CN115165681A - Shale reservoir particle structure directional analysis method, system, equipment and terminal - Google Patents
Shale reservoir particle structure directional analysis method, system, equipment and terminal Download PDFInfo
- Publication number
- CN115165681A CN115165681A CN202210782915.7A CN202210782915A CN115165681A CN 115165681 A CN115165681 A CN 115165681A CN 202210782915 A CN202210782915 A CN 202210782915A CN 115165681 A CN115165681 A CN 115165681A
- Authority
- CN
- China
- Prior art keywords
- shale
- particle
- orientation
- directional
- arrangement
- 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.)
- Granted
Links
- 239000002245 particle Substances 0.000 title claims abstract description 260
- 238000004458 analytical method Methods 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000013178 mathematical model Methods 0.000 claims abstract description 7
- 239000011148 porous material Substances 0.000 claims description 54
- 238000009826 distribution Methods 0.000 claims description 27
- 238000012512 characterization method Methods 0.000 claims description 26
- 238000012545 processing Methods 0.000 claims description 25
- 230000008859 change Effects 0.000 claims description 10
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 10
- 230000002776 aggregation Effects 0.000 claims description 8
- 238000004220 aggregation Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 6
- 208000035126 Facies Diseases 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- 239000003086 colorant Substances 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 238000003706 image smoothing Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims 1
- 230000001788 irregular Effects 0.000 claims 1
- 238000000445 field-emission scanning electron microscopy Methods 0.000 abstract 1
- 239000005416 organic matter Substances 0.000 description 39
- 239000004927 clay Substances 0.000 description 29
- 239000011435 rock Substances 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 7
- 238000004445 quantitative analysis Methods 0.000 description 7
- 238000002474 experimental method Methods 0.000 description 6
- 239000004576 sand Substances 0.000 description 6
- 230000009471 action Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 239000013078 crystal Substances 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000000704 physical effect Effects 0.000 description 3
- 238000002441 X-ray diffraction Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011496 digital image analysis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006355 external stress Effects 0.000 description 2
- 229910052500 inorganic mineral Inorganic materials 0.000 description 2
- 239000011707 mineral Substances 0.000 description 2
- 238000011158 quantitative evaluation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000002734 clay mineral Substances 0.000 description 1
- 238000005056 compaction Methods 0.000 description 1
- 238000007596 consolidation process Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 238000010894 electron beam technology Methods 0.000 description 1
- 238000000349 field-emission scanning electron micrograph Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000000265 homogenisation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000001878 scanning electron micrograph Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000003079 shale oil Substances 0.000 description 1
- 238000010008 shearing Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
- G01N15/088—Investigating volume, surface area, size or distribution of pores; Porosimetry
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N2015/0294—Particle shape
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Health & Medical Sciences (AREA)
- Dispersion Chemistry (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention belongs to the technical field of shale reservoir particle structure directional analysis, and discloses a shale reservoir particle structure directional analysis method, a shale reservoir particle structure directional analysis system, shale reservoir particle structure directional analysis equipment and a shale reservoir particle structure directional analysis terminal. The invention takes the mud shale with the length of Ordos basin of 7 as an example, establishes a particle structure directional entropy mathematical model, and quantitatively represents the directional arrangement characteristics of the five types of shale lithofacies particles. Meanwhile, the shale particle microstructure parameters are obtained by applying a multi-scale multi-view FE-SEM splicing and image recognition method, and the method ensures the photo precision and can obtain data on the photo in a wider range.
Description
Technical Field
The invention belongs to the technical field of shale reservoir granular structure directional analysis, and particularly relates to a shale reservoir granular structure directional analysis method, a shale reservoir granular structure directional analysis system, shale reservoir granular structure directional analysis equipment and a shale reservoir granular structure directional analysis terminal.
Background
The directional arrangement degree of shale particles determines the characteristics of pore size, distribution, pore throat connectivity, fluid mobility and the like, and generally, the higher the directional arrangement degree of the particles is, the better the physical property of a reservoir is, and a channel is provided for the storage and migration of oil and gas. The directional arrangement degree of the shale particles not only changes the physical properties of the rock, but also reflects the compaction strain of the rock, thereby determining the development degree of rock cracks, the difference of the shale particles expressed on mechanics influences the drilling and fracturing design, has important significance for analyzing the directional arrangement degree of the shale particles, and can analyze the engineering properties of the rock on the mechanism. The shale has a layered structure which is a macroscopic embodiment of particle arrangement and combination mode, and the macroscopic property of the shale is influenced by the microstructure to a certain extent. In addition, the mechanical properties, seepage characteristics, productivity and the like of the shale are controlled by the microstructure characteristics of the shale. Therefore, the exploration of the directional structural characteristics of the shale particles is beneficial to further identifying a high-quality reservoir, and the method has important theoretical significance and practical significance for promoting the exploration and development of unconventional oil and gas reservoirs. The shale particle directional arrangement structure mechanism is analyzed, the engineering property of the rock can be analyzed from a new angle, the development efficiency of the oil and gas industry is improved by combining the geological-engineering integrated idea, and a basis is provided for formulating a reasonable development scheme on site.
The shale particle arrangement directionality is an important index in quantitative analysis of microstructures, and can reflect the spatial arrangement condition of particles or pores of rocks under the action of external loads such as consolidation, shearing and the like. At present, scholars at home and abroad analyze the directional arrangement structure of the shale particles, including a high-resolution X-ray experiment, a susceptibility experiment, a permeability method, a sound wave experiment, a polarized light microphotometric experiment, a computer image method and the like.
The quantification of the shale particle structure arrangement mode can be divided into four aspects: a particle orientation analysis method based on an X-ray diffraction experiment; a particle quantitative analysis method based on birefringence of a polarizing microscope; a particle quantitative analysis method based on particle size fractal dimension; particle quantitative analysis method based on computer image.
