Method and device for identifying strike-slip fracture of ultra-deep fault control fracture-vuggy carbonate oil and gas reservoir

Abstract

The invention discloses a strike-slip fracture identification method and device for an ultra-deep fault control fracture-vuggy carbonate reservoir, and the method comprises the steps: carrying out the denoising and fracture enhancement processing of a seismic data volume; determining a plurality of seismic attributes of the seismic data volume after fracture enhancement processing; seismic attributes for fracture identification are screened out, and multi-attribute fusion is carried out to obtain fusion attributes; carrying out ant tracking processing on the fusion attributes; and performing convolution filtering processing on the fusion attribute after ant tracking processing, and determining a fracture position. The strike-slip fracture identification result is less affected by earthquake noise, the fracture structure is clear, and longitudinal and transverse development characteristics conform to geological knowledge; the fracture identification result provides favorable conditions for strike-slip fracture reservoir control and reservoir control analysis.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of petroleum geological exploration, and particularly relates to a strike-slip fault identification method and device for super-deep fault-controlled fracture-vug carbonate rock oil and gas reservoirs. BACKGROUND

[0002] For fault-controlled fracture-vug carbonate rock oil and gas reservoirs, a strike-slip fault causes strata to be broken along a fault plane, and is reformed by later-stage dissolution to form fracture-vug reservoir spaces, and deep oil and gas migrates along the strike-slip fault to be accumulated in the fracture-vug reservoir. As can be seen, accurately depicting the spatial structure of the strike-slip fault is crucial for the exploration and development of the fault-controlled fracture-vug oil and gas reservoir. Three-dimensional seismic data is the most powerful method for studying deep faults, and a common technical method is to identify a fault by selecting appropriate seismic attributes, and then to interpret the fault distribution based on artificial understanding of the structure. However, for carbonate strike-slip faults, the lateral variation is complex, and in the case of continuously changing compression and tension stress, various forms such as pull-apart basins, compression uplifts, echelon, horse-tail, and translation appear alternately in segments, which easily causes artificial interpretation to be subjective and to lose details, and is not conducive to the study of fault-controlled reservoirs. The existing seismic attributes all have limitations, such as the difficulty of a geometric attribute in identifying translation strike-slip faults with small fault separations, and the great influence of signal-to-noise ratio on a signal attribute, so the effect of a single seismic attribute is poor. Based on the above problems and difficulties, it is necessary to further study the method of depicting strike-slip faults by seismic attributes, and to explore a technical method with better depiction capability. SUMMARY

[0003] In view of the above problems, the application discloses a strike-slip fault identification method for super-deep fault-controlled fracture-vug carbonate rock oil and gas reservoirs, comprising the following steps:

[0004] Performing denoising and fault enhancement processing on the seismic data volume;

[0005] Determining multiple seismic attributes of the seismic data volume after the fault enhancement processing;

[0006] Screening out seismic attributes for identifying faults, and performing multi-attribute fusion to obtain a fused attribute;

[0007] Performing ant tracking processing on the fused attribute;

[0008] Performing convolution filtering processing on the fused attribute after the ant tracking processing to determine the position of the fault.

[0009] More further, the denoising and fault enhancement processing on the seismic data volume comprises the following steps:

[0010] Calculating the directional derivative of each point of the three-dimensional seismic data volume, and constructing a gradient structure tensor matrix using the directional derivative;

[0011] Perform eigenvalue decomposition on the gradient structure tensor matrix to obtain eigenvalues and eigenvectors;

[0012] According to the eigenvalues and eigenvectors, the dip angle and strike of the seismic event are determined, and the position of the fracture is determined according to the dip angle information of the seismic event;

[0013] Noise is filtered along the direction of the seismic event, and fracture enhancement is performed at the fracture.

[0014] Further, the screened seismic attributes for identifying fractures include enhanced coherence, dip angle, coherence, curvature, ant tracking and similarity.

[0015] Further, the specific steps of screening the seismic attributes for identifying fractures are as follows:

[0016] The coincidence of all drilling and seismic attribute identified fracture identification results is counted, and the coincidence rate is calculated;

[0017] Screening the seismic attributes with a coincidence rate greater than a first threshold.

[0018] Further, the specific steps of performing multi-attribute fusion to obtain a fusion attribute are as follows:

[0019] Standardize the screened seismic attribute values;

[0020] Assign different weights to the standardized seismic attributes according to the coincidence rate;

[0021] Perform multi-attribute fusion on the standardized seismic attributes according to the assigned weights to obtain a fusion attribute.

