CN114137613B - Formation fracture identification method, system, storage medium and electronic equipment - Google Patents
Formation fracture identification method, system, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN114137613B CN114137613B CN202010916370.5A CN202010916370A CN114137613B CN 114137613 B CN114137613 B CN 114137613B CN 202010916370 A CN202010916370 A CN 202010916370A CN 114137613 B CN114137613 B CN 114137613B
- Authority
- CN
- China
- Prior art keywords
- matrix
- coherence
- coherent
- fracture
- normalized
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 83
- 238000003860 storage Methods 0.000 title claims abstract description 13
- 230000015572 biosynthetic process Effects 0.000 title claims description 32
- 239000011159 matrix material Substances 0.000 claims abstract description 288
- 230000001427 coherent effect Effects 0.000 claims abstract description 121
- 238000012545 processing Methods 0.000 claims abstract description 85
- 238000010606 normalization Methods 0.000 claims description 21
- 238000013461 design Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000003384 imaging method Methods 0.000 abstract description 22
- 230000035945 sensitivity Effects 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000005755 formation reaction Methods 0.000 description 26
- 230000004069 differentiation Effects 0.000 description 20
- 238000005516 engineering process Methods 0.000 description 19
- 230000008569 process Effects 0.000 description 14
- 230000035699 permeability Effects 0.000 description 9
- 230000008859 change Effects 0.000 description 8
- 230000004044 response Effects 0.000 description 8
- 230000000704 physical effect Effects 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 4
- 239000013598 vector Substances 0.000 description 4
- 238000005520 cutting process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000011435 rock Substances 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/34—Displaying seismic recordings or visualisation of seismic data or attributes
- G01V1/345—Visualisation of seismic data or attributes, e.g. in 3D cubes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/362—Effecting static or dynamic corrections; Stacking
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/30—Noise handling
- G01V2210/32—Noise reduction
- G01V2210/322—Trace stacking
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/64—Geostructures, e.g. in 3D data cubes
- G01V2210/642—Faults
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/70—Other details related to processing
- G01V2210/74—Visualisation of seismic data
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a stratum fracture identification method, a stratum fracture identification system, a storage medium and electronic equipment, and relates to the technical field of oil and gas exploration, wherein the stratum fracture identification method comprises the following steps: acquiring post-stack seismic data of a work area to be studied; performing seismic coherence processing on the post-stack seismic data to obtain a coherence matrix; increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix to obtain a differentiated coherence matrix; normalizing the elements in the differentiated coherent matrix to obtain a normalized coherent matrix; the difference between the elements representing the fractured region and the non-fractured region in the normalized coherence matrix is increased to obtain an enhanced coherence matrix. The beneficial effects of the invention are as follows: the sensitivity of the coherence attribute to the medium-small scale fracture can be greatly increased, the medium-small scale fracture can be better displayed on the coherence plane, the imaging of the large fracture is more accurate, and the practicability and applicability of the seismic coherence algorithm are improved.
Description
Technical Field
The invention belongs to the technical field of oil and gas exploration, and particularly relates to a stratum fracture identification method, a stratum fracture identification system, a storage medium and electronic equipment.
Background
Due to the existence of structural stresses, the subsurface formations may inevitably fracture, and thus faults may develop. In the field of oil and gas exploration, faults can be used as not only a channel for oil and gas migration but also a boundary of a broken block oil and gas field, so that the distribution of the oil and gas field can be effectively controlled. Accurate identification of faults is therefore of great importance for the exploration and development of oil and gas fields. Coherence techniques are an effective means for fracture identification, to the extent that variations in the transverse direction of the seismic response can be manifested. The technology has been developed for many times from the beginning, and the first generation coherent technology based on normalized cross correlation, the second generation coherent technology based on multi-channel similarity and the third generation coherent technology based on eigenvalue structure are commonly used at present. These coherent techniques are advantageous in scientific production, but generally the application of third generation coherent techniques is optimal.
Although the third generation coherent technology improves resolution to a certain extent compared with the first two generation coherent technology, the third generation coherent technology still has serious noise interference, and is easy to image by taking noise and other interference as low coherence values, so that imaging inaccuracy is caused. Meanwhile, aiming at medium-small-scale fracture with unobvious performance characteristics, the third-generation coherent technology is poor in imaging or even can not image. These drawbacks severely limit the detection and identification of small and medium-sized breaks in practical production applications of third generation coherent technology.
Disclosure of Invention
The invention provides a stratum fracture identification method, a system, a storage medium and electronic equipment, which are based on the technical problem that the third-generation coherent technology cannot accurately detect and identify medium-small-scale fracture in actual production and application.
In a first aspect, an embodiment of the present invention provides a method for identifying a fracture of a formation, including:
Acquiring post-stack seismic data of a work area to be studied;
Performing seismic coherence processing on the post-stack seismic data to obtain a coherence matrix;
Increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix to obtain a differentiated coherence matrix;
Normalizing the elements in the differentiated coherent matrix to obtain a normalized coherent matrix;
Increasing the difference between elements representing the fracture area and the non-fracture area in the normalized coherence matrix to obtain an enhanced coherence matrix;
And analyzing the fractures of different scales of the work area to be researched by utilizing the coherence matrix of the enhancement treatment.
Optionally, the increasing the difference between the elements in the coherence matrix that represent the fractured region and the non-fractured region obtains a differentiated coherence matrix, including:
Increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix by using a first preset calculation method to obtain a differentiated coherence matrix; wherein the first pre-design formula is:
C_dif＝(C3-C·α)·104
Wherein, c_dif is a differentiated coherent matrix, C 3 is a coherent matrix, C is a matrix with the same size as the coherent matrix, and the elements in the matrix are all 1, and α is a constant.
Optionally, the normalizing the elements in the differentiated coherent matrix to obtain a normalized coherent matrix includes:
normalizing the elements in the differentiated coherent matrix by using a second pre-design algorithm to obtain a normalized coherent matrix; wherein the second pre-design formula is:
Where c_dif_norm is a normalized coherence matrix, c_dif is a differentiated coherence matrix, (c_dif) min is the minimum value of the elements in the coherence matrix, and (c_dif) max is the maximum value of the elements in the coherence matrix.
Optionally, said increasing the difference between the elements representing the fractured region and the non-fractured region in the normalized coherence matrix, obtaining an enhanced coherence matrix, comprising:
for each element in the normalized coherence matrix, performing the steps of:
judging whether the value of an element in the normalized coherent matrix is larger than a preset threshold value or not;
When the value of the element in the normalized coherent matrix is larger than a preset threshold value, adding the element larger than the preset threshold value with a preset added value to obtain a new element;
When the value of the element in the normalized coherence matrix is smaller than the preset threshold value, subtracting the additional value from the element smaller than the preset threshold value to obtain a new element so as to obtain the coherence matrix for enhancement processing.
