CN111290021A - Carbonate rock fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition - Google Patents
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Abstract
The application provides a carbonate rock fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition, which comprises the following steps of: obtaining an amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval; performing convolution processing on the amplitude gradient vector of each point to obtain the amplitude gradient vector of each point after convolution; obtaining a gradient structure tensor based on the amplitude gradient vector of each point after convolution; obtaining an eigenvalue and an eigenvector of a gradient structure tensor according to the amplitude variation direction attribute to obtain a gradient structure tensor anisotropic parameter data volume; amplitude rate of change is extracted in the gradient structure tensor anisotropy parameter data volume for identifying the fracture. By the technical scheme, the influences of the formation inclination angle and the formation bending can be eliminated, so that the purpose of clearly identifying the cracks is achieved.
Description
Technical Field
The invention relates to the technical field of crack identification, in particular to a carbonate rock crack hole enhanced identification method based on gradient structure tensor spectrum decomposition.
Background
The fractured reservoir is one main type of carbonate reservoir, and for the seismic identification of the fractured reservoir of carbonate, the existing common technology is the seismic amplitude spatial change rate attribute. The carbonate stratum is seriously deformed under the action of an extrusion structure, the original crack characteristics are difficult to identify, the amplitude space change rate is obtained according to the time slice of an earthquake amplitude body, a good effect is obtained under the mild structure condition, but when the structure horizon is more complex, the influence of the stratum inclination angle is received, and the crack identification is unclear.
Disclosure of Invention
Objects of the invention
The invention aims to provide a carbonate rock fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition so as to solve the problem that the fracture identification is unclear.
(II) technical scheme
In order to solve the above problems, a first aspect of the present invention provides a carbonate rock fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition, including the following steps: obtaining an amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval; performing convolution processing on the amplitude gradient vector of each point, wherein the convolution processing is used for reducing interference data and obtaining the amplitude gradient vector of each point after convolution; obtaining a gradient structure tensor based on the amplitude gradient vector of each point after convolution; obtaining an eigenvalue and an eigenvector of the gradient structure tensor by using the amplitude variation direction attribute, and removing interference data to obtain a gradient structure tensor anisotropic parameter data volume; extracting an amplitude rate of change in the gradient structure tensor anisotropy parameter data volume, the amplitude rate of change being used to identify a fracture.
Further, the obtaining an amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval includes: based on the three-dimensional seismic data volume, aiming at the target interval, obtaining the inclination angle and the azimuth angle of each time point in the three-dimensional seismic data volume; establishing a local coordinate system of each time point based on the inclination angle and the azimuth angle of each time point; and obtaining an amplitude gradient vector through a formula based on the local coordinate system.
Further, the amplitude gradient vector of each point is convoluted by a Gaussian window function. Further, the obtaining of the eigenvalue and the eigenvector of the gradient structure tensor by using the amplitude variation direction attribute is used to remove interference data, and the obtaining of the anisotropic parameter data volume of the gradient structure tensor specifically includes: and obtaining the gradient structure tensor anisotropy parameter data volume by a formula | T ν - λ ν | ═ 0.
According to another aspect of the invention, a carbonate rock fracture-cave enhanced identification device based on gradient structure tensor spectrum decomposition is provided, which comprises: the first calculation unit is used for obtaining the amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval; the convolution processing unit is used for performing convolution processing on the amplitude gradient vector of each point, reducing interference data and obtaining the amplitude gradient vector of each point after convolution; the second calculation unit is used for obtaining a gradient structure tensor based on the amplitude gradient vector of each point after convolution; the third calculation unit is used for obtaining an eigenvalue and an eigenvector of the gradient structure tensor according to the amplitude variation direction attribute, removing interference data and obtaining a gradient structure tensor anisotropic parameter data body, wherein the gradient structure tensor anisotropic parameter data body comprises parameter data of all points in the three-dimensional seismic data body; and the extraction unit is used for extracting an amplitude change rate in the gradient structure tensor anisotropic parameter data volume, and the amplitude change rate is used for identifying cracks.