(1) Particle orientation analysis method based on X-ray diffraction experiment
As early as 50 s in the nineteenth century, brindley used the ratio of diffraction hardness of the 001 crystal face to the 020 crystal face of flat clay mineral particles to characterize the degree of orientation. Subsequently, martin improved the "diffraction peak ratio" (PR) concept, defined as the ratio of the 002 to 020 reflection amplitudes, i.e.:
PR = reflection amplitude (002)/reflection amplitude (020)
Gillott proposes the concept of "structure index" (FI), using the ratio of the areas of the diffraction peaks on the section of the plane of parallel (P) and perpendicular (V) orientation of the particles as a calculation index of the degree of orientation, namely:
FI＝V/(P+V)
the calculated values of the structural indices are distributed in the interval [0,0.5], 0 indicating a high degree of directional alignment of the particles and 0.5 indicating a completely random orientation of the particles.
The crystal face diffraction hardness formula representing the orientation degree is proposed by Tanluo, and the formula is as follows:
the method is not limited to parallel and random samples, and the orientation degree is directly related to the relative particle ratio, so that the significance is clear.
(2) Particle quantitative analysis method based on birefringence of polarizing microscope
Lafeber analyzes the degree of orientation of flat particles using polarization microscope birefringence. Morgenstrin proposes a semi-quantitative calculation method of particle arrangement on the basis, namely, the ratio of the minimum value to the maximum value of the refraction hardness is used as a judgment standard of particle directionality, the value is distributed between 0 and 1, and the larger the value is, the poorer the directionality is.
(3) Particle quantitative analysis method based on particle size fractal dimension
In the nineties of the world, fractal theory is introduced into the field of clay microstructure, and a new method and an effective way are provided for quantitative characterization.
Zhang Cheng Han is based on the software of independent design research and development, has proposed a quantitative evaluation clay microstructure's method, and has obtained the calculation method of the porosity under different directions. The blurelin analyzes the clay collapse mechanism, provides concepts of 'structural elements' and 'structural parameters', and considers that the clay structural state can be generalized by four characteristics of particle morphology, arrangement and combination mode, porosity, contact relation and the like.
(4) Particle quantitative analysis method based on computer image analysis
By using a computer image analysis method, the microstructure characteristics of the shale particles, such as pores, particle size, morphology, directionality and the like, can be analyzed and represented from multiple angles. In this regard, scientists such as Morgenstrin, tovey, jongerius, etc. have made significant contributions, however, the use of statistical methods by the scientists is limited to the orientation and size of the particles or pores. Wu Yi Xiang proposes the concept of "structural state" to describe the arrangement structural state of particles, and defines it as "structural entropy", as follows:
E arrangement of ＝-∑P(i)logP(i)
Wherein P (i) is the probability value of particle distribution in the ith interval, and the larger the value is, the higher the directionality is, and the worse the directionality is.
The analysis method based on clay mechanics such as Sheer definition provides a structural potential which can characterize the particle arrangement and can also depict the particle connection:
wherein S is 0 、S r 、S s The deformation amounts of the original, saturated and remolded cores under pressure P are respectively.
At present, the prior art carries out a great deal of analysis work on the shale particle directional arrangement structure, and the analysis result shows that the particle directional arrangement structure is the result of the combined action of various complex factors. However, the existing analysis means for quantitatively characterizing the directional arrangement structure of the shale particles is lack.
Through the above analysis, the problems and defects of the prior art are as follows: the existing analysis of quantitative characterization of the directional arrangement structure of shale particles can only characterize local features of a certain area under the microscopic scale of rock, but cannot analyze the arrangement condition of the particles of the whole rock, and the existing method is semi-quantitative evaluation, has subjectivity, and lacks quantitative characterization of a mathematical model on the arrangement condition of the shale particles.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a shale reservoir particle structure directional analysis method, a system, equipment and a terminal.
The invention is realized in such a way, and the shale reservoir particle structure directional analysis method comprises the following steps:
based on the shape, orientation and aggregation condition of shale particles and the pore shape and orientation factors, a structure orientation entropy mathematical model for representing the arrangement orientation of the particles is established, and the structure orientation entropy mathematical model is used for evaluating different types of facies orientation structures and quantitatively representing the orientation arrangement characteristics of the shale particles.
Further, the shale reservoir particle structure directional analysis method comprises the following steps:
firstly, determining the characterization parameters of the directional structure;
step two, obtaining and processing the characterization parameters of the directional structure;
and step three, constructing an oriented structure representation model and carrying out oriented analysis on the shale reservoir particle structure.
Further, the determining of the oriented structure characterization parameters in the first step comprises:
analyzing the shape, orientation and aggregation condition of two sample particles and the shape and orientation of pores, and adopting the concept of fractal dimension to respectively use the fractal dimension of particle flatness distribution, the fractal dimension of particle orientation, the fractal dimension of particle size distribution, the fractal dimension of pore flatness distribution and the fractal dimension of pore orientation to characterize factors; and identifying the picture by means of ImageJ image processing technology, and determining parameters influencing the directional arrangement of the shale particles.
Wherein, the particle orientation fractal dimension, the particle size distribution fractal dimension and the pore orientation fractal dimension value of the two samples show the fluctuation with the amplitude ranging from 6.495 to 7.030 percent, and the particle flatness distribution fractal dimension values are relatively close to each other, so the change is neglected.
Further, in the second step, a multi-scale multi-view FE-SEM splicing and image recognition method is applied to obtain shale particle microstructure parameters, and the directional structure characterization parameter obtaining and processing comprises the following steps:
(1) Splicing and identification of FE-SEM (electron field microscopy-scanning electron microscope) picture
Fixing the magnification of the shot picture at 10000 times; successively and continuously shooting photos of the original section sample without polishing treatment from head to tail, and sequentially splicing the shot photos; identifying and identifying through software ImageJ to obtain microstructure characteristics; and identifying the spliced pictures, and performing gray processing on the identified images by using ImageJ software to obtain digital images consisting of black and white colors.