Optionally, after obtaining the coherence matrix of the enhancement process, the method further comprises:
Resetting elements larger than 1 in the enhancement processing coherence matrix to 1 to obtain a new enhancement processing coherence matrix.
Optionally, the performing seismic coherence processing on the post-stack seismic data to obtain a coherence matrix includes:
Performing seismic coherence processing on the post-stack seismic data by using a third-generation coherence algorithm to obtain a coherence matrix; wherein, the third generation coherent algorithm is:
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
In a second aspect, an embodiment of the present invention provides a formation fracture identification system, including:
the acquisition module is configured to acquire post-stack seismic data of a work area to be researched;
the seismic coherence processing module is configured to perform seismic coherence processing on the post-stack seismic data to obtain a coherence matrix;
A differentiating module configured to increase a difference between elements representing the fractured region and the non-fractured region in the coherence matrix, to obtain a differentiated coherence matrix;
the normalization module is configured to normalize elements in the differentiated coherent matrix to obtain a normalized coherent matrix;
An enhancement processing module configured to increase the difference between elements representing the fractured region and the non-fractured region in the normalized coherence matrix, to obtain an enhancement processed coherence matrix;
And the analysis module is used for analyzing the fractures of different scales of the work area to be researched by utilizing the coherence matrix of the enhancement treatment.
Optionally, the differentiating module is specifically configured to:
Increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix by using a first preset calculation method to obtain a differentiated coherence matrix; wherein the first pre-design formula is:
C_dif＝(C3-C·α)·104
Wherein, c_dif is a differentiated coherent matrix, C 3 is a coherent matrix, C is a matrix with the same size as the coherent matrix, and the elements in the matrix are all 1, and α is a constant.
Optionally, the normalization module is specifically configured to:
normalizing the elements in the differentiated coherent matrix by using a second pre-design algorithm to obtain a normalized coherent matrix; wherein the second pre-design formula is:
Where c_dif_norm is a normalized coherence matrix, c_dif is a differentiated coherence matrix, (c_dif) min is the minimum value of the elements in the coherence matrix, and (c_dif) max is the maximum value of the elements in the coherence matrix.
Optionally, the enhancement processing module includes:
a judging unit configured to judge whether a value of an element in the normalized coherence matrix is greater than a preset threshold;
The enhancement processing unit is configured to enable elements larger than a preset threshold value to be added with a preset added value when the values of the elements in the normalized coherent matrix are larger than the preset threshold value, so that new elements are obtained; when the value of the element in the normalized coherence matrix is smaller than the preset threshold value, subtracting the additional value from the element smaller than the preset threshold value to obtain a new element so as to obtain the coherence matrix for enhancement processing.
Optionally, the system further comprises:
And the post-processing module is configured to reset elements larger than 1 in the enhancement processing coherence matrix to 1so as to obtain a new enhancement processing coherence matrix.
Optionally, the seismic coherence processing module is specifically configured to:
Performing seismic coherence processing on the post-stack seismic data by using a third-generation coherence algorithm to obtain a coherence matrix; wherein, the third generation coherent algorithm is:
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
In a third aspect, an embodiment of the present invention provides a storage medium having stored thereon program code that, when executed by a processor, implements a formation fracture identification method according to any of the above embodiments.
In a fourth aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes a memory and a processor, and the memory stores program code that can be executed on the processor, and the program code is executed by the processor, to implement a method for identifying a formation fracture according to any one of the foregoing embodiments.
According to the stratum fracture identification method provided by the embodiment of the invention, the sensitivity of the coherence attribute to medium-small scale fracture can be greatly increased by carrying out differentiation treatment, normalization treatment and enhancement treatment on the coherence matrix obtained by the third-generation coherence technology calculation, the medium-small scale fracture can be better displayed on the coherence plane, the imaging of the large fracture is more accurate, and the practicability and applicability of the seismic coherence algorithm are improved, so that the coherence attribute can be better applied to actual fracture detection and identification.
Drawings
The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The drawings included herein are:
FIG. 1 is a schematic flow chart of a method for identifying formation fracture according to an embodiment of the present invention;
FIG. 2 shows a schematic diagram of a time domain seismic record containing simple faults;
FIG. 3 is a graph showing a comparison of conventional coherence properties of a time domain seismic record containing a simple fault and coherence enhancement properties processed by the method of the present invention
FIG. 4 shows a schematic diagram of a depth domain seismic record containing complex faults in a region;
FIG. 5 shows a comparison of conventional coherence properties of depth domain seismic records containing complex faults and coherence enhancement properties processed by the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following detailed description of the implementation method of the present invention will be given with reference to the accompanying drawings and examples, by which the technical means are applied to solve the technical problems, and the implementation process for achieving the technical effects can be fully understood and implemented accordingly.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Example 1
According to an embodiment of the present invention, a method for identifying a fracture of a formation is provided, and fig. 1 shows a schematic flow chart of a method for identifying a fracture of a formation according to an embodiment of the present invention, and as shown in fig. 1, the method for identifying a fracture of a formation may include: steps 110 to 160.
In step 110, post-stack seismic data for a work area to be studied is acquired.
Here, the seismic wave signals are acquired by the acquisition device to obtain a seismic data volume, and then the seismic data volume is subjected to superposition processing to obtain post-stack seismic data. By processing post-stack seismic data, data reflecting seismic attributes, which are special measures of geometry, kinematic, dynamic and statistical properties of the seismic waves, can be extracted therefrom.
The seismic wave signals are affected by factors such as formation lithology, physical properties and the like in the process of underground formation propagation to generate corresponding changes, and the method is a complex reflection of the comprehensive characteristics of underground reservoirs. Spatial variations in the physical properties of the subsurface formation rock, etc., necessarily result in variations in the characteristics of the seismic reflection wave, which in turn affect the seismic attributes. Particularly when the reservoir contains oil and gas, the seismic response characteristics of the reservoir can be correspondingly changed, and corresponding seismic attributes can also be reflected. The theoretical basis for predicting oil gas by the seismic attribute technology is as follows: the seismic attributes carry information about the subsurface formations, while some form of inherent association must exist between the seismic attributes and the oil and gas properties of the reservoir.
In step 120, the post-stack seismic data is subjected to seismic coherence processing to obtain a coherence matrix.
Here, the coherence matrix is obtained by performing seismic coherence processing on post-stack seismic data using a coherence technique. The coherence matrix refers to a new data volume obtained by coherent processing of post-stack seismic data. The data can reflect the development of the fracture (crack).