Further, the first calculation unit includes: the first calculation subunit is used for obtaining the inclination angle and the azimuth angle of each time point in the three-dimensional seismic data body aiming at the target interval on the basis of the three-dimensional seismic data body; a local coordinate system establishing unit that establishes a local coordinate system of each time point based on the inclination and azimuth of each time point; and the second calculation subunit obtains the amplitude gradient vector through a formula based on the local coordinate system.
Further, the convolution processing unit performs convolution processing on the amplitude gradient vector of each point by using a gaussian window function.
Further, the eigenvalue and the eigenvector of the gradient structure tensor are obtained by using the amplitude variation direction attribute, and are used for removing interference data to obtain a gradient structure tensor anisotropic parameter data volume; the method specifically comprises the following steps: and obtaining the gradient structure tensor anisotropy parameter data volume by a formula | T ν - λ ν | ═ 0.
According to another aspect of the present invention, there is provided an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the computer program to implement the steps of the method in any of the above-mentioned technical solutions.
According to another aspect of the invention, there is provided a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of the above aspects.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
through the crack identification method and the crack identification device, the influence of the formation dip angle is eliminated by aiming at the amplitude variation direction attribute, and compared with the original seismic data, the technical scheme obviously improves the identification definition of the crack texture, improves the cave identification capability and obviously identifies the boundary characteristics of the intersection of the cracks and the holes. Compared with algorithms such as a coherent algorithm and a curvature algorithm based on a characteristic structure, the Gradient Structure Tensor (GST) method can better overcome discontinuity artifacts caused by steep dip stratum and can clearly reflect real micro fracture, crack and texture information.
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Fig. 1 is a flowchart of a carbonate fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition according to a first embodiment of the present invention;
FIG. 2 is a flowchart of step S100 in FIG. 1;
FIG. 3 is a river channel identification diagram of the original three-dimensional earthquake along-bed amplitude change rate;
fig. 4 is an amplitude change rate river channel identification diagram after gradient structure tensor enhancement processing;
FIG. 5 is a graph of the identification of the amplitude change rate of the seismic data along the layer in the destination;
FIG. 6 is a graph of gradient structure tensor anisotropy parameter data volume along layer amplitude rate of change attribute identification;
fig. 7 is a schematic structural diagram of a carbonate fracture-cave enhanced identification device based on gradient structure tensor spectrum decomposition according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of the first computing unit in fig. 7.
Reference numerals:
1: a first calculation unit; 11: a first calculation subunit; 12: a local coordinate system establishing unit; 13: a second calculation subunit; 2: a convolution processing unit; 3: a second computational element; 4: and a third calculation unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The described embodiments are only some, but not all embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention will be described in more detail below with reference to the accompanying drawings.
Structure tensor analysis is a new attribute analysis method introduced into seismic interpretation from the image processing field in recent years. The essence of the method is that the seismic data are regarded as images, and automatic detection of the geological target body is realized by identifying different cracks in the seismic images.
The technical scheme of the gap enhancement identification is that gradient structure tensor analysis is carried out, the gradient structure tensor analysis is carried out on a target interval of an original seismic data body, an amplitude variation direction attribute is utilized to obtain a gradient structure tensor anisotropic gradient structure anisotropic parameter data body, and then the target layer is extracted to reflect the gap hole development distribution of the target interval along a layer amplitude variation rate attribute.
Fig. 1 is a flowchart of a carbonate fracture-cave enhanced identification method based on gradient structure tensor spectral decomposition according to a first embodiment of the present invention.
In a first embodiment of the invention, a carbonate fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition is provided. As shown in fig. 1, the method comprises the following steps:
and S100, obtaining the amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval. The three-dimensional seismic data volume is three-dimensional seismic data obtained by field three-dimensional acquisition and digital processing of seismic data based on reflection of seismic waves, underground structure and stratum property information can be obtained, oil and gas are guided to be searched, and amplitude is one of basic information of the seismic data.
Fig. 2 is a flowchart of step S100 in fig. 1.
As shown in fig. 2, in some embodiments, deriving an amplitude gradient vector for each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the interval of interest comprises:
step S101, obtaining the inclination angle and the azimuth angle of each time point in a three-dimensional seismic data body aiming at a target interval based on the three-dimensional seismic data body;
step S102, establishing a local coordinate system of each time point based on the inclination angle and the azimuth angle of each time point;
and step S103, obtaining an amplitude gradient vector through a formula based on the local coordinate system.
