Fracture prediction method, electronic equipment and medium based on prestack AVO attributes

Abstract

The present application discloses a fracture prediction method, electronic device and medium based on prestack AVO attributes. The method may include: extracting the AVO attributes of prestack seismic gathers to obtain the intercept data volume and gradient data volume of the prestack AVO attributes; extracting the apparent dip angles in the x and y directions according to the intercept data volume and the gradient data volume; fitting the trend surface according to the apparent dip angle, and then calculating the curvature attribute; and predicting fractures according to the curvature attribute. The present invention can improve the accuracy of small-scale fracture and fracture prediction and provide more powerful technical support for exploration and development.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration and development, and more specifically, to a fracture prediction method based on pre-stack AVO attributes, electronic equipment and medium. Background Art

[0002] In oil and gas exploration, cracks, as a common geological phenomenon, have a great impact on oil and gas migration and storage. They can not only improve the permeability of the reservoir, but also serve as a hydrocarbon reservoir. The traditional research method is to detect cracks using geological and logging methods such as core, outcrop description and logging data analysis. These crack detection results are only valid at specific well locations. The detection of inter-well cracks requires the use of other geological measurement results.

[0003] With the development of the oil and gas exploration industry, the use of seismic attributes for fracture prediction is one of the common technical means at present, and positive progress has been made in theoretical research and technical methods. The fracture prediction of post-stack data mainly includes the following technical means: coherence body technology, which mainly predicts fractures by using coherence technology and analyzing the coherence differences between seismic traces; curvature body fracture detection technology, which determines the development and direction of fractures by the size of curvature; ant body tracking technology, which mainly spreads ant bodies in the work area. When the ant body finds the target area, it will send a signal to guide other ant bodies to follow up, and then mark the fracture development block; dip angle azimuth fracture prediction method, this method mainly uses the changes in the dip angle and azimuth angle of the fracture development area in space to indirectly analyze the fracture development situation.

[0004] AVO / AVA (AVO, Amplitude Versus Offset abbreviation; AVA, Amplitude Versus Angle abbreviation) technology is an important lithology interpretation technology in current exploration, development and production. With the gradual deepening of exploration and development, complex and concealed oil and gas reservoirs have become the current exploration hotspots. AVO technology can make full use of pre-stack seismic data to obtain information related to the lithology and fluid properties of formations and reservoirs, and plays an important role in oil and gas reservoir detection and reservoir characteristics description. Based on AVO technology, the technology of detecting fractures by using the propagation velocity, time, and amplitude attributes of seismic waves in anisotropic media with the change of shot offset and azimuth is called AVAZ (Amplitude Versus Azimuth) technology. On the pre-stack gather data containing azimuth information, the abnormalities of seismic wave travel time or amplitude caused by azimuthal anisotropy and the AVA variation characteristics at different azimuths are comprehensively analyzed, the anisotropic response characteristics are analyzed, and then the azimuthal AVA attributes are used to characterize the fractures and stress states. It is currently the most commonly used pre-stack fracture prediction method.

[0005] Since the pre-stack gathers containing azimuth information, such as CMP gathers, stacked sections and post-stack migration or pre-stack migration sections have obvious differences in structural morphology and resolution, the prediction results and structural interpretation results often do not match. OVT domain three-dimensional pre-stack time migration processing has become a processing technology for fracture prediction or karst fracture cave prediction, which can overcome the mismatch between gather data containing azimuth information and processing result data, but the OVT domain imaging technology is still under development and improvement and has not yet fully matured.

[0006] Therefore, it is necessary to develop a fracture prediction method, electronic equipment and medium based on prestack AVO attributes.

[0007] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0008] The present invention proposes a fracture prediction method, electronic equipment and medium based on pre-stack AVO attributes, which can improve the accuracy of small-scale fault and fracture prediction and provide more powerful technical support for exploration and development.

[0009] In a first aspect, an embodiment of the present disclosure provides a method for predicting cracks based on prestack AVO attributes, including:

[0010] Extract the AVO attributes of pre-stack seismic gathers and obtain the intercept data volume and gradient data volume of pre-stack AVO attributes;

[0011] Extracting the apparent inclination angles in the x and y directions according to the intercept data volume and the gradient data volume;

[0012] According to the apparent inclination angle, a trend surface is fitted, and then the curvature attribute is calculated;

[0013] Crack prediction based on curvature properties.

[0014] Preferably, the AVO attribute of the pre-stack seismic gather is extracted by formula (1):

[0015] r θ =P+G sin 2 θ (1)

[0016] Among them, r θ is the reflection coefficient, θ is the incident angle, P is the intercept data volume, G is the gradient data volume, V p is the average value of the P-wave velocity of the underlying and overlying strata, V sis the average shear wave velocity of the underlying and overlying strata, ρ is the average density of the underlying and overlying strata, ΔV p is the difference in P-wave velocity between the underlying strata and the overlying strata, ΔV s is the difference in shear wave velocity between the underlying stratum and the overlying stratum, and Δρ is the difference in density between the underlying stratum and the overlying stratum.

