CN115494551B - Crack prediction method based on pre-stack AVO attribute, electronic equipment and medium - Google Patents
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Abstract
The application discloses a crack prediction method based on pre-stack AVO attributes, electronic equipment and a medium. The method comprises the steps of extracting AVO attributes of a pre-stack seismic trace set, obtaining intercept data bodies and gradient data bodies of the pre-stack AVO attributes, extracting view angles in the x and y directions according to the intercept data bodies and the gradient data bodies, fitting trend surfaces according to the view angles, further calculating curvature attributes, and carrying out crack prediction according to the curvature attributes. The application can improve the precision of small-scale fracture and crack prediction and provides more powerful technical support for exploration and development.
Description
Technical Field
The invention relates to the field of petroleum and natural gas exploration and development, in particular to a crack prediction method based on pre-stack AVO attributes, electronic equipment and a medium.
Background
In oil and gas exploration, cracks are a common geological phenomenon, have great influence on oil and gas migration and storage, and can improve the permeability of a reservoir and serve as a hydrocarbon reservoir. The traditional research method is to carry out crack detection by using geology such as core, outcrop description, logging data analysis and the like and logging methods, and the crack detection results are only valid at specific well positions. Other geological measurements are used for the detection of inter-well fractures.
With the development of the oil and gas exploration industry, the use of seismic attributes for fracture prediction is one of the current common technical means, and active progress is made in theoretical research and technical methods. The crack prediction method mainly comprises the following technical means of carrying out crack prediction by using a coherent technology and analyzing coherence differences among seismic channels, a curvature body crack detection technology and an ant body tracking technology, wherein the development and trend of a crack are judged by the curvature, ant bodies are mainly scattered in a work area, when the ant bodies find a target area, signals are sent out to guide the following of the ant bodies, so that crack development block marking is carried out, and an inclination angle azimuth angle crack prediction method is mainly used for indirectly analyzing the crack development condition by utilizing the change of inclination angles and azimuth angles of the crack development area in space.
The AVO/AVA (AVO, amplitude Versus Offset abbreviation; AVA, amplitude Versus Angle abbreviation) technology is an important lithology interpretation technology in the current exploration, development and production. With the progressive penetration of exploration and development, complex hidden oil and gas reservoirs become the current exploration hot spot, and AVO technology can fully utilize prestack seismic data to obtain information related to lithology and fluid properties of stratum and reservoir, and plays an important role in oil and gas reservoir detection and reservoir characteristic description. Based on the AVO technology, the technology of detecting cracks by utilizing the variation of the propagation speed, time and amplitude attribute of seismic waves in an anisotropic medium along with offset and azimuth is called AVAZ (Amplitude Versus Azimuth) technology. On pre-stack gather data containing azimuth information, anomaly of seismic wave travel time or amplitude caused by azimuth anisotropy and AVA change characteristics at different azimuth angles are comprehensively analyzed, anisotropic response characteristics are analyzed, and then azimuth AVA attributes are utilized to describe cracks and stress states, so that the method is the most commonly used pre-stack crack prediction method at present.
Because of the obvious difference between the superimposed profile and the post-stack offset or pre-stack offset profile in terms of structural morphology, resolution, etc., the predicted result and the structural interpretation result often have the problem of mismatch due to the pre-stack gathers containing azimuth information, such as CMP gathers. The three-dimensional pre-stack time migration processing of the OVT domain is a processing technology facing crack prediction or karst fracture-cave prediction, the problem of mismatching between gather data containing azimuth information and processing result data can be solved, but the current OVT domain imaging technology is still not fully mature in development and perfection.
Therefore, it is necessary to develop a crack prediction method based on pre-stack AVO properties, an electronic device, and a medium.
The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a crack prediction method, electronic equipment and medium based on pre-stack AVO attributes, which can improve the precision of small-scale fracture and crack prediction and provide more powerful technical support for exploration and development.