In the embodiment of the invention, based on the line, the channel and the two-way travel time direction of the three-dimensional seismic data volume, the derivative of the amplitude along the directions of x, y and z obtains an amplitude gradient vector through a formula:
in the formula: x, y and z are respectively the line, the channel and the two-way travel time direction of the three-dimensional seismic data body; g1, g2, g3 are the directional derivatives of the amplitude in the x, y, z directions, respectively.
The calculation of the amplitude gradient vector is a basic step of calculating the amplitude gradient tensor, and the amplitude change between the point and the x, y and z direction reflected by the amplitude gradient vector cannot directly reflect the abnormality of the geologic body. The amplitude gradient tensor is a high-order matrix of the amplitude gradient vector, and can identify and eliminate amplitude gradient abnormality caused by formation dip angle and bending according to the spatial change of the amplitude gradient, so that the purpose of identifying the abnormal geologic body is achieved.
And calculating the directional derivatives g1, g2 and g3 of the amplitude of each point along the x, y and z directions (calculating the amplitude change of each point according to the adjacent points in the x, y and z directions) to obtain a precise amplitude gradient field of a three-dimensional space, thereby laying a foundation for accurately calculating the amplitude gradient tensor.
And step S200, performing convolution processing on the amplitude gradient vector of each point for reducing interference data to obtain the amplitude gradient vector of each point after convolution.
In some embodiments, the amplitude gradient vector for each point is convolved with a Gaussian window function.
In the embodiment of the present invention, the convolution processing of the amplitude gradient vector of each point by using the gaussian window function is as follows:
i∈{1，2，3}(2)
in the formula: sigmagAs scale parameter, G (w, σ)g) Is a gaussian kernel function.
The formula (2) is subjected to convolution processing, namely the gradient value of one point is replaced by the weighted average value of the gradient values of the points around the point, so that more accurate data can be obtained, and the purposes of denoising, filtering and reducing interference influence are achieved.
And step S300, obtaining a gradient structure tensor based on the amplitude gradient vector of each point after convolution.
In the embodiment of the present application, the gradient structure tensor formula is calculated as follows:
the gradient structure tensor in the formula (3) is a high-order matrix of the amplitude gradient vector, and reflects the multi-linear relation of the gradient.
And step S400, obtaining an eigenvalue and an eigenvector of the gradient structure tensor by using the amplitude variation direction attribute for removing interference data so as to obtain a gradient structure tensor anisotropic parameter data volume. The eigenvectors of the gradient structure tensor represent the local direction and the eigenvalues represent the coherence. The anisotropic gradient structure tensor parameter data volume can distinguish different textures of the seismic image through analysis, wherein the different textures mainly comprise crack textures so as to distinguish the crack textures from non-crack textures.
In the embodiment of the present application, the formula of the eigenvalue and the eigenvector of the gradient structure tensor is:
|Tν-λν|＝0 (4)
in the formula: lambda and v are respectively a characteristic value and a characteristic vector, the solved characteristic value and characteristic vector are respectively (lambda 1, lambda 2 and lambda 3) and (v 1, v 2 and v 3), the matrix T is a real symmetric matrix, so that lambda 1 is more than or equal to lambda 2 and more than or equal to lambda 3, and 3 characteristic vectors lambda 1, lambda 2 and lambda 3 are orthogonal in pairs.
The amplitude change rate is normally calculated according to the amplitude change of two horizontally adjacent points, if the stratum is horizontal, the amplitude change rate of the stratum information along the stratum is all 0, once the geologic bodies such as cracks, faults, riverways and the like develop along the stratum, the amplitude change rate of the geologic body information is more than 0, and the geologic body information is easy to identify; however, due to the inclination or bending of the stratum, the amplitude change rate of two adjacent points is greater than 0, which is not favorable for the identification of the geologic body. Therefore, the amplitude change rate of the inclined or bent stratum is 0, and the directional characteristic vector v containing the direction is processed mainly aiming at the direction attribute of the amplitude change amount, wherein the directional characteristic vector represents the direction of the amplitude change of 0, and the directional characteristic vector v can be used for eliminating the influence of the stratum inclination and the stratum bending on the amplitude change rate and greatly improving the identification precision of the geological abnormal body, so that the method is more suitable for identifying the cracks with small scale (less than 5M). Compared with the original seismic data, the method and the device have the advantages that the recognition definition of crack textures is obviously improved, the cave recognition capability is also improved, and the boundary characteristic recognition of the intersection of the cracks and the caves is more obvious.
