diffusion and three-dimensional multi-level median filtering in step three refers to combining anisotropic diffusion filtering with alpha filtering. Alpha filtering is then applied to the anisotropic diffusion filtered data. Anisotropic diffusion filtering enhances the discontinuities in fault, fracture, or cavitation data, while three-dimensional multi-level median filtering effectively improves the signal-to-noise ratio. The combination of both enhances the discontinuities in seismic data while improving the signal-to-noise ratio. The anisotropic diffusion filtering calculation formula is as follows:

[0054]

[0055] In the above formula, For earthquake data, For diffusion time, For divergence calculation, Let be the diffusion tensor. The formula for calculating the three-dimensional multi-level median filter is as follows:

[0056]

[0057] In the above formula, The amplitude of a point in 3D seismic data. for The amplitude of the vibration at 26 adjacent points in the surrounding space. This is the filtered output value. This is to obtain an intermediate value.

[0058] The calculation of the structural tensor coherence attribute volume in step four mainly includes two parts: first, calculating the directional derivative vector of each point in the 3D seismic data and constructing the structural tensor; and finally, obtaining the structural tensor coherence attribute volume by calculating the eigenvalues ​​and eigenvectors of its structural tensor matrix. This method can effectively characterize the structural features in seismic data and is generally used as a basic morphological identification method for fractures and fracture-cavities.

[0059] The multi-azimuth frequency attenuation gradient in step four refers to extracting the frequency attenuation gradient from multiple azimuth traces of the pre-stack data, and finally obtaining the crack density inversion result through ellipse fitting. The expression for the frequency attenuation gradient is as follows:

[0060]

[0061] The location of the maximum energy point in the instantaneous amplitude spectrum is ( , The location of the minimum point is ( ). , The frequency attenuation gradient mainly describes the rate at which the energy in the high-frequency band of seismic data attenuates as the frequency increases. If factors such as cracks exist, the absolute value of the attenuation gradient will increase. Elliptical azimuth fitting is mainly guided by the reflection coefficient formula of seismic signal amplitude as a function of azimuth angle (Ruger, 1996). It usually describes the azimuth anisotropy of different attributes in the same CMP (Common Middle Point) gather, that is, the reflection information received by the same underground point from different azimuths is different. We arrange the information of the same reflection point in different azimuths in a ring according to different azimuth angles (0-360 degrees). The distance from the center point to the ring represents the attribute value of the same reflection point in different azimuths. When the attribute values ​​of the same reflection point in different azimuths are the same, it is isotropic (no difference in different azimuths), and there are often no cracks. When the attribute values ​​of the same reflection point in different azimuths are different, the ring arrangement is an ellipse. The reflection point has azimuth anisotropy. Its major axis represents the crack direction, and the ratio of the major axis to the minor axis represents the crack density. Figure 4 As shown.

[0062] The attribute fusion calculation under adjustable parameters in step four involves fusing the structural tensor coherence volume with the crack density inversion results, and mainly includes the following steps:

[0063] (1) Establish a matrix from the two attribute bodies ,in The values ​​are mainly for the i-th attribute and the j-th sample point, where a is the number of attribute volumes and b is the number of sample points for each attribute. Standardization is performed to eliminate the influence between different units.

[0064] (2) Calculate the correlation coefficients of the standardized attribute matrices. The formula is as follows:

[0065]

[0066] In the above formula, Let be the correlation coefficient, and R be the correlation coefficient matrix. The matrix established for the attribute body, For the attribute values ​​of the sample points, The number of attribute bodies.

[0067] (3) Obtain the eigenvector matrix by finding the non-negative eigenvalues ​​of the correlation matrix R. H And calculate the principal component components. F= HD.

[0068] (4) Set a threshold factor k. If the variance of the first i principal components accounts for a proportion of the total variance greater than or equal to the threshold factor k, then the original attribute sample can be represented by the first i principal components.

[0069] (5) The principal components are fused together, and the threshold factor k is adjusted according to the comprehensive evaluation of the fusion effect and geological conditions to achieve the best fusion.

[0070] The shadow relief display mentioned in step five refers to a technology that integrates computer graphics, optical principles, and data visualization to transform two-dimensional planar data (such as elevation, grayscale, or attribute values) into a relief image with depth perception. Its core principle is to reconstruct spatial form using light and shadow relationships, allowing the observer to perceive the protrusions and depressions of a surface through changes in visual shadows, such as... Figure 2 , Figure 3 As shown.

[0071] The above are the embodiments listed in this example. However, this example is not limited to the optional embodiments described above. Those skilled in the art can arbitrarily combine the above methods to obtain other various embodiments. Anyone can derive other various forms of embodiments under the guidance of this example. The above specific embodiments should not be construed as limiting the scope of protection of this example. The scope of protection of this example should be defined in the claims.