In a first aspect, an embodiment of the present disclosure provides a method for predicting a crack based on a prestack AVO attribute, including:
extracting AVO attributes of the pre-stack seismic trace set to obtain intercept data volumes and gradient data volumes of the pre-stack AVO attributes;
Extracting the visual inclination angles in the x and y directions according to the intercept data volume and the gradient data volume;
Fitting a trend surface according to the viewing angle, and further calculating curvature attributes;
And carrying out crack prediction according to the curvature attribute.
Preferably, the AVO properties of the pre-stack seismic gather are extracted by equation (1):
rθ＝P+G sin2θ (1)
where r θ is the reflection coefficient, θ is the angle of incidence, P is the intercept data volume, G is a gradient data volume, and the gradient data volume,V p is the average of the compressional velocity of the underburden and the overburden, V s is the average of the shear velocity of the underburden and the overburden, ρ is the average of the density of the underburden and the overburden, Δv p is the difference between the compressional velocity of the underburden and the overburden, Δv s is the difference between the shear velocity of the underburden and the overburden, and Δρ is the difference between the density of the underburden and the overburden.
Preferably, the trend surface is fitted by equation (2):
z(x,y)=Ax2+By2+Cxy+Dx+Ey+F (2)
Wherein z (x, y) is a trend surface, z, x, y are Gao Chengji geographic coordinates of observation points on the trend surface, A, B, C, D, E, F are fitting coefficients, D=p, e=q, p, q being the viewing angles in the x, y directions, respectively.
Preferably, the curvature attribute includes average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature.
Preferably, the average curvature is:
The gaussian curvature is:
Where K m is the average curvature and K g is the Gaussian curvature.
Preferably, the maximum curvature is:
the minimum curvature is:
Where K max is the maximum curvature and K min is the minimum curvature.
Preferably, the maximum positive curvature is:
The minimum negative curvature is:
Where K + is the maximum positive curvature and K - is the minimum negative curvature.
Preferably, performing crack prediction according to curvature properties includes:
the higher the degree of bending of the formation due to stress, the greater the value of the curvature, and the corresponding fracture or crack will develop.
As a specific implementation of an embodiment of the present disclosure,
In a second aspect, embodiments of the present disclosure further provide an electronic device, including:
a memory storing executable instructions;
And the processor runs the executable instructions in the memory to realize the crack prediction method based on the pre-stack AVO attribute.
In a third aspect, the disclosed embodiments also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the crack prediction method based on pre-stack AVO attributes.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the present invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
Fig. 1 shows a flow chart of the steps of a method of crack prediction based on pre-stack AVO properties according to an embodiment of the invention.
Fig. 2a and 2b show a comparative schematic of pre-stack seismic gathers before and after an optimization process, respectively, according to one embodiment of the invention.
Fig. 3a and 3b show schematic diagrams of an original seismic superposition profile and an intercept property profile, respectively, according to an embodiment of the invention.
Fig. 4a and 4b show schematic plan views of minimum negative curvature extracted based on seismic superimposed data and intercept properties, respectively, according to one embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
The invention provides a crack prediction method based on pre-stack AVO attributes, which comprises the following steps:
Extracting AVO attributes of the pre-stack seismic trace set to obtain an intercept data volume and a gradient data volume of the pre-stack AVO attributes, and in one example, extracting the AVO attributes of the pre-stack seismic trace set by the formula (1):
rθ＝P+G sin2θ (1)
where r θ is the reflection coefficient, θ is the angle of incidence, P is the intercept data volume, G is a gradient data volume, and the gradient data volume,V p is the average of the compressional velocity of the underburden and the overburden, V s is the average of the shear velocity of the underburden and the overburden, ρ is the average of the density of the underburden and the overburden, Δv p is the difference between the compressional velocity of the underburden and the overburden, Δv s is the difference between the shear velocity of the underburden and the overburden, and Δρ is the difference between the density of the underburden and the overburden.