[0022] Further, the specific steps of performing convolution filtering on the fusion attribute after ant tracking processing to determine the position of the fracture are as follows:

[0023] Gaussian smoothing is used to perform noise reduction processing on the fusion attribute after ant tracking processing;

[0024] Sobel operator is used to perform edge enhancement processing on the noise-reduced fusion attribute to determine the position of the fracture.

[0025] The application also discloses a strike-slip fracture identification device for ultra-deep fault-controlled fracture-vug carbonate reservoirs, comprising:

[0026] A denoising and fracture enhancement processing unit is used to denoise and enhance the fracture of the seismic data body;

[0027] A determination unit is used to determine a plurality of seismic attributes of the seismic data body after fracture enhancement processing;

[0028] A fusion unit is configured to screen out a seismic attribute for identifying a fracture and perform multi-attribute fusion to obtain a fused attribute.

[0029] An ant tracking processing unit is configured to perform ant tracking processing on the fused attribute.

[0030] A convolution filter processing unit is configured to perform convolution filter processing on the fused attribute after the ant tracking processing to determine the position of the fracture.

[0031] Further, the denoising and fracture enhancement processing unit is specifically configured to:

[0032] Calculate a directional derivative of each point of a three-dimensional seismic data body, and construct a gradient structure tensor matrix using the directional derivative;

[0033] Perform eigenvalue decomposition on the gradient structure tensor matrix to obtain eigenvalues and eigenvectors;

[0034] Determine the dip angle and strike of a seismic event according to the eigenvalues and eigenvectors, and determine the position of the fracture according to the dip angle information of the seismic event;

[0035] Filter out noise along the direction of the seismic event, and perform fracture enhancement at the fracture.

[0036] Further, the fusion unit is specifically configured to:

[0037] Statistically analyze the coincidence of the fracture identification results of all the drilling and the seismic attribute identification, and calculate a coincidence rate;

[0038] Screen out the seismic attribute with the coincidence rate greater than a first threshold value.

[0039] Further, the convolution filter processing unit is specifically configured to:

[0040] Perform denoising processing on the fused attribute after the ant tracking processing by using Gaussian smoothing;

[0041] Perform edge enhancement processing on the fused attribute after the denoising processing by using a Sobel operator to determine the position of the fracture.

[0042] Compared with the prior art, the embodiments of the present application have at least the following advantages: the strike-slip fracture identification result is less affected by seismic noise, the fracture structure is clear, and the longitudinal and transverse development characteristics are consistent with geological understanding; and the fracture identification result provides favorable conditions for strike-slip fracture control and reservoir analysis.

[0043] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure as indicated in the description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0045] Figure 1 The original seismic data profile of a transverse strike-slip fault is shown according to an embodiment of the present application;

[0046] Figure 2 The profile after the fault enhancement filtering of the original seismic data profile is shown according to an embodiment of the present application;

[0047] Figure 3 The profile after the preferred and fused seismic attribute profile is shown according to an embodiment of the present application;

[0048] Figure 4 The profile after the ant tracking processing is shown according to an embodiment of the present application;

[0049] Figure 5 The profile after the convolution filtering processing is shown according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely explain the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present application.

[0051] The strike-slip fault has a complex internal structure, and in the changing extrusion and tension stress, multiple structural patterns appear alternately in segments. At the same time, the exploration and development of the fault-controlled fracture-cave oil and gas reservoir put forward higher requirements for the fine depiction of the strike-slip fault. The horizontal segmentation, vertical stratification and main and secondary fault plane combination characteristics of the fault need more in-depth research. The conventional seismic attribute combined with the depiction means of artificial interpretation is difficult to meet the requirements. The purpose of the present application is to solve the problem that the existing technology cannot accurately depict the internal structure of the super-deep strike-slip fault.

[0052] The present application provides a kind of super-deep fault-controlled fracture-cave type carbonate rock oil and gas reservoir strike-slip fault identification method, comprising the following steps:

[0053] Step 1: denoising and fault enhancement processing is carried out on the seismic data body.