The digital image is composed of a series of pixels with different gray values, the pixel value 0 represents a black area, the pixel value 255 represents a white area, and the pixel values are represented by a discrete function matrix f (x, y):
in the formula, a and b represent the row number and the column number of the pixel points in the corresponding pixel dot matrix in the whole image, and n and m represent the row number and the column number of the pixel points contained in the image.
(2) The geometric parameters of the particles were calculated by means of ImageJ software: and importing the identified image into image processing software ImageJ to obtain a numerical image with black and white tone.
Further, the operation process of the ImageJ image processing software is as follows:
(1) gray level conversion: importing a target picture into software, and carrying out gray level conversion on the picture;
(2) setting a gray threshold: adjusting the threshold value of the picture according to the gray value of the picture to enable the picture to present a geometric shape with complete grain diameter, keeping the threshold value concentrated in a stable range, and extracting the grains according to the threshold value;
(3) automatic denoising: manually correcting the picture, or removing the noise points in a filtering and image smoothing mode, and analyzing the noise points in an automatic denoising mode;
(4) acquisition of geometric parameters: setting a proper scale for the picture according to the magnification of the scanning electron microscope; calculating the geometric parameters of the particles, wherein the selected items comprise particle size, flatness, perimeter, equivalent ellipse perimeter and convexity of the particles, and calibrating and counting the particles;
(5) and (3) exporting data: exporting and converting data, outputting and storing all the data in an Excel table, and processing the data to obtain particle geometric information; the identified information values are the area, pixel value, X and Y coordinate positions of the particle, the perimeter, width, length, height and angle of the particle, the Feret diameter of the particle, the roundness of the particle, the sphericity of the particle, and the convexity parameters.
Further, the constructing of the oriented structure characterization model in the third step includes:
shale structure directional entropy based on influence of pore arrangement structure
The entropy of particle size, the entropy of particle arrangement and the entropy of pore arrangement is expressed by the following formula:
in the formula, E di The particle arrangement entropy is used for expressing a particle directional fractal dimension value; e pd Representing a particle size fractal dimension value by particle size entropy; e bi The pore orientation fractal dimension value is represented as the pore arrangement entropy.
Shale
The value is equivalent to the average value of the particle arrangement entropy, the particle size entropy and the pore arrangement entropy, and the weight coefficients are all 1/3 as follows:
wherein,
the larger the value is,
the higher the degree of disorder of arrangement is, the more disorder of arrangement is, the less obvious regularity is provided, and the more unstable the system is;
smaller values indicate lower disorder, more directional alignment, more stable system,
another object of the present invention is to provide a shale reservoir particle structure directional analysis system applying the shale reservoir particle structure directional analysis method, the shale reservoir particle structure directional analysis system comprising:
the parameter determination module is used for determining the characterization parameters of the directional structure;
the parameter processing module is used for acquiring and processing the characterization parameters of the directional structure;
and the structure analysis module is used for constructing a directional structure representation model and directionally analyzing the shale reservoir particle structure.
It is a further object of the present invention to provide a computer arrangement comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the shale reservoir grain structure orientation analysis method.
It is a further object of the present invention to provide a computer readable storage medium, storing a computer program which, when executed by a processor, causes the processor to perform the shale reservoir grain structure orientation analysis method.
The invention further aims to provide an information data processing terminal which is used for realizing the shale reservoir particle structure directional analysis system.
In combination with the technical solutions and the technical problems to be solved, please analyze the advantages and positive effects of the technical solutions to be protected in the present invention from the following aspects:
first, aiming at the technical problems existing in the prior art and the difficulty in solving the problems, the technical problems to be solved by the technical scheme of the present invention are closely combined with the technical scheme to be protected and the results and data in the research and development process, and some creative technical effects brought after the problems are solved are analyzed in detail and deeply. The specific description is as follows:
the directional entropy model is used for evaluating the directionality of the arrangement structure of the long 7 shale particles in different types of Ordos basins. According to the invention, on the basis of analysis of a large number of FE-SEM images, three shale orientation arrangement influence factors of particle size, particle arrangement and pore arrangement characteristics are summarized, and a 'structure orientation entropy' model suitable for quantitative characterization of the orientation degree of the long 7 shale is established by means of a 'structure entropy' concept; based on FE-SEM characterization and structural orientation entropy calculations for 79 samples, a threshold (0.85) for orientation and random distribution of long 7 shale particles was established.
The invention is improved in application
Model, long 7 shale is obtained
The distribution range of (1) (0.78-0.93); when in use
When the particles are distributed directionally, the particles have the characteristic of spindle shape; when in use
The shale particles are randomly distributed. Analysis of five types of rock facies (middle and high organic clay shale, middle and high organic sand shale, low organic clay shale, low organic mixed shale, low organic sand shale) of long 7 shale
The oriented arrangement of particles in different lithofacies shows obvious difference, and the clay shale with medium and high organic matter content
(0.78-0.85) is the smallest, and the particle arrangement has stronger directionality.
Secondly, considering the technical scheme as a whole or from the perspective of products, the technical effect and advantages of the technical scheme to be protected by the invention are specifically described as follows:
the method takes the mud shale with the length of 7 Erdos basins as an example, establishes a particle structure directional entropy mathematical model, quantitatively represents the directional arrangement characteristics of shale particles, and analyzes the directional arrangement characteristics and the cause of the shale particles in different deposition and mechanical environments. The method for acquiring the microstructure parameters of the shale particles by using the multi-scale multi-view FE-SEM splicing and image recognition method ensures the photo precision and can acquire data on the photo in a wider range. Meanwhile, the geometric characteristic parameters of the particles are calculated by means of ImageJ software, and the characteristics of the particles can be accurately and quickly identified by using the ImageJ image identification software.