Specifically, the advanced data preparation comprises the steps that ① seismic data are post-stack data and pre-stack seismic gathers, the pre-stack seismic gathers are gather data which are not subjected to superposition processing, such as CMP (common center point) gathers, CRP (common reflection point) gathers or CRS (common reflection point surface element) gathers, the superposition seismic data are data subjected to post-stack or pre-stack migration processing, ② geological data collection comprises logging, rock core, oil/gas test and other data, and ③ logging data comprise imaging logging data and fracture interpretation results.
According to the method, optimization processing is carried out on pre-stack seismic gathers, namely ① gather leveling processing is carried out, time shift amounts relative to zero offset are obtained through calculation correlation of all pre-stack gathers and template gathers (superposition data) in a given time window, the time shift amounts of the whole gather are obtained through gradual sliding of the time window, the gather is leveled according to the residual time shift amount theory, random noise of the gather is removed through ② singular value decomposition, singular value decomposition is a data processing method based on matrix decomposition, pre-stack CRP gather data are decomposed into a plurality of singular values, wherein each singular value corresponds to a part of energy in the data, the larger singular value represents the better energy coherence, the larger singular value is removed, and therefore the aim of reconstructing the data and denoising is achieved. And the pre-stack seismic trace set is optimized, so that the quality of trace set data can be improved.
The AVO attribute extraction method adopts Aki-Richards approximate formula:
When the incidence angle is small to medium (θ <30 °), it can be expressed as formula (1), and then the intercept data volume and the gradient data volume of the prestack AVO attribute are obtained. The amplitude value of the intercept section is obtained by inversion of information of amplitude along with offset, so that amplitude distortion phenomenon caused by non-in-phase superposition can be avoided, the resolution of seismic data can be improved, and the gradient data body is the amplitude of amplitude along with offset, and when oil-containing gas or cracks develop, the gradient value is abnormal.
And extracting the visual dip angles in the x and y directions according to the intercept data volume and the gradient data volume.
Specifically, the apparent inclination angles in the x and y directions are extracted by formulas (10) and (11):
p=kx/ω (10)
q=ky/ω (11)
Wherein p and q are viewing angles in x and less directions, k x、ky is instantaneous wave number in x and y directions, and ω is instantaneous frequency. k x、ky and omega can be calculated by a complex seismic trace analysis method.
Fitting the trend surface according to the apparent dip angle, and further calculating the curvature attribute, in one example, fitting the trend surface by the formula (2):
z(x,y)=Ax2+By2+Cxy+Dx+Ey+F (2)
Wherein z (x, y) is a trend surface, z, x, y are Gao Chengji geographic coordinates of observation points on the trend surface, A, B, C, D, E, F are fitting coefficients, D=p, e=q, p, q being the viewing angles in the x, y directions, respectively.
In one example, the curvature attributes include average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature.
In one example, the average curvature is:
the gaussian curvature is:
Where K m is the average curvature and K g is the Gaussian curvature.
In one example, the maximum curvature is:
the minimum curvature is:
Where K max is the maximum curvature and K min is the minimum curvature.
In one example, the maximum positive curvature is:
The minimum negative curvature is:
Where K + is the maximum positive curvature and K - is the minimum negative curvature.
Specifically, after obtaining the pre-stack AVO attribute intercept and gradient data volume, the viewing angles p and q in the x and y directions are extracted, and then the curvature attribute is calculated by using a trend surface analysis method. The trend surface analysis method is a method of expressing the distribution and the change trend of data in space in a form of a simulated curved surface. When the quadric surface equation is adopted, the trend surface fitting formula is formula (2), so that expressions of average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature and minimum negative curvature are obtained, and the vision inclination angles p and q extracted based on AVO attributes are substituted into the formula, so that the corresponding curvature attribute body can be calculated.
In one example, performing crack prediction based on curvature attributes includes:
the higher the degree of bending of the formation due to stress, the greater the value of the curvature, and the corresponding fracture or crack will develop.