[0054] Seismic data is post-stack de-noising to reduce the effect of noise on seismic data and highlight the true signal response. Fracture enhancement filtering can weaken the effect of noise and make the fracture clearer. The process is as follows: 1) Calculate the gradient vector (directional derivative) of each point of the 3D seismic data volume, and use the directional derivative to construct the gradient structure tensor matrix:

[0055]

[0056] Where T is the gradient structure tensor matrix; u(x, y, z) represents the 3D seismic data volume. GS

[0057] 2) Perform eigenvalue decomposition on the gradient structure tensor matrix to obtain 3 eigenvalues and eigenvectors;

[0058] 3) Determine the dip and strike of the seismic event according to the eigenvalues and eigenvectors, and determine the location of the fracture according to the dip information of the seismic event;

[0059] The three eigenvectors can form a local orthogonal coordinate system, where the eigenvector corresponding to the largest eigenvalue is the direction in which the seismic event changes the fastest, representing the normal direction of the event, so that the dip and strike of the local seismic event can be calculated. Fractures usually cause sudden changes in the dip of seismic events, and the location of the fracture can be determined according to the dip information;

[0060] 4) Filter out noise along the direction of the seismic event and enhance at the fracture. The processing effect examples are shown in Figure 1 and Figure 2 . Figure 1 is the original seismic profile, Figure 2 is the seismic profile after enhancement processing, and the fault segment of the seismic event is more obvious after processing, and the longitudinal structure of the fracture is easier to distinguish.

[0061] Step 2: Calculate a variety of seismic attributes from the fracture-enhanced seismic data volume.

[0062] The seismic attributes that can reflect the development of fractures and are directly calculated from seismic data mainly include two categories: geometric and signal.

[0063] Geometric attributes detect changes in the spatial shape of seismic events caused by fractures, including curvature, dip, stress field, etc. For example, the average curvature attribute is calculated as follows:

[0064] First, use the least squares method to obtain the fitting formula of the local surface:

[0065] z(x, y) = ax 2 +by 2 ​+ cxy + dx + ey + h (2)

[0066] where x, t, z are spatial coordinates, a, b, c, d, e, h are coefficients of the surface equation.

[0067] Then the average curvature is calculated:

[0068]

[0069] where K m is the average curvature.

[0070] Signal class attributes detect the changes of seismic amplitude, frequency, phase caused by the fracture, including coherence, discontinuity, similarity, symmetry, edge detection, variance, etc. For example, similarity describes the correlation between local seismic data and overall seismic data:

[0071]

[0072] where Semb(z) is the similarity; τ is the time offset; N is the calculation window; F m (z) represents the mth trace of seismic data; z is time or depth; M is the maximum number of seismic traces.

[0073] These seismic attributes can identify fractures to some extent, but all have certain limitations. Different fracture characteristics can be identified by different seismic attributes. Therefore, after calculating these seismic attributes, the seismic attributes that are better for identifying carbonate strike-slip fractures need to be selected.

[0074] Step 3: Select several seismic attributes that are better for identifying fractures, and perform multi-attribute fusion to obtain a fused attribute.

[0075] The method for selecting seismic attributes is to compare with drilling. When drilling through carbonate fractures, emptying, mud loss, or gas logging display often occurs. The coincidence of all drilling and seismic attribute identification of fracture identification results is calculated, and the coincidence rate is calculated, as shown in Table 1, and the seismic attributes with a coincidence rate of more than 70% are selected.

[0076] Table 1: Coincidence rate statistics of multiple seismic attributes and drilling

[0077]

[0078] where the selected seismic attributes include enhanced coherence, dip, coherence, curvature, ant body, and similarity.

[0079] The selected several seismic attributes are fused to obtain a fused attribute data, such as Figure 3As shown in Fig. 4, the strike-slip fault structure can be clearly reflected. In the seismic attribute fusion, the range of each seismic attribute value screened is first normalized, and then each seismic attribute after the normalization is assigned different weights according to the coincidence rate.

[0080]

[0081] wherein X norm is normalized; X is the seismic attribute value; μ is the mean value; and σ is the standard deviation.

[0082] The normalized seismic attributes are fused according to the assigned weights to obtain the fused attribute. The method of the seismic attribute fusion is as follows:

[0083]

[0084] wherein P is the fused attribute; L is the number of seismic attributes participating in the fusion; P i is the normalized i-th seismic attribute; and W i is the weight of the normalized i-th seismic attribute.

[0085] Step 4: The fused attribute obtained in Step 3 is subjected to ant tracking processing.