The technical scheme is more sensitive to the carbonate fracture cavity and can well identify the fracture cavity.
In step S500, an amplitude change rate is extracted from the gradient tensor anisotropic parameter data volume, and the amplitude change rate is used for identifying a crack.
In some embodiments, the step of identifying further comprises:
and S600, performing fracture imaging based on the amplitude change rate to reflect the fracture distribution of the target interval.
In addition, the technical scheme is not limited to identifying carbonate gaps, and is also suitable for identifying geological phenomena such as fractures, internal textures of rivers and reefs, parallel and sub-parallel structures, inclined bedding, wavy bedding and the like.
Fig. 3 is a river channel identification diagram of the original three-dimensional earthquake along-bed amplitude change rate.
Fig. 4 is an amplitude change rate river channel identification diagram after gradient structure tensor enhancement processing.
In an exemplary embodiment provided by the application, as shown in fig. 3, a target stratum of a certain experimental area is a gentle slope area with an inclination angle of 4-5 degrees, small-scale riverway development is realized along the layer, riverway features (S-shaped bending attribute features in fig. 3) can be identified by amplitude change rate extracted along the target stratum, and the riverway features identified by an original three-dimensional earthquake are weak.
As shown in fig. 4, by establishing amplitude gradient structure tensor characteristic parameter data, not only the influence of the formation dip angle can be eliminated, but also the coherence identification of the development part of the river channel and the surrounding environment can be enhanced, and the amplitude change rate energy of the river channel is obviously enhanced.
FIG. 5 is a graph of the identification of the amplitude change rate of seismic data along a layer in a destination.
Figure 6 is a graph of gradient structure tensor anisotropy parameter data volume along layer amplitude rate of change attribute identification.
In another exemplary embodiment provided herein, as shown in FIG. 5, the seismic data in the target zone has a property of rate of change of amplitude along the zone, and the dip angle of the formation in the target zone is 20-22, wherein the hole (bead-like feature in the figure) is well identified, but the dip angle is large, and the crack development is substantially unrecognizable.
As shown in fig. 6, gradient structure tensor anisotropy parameter data volume has an amplitude change rate attribute along a layer, light-colored textures represent fracture development characteristics, and the textures are clearly visible due to elimination of the amplitude change influence of a formation dip angle, which is enough to prove that the identification capability of the amplitude change rate on the fracture is stronger by performing gradient structure tensor processing on the formation.
Fig. 7 is a schematic structural diagram of a carbonate fracture-cave enhanced identification device based on gradient structure tensor spectrum decomposition according to an embodiment of the present invention.
The invention also provides a carbonate rock fracture-cave enhanced identification device based on gradient structure tensor spectrum decomposition, as shown in fig. 7, which mainly comprises: a first calculation unit 1, a convolution processing unit 2, a second calculation unit 3, and a third calculation unit 4.
The first computing unit 1 is used for obtaining an amplitude gradient vector of each point in the three-dimensional seismic data body based on the three-dimensional seismic data body and the target interval.
The convolution processing unit 2 is used for performing convolution processing on the amplitude gradient vector of each point, and is used for improving the signal-to-noise ratio, reducing interference data and obtaining the amplitude gradient vector of each point after convolution.
The second calculation unit 3 obtains a gradient structure tensor based on the amplitude gradient vector of each point after convolution.
The third calculating unit 4 obtains an eigenvalue and an eigenvector of the gradient structure tensor by using the amplitude variation direction attribute, and is used for removing interference data of the formation tendency to obtain a gradient structure tensor anisotropic parameter data volume. The use of the mean gradient structure tensor can characterize textural features within a region and also serves to suppress abrupt changes in the structure tensor due to noise.
The extraction unit is used for extracting an amplitude change rate in the gradient structure tensor anisotropic parameter data volume, and the amplitude change rate is used for identifying the crack.