Thirdly, the expected income and commercial value after the technical scheme of the invention is converted are as follows: according to the invention, through the methods of experimental testing, imageJ software identification, digital-analog model calculation and the like, the directional arrangement degree of shale particles can be accurately and quantitatively evaluated from a microscopic level, and the method has important values on exploitation of shale oil gas, reservoir fracturing evaluation, reservoir physical property estimation and the like, and provides an important index for reservoir evaluation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a shale reservoir particle structure directional analysis method provided by an embodiment of the invention;
FIG. 2 is a transmission electron microscope photograph of shale particles provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a multi-scale multi-view stitching method and particle recognition according to an embodiment of the present invention; a is the vision field multiple determination; b, shooting pictures in a connecting manner; c, image splicing; d is mineral particle identification;
FIG. 4 is a diagram illustrating an image digitization process according to an embodiment of the invention;
FIG. 5 is a graph of Kath #10FE-SEM artificial particle identification and ImageJ software identification of shale samples provided by the embodiment of the invention;
FIG. 6 is a bar graph of particle size distribution fractal dimension of shale samples provided by an embodiment of the present invention; HC: clay shale with medium and high organic matters; HS: medium and high organic matter sandy shale; LC: low organic matter clay shale; LM: low organic matter mixed shale; LS: low organic matter sandy shale;
fig. 7 is a bar graph of oriented fractal dimension of particles of a long 7 shale sample provided by an embodiment of the present invention; HC: medium and high organic clay shale; HS: medium and high organic matter sandy shale; LC: low organic matter clay shale; LM: low organic matter mixed shale; LS: low organic matter sandy shale;
FIG. 8 is a bar graph of pore orientation fractal dimension of a long 7 shale sample provided by an embodiment of the present invention; HC: clay shale with medium and high organic matters; HS: medium and high organic matter sandy shale; LC: low organic matter clay shale; LM: low organic matter mixed shale; LS: low organic matter sandy shale;
FIG. 9 is an entropy bar chart of particle arrangement structure of a long 7 shale sample provided by an embodiment of the invention; HC: clay shale with medium and high organic matters; HS: medium and high organic matter sandy shale; LC: low organic matter clay shale; LM: low organic matter mixed shale; LS: low organic matter sandy shale;
fig. 10 is a graph illustrating a directional entropy and arrangement dominance directional diagram coupling relationship of shale particle structures according to an embodiment of the present invention;
FIG. 11 is a particle alignment dominance pattern for different types of facies provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a shale reservoir particle structure directional analysis method, a system, equipment and a terminal, and the invention is described in detail below with reference to the accompanying drawings.
1. Illustrative embodiments are explained. This section is an explanatory embodiment expanding on the claims so as to fully understand how the present invention is embodied by those skilled in the art.
As shown in fig. 1, the shale reservoir particle structure directional analysis method provided by the embodiment of the invention includes the following steps:
s101, determining the characterization parameters of the directional structure;
s102, acquiring and processing the characterization parameters of the directional structure;
s103, constructing an oriented structure representation model and carrying out oriented analysis on the shale reservoir particle structure.
As a preferred embodiment, the shale reservoir particle structure directional analysis method provided by the embodiment of the present invention specifically includes the following steps:
1. determination of orientation structure characterizing parameters
The analysis compares the shale samples (A, B) with different orientation arrangement degrees, and the result shows that when the mineral particles change from disorder to order state, the aggregation condition, the orientation and the spatial position of the particles, the shape and the arrangement characteristics of pores and the like of the particles tend to be more balanced (see figure 2).
In order to further determine parameters influencing the shale particle directional arrangement, the shape, orientation, aggregation condition, pore shape, orientation and the like of two sample particles are analyzed, and the fractal dimension concept is adopted, and the factors are respectively represented by a particle flatness distribution fractal dimension, a particle directional fractal dimension, a particle size distribution fractal dimension, a pore flatness distribution fractal dimension and a pore directional fractal dimension. The photos were identified by ImageJ image processing technique and the specific parameters calculated are given in table 1. According to analysis results, the particle orientation fractal dimension, the particle size distribution fractal dimension and the pore orientation fractal dimension value of the two samples show fluctuation with the amplitude ranging from 6.495 to 7.030%, the particle flatness distribution fractal dimension values are relatively close, and the change can be ignored. Therefore, the analysis considers that the ordering of shale particle arrangement is not only related to the orientation and aggregation condition of the particles, but also greatly influenced by the direction of the pores, but is not greatly related to the shape of the particles and the pores.
TABLE 1 analytical dimensional values for shale grain and pore structure
| Parameter value | Sample A | Sample B | Rate of change (%) |
| Fractal dimension of particle flatness distribution | 0.9541 | 0.9536 | 0.052 |
| Particle oriented fractal dimension | 0.9654 | 0.9027 | 6.495 |
| Fractal dimension of particle size distribution | 0.8521 | 0.7922 | 7.030 |
| Pore flatness distribution fractal dimension | 0.9442 | 0.9445 | 0.032 |
| Pore oriented fractal dimension | 0.9235 | 0.8624 | 6.616 |
2. Acquisition and processing of oriented structure characterization parameters
The shale has strong heterogeneity, and a single scanning electron microscope photo cannot reflect the real microstructure characteristics of the rock. In order to overcome the influence of heterogeneity and enable experimental data to be more accurate, the shale particle microstructure parameters are obtained by applying a multi-scale multi-view FE-SEM splicing and image recognition method. The method ensures the precision of the photo, and can acquire data on the photo in a larger range.
(1) Splicing and identifying FE-SEM (electron beam scanning microscope) pictures
According to the existing analysis results, the magnification of the shot picture is fixed at 10000 ×. The method comprises the steps of continuously shooting photos of an original section sample from head to tail sequentially (the sample is not polished), splicing the shot photos in sequence, and identifying the processed photos through software (ImageJ), so that microstructure characteristics can be obtained comprehensively.
As shown in fig. 3, the identification work is performed on the spliced photo, and the black color in the figure is the identified shale particles. And then, carrying out gray processing on the identified image by using ImageJ software to obtain a digital image consisting of black and white colors, wherein the black area in the image represents the area of the particles.