Among them, the coherence technique is an important seismic attribute technique, which converts a three-dimensional seismic data volume into a coherent data volume by calculating the similarity of waveforms of adjacent seismic traces, highlighting the discontinuity characteristics of the waveforms. Therefore, the coherence can measure the transverse change of the earthquake response caused by the change of factors such as the structure, stratum, lithology, oil gas and the like, thereby effectively revealing geological phenomena such as faults, cracks, lithology edges, unconformity and the like and reflecting the plane spread of geological abnormal characteristics.
In step 130, the difference between the elements in the coherence matrix that represent the fractured region and the non-fractured region is increased to obtain a differentiated coherence matrix.
Here, after the coherence matrix is obtained, a differentiation process is performed to macroscopically increase the difference between the coherence values of the broken area and the non-broken area, so that the broken area has an overall appearance on the coherence plane. That is, the difference between the coherence values of the elements representing the broken area and the non-broken area in the coherence matrix is increased, and the broken area and the non-broken area are differentiated, thereby obtaining a differentiated coherence matrix.
In step 140, elements in the differentiated coherence matrix are normalized to obtain a normalized coherence matrix.
Here, the values of the elements in the differentiated coherent matrix obtained through the differentiation process may have negative values, so that a normalization process is performed on the basis of the differentiated coherent matrix, and the values of the elements in the differentiated coherent matrix are normalized to be between 0 and 1, thereby obtaining a normalized coherent matrix.
The normalization is a way of simplifying computation, namely, an expression with dimension is transformed into an expression without dimension to become a scalar. I.e. the data is changed to (0, 1) or to a fraction between (1, 1). The method is mainly used for conveniently providing data processing, and mapping the data to the range of 0-1 for processing, so that the method is more convenient and rapid. The dimensionless expression is changed into the dimensionless expression, so that indexes with different units or magnitudes can be compared and weighted conveniently.
In step 150, the difference between the elements representing the fractured and non-fractured regions in the normalized coherence matrix is increased to obtain an enhanced coherence matrix.
Here, the difference between the elements representing the fractured region and the non-fractured region in the normalized coherence matrix is increased in order to further highlight the coherence value of the fractured region. The small-scale fracture is displayed as much as possible, and the large-scale fracture is displayed more clearly and accurately. The obtained enhancement processed coherent matrix can be used as a coherent body of a work area to be researched, the coherent body can better display medium-small scale fracture on a coherent plane, and imaging of large-scale fracture is more accurate.
In step 160, the coherence matrix of the enhancement process is used to analyze the fractures of different dimensions of the work area under investigation.
Here, the enhancement-processed coherence matrix can better display medium-small scale fractures on a coherence plane and enable imaging of large fractures to be more accurate, so that fractures of different scales of a work area to be studied can be analyzed through the enhancement-processed coherence matrix.
The cracks (breaks) are classified into micro-cracks, small-scale cracks and large-scale cracks according to the scale.
Where microcracks refer to cracks that can be observed on a core sheet, typically in the micrometer scale, the microcracks are numerous in the reservoir, typically associated with the matrix, so the microcracks are actually part of the matrix.
Small-scale fractures generally refer to fractures that can be observed on core and imaging logs, typically ranging from tens of centimeters to tens of meters in length. Seen on the imaging log and core is the height of the small scale fracture. The small-scale cracks are interwoven into a net in a three-dimensional space to form seepage channels of oil gas. Fracture density, fracture opening, fracture length, and degree of fracture azimuth variation are key factors in determining fracture permeability.
Large scale fractures generally refer to fractures at the seismic level, typically from tens of meters to thousands of meters in length. The large-scale cracks have large transverse extension length, vertical cutting layer depth and high permeability, the small-scale cracks in the oil reservoir provide permeability of the oil reservoir, and the large-scale cracks determine the heterogeneity of the oil reservoir.
In the embodiment, the coherence matrix obtained through calculation of the coherence technology is subjected to differentiation treatment, normalization treatment and enhancement treatment, so that the sensitivity of the coherence attribute to medium-small scale fracture can be greatly increased, the medium-small scale fracture can be better displayed on a coherence plane, the imaging of the large fracture is more accurate, the practicability and applicability of the seismic coherence algorithm are improved, and the coherence attribute can be better applied to actual fracture detection and identification.
Example two
Based on the above embodiment, the second embodiment of the present invention may also provide a method for identifying a fracture of a stratum. The formation fracture identification method may include: step 210 to step 250.
In step 210, post-stack seismic data for a work area to be studied is acquired.
Here, the seismic wave signals are acquired by the acquisition device to obtain a seismic data volume, and then the seismic data volume is subjected to superposition processing to obtain post-stack seismic data. By processing post-stack seismic data, data reflecting seismic attributes, which are special measures of geometry, kinematic, dynamic and statistical properties of the seismic waves, can be extracted therefrom.
The seismic wave signals are affected by factors such as formation lithology, physical properties and the like in the process of underground formation propagation to generate corresponding changes, and the method is a complex reflection of the comprehensive characteristics of underground reservoirs. Spatial variations in the physical properties of the subsurface formation rock, etc., necessarily result in variations in the characteristics of the seismic reflection wave, which in turn affect the seismic attributes. Particularly when the reservoir contains oil and gas, the seismic response characteristics of the reservoir can be correspondingly changed, and corresponding seismic attributes can also be reflected. The theoretical basis for predicting oil gas by the seismic attribute technology is as follows: the seismic attributes carry information about the subsurface formations, while some form of inherent association must exist between the seismic attributes and the oil and gas properties of the reservoir.
In step 220, the post-stack seismic data is subjected to seismic coherence processing to obtain a coherence matrix.
Here, the coherence matrix is obtained by performing seismic coherence processing on post-stack seismic data using a coherence technique. The coherence matrix refers to a new data volume obtained by coherent processing of post-stack seismic data. The data can reflect the development of the fracture (crack).
In an alternative embodiment, in step 220, the post-stack seismic data is subjected to a seismic coherence process to obtain a coherence matrix, including:
Performing seismic coherence processing on the post-stack seismic data by using a third-generation coherence algorithm to obtain a coherence matrix; wherein, the third generation coherent algorithm is:
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
Here, the coherence technique is an important seismic attribute technique that converts a three-dimensional seismic data volume into a coherent data volume by computing the similarity of waveforms of adjacent seismic traces, highlighting the discontinuity characteristics of the waveforms. Therefore, the coherence can measure the transverse change of the earthquake response caused by the change of factors such as the structure, stratum, lithology, oil gas and the like, thereby effectively revealing geological phenomena such as faults, cracks, lithology edges, unconformity and the like and reflecting the plane spread of geological abnormal characteristics.
The third generation coherent volume algorithm is obtained by calculating eigenvalues of a seismic data volume, in algorithm analysis, firstly, a plurality of channels of seismic data are extracted from a given analysis time window to generate sample point vectors, and the sample point vectors form a matrix:
The covariance matrix corresponding to the matrix is:
the covariance matrix is a symmetric, semi-positive definite matrix with all eigenvalues greater than or equal to 0.