Claims

1. A method for well leakage early warning using earthquake prediction results, characterized in that, include: Basic data acquisition: Collect and organize post-stack depth-domain seismic data and pre-stack time-domain data; Regional geological analysis: Based on regional drilling and well leakage conditions, complete regional well leakage analysis; based on the depth domain 3D seismic interpretation results, determine the type, scale, and distribution characteristics of faults in the work area; Seismic data preprocessing: Anisotropic diffusion and three-dimensional multi-level median method combined filtering are performed on post-stack depth domain seismic data; Fine-grained seismic characterization of small faults and karst cavities: First, based on preprocessed post-stack depth domain seismic data, coherent structural tensor attribute volume calculations are performed to characterize the basic morphology of small faults and karst cavities within the region. Then, pre-stack multi-azimuth frequency attenuation gradients are performed using pre-stack seismic data to obtain fracture density results, characterizing the anisotropic differences caused by fracture zones within the region. Finally, principal component analysis calculations under adjustable parameters are performed on the fracture density inversion results and coherent structural tensor data to achieve morphological characterization of small faults and karst cavities. The shadow relief of the characterization results shows that the attribute values ​​of the detailed seismic characterization results of small faults and karst fissures are converted into shadow relief to represent the changes in convexity and depression, and to highlight the size and scale of small faults and karst fissures.

2. The method for well leakage early warning using earthquake prediction results according to claim 1, characterized in that, In the aforementioned seismic data preprocessing steps, anisotropic diffusion filtering calculations are performed according to the following method: In the above formula, For earthquake data, For diffusion time, For divergence calculation, For the diffusion tensor.

3. The method for well leakage early warning using earthquake prediction results according to claim 1, characterized in that, In the aforementioned seismic data preprocessing steps, three-dimensional multi-level median filtering is calculated according to the following method: In the above formula, The amplitude of a point in 3D seismic data. for The amplitude of the vibration at 26 adjacent points in the surrounding space. This is the filtered output value. To obtain the median value, A i (s) represents the range of values ​​after median processing.

4. The method for well leakage early warning using earthquake prediction results according to claim 1, characterized in that, In the detailed seismic characterization steps for small fractures and cavities, the calculation of the structural tensor coherence attribute volume includes: calculating the directional derivative vector of each point in the three-dimensional seismic data and constructing a structural tensor; and obtaining the structural tensor coherence attribute volume by calculating the eigenvalues ​​and eigenvectors of its structural tensor matrix.

5. The method for well leakage early warning using earthquake prediction results according to claim 1, characterized in that, In the detailed seismic characterization steps for small fractures and cavities, the multi-azimuth frequency attenuation gradient is determined according to the following method: The location of the point with the maximum amplitude energy in the instantaneous amplitude spectrum is ( , The location of the minimum point is ( , ), For frequency attenuation gradient, E For amplitude energy, , These represent the frequency and amplitude energy of the maximum energy point, respectively. , These represent the frequency and amplitude energy of the minimum energy point, respectively.

6. The method for well leakage early warning using earthquake prediction results according to claim 1, characterized in that, In the detailed seismic characterization steps for small fractures and cavities, the attribute fusion calculation under adjustable parameters includes: Establish a matrix from the two attribute bodies. ,in The main data consists of the attribute value of the i-th attribute and the j-th sample point, where a is the number of attribute volumes and b is the number of sample points for each attribute. Standardization is performed to eliminate the influence between different units. Calculate the correlation coefficients of the standardized attribute matrices; By finding the non-negative eigenvalues ​​of the correlation matrix R, the eigenvector matrix can be obtained. H And calculate the principal component components. F=HD ;in, D A matrix created for the attribute body; Set a threshold factor k. If the variance of the first i principal components accounts for a proportion of the total variance that is greater than or equal to the threshold factor k, then the original attribute sample can be represented by the first i principal components. The first i principal components are fused, and the threshold factor k is adjusted based on a comprehensive evaluation of the fusion effect and geological conditions.

7. The method for well leakage early warning using earthquake prediction results according to claim 6, characterized in that, The correlation coefficients of the standardized attribute matrices are calculated as follows: In the above formula, Here, R is the correlation coefficient, and is the correlation coefficient matrix. The matrix established for the attribute body, For the attribute values ​​of the sample points, The number of attribute bodies.

8. The method for well leakage early warning using earthquake prediction results according to claim 6, characterized in that, The first i principal component components are fused using the following method: The first i principal component components are rearranged into a new eigenvector matrix W, resulting in a new attribute matrix. , here A matrix created for the attribute body.

9. The method for well leakage early warning using earthquake prediction results according to claim 6, characterized in that, Adjust the threshold factor k as follows: Attribute body If the spatial correlation with the number of lost wells is greater than 80%, no adjustment is needed; if it is less than 80%, the k-factor should be increased.

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