[0086] Ant tracking is a commonly used method for processing seismic attributes, which can refine the fault features and enhance the fault continuity. The principle of the technology is to set a large number of electronic "ants" in the seismic data volume, and let each "ant" move forward along the possible fault surface while emitting "pheromones". The "ants" moving forward along the fault can track the fault surface, and if the expected fault surface is encountered, the "pheromones" will make very obvious marks. While those surfaces that are not likely to be faults will not be marked or only be marked with less obvious marks. The processing effect is shown in Fig. 5. Figure 4 As shown in Fig. 5, compared with Fig. 4, the resolution is higher, and each fault surface is more clearly identified. Figure 3

[0087] Step 5: The fused attribute after the ant tracking processing in Step 4 is subjected to convolution filtering processing to determine the position of the fault.

[0088] Convolution filtering is a commonly used method of image processing, and in the present application, its main role is to enhance the edges of the fault. The algorithm of edge enhancement is mainly based on the first or second derivative of the image, but the derivative is usually very sensitive to noise, so it is necessary to use a smoothing filter to improve the noise effect. In the present application, Gaussian smoothing is used to perform noise reduction processing on the fused attribute after the ant tracking processing, and the sobel operator is used to perform edge enhancement processing on the noise reduction processed fused attribute to determine the position of the fault.

[0089] ​Gaussian smoothing is weighted average according to the value of the pixel point and its neighborhood points to be filtered by Gaussian filter, which can effectively smooth the noise in the image. Gaussian filter is Gaussian distribution in horizontal and vertical directions, which highlights the weight of the center point in the smoothing calculation. Its calculation process is the convolution operation of the original image and the Gaussian filter:

[0090] (f*g)(t)=∫ R f(x)g(t-x)dx (7)

[0091] Wherein, f is the image to be processed;G is the filter or convolution kernel;T is time;R is the calculation range.

[0092] The following is a 3x3 Gaussian filter:

[0093]

[0094] Sobel operator is also a convolution kernel, which can highlight the edges of the image after operation. Sobel horizontal and vertical operators are respectively:

[0095]

[0096]

[0097] Wherein, Sobel X is the Sobel horizontal operator;Sobel Y is the Sobel vertical operator.

[0098] Convolution operation:

[0099]

[0100] Wherein, d x is the horizontal convolution result;D y is the vertical convolution result.

[0101] Fracture can be considered as the local edge of the image, and the edge of the fracture is more prominent and clear after processing by convolution filter. The processing effect is shown in Figure 5 , compared with Figure 4 , the longitudinal continuity of the fracture is further improved.

[0102] The recognition result of strike-slip fracture of the application is less affected by seismic noise, the fracture structure is clear, the internal structure of the fracture can be accurately described, and the longitudinal and transverse development characteristics are consistent with geological understanding;The recognition result of the fracture provides favorable conditions for strike-slip fracture control and reservoir analysis.

[0103] Based on the above-mentioned strike-slip fracture identification method of ultra-deep fault-controlled fracture-cave type carbonate rock oil and gas reservoir, the embodiment provides a strike-slip fracture identification device for ultra-deep fault-controlled fracture-cave type carbonate rock oil and gas reservoir, comprising:

[0104] a denoising and fracture enhancement processing unit, configured to perform denoising and fracture enhancement processing on the seismic data volume;

[0105] a determination unit, configured to determine a plurality of seismic attributes of the seismic data volume after the fracture enhancement processing;

[0106] a fusion unit, configured to filter out seismic attributes that identify fractures, and perform multi-attribute fusion to obtain a fused attribute;

[0107] an ant tracking processing unit, configured to perform ant tracking processing on the fused attribute;

[0108] a convolution filter processing unit, configured to perform convolution filter processing on the fused attribute after the ant tracking processing, to determine the position of the fracture.

[0109] In some embodiments, the denoising and fracture enhancement processing unit is specifically configured to:

[0110] calculate the directional derivative of each point of the three-dimensional seismic data volume, and construct a gradient structure tensor matrix using the directional derivative;

[0111] perform eigenvalue decomposition on the gradient structure tensor matrix to obtain eigenvalues and eigenvectors;

[0112] determine the dip angle and strike of a seismic event according to the eigenvalues and eigenvectors, and determine the position of the fracture according to the dip angle information of the seismic event;

[0113] filter out noise along the direction of the seismic event, and perform fracture enhancement at the fracture.

[0114] In some embodiments, the fusion unit is specifically configured to:

[0115] statistically determine the coincidence of all drilling and seismic attribute-identified fracture identification results, and calculate a coincidence rate;

[0116] filter out seismic attributes with a coincidence rate greater than a first threshold.