Calculating eigenvalues and eigenvectors of the covariance matrix by the third-generation coherent algorithm, wherein the third-generation coherent algorithm is as follows:
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
Thus, the third-generation coherence algorithm is utilized to perform seismic coherence processing on the post-stack seismic data, and a coherence matrix is obtained.
In step 230, the difference between the elements in the coherence matrix that represent the fractured region and the non-fractured region is increased to obtain a differentiated coherence matrix.
Here, after the coherence matrix is obtained, a differentiation process is performed to macroscopically increase the difference between the coherence values of the broken area and the non-broken area, so that the broken area has an overall appearance on the coherence plane. That is, the difference between the coherence values of the elements representing the broken area and the non-broken area in the coherence matrix is increased, and the broken area and the non-broken area are differentiated, thereby obtaining a differentiated coherence matrix.
In an alternative embodiment, in step 230, increasing the difference between the elements in the coherence matrix that represent the fractured region and the non-fractured region to obtain a differentiated coherence matrix includes:
Increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix by using a first preset calculation method to obtain a differentiated coherence matrix; wherein the first pre-design formula is:
C_dif＝(C3-C·α)·104
Wherein, c_dif is a differentiated coherent matrix, C 3 is a coherent matrix, C is a matrix with the same size as the coherent matrix, and the elements in the matrix are all 1, and α is a constant.
Here, the differentiation processing is performed on the basis of the acquisition of the coherence matrix C 3, and the purpose of the differentiation processing is to macroscopically increase the difference between the coherence values of the broken area and the non-broken area, so that the broken area has an overall appearance on the coherence plane. Specifically, the differentiation processing is performed through a first preset calculation type.
It is noted that α is a constant that is used to control the degree of differentiation.
In step 240, elements in the differentiated coherence matrix are normalized to obtain a normalized coherence matrix.
Here, the values of the elements in the differentiated coherent matrix obtained through the differentiation process may have negative values, so that a normalization process is performed on the basis of the differentiated coherent matrix, and the values of the elements in the differentiated coherent matrix are normalized to be between 0 and 1, thereby obtaining a normalized coherent matrix.
The normalization is a way of simplifying computation, namely, an expression with dimension is transformed into an expression without dimension to become a scalar. I.e. the data is changed to (0, 1) or to a fraction between (1, 1). The method is mainly used for conveniently providing data processing, and mapping the data to the range of 0-1 for processing, so that the method is more convenient and rapid. The dimensionless expression is changed into the dimensionless expression, so that indexes with different units or magnitudes can be compared and weighted conveniently.
In an optional embodiment, in step 240, normalizing the elements in the differentiated coherence matrix to obtain a normalized coherence matrix includes:
normalizing the elements in the differentiated coherent matrix by using a second pre-design algorithm to obtain a normalized coherent matrix; wherein the second pre-design formula is:
Where c_dif_norm is a normalized coherence matrix, c_dif is a differentiated coherence matrix, (c_dif) min is the minimum value of the elements in the coherence matrix, and (c_dif) max is the maximum value of the elements in the coherence matrix.
Here, the values of the internal elements of the differentiated coherent matrix obtained after the differentiation process may have negative conditions, so that a normalization process is performed on the basis of the differentiated coherent matrix, and the values of the elements in the differentiated coherent matrix are normalized to between 0 and 1.
In this embodiment, a linear function normalization method is adopted, specifically, normalization processing is performed by using a second preset calculation formula, where the second preset calculation formula is:
Where c_dif_norm is a normalized coherence matrix, c_dif is a differentiated coherence matrix, (c_dif) min is the minimum value of the elements in the coherence matrix, and (c_dif) max is the maximum value of the elements in the coherence matrix.
Thus, the normalization process converts the range of coherence values to between 0 and 1, ensuring its rationality.
In step 250, the difference between the elements representing the fractured and non-fractured regions in the normalized coherence matrix is increased to obtain an enhanced coherence matrix.
Here, the difference between the elements representing the fractured region and the non-fractured region in the normalized coherence matrix is increased in order to further highlight the coherence value of the fractured region. The small-scale fracture is displayed as much as possible, and the large-scale fracture is displayed more clearly and accurately. The obtained enhancement processed coherent matrix can be used as a coherent body of a work area to be researched, the coherent body can better display medium-small scale fracture on a coherent plane, and imaging of large-scale fracture is more accurate.
The cracks (breaks) are classified into micro-cracks, small-scale cracks and large-scale cracks according to the scale.
Where microcracks refer to cracks that can be observed on a core sheet, typically in the micrometer scale, the microcracks are numerous in the reservoir, typically associated with the matrix, so the microcracks are actually part of the matrix.
Small-scale fractures generally refer to fractures that can be observed on core and imaging logs, typically ranging from tens of centimeters to tens of meters in length. Seen on the imaging log and core is the height of the small scale fracture. The small-scale cracks are interwoven into a net in a three-dimensional space to form seepage channels of oil gas. Fracture density, fracture opening, fracture length, and degree of fracture azimuth variation are key factors in determining fracture permeability.
Large scale fractures generally refer to fractures at the seismic level, typically from tens of meters to thousands of meters in length. The large-scale cracks have large transverse extension length, vertical cutting layer depth and high permeability, the small-scale cracks in the oil reservoir provide permeability of the oil reservoir, and the large-scale cracks determine the heterogeneity of the oil reservoir.
In an alternative embodiment, in step 250, increasing the difference between the elements in the normalized coherence matrix that represent the fractured region and the non-fractured region to obtain an enhanced coherence matrix includes:
for each element in the normalized coherence matrix, performing the steps of:
judging whether the value of an element in the normalized coherent matrix is larger than a preset threshold value or not;
When the value of the element in the normalized coherent matrix is larger than a preset threshold value, adding the element larger than the preset threshold value with a preset added value to obtain a new element;
When the value of the element in the normalized coherence matrix is smaller than the preset threshold value, subtracting the additional value from the element smaller than the preset threshold value to obtain a new element so as to obtain the coherence matrix for enhancement processing.
Here, it is determined for each element in the normalized coherence matrix whether the value of each element in the normalized coherence matrix is greater than a preset threshold. When the value of the element in the normalized coherent matrix is larger than a preset threshold value, adding the element larger than the preset threshold value with a preset added value to obtain a new element; when the value of the element in the normalized coherence matrix is smaller than the preset threshold value, subtracting the additional value from the element smaller than the preset threshold value to obtain a new element, thereby obtaining the coherence matrix for enhancement processing.