The digital image is composed of a series of pixels with different gray values, wherein a pixel value 0 represents a black area, a pixel value 255 represents a white area, and can be represented by a discrete function matrix f (x, y):
in the formula, a and b represent the row number and the column number of the pixel points in the corresponding pixel dot matrix in the whole image, and n and m represent the row number and the column number of the pixel points contained in the image. Fig. 4 is a schematic diagram of a digital image after the shale photograph is digitized, the gray value in the black-and-white image is composed of 0 and 255, and the shape of the particle is accurately represented by a discrete function corresponding to the gray value or the chromaticity of the digital image.
(2) Calculation of geometric parameters of particles by means of ImageJ software
The identified images are imported into image processing software to obtain numerical images with black and white tones, and the analysis adopts ImageJ image processing software developed by National Institutes of health. The software can obtain the basic geometrical parameters of the diameter, orientation, density and the like of the particles, and on the basis of the basic geometrical parameters, the shape parameters of the particles, such as granularity, flatness, fourier shape index, radius corner index and the like, are calculated.
The specific operation process of the ImageJ image processing software is as follows:
(1) and (5) gray level transformation. Importing a target picture into software, and carrying out gray level conversion on the picture, wherein the specific operations are as follows: image-Type-8-bit in the menu option column transforms the Image into an 8-bit grayscale Image.
(2) And setting a gray threshold value. And adjusting the threshold of the picture according to the gray value of the picture to enable the picture to present a geometric shape with complete particle size, keeping the threshold concentrated in a stable range, and extracting the particles according to the threshold. The specific operation is as follows: image-Adjust-Threshold in the menu options bar.
(3) And (6) automatically denoising. A large amount of noise still exists in the image after the threshold processing, and the existence of the noise can reduce the accuracy of the particle count and the geometric shape parameters, so that the image needs to be denoised. The noise is removed by manually rectifying the picture or smoothing the image through filtering. The analysis of the invention adopts an automatic denoising mode, and the specific operation is as follows: process-Noise-Despeckle.
(4) And acquiring geometric parameters. Firstly, a proper scale is set for a picture according to the magnification of a scanning electron microscope, and the specific operation is as follows: analyze-Set scale. And then, calculating the geometric parameters of the particles, selecting items to be measured, calibrating and counting the particles, wherein the items to be calculated in the analysis comprise particle size, flatness, perimeter, equivalent ellipse perimeter, convexity and the like. The specific operation is as follows: analyze-Set measurement-Analyze partitions.
(5) And exporting the data. Exporting and converting data, outputting all the data and storing the data in an Excel table, and processing the data to obtain the geometric information of the particles. 1195 shale particles are identified, and the identified information values are series of parameters such as the area, the pixel value, the X and Y coordinate positions, the perimeter, the width, the length, the height and the angle of the particles, the Feret diameter of the particles, the roundness of the particles, the sphericity and the convexity of the particles and the like.
3. Oriented structure characterization model
In 1865, clausius, german physicist, presented the concept of "entropy," representing a measure of the "degree of confusion" of a system. "entropy" at the microscopic level refers to the disorder of microscopic particles in the system, and may represent the uniformity of the spatial distribution of energy at the macroscopic level. Thus, "entropy" is a statistical feature of a system of different states, and is a bridge between macroscopic states and microscopic states. Generally, the higher the entropy value of a system, the more unstable the system, and vice versa. The former analysis considers that the shale is a complex structure composed of various elements, when the external environment changes, the chaos degree of the various elements of the shale can correspondingly change, and the entropy value can also change. Analysis by Zenitrene and the like considers that the shale particle size characteristics and arrangement characteristics are most remarkably changed under the action of external stress. Thus, "microstructural entropy" is defined as a function of the entropy of shale particle size and the entropy of arrangement.
The analysis finds that the shale particle orientation arrangement is not only related to the orientation and aggregation condition of the shale particles (particle orientation fractal dimension, particle size distribution fractal dimension), but also is greatly influenced by the arrangement characteristics of the pores (pore orientation fractal dimension). Therefore, on the basis of Wuyi auspicious and Zengyubin analysis, the invention considers the influence of pore arrangement structure and the directional entropy of shale structure
The entropy of the particle size, the entropy of the particle arrangement and the entropy of the pore arrangement is expressed by the following formulas:
in the formula, E di As entropy of particle alignment (particle oriented fractal dimension)Value), E) pd Entropy of particle size (fractal dimension of particle size), E bi Entropy of pore arrangement (pore orientation fractal dimension value).
Under the action of external stress, the microstructure of shale particles is changed to some extent. Shale particles are gradually decomposed into particles with smaller particle sizes under the action of external force, and the arrangement characteristics of the particles tend to be oriented; meanwhile, the pores tend to be more elongated, and the core is layered or has page-like development. The change of the directional arrangement of the shale particles causes the particle size, the particle orientation and the pore orientation to be changed simultaneously, and the change rates of the three parameters are similar. Thus shale
The value is equivalent to the average value of the particle arrangement entropy, the granularity entropy and the pore arrangement entropy, the weight coefficients are all 1/3, and are as follows:
The shale reservoir particle structure directional analysis system provided by the embodiment of the invention comprises:
the parameter determination module is used for determining the characterization parameters of the directional structure;
the parameter processing module is used for acquiring and processing the characterization parameters of the directional structure;
and the structure analysis module is used for constructing a directional structure representation model and directionally analyzing the shale reservoir particle structure.
The technical solution of the present invention will be further described with reference to the following specific examples.
Example 1: characterization of oriented structure of long 7 shale particles
A total of 35 samples of different types of shale were selected in the drill core and field profiles. Based on the multi-scale multi-view scanning electron microscope identification technology, the entropy values of the particle arrangement structures of 35 long 7 shale sample particles are calculated by combining ImageJ software. A total scanning electron micrograph of 1764 is taken, and 42351 shale particles are identified.