[0117] In some embodiments, the convolution filter processing unit is specifically configured to:

[0118] perform denoising processing on the fused attribute after the ant tracking processing using Gaussian smoothing;

[0119] perform edge enhancement processing on the denoised fused attribute using a Sobel operator to determine the position of the fracture.

[0120] Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood that modifications can be made to the foregoing embodiments, or additional implementations of the present application can be implemented, without departing from the spirit or scope of the application. Accordingly, the present application is not limited except as by the appended claims.

Claims

1. A method for identifying strike-slip faults in super-deep, fault- controlled, vuggy carbonate reservoirs, characterized in that, The method comprises the following steps: de-noising and fracture enhancement processing of a seismic data volume; determining a plurality of seismic attributes of the seismic data volume after fracture enhancement processing; screening out seismic attributes for identifying fractures, and performing multi-attribute fusion to obtain a fused attribute; ant tracking processing of the fused attribute; convolution filtering processing of the fused attribute after ant tracking processing to determine the position of the fracture.

2. The method according to claim 1, wherein, The de-noising and fracture enhancement processing of the seismic data volume comprises the following steps: calculating the directional derivative of each point of the three-dimensional seismic data volume, and constructing a gradient structure tensor matrix using the directional derivative; performing eigenvalue decomposition on the gradient structure tensor matrix to obtain eigenvalues and eigenvectors; determining the dip angle and strike of a seismic event according to the eigenvalues and eigenvectors, and determining the position of the fracture according to the dip angle information of the seismic event; filtering out noise along the direction of the seismic event, and performing fracture enhancement at the fracture.

3. The method according to claim 1, wherein, The screened seismic attributes for identifying fractures include enhanced coherence, dip angle, coherence, curvature, ant body and similarity.

4. The method according to claim 1, wherein, The specific steps of screening out the seismic attributes for identifying fractures are as follows: statistically analyzing the coincidence of all drilling and seismic attribute-identified fracture identification results, and calculating the coincidence rate; screening out seismic attributes with a coincidence rate greater than a first threshold value.

5. The method according to claim 4, wherein, The specific steps of performing multi-attribute fusion to obtain a fused attribute are as follows: standardizing each seismic attribute value screened out; assigning different weights to each seismic attribute after standardization according to the coincidence rate; performing multi-attribute fusion on each seismic attribute after standardization according to the assigned weights to obtain a fused attribute.

6. The method according to claim 1, wherein, The specific steps of performing convolution filtering processing on the fused attribute after ant tracking processing to determine the position of the fracture are as follows: performing noise reduction processing on the fused attribute after ant tracking processing using Gaussian smoothing; performing edge enhancement processing on the noise-reduced fused attribute using a Sobel operator to determine the position of the fracture.

7. A device for identifying strike-slip faults in ultra-deep, fault-controlled, vuggy carbonate reservoirs, characterized in that, The method comprises the following steps: a de-noising and fracture enhancement processing unit for de-noising and fracture enhancement processing of a seismic data volume; a determination unit for determining a plurality of seismic attributes of the seismic data volume after fracture enhancement processing; a fusion unit for screening out seismic attributes for identifying fractures, and performing multi-attribute fusion to obtain a fused attribute; an ant tracking processing unit for ant tracking processing of the fused attribute; a convolution filtering processing unit for convolution filtering processing of the fused attribute after ant tracking processing to determine the position of the fracture.

8. The apparatus according to claim 7, wherein, The de-noising and fracture enhancement processing unit is specifically configured to: calculate the directional derivative of each point of the three-dimensional seismic data volume, and construct a gradient structure tensor matrix using the directional derivative; perform eigenvalue decomposition on the gradient structure tensor matrix to obtain eigenvalues and eigenvectors; determine the dip angle and strike of a seismic event according to the eigenvalues and eigenvectors, and determine the position of the fracture according to the dip angle information of the seismic event; filter out noise along the direction of the seismic event, and perform fracture enhancement at the fracture.

9. The apparatus according to claim 7, wherein, The fusion unit is specifically configured to: statistically analyze the coincidence of all drilling and seismic attribute-identified fracture identification results, and calculate the coincidence rate; screen out seismic attributes with a coincidence rate greater than a first threshold value.

10. The apparatus according to claim 7, wherein, The convolution filtering processing unit is specifically configured to: Gaussian smoothing is used to denoise the fusion attribute after ant tracking processing; Sobel operator is used to enhance the edge of the denoised fusion attribute to determine the position of the fracture.

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