It should be noted that the preset threshold is determined according to the values of the fracture region and the non-fracture region, and the purpose of the preset threshold is to clearly distinguish the fracture region and the non-fracture region. The additional value is determined according to actual needs, and the purpose of the additional value is to obviously distinguish elements representing the fracture area and the non-fracture area in the coherent matrix.
In step 260, the coherence matrix of the enhancement process is used to analyze the fractures of different dimensions of the work area under investigation.
Here, the enhancement-processed coherence matrix can better display medium-small scale fractures on a coherence plane and enable imaging of large fractures to be more accurate, so that fractures of different scales of a work area to be studied can be analyzed through the enhancement-processed coherence matrix.
In this embodiment, the enhancement treatment is to further highlight the coherence value of the fracture region, so that small fracture is revealed as much as possible, and large fracture is revealed more clearly and accurately.
In an alternative embodiment, after obtaining the coherence matrix of the enhancement process, the method further comprises:
Resetting elements larger than 1 in the enhancement processing coherence matrix to 1 to obtain a new enhancement processing coherence matrix.
Here, to prevent the added value of the coherence value in the coherence matrix of the enhancement processing from being greater than 1, all the coherence values greater than 1 in the coherence matrix of the enhancement processing are classified as 1 after the coherence matrix of the enhancement processing is obtained.
In the embodiment, the coherence matrix obtained through calculation of the coherence technology is subjected to differentiation treatment, normalization treatment and enhancement treatment, so that the sensitivity of the coherence attribute to medium-small scale fracture can be greatly increased, the medium-small scale fracture can be better displayed on a coherence plane, the imaging of the large fracture is more accurate, the practicability and applicability of the seismic coherence algorithm are improved, and the coherence attribute can be better applied to actual fracture detection and identification.
FIG. 2 shows a schematic representation of a time domain seismic record with simple faults, as shown in FIG. 2, it can be seen that the fault angle is relatively gentle and the cross-section reflection is relatively weak.
Fig. 3 shows a comparison of conventional coherence properties of a time domain seismic record containing a simple fault and coherence enhancement properties processed by the method of the present invention, as shown in fig. 3, where (a) in fig. 3 is the conventional coherence properties and (b) in fig. 3 is the coherence enhancement properties processed by the method of the present invention. It can be seen that conventional coherence properties make it difficult to identify the fault and appear insignificant at the fault location. The effect is greatly improved after the coherence enhancement by the method provided by the invention, the position of the fault can be obviously seen in the diagram (b) in fig. 3, and the fault morphology and the spread are basically consistent with the original record. The stratum fracture identification method provided by the invention is proved to be capable of highlighting the fracture area as a low coherence value, and the imaging of the small-scale fracture with unobvious representation characteristics is realized.
FIG. 4 shows a schematic representation of a depth domain seismic record containing complex faults in a region, where 4, and not 3, of the layer interfaces containing distinct faults are seen in FIG. 4.
Fig. 5 shows a comparison of conventional coherence properties of a depth domain seismic record containing complex faults and coherence enhancement properties processed by the method of the present invention, where fig. 5 (c) and (d) are graphs of conventional coherence properties corresponding to the record and coherence enhancement properties processed by the method of the present invention, respectively. Fig. 5 (c) shows faults in the layer interfaces with 4 obvious faults, but is insensitive to faults in the layer interfaces with 3 which are not obvious in appearance, so that imaging cannot be performed. After the coherence enhancement attribute is processed by the method provided by the invention, faults in the 3 layer interfaces which are not obvious are clearly shown in fig. 5 (d), and faults in the 4 layer interfaces which contain obvious faults are more obvious.
Therefore, the stratum fracture identification method provided by the invention can better solve the imaging problem of small and medium-scale fracture with unobvious performance characteristics and further enhance the imaging of fracture with obvious performance characteristics.
Example III
There is also provided, in accordance with an embodiment of the present invention, a formation fracture identification system, including:
the acquisition module is configured to acquire post-stack seismic data of a work area to be researched;
the seismic coherence processing module is configured to perform seismic coherence processing on the post-stack seismic data to obtain a coherence matrix;
A differentiating module configured to increase a difference between elements representing the fractured region and the non-fractured region in the coherence matrix, to obtain a differentiated coherence matrix;
the normalization module is configured to normalize elements in the differentiated coherent matrix to obtain a normalized coherent matrix;
An enhancement processing module configured to increase the difference between elements representing the fractured region and the non-fractured region in the normalized coherence matrix, to obtain an enhancement processed coherence matrix;
And the analysis module is used for analyzing the fractures of different scales of the work area to be researched by utilizing the coherence matrix of the enhancement treatment.
Here, the seismic wave signals are acquired by the acquisition device to obtain a seismic data volume, and then the seismic data volume is subjected to superposition processing to obtain post-stack seismic data. By processing post-stack seismic data, data reflecting seismic attributes, which are special measures of geometry, kinematic, dynamic and statistical properties of the seismic waves, can be extracted therefrom.
The seismic wave signals are affected by factors such as formation lithology, physical properties and the like in the process of underground formation propagation to generate corresponding changes, and the method is a complex reflection of the comprehensive characteristics of underground reservoirs. Spatial variations in the physical properties of the subsurface formation rock, etc., necessarily result in variations in the characteristics of the seismic reflection wave, which in turn affect the seismic attributes. Particularly when the reservoir contains oil and gas, the seismic response characteristics of the reservoir can be correspondingly changed, and corresponding seismic attributes can also be reflected. The theoretical basis for predicting oil gas by the seismic attribute technology is as follows: the seismic attributes carry information about the subsurface formations, while some form of inherent association must exist between the seismic attributes and the oil and gas properties of the reservoir.
And obtaining a coherence matrix by performing seismic coherence processing on the post-stack seismic data by using a coherence technique. The coherence matrix refers to a new data volume obtained by coherent processing of post-stack seismic data. The data can reflect the development of the fracture (crack).
Among them, the coherence technique is an important seismic attribute technique, which converts a three-dimensional seismic data volume into a coherent data volume by calculating the similarity of waveforms of adjacent seismic traces, highlighting the discontinuity characteristics of the waveforms. Therefore, the coherence can measure the transverse change of the earthquake response caused by the change of factors such as the structure, stratum, lithology, oil gas and the like, thereby effectively revealing geological phenomena such as faults, cracks, lithology edges, unconformity and the like and reflecting the plane spread of geological abnormal characteristics.
After the coherence matrix is obtained, differentiation processing is performed to macroscopically increase the difference between the coherence values of the fracture region and the non-fracture region, so that the fracture region has an overall appearance on the coherence plane. That is, the difference between the coherence values of the elements representing the broken area and the non-broken area in the coherence matrix is increased, so that the broken area and the non-broken area can be clearly distinguished, and a differentiated coherence matrix is obtained.