[0017] Preferably, the trend surface is fitted by formula (2):

[0018] z(x,y)=Ax 2 +By 2 +Cxy+Dx+Ey+F (2)

[0019] Among them, z(x, y) is the trend surface, z, x, y are the elevation and geographic coordinates of the observation point on the trend surface, A, B, C, D, E, F are fitting coefficients, D=p, E=q, where p and q are the apparent inclination angles in the x and y directions respectively.

[0020] Preferably, the curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, and minimum negative curvature.

[0021] Preferably, the average curvature is:

[0022]

[0023] The Gaussian curvature is:

[0024]

[0025] Among them, K m is the mean curvature, K g is the Gaussian curvature.

[0026] Preferably, the maximum curvature is:

[0027]

[0028] The minimum curvature is:

[0029]

[0030] Among them, K max is the maximum curvature, K min is the minimum curvature.

[0031] Preferably, the maximum positive curvature is:

[0032]

[0033] The minimum negative curvature is:

[0034]

[0035] Among them, K + is the maximum positive curvature, K - is the minimum negative curvature.

[0036] Preferably, predicting cracks according to curvature attributes includes:

[0037] The higher the degree of bending of the rock layer due to force, the greater the curvature value, and the more developed the corresponding faults or cracks.

[0038] As a specific implementation method of the embodiment of the present disclosure,

[0039] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0040] A memory storing executable instructions;

[0041] A processor runs the executable instructions in the memory to implement the fracture prediction method based on pre-stack AVO attributes.

[0042] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for predicting fractures based on pre-stack AVO attributes is implemented.

[0043] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0045] Figure 1 A flowchart showing the steps of a fracture prediction method based on prestack AVO attributes according to an embodiment of the present invention.

[0046] Figure 2a and Figure 2b Schematic diagrams showing comparison of pre-stack seismic gathers before and after optimization processing according to an embodiment of the present invention are shown respectively.

[0047] Figure 3a and Figure 3bSchematic diagrams of an original seismic stacking section and an intercept attribute section according to an embodiment of the present invention are respectively shown.

[0048] Figure 4a and Figure 4b A schematic diagram of a plane of minimum negative curvature extracted based on seismic stacking data and intercept attributes according to an embodiment of the present invention is shown respectively. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0050] The present invention provides a crack prediction method based on prestack AVO attributes, comprising:

[0051] The AVO attributes of the prestack seismic gathers are extracted to obtain the intercept data volume and the gradient data volume of the prestack AVO attributes. In one example, the AVO attributes of the prestack seismic gathers are extracted by formula (1):

[0052] r θ =P+G sin 2 θ (1)

[0053] Among them, r θ is the reflection coefficient, θ is the incident angle, P is the intercept data volume, G is the gradient data volume, V p is the average value of the P-wave velocity of the underlying and overlying strata, V s is the average shear wave velocity of the underlying and overlying strata, ρ is the average density of the underlying and overlying strata, ΔV p is the difference in P-wave velocity between the underlying strata and the overlying strata, ΔV s is the difference in shear wave velocity between the underlying stratum and the overlying stratum, and Δρ is the difference in density between the underlying stratum and the overlying stratum.

[0054] Specifically, data preparation is carried out first: ① seismic data includes post-stack data and pre-stack seismic gathers. Pre-stack seismic gathers are CMP (common center point) gathers, CRP (common reflection point) gathers or CRS (common reflection point surface) gathers that have not been stacked; stacked seismic data are data that have undergone post-stack or pre-stack migration processing; ② geological data collection includes logging, core, oil / gas testing and other data; ③ logging data includes imaging logging data and fracture interpretation results.

[0055] Optimize the processing of pre-stack seismic gathers: ① Gather flattening, calculate the correlation between each pre-stack trace and the template trace (stacked data) within a given time window to obtain the time shift relative to the zero offset, gradually slide the time window to obtain the time shift of the entire gather, and selectively correct it according to the residual time shift theory to flatten the gather; ② Use singular value decomposition to remove random noise in the gather. Singular value decomposition is a data processing method based on matrix decomposition. The pre-stack CRP gather data is decomposed into several singular values, each of which corresponds to a part of the energy in the data. The larger the singular value, the better the corresponding energy coherence. The part with larger singular values ​​is eliminated, so as to reconstruct the data and achieve the purpose of denoising. Optimizing the pre-stack seismic gathers can improve the quality of the gather data.