(1) Fractal dimension of particle size distribution of different types of lithofacies
The particle size fractal dimension value represents the distribution of the particle sizes of the particles in a uniform manner. The larger the value, the more non-uniform the particle size and the worse the homogenization. Assuming that the diameter of the particle is D s Divide it equally into n equal parts in sigma increments if the shale particles are at [ i, i +1 ]]Probability of interval being P i (σ), the corresponding probability series of each interval is P 1 (σ)，P 2 (σ)，…，P n (sigma). If the azimuth increment σ is changed, another set of corresponding probability series values is obtained. When the sigma tends to be 0, the following relation is applied to solve the particle size fractal dimension value D of the shale particles ps ：
The method is a fractal dimension algorithm in the information dimension meaning and takes ln sigma to sigma P i (σ)·ln(1/P i (σ)) is linearly related in a log-log coordinate system to judge the fractal dimension, and the negative value of the slope is used as a value.
Particle size fractal dimension values of five long 7 shale are shown in fig. 6. The particle size fractal dimension values are distributed within the range of 0.7668-0.9542, the particle size fractal dimension values of different lithofacies have certain difference, and the average value of the particle size fractal dimension values of the middle-high organic matter clay shale, the middle-high organic matter sand shale, the low organic matter clay shale, the low organic matter mixed shale and the low organic matter sand shale is 0.8399, 0.8381, 0.8615, 0.8901 and 0.8696.
(2) Grain oriented fractal dimension for different types of lithofacies
The particle orientation fractal dimension represents the size of the particle arrangement orientation degree, and is defined as that the particles have obvious functional relation between the coarse-vision scale and the observed number thereof within a certain scale range. Assuming that the particle has an azimuthal angle α in the range of 0, π]It is divided into n equal parts in alpha increments if the shale particles are [ i, i +1 ]]Probability of interval being P 1 (α) and the corresponding probability series of each interval is P 1 (σ)，P 2 (σ)，…，P n (sigma). If the azimuth increment alpha is changed, another set of corresponding probability series values is obtained. When alpha tends to 0, the following relation is applied to obtain the directional fractal dimension value D of the shale particles di ：
The method is based on fractal dimension algorithm in information dimension meaning and uses ln alpha to sigma P i (α)·ln(1/P i (alpha)) in a double logarithmic coordinate system to judge the fractal dimension, and taking the negative value of the slope as D di The value is obtained. In fact, when α is constant, Σ P i (α)·ln(1/P i And (. Alpha)) is the structural entropy. Shale particle directional fractal dimension value D di The larger the size, the poorer the directionality of the shale particle arrangement, and vice versa, the shale particles are arranged in order and have directionality.
Based on a multi-scale and multi-view scanning electron microscope identification technology, angle values of representative 35 shale sample particles are respectively calculated by combining ImageJ software, and the result shows that the oriented fractal dimension value of the particles has obvious amplitude change and changes in the interval of [0.7845,0.9874] (see figure 7). The grain oriented fractal dimension values of different types of shale are greatly different, and the grain oriented fractal dimension values of the shale with medium and high organic matter and sandy matter, the shale with low organic matter and mixed shale and the shale with low organic matter and sandy matter are respectively distributed in the ranges of 0.7868-0.9310, 0.8863-0.9905, 0.8268-0.9310, 0.9097-0.9954 and 0.9236-0.9824, and the average values are respectively 0.8399, 0.9381, 0.8815, 0.9296 and 0.9509. The oriented fractal dimension value of the particles of the clay shale with medium and high organic matters is obviously smaller than that of other lithofacies, and the oriented property of the arrangement of the lithofacies particles is proved to be higher.
(3) Pore-oriented fractal dimension for different types of facies
The directional fractal dimension value of the pore reflects the size of the directional degree of pore arrangement, the calculation method is similar to the directional fractal dimension of the particle, and the directional fractal dimension value of the pore also reflects the directional degree of particle arrangement to a certain extent, as follows:
where α is the angle of orientation of the pore and the corresponding probability for each interval is P i (α)。
On the basis of identifying the multi-scale and multi-view FE-SEM pictures, the oriented fractal dimension values of 35 typical shale sample pores in the research area are subjected to statistical analysis, and the calculation result is shown in figure 8. The different types of shale have larger difference of the pore orientation fractal dimension values, wherein the pore orientation fractal dimension values of the shale with medium and high organic matter clay, the shale with medium and high organic matter sand, the shale with low organic matter clay, the shale with low organic matter mixture and the shale with low organic matter sand are distributed in the interval of 0.7820-0.9578, 0.9186-0.9775, 0.8620-0.9710, 0.9650-0.9870 and 0.9536-0.98240, and the average values are 0.8386, 0.9685, 0.9277, 0.9763 and 0.9709 respectively. The oriented fractal dimension value of the particles of the clay shale with medium and high organic matters is obviously smaller than that of other lithofacies, and the oriented property of the arrangement of the lithofacies particles is higher.
(4) Structural directional entropy of different types of lithofacies
The particle value was calculated based on the particle size fractal dimension value, particle oriented fractal dimension value, pore oriented fractal dimension value of the shale sample, as shown in fig. 9. The research result shows that the values are distributed in the interval of 0.7882-0.9309. The values of the clay shale with medium and high organic matter, the clay shale with low organic matter, the mixed clay shale with low organic matter and the clay shale with low organic matter are respectively distributed in 0.7800-0.9010, 0.8853-0.9490, 0.8410-0.9560, 0.8810-0.9570 and 0.960-0.9640, and the average values are respectively 0.839, 0.9121, 0.8884, 0.9271 and 0.9318. The values of different types of shale show larger difference, the directional entropy value of the structure of the clay shale with medium and high organic matters fluctuates greatly, but the value is obviously smaller than other four lithofacies, and the higher the directional arrangement degree is, the better the directionality is. The average values of the five types of rock phase values are arranged from small to large in sequence as follows: medium high organic matter clay shale < low organic matter clay shale < medium high organic matter sandy shale < low organic matter mixed shale < low organic matter sandy shale.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A shale reservoir particle structure directional analysis method is characterized by comprising the following steps:
based on the shape, orientation and aggregation condition of shale particles and the pore shape and orientation factors, a structure orientation entropy mathematical model for representing the arrangement orientation of the particles is established, and the structure orientation entropy mathematical model is used for evaluating different types of facies orientation structures and quantitatively representing the orientation arrangement characteristics of the shale particles.