The values of the elements in the differentiated coherent matrix obtained after the differentiation processing may have negative values, so that a normalization processing is performed on the basis of the differentiated coherent matrix, and the values of the elements in the differentiated coherent matrix are normalized to be between 0 and 1, so that a normalized coherent matrix is obtained.
The normalization is a way of simplifying computation, namely, an expression with dimension is transformed into an expression without dimension to become a scalar. I.e. the data is changed to (0, 1) or to a fraction between (1, 1). The method is mainly used for conveniently providing data processing, and mapping the data to the range of 0-1 for processing, so that the method is more convenient and rapid. The dimensionless expression is changed into the dimensionless expression, so that indexes with different units or magnitudes can be compared and weighted conveniently.
Furthermore, by increasing the difference between the elements representing the fracture region and the non-fracture region in the normalized coherence matrix, the purpose is to further highlight the coherence value of the fracture region, so that the small-scale fracture is revealed as much as possible, and the large-scale fracture is revealed more clearly and accurately. The obtained enhancement processed coherent matrix can be used as a coherent body of a work area to be researched, the coherent body can better display medium-small scale fracture on a coherent plane, and imaging of large-scale fracture is more accurate.
The cracks (breaks) are classified into micro-cracks, small-scale cracks and large-scale cracks according to the scale.
Where microcracks refer to cracks that can be observed on a core sheet, typically in the micrometer scale, the microcracks are numerous in the reservoir, typically associated with the matrix, so the microcracks are actually part of the matrix.
Small-scale fractures generally refer to fractures that can be observed on core and imaging logs, typically ranging from tens of centimeters to tens of meters in length. Seen on the imaging log and core is the height of the small scale fracture. The small-scale cracks are interwoven into a net in a three-dimensional space to form seepage channels of oil gas. Fracture density, fracture opening, fracture length, and degree of fracture azimuth variation are key factors in determining fracture permeability.
Large scale fractures generally refer to fractures at the seismic level, typically from tens of meters to thousands of meters in length. The large-scale cracks have large transverse extension length, vertical cutting layer depth and high permeability, the small-scale cracks in the oil reservoir provide permeability of the oil reservoir, and the large-scale cracks determine the heterogeneity of the oil reservoir.
Optionally, the differentiating module is specifically configured to:
Increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix by using a first preset calculation method to obtain a differentiated coherence matrix; wherein the first pre-design formula is:
C_dif＝(C3-C·α)·104
Wherein, c_dif is a differentiated coherent matrix, C 3 is a coherent matrix, C is a matrix with the same size as the coherent matrix, and the elements in the matrix are all 1, and α is a constant.
Here, the differentiation processing is performed on the basis of the acquisition of the coherence matrix C 3, and the purpose of the differentiation processing is to macroscopically increase the difference between the coherence values of the broken area and the non-broken area, so that the broken area has an overall appearance on the coherence plane. Specifically, the differentiation processing is performed through a first preset calculation type.
It is noted that α is a constant that is used to control the degree of differentiation.
Optionally, the normalization module is specifically configured to:
normalizing the elements in the differentiated coherent matrix by using a second pre-design algorithm to obtain a normalized coherent matrix; wherein the second pre-design formula is:
Where c_dif_norm is a normalized coherence matrix, c_dif is a differentiated coherence matrix, (c_dif) min is the minimum value of the elements in the coherence matrix, and (c_dif) max is the maximum value of the elements in the coherence matrix.
Here, the values of the elements in the differentiated coherent matrix obtained through the differentiation process may have negative values, so that a normalization process is performed on the basis of the differentiated coherent matrix, and the values of the elements in the differentiated coherent matrix are normalized to be between 0 and 1, thereby obtaining a normalized coherent matrix.
The normalization is a way of simplifying computation, namely, an expression with dimension is transformed into an expression without dimension to become a scalar. I.e. the data is changed to (0, 1) or to a fraction between (1, 1). The method is mainly used for conveniently providing data processing, and mapping the data to the range of 0-1 for processing, so that the method is more convenient and rapid. The dimensionless expression is changed into the dimensionless expression, so that indexes with different units or magnitudes can be compared and weighted conveniently.
Optionally, the enhancement processing module includes:
a judging unit configured to judge whether a value of an element in the normalized coherence matrix is greater than a preset threshold;
The enhancement processing unit is configured to enable elements larger than a preset threshold value to be added with a preset added value when the values of the elements in the normalized coherent matrix are larger than the preset threshold value, so that new elements are obtained; when the value of the element in the normalized coherence matrix is smaller than the preset threshold value, subtracting the additional value from the element smaller than the preset threshold value to obtain a new element so as to obtain the coherence matrix for enhancement processing.
Here, it is determined for each element in the normalized coherence matrix whether the value of each element in the normalized coherence matrix is greater than a preset threshold. When the value of the element in the normalized coherent matrix is larger than a preset threshold value, adding the element larger than the preset threshold value with a preset added value to obtain a new element; when the value of the element in the normalized coherence matrix is smaller than the preset threshold value, subtracting the additional value from the element smaller than the preset threshold value to obtain a new element, thereby obtaining the coherence matrix for enhancement processing.
It should be noted that the preset threshold is determined according to the values of the fracture region and the non-fracture region, and the purpose of the preset threshold is to clearly distinguish the fracture region and the non-fracture region. The additional value is determined according to actual needs, and the purpose of the additional value is to obviously distinguish elements representing the fracture area and the non-fracture area in the coherent matrix.
Optionally, the system further comprises:
And the post-processing module is configured to reset elements larger than 1 in the enhancement processing coherence matrix to 1so as to obtain a new enhancement processing coherence matrix.
Here, to prevent the added value of the coherence value in the coherence matrix of the enhancement processing from being greater than 1, all the coherence values greater than 1 in the coherence matrix of the enhancement processing are classified as 1 after the coherence matrix of the enhancement processing is obtained.
Optionally, the seismic coherence processing module is specifically configured to:
Performing seismic coherence processing on the post-stack seismic data by using a third-generation coherence algorithm to obtain a coherence matrix; wherein, the third generation coherent algorithm is:
/>
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
Here, the coherence technique is an important seismic attribute technique that converts a three-dimensional seismic data volume into a coherent data volume by computing the similarity of waveforms of adjacent seismic traces, highlighting the discontinuity characteristics of the waveforms. Therefore, the coherence can measure the transverse change of the earthquake response caused by the change of factors such as the structure, stratum, lithology, oil gas and the like, thereby effectively revealing geological phenomena such as faults, cracks, lithology edges, unconformity and the like and reflecting the plane spread of geological abnormal characteristics.
The third generation coherent volume algorithm is obtained by calculating eigenvalues of a seismic data volume, in algorithm analysis, firstly, a plurality of channels of seismic data are extracted from a given analysis time window to generate sample point vectors, and the sample point vectors form a matrix:
The covariance matrix corresponding to the matrix is:
the covariance matrix is a symmetric, semi-positive definite matrix with all eigenvalues greater than or equal to 0.