FIG. 8 is a schematic structural diagram of the first computing unit in FIG. 7;
in some embodiments, as shown in fig. 8, the first calculation unit 1 includes: the first calculating subunit 11 is configured to obtain, for a target interval, an inclination angle and an azimuth angle of each time point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume; a local coordinate system establishing unit 12 that establishes a local coordinate system of each time point based on the inclination and azimuth of each time point; the second calculation subunit 13 obtains an amplitude gradient vector by a formula based on the local coordinate system.
In some embodiments, the convolution processing unit 2 performs convolution processing on the amplitude gradient vector of each point using a gaussian window function.
In some embodiments, the obtaining of the eigenvalue and the eigenvector of the gradient structure tensor by using the amplitude variation direction attribute is used to remove interference data, and the obtaining of the gradient structure tensor anisotropic parameter data volume specifically includes: and obtaining the gradient structure tensor anisotropy parameter data volume by a formula | T ν - λ ν | ═ 0. Wherein, λ and ν are respectively a characteristic value and a characteristic vector, the characteristic value and the characteristic vector are respectively (λ 1, λ 2, λ 3) and (ν 1, ν 2, ν 3), the matrix T is a real symmetric matrix, so λ 1 ≥ λ 2 ≥ λ 3 > 0, and 3 characteristic vectors λ 1, λ 2, λ 3 are orthogonal in pairs. The eigenvectors of the gradient structure tensor represent the local direction and the eigenvalues represent the coherence.
In some embodiments, the identification means further comprises: and the imaging unit is used for carrying out fracture imaging based on the amplitude change rate so as to reflect the fracture distribution of the target interval.
The invention further provides an electronic device, which comprises a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, and the steps of any method in the technical scheme are realized when the processor executes the computer program.
The invention also provides a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform any of the methods described above.
The technical scheme of the application identifies the seam hole principle: the dip angle and the azimuth angle of the geologic body are described by mainly utilizing the gradient vectors, the structural characteristics of seismic data are described by adopting the spectral decomposition of the gradient tensor matrix, and particularly, the influence of the formation dip angle is eliminated aiming at the direction attribute of the amplitude variation, so that the aim of identifying cracks is fulfilled.
The technical scheme of the invention has the following beneficial technical effects:
through the identification method and the identification device, the influence of the stratigraphic dip angle is eliminated by aiming at the amplitude variation direction attribute, and compared with the original seismic data, the identification definition of crack textures is obviously improved by adopting the technical scheme, the cave identification capability is also improved, and the boundary characteristics of the intersection of the cracks and the caves are also identified more obviously. Compared with algorithms such as a coherent algorithm and a curvature algorithm based on a characteristic structure, the Gradient Structure Tensor (GST) method can better overcome discontinuity artifacts caused by steep dip stratum and can clearly reflect real micro fracture, crack and texture information.
The invention has been described above with reference to embodiments thereof. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the invention, and these alternatives and modifications are intended to be within the scope of the invention.
Claims (10)
1. The carbonate rock fracture-cave enhanced identification method based on gradient structure tensor spectrum decomposition is characterized by comprising the following steps of:
obtaining an amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval;
performing convolution processing on the amplitude gradient vector of each point, wherein the convolution processing is used for reducing interference data and obtaining the amplitude gradient vector of each point after convolution;
obtaining a gradient structure tensor based on the amplitude gradient vector of each point after convolution;
obtaining an eigenvalue and an eigenvector of the gradient structure tensor by using the amplitude variation direction attribute, and removing interference data to obtain a gradient structure tensor anisotropic parameter data volume;
extracting an amplitude rate of change in the gradient structure tensor anisotropy parameter data volume, the amplitude rate of change being used to identify a fracture.
2. The identification method according to claim 1,
the obtaining of the amplitude gradient vector of each point in the three-dimensional seismic data volume based on the three-dimensional seismic data volume and the target interval comprises:
based on the three-dimensional seismic data volume, aiming at the target interval, obtaining the inclination angle and the azimuth angle of each time point in the three-dimensional seismic data volume;
establishing a local coordinate system of each time point based on the inclination angle and the azimuth angle of each time point;
and obtaining an amplitude gradient vector through a formula based on the local coordinate system.
3. The identification method according to claim 1,
and performing convolution processing on the amplitude gradient vector of each point by using a Gaussian window function.