2. The shale reservoir particle structure directional analysis method of claim 1, comprising the steps of:
firstly, determining the characterization parameters of the directional structure;
step two, obtaining and processing the characterization parameters of the directional structure;
and step three, constructing an oriented structure representation model and carrying out oriented analysis on the shale reservoir particle structure.
3. The shale reservoir particle structure oriented analysis method of claim 2, wherein the determination of the oriented structure characterization parameter in the first step comprises:
analyzing the shape, orientation and aggregation condition of two sample particles and the shape and orientation of pores, and adopting the concept of fractal dimension to respectively use the fractal dimension of particle flatness distribution, the fractal dimension of particle orientation, the fractal dimension of particle size distribution, the fractal dimension of pore flatness distribution and the fractal dimension of pore orientation to characterize factors; identifying the picture by means of an ImageJ image processing technology, and determining parameters influencing the directional arrangement of the shale particles;
wherein, the particle orientation fractal dimension, the particle size distribution fractal dimension and the pore orientation fractal dimension value of the two samples show the fluctuation with the amplitude ranging from 6.495 to 7.030 percent, and the particle flatness distribution fractal dimension values are relatively close to each other, so the change is neglected.
4. The shale reservoir particle structure directional analysis method as claimed in claim 2, wherein in the second step, shale particle microstructure parameters are obtained by a multi-scale and multi-view FE-SEM stitching and image recognition method, and the directional structure characterization parameter obtaining and processing comprises:
(1) Splicing and identification of FE-SEM (electron field microscopy-scanning electron microscope) picture
Fixing the magnification of the shot picture at 10000 times; continuously shooting photos of the original section sample which is not polished end to end, and sequentially splicing the shot photos; identifying and identifying through software ImageJ to obtain microstructure characteristics; performing identification work on the spliced pictures, and performing gray processing on the identified images by using ImageJ software to obtain digital images consisting of black and white colors;
the digital image is composed of a series of pixels with different gray values, the pixel value 0 represents a black area, the pixel value 255 represents a white area, and the pixel values are represented by a discrete function matrix f (x, y):
in the formula, a and b represent the row number and the column number of a pixel point in a corresponding pixel dot matrix in the whole image, and n and m represent the row number and the column number of the pixel point contained in the image;
(2) Geometric parameters of the particles were calculated by means of ImageJ software: and importing the identified image into image processing software ImageJ to obtain a numerical image with black and white tone.
5. The shale reservoir grain structure directional analysis method of claim 4, wherein the ImageJ image processing software operates as follows:
(1) gray level conversion: importing a target picture into software, and carrying out gray level conversion on the picture;
(2) setting a gray threshold: adjusting the threshold value of the picture according to the gray value of the picture to enable the picture to present a geometric shape with complete grain diameter, keeping the threshold value concentrated in a stable range, and extracting the grains according to the threshold value;
(3) automatic denoising: manually correcting the picture, or removing the noise points in a filtering and image smoothing mode, and analyzing the noise points in an automatic denoising mode;
(4) acquisition of geometric parameters: setting a proper scale for the picture according to the magnification of the scanning electron microscope; calculating the geometric parameters of the particles, wherein the selected items comprise particle size, flatness, perimeter, equivalent ellipse perimeter and convexity of the particles, and calibrating and counting the particles;
(5) and (3) exporting data: exporting and converting data, outputting and storing all the data in an Excel table, and processing the data to obtain particle geometric information; the identified information values are the area, pixel value, X and Y coordinate positions of the particle, the perimeter, width, length, height and angle of the particle, the Feret diameter of the particle, the roundness of the particle, the sphericity of the particle and the convexity parameters.
6. The shale reservoir particle structure oriented analysis method of claim 2, wherein the construction of the oriented structure characterization model in the third step comprises:
shale structure directional entropy based on influence of pore arrangement structure
The entropy of particle size, the entropy of particle arrangement and the entropy of pore arrangement is expressed by the following formula:
in the formula, E di Expressing the grain orientation fractal dimension value as the grain arrangement entropy; e pd The particle size entropy represents a particle size fractal dimension value; e bi Expressing a pore orientation fractal dimension value for pore arrangement entropy;
shale
The value is equivalent to the average value of the particle arrangement entropy, the particle size entropy and the pore arrangement entropy, and the weight coefficients are all 1/3 as follows:
wherein,
the larger the value is,
the higher the degree of disorder of the arrangement is, the more disordered the arrangement is, the more irregular the arrangement is, the more unstable the system is;
smaller values indicate lower disorder, more directional alignment, more stable system,
7. a shale reservoir particle structure directional analysis system applying the shale reservoir particle structure directional analysis method as claimed in any one of claims 1 to 6, wherein the shale reservoir particle structure directional analysis system comprises:
the parameter determining module is used for determining the characterization parameters of the directional structure;
the parameter processing module is used for acquiring and processing the characterization parameters of the directional structure;
and the structure analysis module is used for constructing a directional structure representation model and directionally analyzing the shale reservoir particle structure.
8. A computer arrangement, characterized in that the computer arrangement comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the shale reservoir grain structure orientation analysis method according to any of claims 1 to 6.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the shale reservoir granular structure orientation analysis method as claimed in any one of claims 1 to 6.