Specifically, the higher the degree of bending of the formation due to stress, the greater the value of the curvature, and the corresponding fracture or fracture develops. Under the condition that well drilling exists, well leakage condition, crack interpretation result and research area fracture development condition are taken as discrimination criteria, curvature attribute matched with the real drilling condition is optimized to predict fracture and crack development condition, and under the condition that geological and well logging data are absent, regional stress background and research area fracture development condition can be utilized to discriminate and optimize curvature attribute for prediction.
The invention also provides electronic equipment which comprises a memory and a processor, wherein the memory stores executable instructions, and the processor runs the executable instructions in the memory so as to realize the crack prediction method based on the pre-stack AVO attribute.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the crack prediction method based on the pre-stack AVO attribute.
In order to facilitate understanding of the solution and the effects of the embodiments of the present invention, three specific application examples are given below. It will be understood by those of ordinary skill in the art that the examples are for ease of understanding only and that any particular details thereof are not intended to limit the present invention in any way.
Example 1
Fig. 1 shows a flow chart of the steps of a method of crack prediction based on pre-stack AVO properties according to an embodiment of the invention.
As shown in FIG. 1, the crack prediction method based on the pre-stack AVO attribute comprises the steps of extracting the AVO attribute of a pre-stack seismic trace set to obtain an intercept data body and a gradient data body of the pre-stack AVO attribute, extracting visual inclination angles in the x and y directions according to the intercept data body and the gradient data body, fitting a trend surface according to the visual inclination angles, further calculating curvature attributes, and carrying out crack prediction according to the curvature attributes, wherein the step 101 is shown in the figure 1.
Fig. 2a and 2b show a comparative schematic of pre-stack seismic gathers before and after an optimization process, respectively, according to one embodiment of the invention.
Since the main purpose of conventional processing is for imaging effect, the problem of prestack gathers is mainly manifested in that ① has low signal-to-noise ratio and poor in-phase continuity, and ② has residual time difference. Meanwhile, the superposition of different offset distances (or angles) has poor waveform consistency, amplitude energy and phase matching, as shown in fig. 2 a. In order to improve the pre-stack AVO attribute extraction effect, the singular value decomposition and gather flattening technology is utilized to perform optimization treatment aiming at the problems, and the signal to noise ratio and the resolution of the gather after the treatment are greatly improved, as shown in fig. 2 b.
Fig. 3a and 3b show schematic diagrams of an original seismic superposition profile and an intercept property profile, respectively, according to an embodiment of the invention. Therefore, after the gather optimization treatment, the influence of non-in-phase superposition caused by the gather residual time difference is eliminated, the intercept attribute extracted based on Aki-Richards approximate formula, namely the zero-offset seismic section, is improved in seismic resolution, and is more sensitive to small-amplitude distortion or dislocation of the in-phase axis of the seismic.
Fig. 4a and 4b show schematic plan views of minimum negative curvature extracted based on seismic superimposed data and intercept properties, respectively, according to one embodiment of the invention.
The comparison analysis of the development condition of the real drilling crack and the development condition of the fracture in the work area and the different curvature attributes shows that the minimum negative curvature is more sensitive to the small-scale fracture. As can be seen by comparing fig. 4a and fig. 4b, the minimum negative curvature attribute plan extracted based on the prestack intercept attribute is more sensitive to small-scale fracture and crack, and the prediction result of crack details is improved. Early fracture in North-west trend in the figure controls development of the water system, and development characteristics of annular karst caves on two sides of the water system are obvious. The actual data test proves that the method has good application effect and can promote small-scale fracture and crack prediction effect.
Example 2
The electronic equipment comprises a memory and a processor, wherein executable instructions are stored in the memory, and the processor runs the executable instructions in the memory to realize the crack prediction method based on the pre-stack AVO attribute.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is for storing non-transitory computer readable instructions. In particular, 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 include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example 3
Embodiments of the present disclosure provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the pre-stack AVO attribute-based fracture prediction method.
A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the present disclosure described above.