4. The identification method according to claim 1, further comprising:
the obtaining of the eigenvalue and the eigenvector of the gradient structure tensor by using the amplitude variation direction attribute is used for removing interference data, and the obtaining of the anisotropic parameter data volume of the gradient structure tensor specifically includes:
and obtaining the gradient structure tensor anisotropy parameter data volume by the formula | Tv- λ v | ═ 0.
5. Carbonate seam hole reinforcing recognition device based on gradient structure tensor spectrum is decomposed, its characterized in that includes:
the first calculation unit (1) is used for obtaining the amplitude gradient vector of each point in the three-dimensional seismic data body based on the three-dimensional seismic data body and the target interval;
the convolution processing unit (2) is used for performing convolution processing on the amplitude gradient vector of each point, reducing interference data and obtaining the amplitude gradient vector of each point after convolution;
a second calculation unit (3) for obtaining a gradient structure tensor based on the amplitude gradient vector of each point after the convolution;
the third calculating unit (4) is used for obtaining an eigenvalue and an eigenvector of the gradient structure tensor by utilizing the amplitude variation direction attribute and removing interference data so as to obtain a gradient structure tensor anisotropic parameter data volume;
and the extraction unit is used for extracting an amplitude change rate in the gradient structure tensor anisotropic parameter data volume, and the amplitude change rate is used for identifying cracks.
6. The identification device according to claim 5, characterized in that the first calculation unit (1) comprises:
the first calculating subunit (11) is used for obtaining the inclination angle and the azimuth angle of each time point in the three-dimensional seismic data body aiming at the target interval on the basis of the three-dimensional seismic data body;
a local coordinate system establishing unit (12) that establishes a local coordinate system for each time point based on the inclination and azimuth of each time point;
and a second calculation subunit (13) for obtaining an amplitude gradient vector by a formula based on the local coordinate system.
7. The identification device according to claim 5, wherein said convolution processing unit (2) performs convolution processing on said amplitude gradient vector of each time point by using a Gaussian window function.
8. The identification device of claim 5, further comprising:
the obtaining of the eigenvalue and the eigenvector of the gradient structure tensor by using the amplitude variation direction attribute is used for removing interference data, and the obtaining of the anisotropic parameter data volume of the gradient structure tensor specifically includes:
and obtaining the gradient structure tensor anisotropy parameter data volume by the formula | Tv- λ v | ═ 0.
9. An electronic device comprising a memory, a processor, a computer program stored in the memory and executable on the processor, wherein,
the processor, when executing the computer program, performs the steps of the method of any of the preceding claims 1-4.
10. A computer-readable medium having non-volatile program code executable by a processor,
the program code causes the processor to perform the method of any of claims 1-4.
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| CN112363220A (en) * | 2020-10-26 | 2021-02-12 | 中国石油天然气集团有限公司 | Fracture-cavity carbonate rock micro reservoir sweet spot prediction method and system |
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| CN114578422A (en) * | 2022-03-08 | 2022-06-03 | 国勘数字地球(北京)科技有限公司 | Method and device for analyzing hydraulic fracturing monitoring result and computer equipment |
| CN114721046A (en) * | 2021-01-05 | 2022-07-08 | 中国石油天然气股份有限公司 | Slot hole detection method and device and computer storage medium |
| CN116165709A (en) * | 2023-01-19 | 2023-05-26 | 中国石油大学(华东) | Oil gas exploration method, device and equipment based on fracture-cavity body recognition |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102879799A (en) * | 2011-07-15 | 2013-01-16 | 中国石油天然气集团公司 | Multi-direction seismic energy gradient difference carbonate karst cave type reservoir identification method |