10. An information data processing terminal, characterized in that the information data processing terminal is used for implementing the shale reservoir granular structure directional analysis system as claimed in claim 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210782915.7A CN115165681B (en) | 2022-07-05 | 2022-07-05 | Shale reservoir granule structure directional analysis method, system, equipment and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210782915.7A CN115165681B (en) | 2022-07-05 | 2022-07-05 | Shale reservoir granule structure directional analysis method, system, equipment and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115165681A true CN115165681A (en) | 2022-10-11 |
| CN115165681B CN115165681B (en) | 2024-09-06 |
Family
ID=83491511
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210782915.7A Active CN115165681B (en) | 2022-07-05 | 2022-07-05 | Shale reservoir granule structure directional analysis method, system, equipment and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115165681B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116296946A (en) * | 2023-03-06 | 2023-06-23 | 中国矿业大学(北京) | Shale stratum development degree characterization method and device based on fractal-fluctuation theory |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070172105A1 (en) * | 2006-01-25 | 2007-07-26 | Claus Bahlmann | System and method for local pulmonary structure classification for computer-aided nodule detection |
| US20140112578A1 (en) * | 2012-10-19 | 2014-04-24 | National Taiwan University Of Science And Technology | Image recognition method and image recognition system |
| CN105809692A (en) * | 2016-03-10 | 2016-07-27 | 中国石油大学(华东) | Quantitative characterization method of shale structures |
| CN109444015A (en) * | 2018-10-31 | 2019-03-08 | 成都理工大学 | More kens, it is multiple dimensioned under shale reservoir microcellular system identification method |
| CN110523997A (en) * | 2019-08-19 | 2019-12-03 | 江苏大学 | A kind of subzero treatment aluminum matrix composite and preparation method thereof of high-entropy alloy particle enhancing |
-
2022
- 2022-07-05 CN CN202210782915.7A patent/CN115165681B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070172105A1 (en) * | 2006-01-25 | 2007-07-26 | Claus Bahlmann | System and method for local pulmonary structure classification for computer-aided nodule detection |
| US20140112578A1 (en) * | 2012-10-19 | 2014-04-24 | National Taiwan University Of Science And Technology | Image recognition method and image recognition system |
| CN105809692A (en) * | 2016-03-10 | 2016-07-27 | 中国石油大学(华东) | Quantitative characterization method of shale structures |
| CN109444015A (en) * | 2018-10-31 | 2019-03-08 | 成都理工大学 | More kens, it is multiple dimensioned under shale reservoir microcellular system identification method |
| CN110523997A (en) * | 2019-08-19 | 2019-12-03 | 江苏大学 | A kind of subzero treatment aluminum matrix composite and preparation method thereof of high-entropy alloy particle enhancing |
Non-Patent Citations (4)
| Title |
|---|
| LI X 等: "Effect of a weak transverse magnetic field on solidification structure during directional solidification", 《ACTA MATERIALIA》, 31 December 2014 (2014-12-31), pages 367 - 381 * |
| XIE XH 等: "Investigation of the Oriented Structure Characteristics of Shale Using Fractal and Structural Entropy Theory", 《FRACTAL AND FRACTIONAL》, vol. 06, no. 12, 30 November 2022 (2022-11-30), pages 1 - 29 * |
| 李毅 等: "定向凝固法制备高强度多级异构共晶熵合金", 《上海金属》, vol. 43, no. 05, 30 September 2021 (2021-09-30), pages 79 - 84 * |
| 解馨慧 等: "基于纳米压痕和FE-SEM技术探究岩页理结构对其微-宏观力学行为的影响", 《地质论评》, vol. 70, no. 1, 25 March 2024 (2024-03-25), pages 319 - 322 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116296946A (en) * | 2023-03-06 | 2023-06-23 | 中国矿业大学(北京) | Shale stratum development degree characterization method and device based on fractal-fluctuation theory |
| CN116296946B (en) * | 2023-03-06 | 2023-08-11 | 中国矿业大学(北京) | Shale stratum development degree characterization method and device based on fractal-fluctuation theory |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115165681B (en) | 2024-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| RU2543698C1 (en) | Analysis of petrographic images for detection of capillary pressure in porous media | |
| CN108734714B (en) | Method for analyzing carbonate rock structure based on Matlab | |
| Qajar et al. | Microtomographic characterization of dissolution-induced local porosity changes including fines migration in carbonate rock | |
| CN115165681A (en) | Shale reservoir particle structure directional analysis method, system, equipment and terminal | |
| CN110349637B (en) | Method and device for predicting volume fraction of transition zone of peripheral interface of aggregate and terminal equipment | |
| CN107655908A (en) | Method and device for constructing digital core | |
| CN105809692A (en) | Quantitative characterization method of shale structures | |
| CN101413871B (en) | Method for calculating porous material hole structure fractal dimension | |
| CN105791635B (en) | Video source modeling denoising method based on GPU and device | |
| CN111426616B (en) | Method and device for acquiring elastic property and pore structure of carbonate rock and storage medium | |
| CN115081210A (en) | Method for calculating flow tortuosity in porous medium through limited walk simulation | |
| Kang et al. | Construction of complex digital rock physics based on full convolution network | |
| CN114427457A (en) | Method for determining logging pentasexual relation of tidal flat facies carbonate reservoir and logging evaluation method | |
| CN110895704B (en) | Microorganism dune complex reservoir type identification method and device and storage medium | |
| CN115112700A (en) | Shale reservoir microstructure characterization method, system, medium, equipment and terminal | |
| CN104112126A (en) | Marble microsection automatically identifying method | |
| CN115205165B (en) | Spraying method of anticorrosive material for industrial machine housing | |
| CN113818867B (en) | Method, system, medium, equipment and application for constructing pseudo capillary pressure curve | |
| CN112461718B (en) | Method for representing relationship between porosity and particle size distribution | |
| CN102901697B (en) | Porosity detection method for soil | |
| CN115564725A (en) | Slope monitoring method and device based on intelligent image identification | |
| CN103295208B (en) | Towards guarantor's characteristic quantification method of geologic body data visualization | |
| CN107292854B (en) | Gray level image enhancement method based on local singularity quantitative analysis | |
| CN117574686B (en) | Method and device for constructing digital rock core, electronic equipment and storage medium | |
| CN118628453A (en) | Quantitative evaluation method for stability of training image based on variation function |
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 |