Such 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., tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention has been given for the purpose of illustrating the benefits of embodiments of the invention only and is not intended to limit embodiments of the invention to any examples given.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (9)
1. A crack prediction method based on pre-stack AVO attribute is characterized by comprising the following steps:
extracting AVO attributes of the pre-stack seismic trace set to obtain intercept data volumes and gradient data volumes of the pre-stack AVO attributes;
Extracting the visual inclination angles in the x and y directions according to the intercept data volume and the gradient data volume;
Fitting a trend surface according to the viewing angle, and further calculating curvature attributes;
Performing crack prediction according to the curvature attribute;
Wherein, extracting AVO attribute of pre-stack seismic trace set by formula (1):
rθ＝P+Gsin2θ (1)
where r θ is the reflection coefficient, θ is the angle of incidence, P is the intercept data volume, G is a gradient data volume, and the gradient data volume,V p is the average of the compressional velocities of the underburden and the overburden, V s is the average of the compressional velocities of the underburden and the overburden, ρ is the average of the densities of the underburden and the overburden, Δv p is the difference between the compressional velocities of the underburden and the overburden, Δv s is the difference between the compressional velocities of the underburden and the overburden, and Δρ is the difference between the densities of the underburden and the overburden;
wherein, the apparent dip angles in the x and y directions are extracted by formulas (10) and (11):
p=kx/ω (10)
q=ky/ω (11)
Wherein p and q are viewing angles in x and y directions, k x、ky is an instantaneous wave number in x and y directions, and ω is an instantaneous frequency.
2. The pre-stack AVO property-based fracture prediction method of claim 1, wherein the trend surface is fitted by equation (2):
z(x,y)=Ax2+By2+Cxy+Dx+Ey+F (2)
Wherein z (x, y) is a trend surface, z, x, y are Gao Chengji geographic coordinates of observation points on the trend surface, A, B, C, D, E, F are fitting coefficients, D=p, e=q, p, q being the viewing angles in the x, y directions, respectively.
3. The pre-stack AVO property-based fracture prediction method of claim 2, wherein the curvature properties include average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature.
4. The pre-stack AVO property-based fracture prediction method of claim 3, wherein the average curvature is:
The gaussian curvature is:
Where K m is the average curvature and K g is the Gaussian curvature.
5. The pre-stack AVO property-based fracture prediction method of claim 4, wherein the maximum curvature is:
the minimum curvature is:
Where K max is the maximum curvature and K min is the minimum curvature.
6. The pre-stack AVO property-based fracture prediction method of claim 3, wherein the maximum positive curvature is:
The minimum negative curvature is:
Where K + is the maximum positive curvature and K - is the minimum negative curvature.
7. The pre-stack AVO property-based fracture prediction method of claim 1, wherein performing fracture prediction according to curvature properties comprises:
the higher the degree of bending of the formation due to stress, the greater the value of the curvature, and the corresponding fracture or crack will develop.
8. An electronic device, the electronic device comprising:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the pre-stack AVO attribute-based fracture prediction method of any of claims 1-7.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the crack prediction method based on pre-stack AVO properties as claimed in any one of claims 1-7.
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| CN107728204A (en) * | 2016-08-11 | 2018-02-23 | 中国石油化工股份有限公司 | Based on the anisotropic crack prediction method of prestack compressional wave and system |
| CN111158053A (en) * | 2019-12-20 | 2020-05-15 | 中石化石油工程技术服务有限公司 | Crack prediction method and device |
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| WO2017062390A1 (en) * | 2015-10-07 | 2017-04-13 | Schlumberger Technology Corporation | Seismic polynomial filter |
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| CN107728204A (en) * | 2016-08-11 | 2018-02-23 | 中国石油化工股份有限公司 | Based on the anisotropic crack prediction method of prestack compressional wave and system |
| CN111158053A (en) * | 2019-12-20 | 2020-05-15 | 中石化石油工程技术服务有限公司 | Crack prediction method and device |
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