Calculating eigenvalues and eigenvectors of the covariance matrix by the third-generation coherent algorithm, wherein the third-generation coherent algorithm is as follows:
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
Example five
According to an embodiment of the present invention, there is also provided a storage medium having stored thereon program code which, when executed by a processor, implements a formation fracture identification method as in any of the above embodiments.
The storage medium may be, for example, flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, server, app application mall, etc.
Example six
According to an embodiment of the present invention, there is further provided an electronic device including a memory and a processor, where the memory stores program code executable on the processor, and the program code is executed by the processor to implement the method for identifying a fracture of a formation according to any one of the above embodiments.
It is to be appreciated that the electronic device can also include an input/output (I/O) interface, as well as a communication component.
Wherein the processor is configured to perform all or part of the steps of the method for predicting thickness of a river as in any one of the embodiments. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor may be an Application SPECIFIC INTEGRATED Circuit (ASIC), a digital signal Processor (DIGITAL SIGNAL Processor, DSP), a digital signal processing device (DIGITAL SIGNAL Processing Device, DSPD), a programmable logic device (Programmable Logic Device, PLD), a field programmable gate array (Field Programmable GATE ARRAY, FPGA), a controller, a microcontroller, a microprocessor or other electronic component implementation for performing the method for predicting thickness of a river according to any of the embodiments.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk or optical disk.
The technical scheme of the invention is described in detail by combining the drawings, and the fact that in the related technology, the third generation coherent technology cannot accurately detect and identify the medium-small-scale fracture in actual production and application is considered. The invention provides a stratum fracture identification method, a stratum fracture identification system, a storage medium and an electronic device, wherein the sensitivity of a coherence attribute to medium-small scale fracture can be greatly increased by carrying out differentiation treatment, normalization treatment and enhancement treatment on a coherence matrix obtained by third-generation coherence technology calculation, the medium-small scale fracture can be better displayed on a coherence plane, the imaging of a large fracture is more accurate, and the practicability and the applicability of a seismic coherence algorithm are improved, so that the coherence attribute can be better applied to actual fracture detection and identification.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Although the embodiments of the present invention are disclosed above, the embodiments are only used for the convenience of understanding the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the present disclosure as defined by the appended claims.
Claims (7)
1. A method of formation fracture identification, comprising:
Acquiring post-stack seismic data of a work area to be studied;
Performing seismic coherence processing on the post-stack seismic data to obtain a coherence matrix;
Increasing the difference value between elements representing the fracture area and the non-fracture area in the coherence matrix by using a first preset calculation method to obtain a differentiated coherence matrix; wherein the first pre-design formula is:
C_dif＝(C3-C·α)·104
Wherein, C_dif is a differentiated coherent matrix, C 3 is a coherent matrix, C is a matrix with the same size as the coherent matrix, the elements in the matrix are all 1, and alpha is a constant;
Normalizing the elements in the differentiated coherent matrix to obtain a normalized coherent matrix;
for each element in the normalized coherence matrix, performing the steps of:
Judging whether the value of an element in the normalized coherent matrix is larger than a preset threshold value or not; when the value of the element in the normalized coherent matrix is larger than a preset threshold value, adding the element larger than the preset threshold value with a preset added value to obtain a new element; when the value of the element in the normalized coherent matrix is smaller than the preset threshold value, subtracting the element smaller than the preset threshold value from the added value to obtain a new element so as to obtain a coherent matrix for enhancement processing;
And analyzing the fractures of different scales of the work area to be researched by utilizing the coherence matrix of the enhancement treatment.
2. The method of claim 1, wherein normalizing the elements in the differentiated coherence matrix to obtain a normalized coherence matrix comprises:
normalizing the elements in the differentiated coherent matrix by using a second pre-design algorithm to obtain a normalized coherent matrix; wherein the second pre-design formula is:
Where c_dif_norm is a normalized coherence matrix, c_dif is a differentiated coherence matrix, (c_dif) min is the minimum value of the elements in the coherence matrix, and (c_dif) max is the maximum value of the elements in the coherence matrix.
3. The method of formation fracture identification of claim 1, wherein after obtaining the coherence matrix of the enhancement treatment, the method further comprises:
Resetting elements larger than 1 in the enhancement processing coherence matrix to 1 to obtain a new enhancement processing coherence matrix.
4. The method of claim 1, wherein performing seismic coherence processing on post-stack seismic data to obtain a coherence matrix comprises:
Performing seismic coherence processing on the post-stack seismic data by using a third-generation coherence algorithm to obtain a coherence matrix; wherein, the third generation coherent algorithm is:
Wherein C 3 is a coherence matrix, lambda j is the J-th eigenvalue in the post-stack seismic data, and J is the total number of eigenvalues.
5. A formation fracture identification system, comprising:
the acquisition module is configured to acquire post-stack seismic data of a work area to be researched;
the seismic coherence processing module is configured to perform seismic coherence processing on the post-stack seismic data to obtain a coherence matrix;
the differentiating module is configured to increase the difference value between the elements representing the fracture area and the non-fracture area in the coherence matrix by using a first preset calculation formula to obtain a differentiated coherence matrix; wherein the first pre-design formula is:
C_dif＝(C3-C·α)·104
Wherein, C_dif is a differentiated coherent matrix, C 3 is a coherent matrix, C is a matrix with the same size as the coherent matrix, the elements in the matrix are all 1, and alpha is a constant;
the normalization module is configured to normalize elements in the differentiated coherent matrix to obtain a normalized coherent matrix;
An enhancement processing module configured to perform the following steps for each element in the normalized coherence matrix:
Judging whether the value of an element in the normalized coherent matrix is larger than a preset threshold value or not; when the value of the element in the normalized coherent matrix is larger than a preset threshold value, adding the element larger than the preset threshold value with a preset added value to obtain a new element; when the value of the element in the normalized coherent matrix is smaller than the preset threshold value, subtracting the element smaller than the preset threshold value from the added value to obtain a new element so as to obtain a coherent matrix for enhancement processing;
And the analysis module is used for analyzing the fractures of different scales of the work area to be researched by utilizing the coherence matrix of the enhancement treatment.
6. A storage medium having program code stored thereon, which when executed by a processor, implements the formation fracture identification method according to any one of claims 1 to 4.