| US20130182538A1 (en) * | 2012-01-12 | 2013-07-18 | Westerngeco L.L.C. | Reverse time migration model dip-guided imaging |
| CN103792576A (en) * | 2014-01-28 | 2014-05-14 | 中国石油天然气股份有限公司 | Reservoir heterogeneous detection method and device based on gradient structure tensor |
| US20140293744A1 (en) * | 2013-04-02 | 2014-10-02 | Bp Corporation North America Inc. | Specular filter (sf) and dip oriented partial imaging (dopi) seismic migration |
| CN107272065A (en) * | 2017-08-04 | 2017-10-20 | 中国石油化工股份有限公司 | The disconnected solution profile testing method of carbonate rock |
| 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 |
| CN107966732A (en) * | 2017-11-10 | 2018-04-27 | 西南石油大学 | The seismic properties change rate acquiring method being oriented to based on space structure |
| CN109116428A (en) * | 2018-07-02 | 2019-01-01 | 中国石油天然气股份有限公司 | Fracture-cavity carbonate reservoir uncertainty modeling method and device |
| CN110749924A (en) * | 2018-07-24 | 2020-02-04 | 中国石油化工股份有限公司 | Fracture zone identification method |
-
2020
- 2020-03-25 CN CN202010216733.4A patent/CN111290021A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102879799A (en) * | 2011-07-15 | 2013-01-16 | 中国石油天然气集团公司 | Multi-direction seismic energy gradient difference carbonate karst cave type reservoir identification method |
| US20130182538A1 (en) * | 2012-01-12 | 2013-07-18 | Westerngeco L.L.C. | Reverse time migration model dip-guided imaging |
| US20140293744A1 (en) * | 2013-04-02 | 2014-10-02 | Bp Corporation North America Inc. | Specular filter (sf) and dip oriented partial imaging (dopi) seismic migration |
| CN103792576A (en) * | 2014-01-28 | 2014-05-14 | 中国石油天然气股份有限公司 | Reservoir heterogeneous detection method and device based on gradient structure tensor |
| 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 |
| CN107272065A (en) * | 2017-08-04 | 2017-10-20 | 中国石油化工股份有限公司 | The disconnected solution profile testing method of carbonate rock |
| CN107966732A (en) * | 2017-11-10 | 2018-04-27 | 西南石油大学 | The seismic properties change rate acquiring method being oriented to based on space structure |
| CN109116428A (en) * | 2018-07-02 | 2019-01-01 | 中国石油天然气股份有限公司 | Fracture-cavity carbonate reservoir uncertainty modeling method and device |
| CN110749924A (en) * | 2018-07-24 | 2020-02-04 | 中国石油化工股份有限公司 | Fracture zone identification method |
Non-Patent Citations (3)
| Title |
|---|
| 王清振 等: "利用梯度结构张量检测盐丘与断层", 《石油地球物理勘探》 * |
| 王震 等: "塔河油田碳酸盐岩断溶体刻画技术研究与应用", 《石油物探》 * |
| 韩革华 等: "塔河油田奥陶系碳酸盐岩缝洞型储层预测技术", 《石油与天然气地质》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111596365A (en) * | 2020-06-18 | 2020-08-28 | 中国海洋石油集团有限公司 | Volcanic eruption rock earthquake interpretation method for carbonate reservoir section of lake-phase under salt |
| CN111596365B (en) * | 2020-06-18 | 2023-11-21 | 中国海洋石油集团有限公司 | Volcanic eruption rock seismic interpretation method aiming at undersalt lake-phase carbonate rock reservoir section |
| CN112363220A (en) * | 2020-10-26 | 2021-02-12 | 中国石油天然气集团有限公司 | Fracture-cavity carbonate rock micro reservoir sweet spot prediction method and system |
| CN114721046A (en) * | 2021-01-05 | 2022-07-08 | 中国石油天然气股份有限公司 | Slot hole detection method and device and computer storage medium |
| CN114167499A (en) * | 2021-12-02 | 2022-03-11 | 大庆油田有限责任公司 | Method and device for automatically identifying biological reef, electronic equipment and storage medium |
| CN114167499B (en) * | 2021-12-02 | 2022-08-26 | 大庆油田有限责任公司 | Method and device for automatically identifying biological reef, electronic equipment and storage medium |
| CN114578422A (en) * | 2022-03-08 | 2022-06-03 | 国勘数字地球(北京)科技有限公司 | Method and device for analyzing hydraulic fracturing monitoring result and computer equipment |
| CN116165709A (en) * | 2023-01-19 | 2023-05-26 | 中国石油大学(华东) | Oil gas exploration method, device and equipment based on fracture-cavity body recognition |
| CN116165709B (en) * | 2023-01-19 | 2024-01-30 | 中国石油大学(华东) | Oil gas exploration method, device and equipment based on fracture-cavity body recognition |
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