[0056] The AVO attribute extraction method uses the Aki-Richards approximation formula:

[0057]

[0058] When the incident angle is small to medium (θ < 30°), it can be expressed as formula (1), and then the intercept data volume and gradient data volume of the pre-stack AVO attribute are obtained. The amplitude value of the intercept section is obtained by inverting the information of the amplitude change with the offset distance, which can avoid the amplitude distortion caused by non-co-phase stacking and improve the resolution of seismic data; the gradient data volume is the amplitude change with the offset distance. When oil and gas or fractures are developed, it will cause abnormal gradient values.

[0059] The apparent inclination angles in the x and y directions are extracted based on the intercept data volume and the gradient data volume.

[0060] Specifically, the apparent inclination angles in the x and y directions are extracted using formulas (10) and (11):

[0061] p=k x / ω (10)

[0062] q=k y / ω (11)

[0063] Among them, p and q are the apparent inclination angles in the x and q directions respectively, and k x , k y are the instantaneous wave numbers in the x and y directions respectively, and ω is the instantaneous frequency. x , k y , ω can be calculated by complex seismic trace analysis method.

[0064] According to the apparent inclination angle, a trend surface is fitted to calculate the curvature attribute. In one example, the trend surface is fitted by formula (2):

[0065] z(x,y)=Ax 2 +By2 +Cxy+Dx+Ey+F (2)

[0066] Among them, z(x, y) is the trend surface, z, x, y are the elevation and geographic coordinates of the observation point on the trend surface, A, B, C, D, E, F are fitting coefficients, D=p, E=q, where p and q are the apparent inclination angles in the x and y directions respectively.

[0067] In one example, the curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, and minimum negative curvature.

[0068] In one example, the mean curvature is:

[0069]

[0070] The Gaussian curvature is:

[0071]

[0072] Among them, K m is the mean curvature, K g is the Gaussian curvature.

[0073] In one example, the maximum curvature is:

[0074]

[0075] The minimum curvature is:

[0076]

[0077] Among them, K max is the maximum curvature, K min is the minimum curvature.

[0078] In one example, the maximum positive curvature is:

[0079]

[0080] The minimum negative curvature is:

[0081]

[0082] Among them, K + is the maximum positive curvature, K - is the minimum negative curvature.

[0083] Specifically, after obtaining the pre-stack AVO attribute intercept and gradient data volume, the apparent dip angles p and q in the x and y directions are extracted, and then the curvature attributes are calculated using the trend surface analysis method. The trend surface analysis method is a method that expresses the spatial distribution and change trend of data in the form of a simulated surface. When the quadratic surface equation is used, the trend surface fitting formula is formula (2), and then the expressions of the mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, and minimum negative curvature are obtained. Substituting the apparent dip angles p and q extracted based on the AVO attributes into the above formulas, the corresponding curvature attribute volume can be calculated.

[0084] Crack prediction is performed according to curvature attributes. In one example, crack prediction according to curvature attributes includes:

[0085] The higher the degree of bending of the rock layer due to force, the greater the curvature value, and the more developed the corresponding faults or cracks.

[0086] Specifically, the higher the bending degree of the rock layer due to stress, the greater the curvature value, and the more developed the corresponding faults or cracks. For the comparative analysis of the effects of different curvature attributes in practical applications, in the case of drilling, the leakage situation, fracture interpretation results and fracture development in the study area are used as the judgment criteria, and the curvature attributes consistent with the actual drilling situation are selected to predict the development of fractures and cracks; in the absence of geological and logging data, the regional stress background and the fracture development in the study area can be used to judge the preferred curvature attributes for prediction.

[0087] The present invention also provides an electronic device, which includes: a memory storing executable instructions; and a processor, which runs the executable instructions in the memory to implement the above-mentioned crack prediction method based on pre-stack AVO attributes.

[0088] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned fracture prediction method based on pre-stack AVO attributes is implemented.

[0089] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0090] Example 1

[0091] Figure 1 A flowchart showing the steps of a fracture prediction method based on prestack AVO attributes according to an embodiment of the present invention.

[0092] like Figure 1As shown, the fracture prediction method based on pre-stack AVO attributes includes: step 101, extracting the AVO attributes of the pre-stack seismic gathers to obtain the intercept data volume and the gradient data volume of the pre-stack AVO attributes; step 102, extracting the apparent dip angles in the x and y directions according to the intercept data volume and the gradient data volume; step 103, fitting the trend surface according to the apparent dip angle, and then calculating the curvature attribute; step 104, predicting fractures according to the curvature attribute.

[0093] Figure 2a and Figure 2b Schematic diagrams showing comparison of pre-stack seismic gathers before and after optimization processing according to an embodiment of the present invention are shown respectively.

[0094] Since the main purpose of conventional processing is to achieve imaging effect, the problems of pre-stack gathers are mainly reflected in: ① low signal-to-noise ratio and poor continuity of event axis; ② residual time difference. At the same time, the consistency of stacked waveforms, amplitude energy and phase matching of different offset distances (or angles) are poor, such as Figure 2a In order to improve the pre-stack AVO attribute extraction effect, the singular value decomposition and gather flattening technology are used to optimize the above problems. After processing, the signal-to-noise ratio and resolution of the gather are greatly improved, as shown in Figure 2b shown.

[0095] Figure 3a and Figure 3b Schematic diagrams of the original seismic stacking section and the intercept attribute section according to an embodiment of the present invention are respectively shown. It can be seen that after the gather optimization processing, the influence of the non-in-phase stacking caused by the residual time difference of the gather is eliminated, and the intercept attribute extracted based on the Aki-Richards approximate formula, that is, the zero-shot offset seismic section, the seismic resolution is improved, and at the same time, it is more sensitive to the small distortion or misalignment of the seismic phase axis.

[0096] Figure 4a and Figure 4b A schematic diagram of a plane of minimum negative curvature extracted based on seismic stacking data and intercept attributes according to an embodiment of the present invention is shown respectively.

[0097] A comparative analysis of the development of actual well fractures and faults in the work area and different curvature attributes revealed that the minimum negative curvature is more sensitive to small-scale faults and cracks. Figure 4a and Figure 4b By comparison, it can be seen that the minimum negative curvature attribute plane map extracted based on the prestack intercept attribute is more sensitive to small-scale faults and cracks, and the prediction results of crack details are improved. The early faults in the northwest direction in the figure control the development of the water system, and the development characteristics of annular caves on both sides of the water system are obvious. Actual data testing confirms that the present invention has a good application effect and can improve the prediction effect of small-scale faults and cracks.

[0098] Example 2

[0099] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned crack prediction method based on pre-stack AVO attributes.

[0100] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0101] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0102] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0103] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0104] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0105] Example 3

[0106] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the pre-stack AVO attribute-based fracture prediction method is implemented.

[0107] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0108] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM boxes).

[0109] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0110] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A fracture prediction method based on prestack AVO attributes, characterized in that: include: Extract the AVO attributes of pre-stack seismic gathers and obtain the intercept data volume and gradient data volume of pre-stack AVO attributes; Extracting the apparent inclination angles in the x and y directions according to the intercept data volume and the gradient data volume; According to the apparent inclination angle, a trend surface is fitted, and then the curvature attribute is calculated; Crack prediction based on curvature properties; The AVO attribute of the pre-stack seismic gather is extracted by formula (1): r θ =P+Gsin 2 i (1) Among them, r θ is the reflection coefficient, θ is the incident angle, P is the intercept data volume, G is the gradient data volume, V p is the average value of the P-wave velocity of the underlying and overlying strata, V s is the average shear wave velocity of the underlying and overlying strata, ρ is the average density of the underlying and overlying strata, ΔV p is the difference in P-wave velocity between the underlying strata and the overlying strata, ΔV s is the difference in shear wave velocity between the underlying strata and the overlying strata, Δρ is the difference in density between the underlying strata and the overlying strata; The apparent inclination angles in the x and y directions are extracted by formulas (10) and (11): p=k x / h (10) q=k y / ω (11) Where p and q are the apparent inclination angles in the x and y directions respectively, and k x , k y are the instantaneous wave numbers in the x and y directions respectively, and ω is the instantaneous frequency.

2. The crack prediction method based on prestack AVO attributes according to claim 1, wherein: The trend surface is fitted by formula (2): z(x,y)=Ax 2 +By 2 +Cxy+Dx+Ey+F (2) Among them, z(x,y) is the trend surface, z, x, y are the elevation and geographic coordinates of the observation point on the trend surface, A, B, C, D, E, F are fitting coefficients, D=p, E=q, where p and q are the apparent inclination angles in the x and y directions respectively.

3. The crack prediction method based on prestack AVO attributes according to claim 2, wherein: The curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, and minimum negative curvature.

4. The crack prediction method based on prestack AVO attributes according to claim 3, wherein: The mean curvature is: The Gaussian curvature is: Among them, K m is the mean curvature, K g is the Gaussian curvature.

5. The crack prediction method based on prestack AVO attributes according to claim 4, wherein: The maximum curvature is: The minimum curvature is: Among them, K max is the maximum curvature, K min is the minimum curvature.

6. The crack prediction method based on prestack AVO attributes according to claim 3, wherein: The maximum positive curvature is: The minimum negative curvature is: Among them, K + is the maximum positive curvature, K - is the minimum negative curvature.

7. The crack prediction method based on prestack AVO attributes according to claim 1, wherein: Crack prediction based on curvature properties includes: The higher the degree of bending of the rock layer due to force, the greater the curvature value, and the more developed the corresponding faults or cracks.

8. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the fracture prediction method based on prestack AVO attributes according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting fractures based on pre-stack AVO attributes described in any one of claims 1 to 7 is implemented.

Images