7. An electronic device comprising a memory, a processor, the memory having stored thereon program code executable on the processor, the program code, when executed by the processor, implementing the formation fracture identification method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010916370.5A CN114137613B (en) | 2020-09-03 | 2020-09-03 | Formation fracture identification method, system, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010916370.5A CN114137613B (en) | 2020-09-03 | 2020-09-03 | Formation fracture identification method, system, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114137613A CN114137613A (en) | 2022-03-04 |
| CN114137613B true CN114137613B (en) | 2024-05-17 |
Family
ID=80438271
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010916370.5A Active CN114137613B (en) | 2020-09-03 | 2020-09-03 | Formation fracture identification method, system, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114137613B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MX2008009621A (en) * | 2008-07-25 | 2010-01-25 | Fidel Reyes Ramos | Method and apparatus for detecting geological faults with a substantial dip based on multi-target optimisation. |
| CN102455436A (en) * | 2010-11-02 | 2012-05-16 | 中国石油大学(北京) | Method for detecting anisotropic fracture of longitudinal noise attenuation prestack wave at limited azimuth angles |
| CN103076623A (en) * | 2011-10-25 | 2013-05-01 | 中国石油化工股份有限公司 | Crack detection method based on prestack coherence |
| CN103869358A (en) * | 2014-02-18 | 2014-06-18 | 中国石油天然气股份有限公司 | Histogram equalization-based fault identification method and equipment |
| CN104216013A (en) * | 2014-09-01 | 2014-12-17 | 电子科技大学 | C3 coherent body method based on wide azimuth data |
| CN105445787A (en) * | 2014-08-11 | 2016-03-30 | 中国石油化工股份有限公司 | Crack prediction method for preferred orientation daughter coherence |
| CN107942373A (en) * | 2017-11-14 | 2018-04-20 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Coherent algorithm based on the detection of Fractured oil and gas reservoir fracture system |
| CN109143348A (en) * | 2017-06-28 | 2019-01-04 | 中国石油化工股份有限公司 | 3D seismic data tomography enhanced processing method |
| CN109407149A (en) * | 2018-10-10 | 2019-03-01 | 电子科技大学 | Seismic coherence data crack detection method based on Hessian matrix |
| CN111025389A (en) * | 2018-10-10 | 2020-04-17 | 中国石油化工股份有限公司 | Multi-scale coherent dimensionality reduction fusion fracture prediction method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9891334B2 (en) * | 2014-04-07 | 2018-02-13 | Schlumberger Technology Corporation | System and methodology for determining fracture attributes in a formation |
| US20190204464A1 (en) * | 2017-12-29 | 2019-07-04 | Paul J. Moore, III | Method and System for Modeling in a Subsurface Region |
-
2020
- 2020-09-03 CN CN202010916370.5A patent/CN114137613B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MX2008009621A (en) * | 2008-07-25 | 2010-01-25 | Fidel Reyes Ramos | Method and apparatus for detecting geological faults with a substantial dip based on multi-target optimisation. |
| CN102455436A (en) * | 2010-11-02 | 2012-05-16 | 中国石油大学(北京) | Method for detecting anisotropic fracture of longitudinal noise attenuation prestack wave at limited azimuth angles |
| CN103076623A (en) * | 2011-10-25 | 2013-05-01 | 中国石油化工股份有限公司 | Crack detection method based on prestack coherence |
| CN103869358A (en) * | 2014-02-18 | 2014-06-18 | 中国石油天然气股份有限公司 | Histogram equalization-based fault identification method and equipment |
| CN105445787A (en) * | 2014-08-11 | 2016-03-30 | 中国石油化工股份有限公司 | Crack prediction method for preferred orientation daughter coherence |
| CN104216013A (en) * | 2014-09-01 | 2014-12-17 | 电子科技大学 | C3 coherent body method based on wide azimuth data |
| CN109143348A (en) * | 2017-06-28 | 2019-01-04 | 中国石油化工股份有限公司 | 3D seismic data tomography enhanced processing method |
| CN107942373A (en) * | 2017-11-14 | 2018-04-20 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Coherent algorithm based on the detection of Fractured oil and gas reservoir fracture system |
| CN109407149A (en) * | 2018-10-10 | 2019-03-01 | 电子科技大学 | Seismic coherence data crack detection method based on Hessian matrix |
| CN111025389A (en) * | 2018-10-10 | 2020-04-17 | 中国石油化工股份有限公司 | Multi-scale coherent dimensionality reduction fusion fracture prediction method and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于相干的精细断裂刻画技术在顺北地区的应用;卢志强;王力;杨瑞召;孟令彬;金圣林;;天然气勘探与开发(第03期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114137613A (en) | 2022-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Walsh et al. | Silver and Chan revisited | |
| US5862100A (en) | Method and system for detecting hydrocarbon reservoirs using statistical normalization of amplitude-versus-offset indicators based upon seismic signals | |
| Lu et al. | Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram | |
| AU2011248992B2 (en) | Windowed statistical analysis for anomaly detection in geophysical datasets | |
| AU2009314458B2 (en) | Windowed statistical analysis for anomaly detection in geophysical datasets | |
| US20230058742A1 (en) | Method and storage medium for quantitative reconstruction of paleowater depth based on milankovitch cycles | |
| CN115390155A (en) | Well logging interpretation method, device, electronic equipment and medium for heterogeneous reservoir | |
| CN114137613B (en) | Formation fracture identification method, system, storage medium and electronic equipment | |
| Donias et al. | New fault attribute based on robust directional scheme | |
| CN113970785A (en) | Method and system for predicting development of underground river crack, storage medium and electronic equipment | |
| CN111077574A (en) | Method, device and system for determining stratum elastic parameters | |
| US20220342108A1 (en) | Method, device and medium for acquiring logging parameters | |
| Chen et al. | Least-squares decomposition with time–space constraint for denoising microseismic data | |
| US20200309978A1 (en) | Direct Hydrocarbon Indicators Analysis Informed by Machine Learning Processes | |
| CN114428298A (en) | Method and device for identifying broken solution banding, electronic equipment and storage medium | |
| CN114076990B (en) | Oil shale reflected energy determination method, system, storage medium and electronic equipment | |
| Huang et al. | Shannon entropy-based seismic local correlation measure and enhancement | |
| Stovas et al. | Fluid-pressure discrimination in anisotropic reservoir rocks—a sensitivity study | |
| Dębski | Bayesian approach to tomographic imaging of rock-mass velocity heterogeneities | |
| CN113970786A (en) | Underground river thickness prediction method and system, storage medium and electronic equipment | |
| Xu et al. | Frequency trend attribute analysis for stratigraphic division and correlation | |
| De Jesus et al. | K-Image-Permeability Characterization Integrating Matrix and Non-Matrix Pore Scales on Pre-Salt Reservoirs | |
| Szalaiová et al. | 3D fracture networks in the crystalline upper crust‐A new seismic model of the Continental Deep Drilling Site (South Germany) | |
| CN117849870A (en) | Multi-scale fault identification method based on sandstone-type uranium deposit frequency division seismic attribute | |
| Malcolm | This issue of Geophysics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |