CN114722648A - Method and device for acquiring core acoustic response - Google Patents
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Abstract
The application provides a method and a device for obtaining core acoustic response, and belongs to the technical field of rock physics response. The core is scanned by adopting a computer tomography scanning technology, the tomography data of the core are obtained, the data can reflect the form information of pores and fractures in the core and can provide a data base for response calculation of a simulated sound wave process, specifically, the data can be subjected to noise reduction by performing filtering processing and binarization processing on the data, and a first space reconstruction graph comprising a pore structure of the core and a second space reconstruction graph comprising a fracture structure of the core are drawn; and moreover, based on the tomography data, a digital core can be established, the acoustic response process can be simulated, and the acoustic response characteristic data of the core can be obtained. Based on the two space reconstruction images and the sound wave response characteristic data, the corresponding relation among the pores, the cracks and the sound wave response characteristics in the rock core can be obtained.
Description
Technical Field
The application relates to the technical field of rock physics response, in particular to a method and a device for acquiring core acoustic response.
Background
In the engineering of seismic exploration, oil logging and the like, the rock physical properties such as the porosity and the mechanical properties of rocks in an oil and gas reservoir of a deep stratum are usually tested by adopting a sound wave response mode, wherein the response characteristics of sound waves to a storage and seepage space and fluid in the deep stratum are key foundations for realizing the identification and evaluation of the reservoir.
The currently commonly adopted method for acquiring the acoustic response of the core comprises the following steps: and performing an acoustic response test based on the core obtained from the reservoir, thereby obtaining the acoustic response characteristic of the core.
However, for the carbonate fracture cavity, the pore structure of the rock is complex and the heterogeneity is strong, so that the acoustic response characteristics are particularly complex, the method for acquiring the core acoustic response can only obtain the integral acoustic attribute of the core, and cannot analyze the response characteristics of the acoustic to each crack and karst cave in the core, so that the efficient exploration and development of the carbonate reservoir of the fracture cavity is hindered.
Disclosure of Invention
The embodiment of the application provides a method and a device for obtaining core acoustic response, which can obtain the corresponding relation among pores, cracks and acoustic response characteristics in a core. The technical scheme is as follows:
in one aspect, a method for obtaining a core acoustic response is provided, and the method includes:
taking out a rock core to be detected from an underground carbonate rock storage interval;
acquiring basic information parameters of the rock core;
scanning the core by adopting a computed tomography technology to obtain tomography data of the core;
processing the fault scanning data of the rock core by adopting a non-local mean filtering algorithm to obtain filtered fault scanning data;
processing the filtered tomography data by adopting a binarization algorithm based on a porosity image segmentation method to obtain two groups of sub-tomography data after binarization processing;
respectively acquiring a first spatial reconstruction graph and a second spatial reconstruction graph corresponding to the core based on the two sets of sub-tomography data after binarization processing, wherein the first spatial reconstruction graph comprises the form of at least part of pores in the core, and the second spatial reconstruction graph comprises the form of at least part of cracks in the core;
simulating an acoustic response process by adopting a finite element analysis mode based on the fault scanning data of the core, and acquiring acoustic response characteristic data of the core;
and acquiring corresponding relations among pores, cracks and acoustic wave response characteristics in the core based on the first spatial reconstruction map, the second spatial reconstruction map and the acoustic wave response characteristic data of the core.
In one possible implementation, the basic information parameter includes: length, diameter, and density.
In one possible implementation manner, the processing the tomographic data of the core by using a non-local mean filtering algorithm to obtain filtered tomographic data includes:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and has no dimension;
c (i) is a normalization factor, dimensionless;
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in the range of omegaiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe area is a positive direction area which takes i as the center and has side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension;
i (i) the gray values of the ith pixel point before processing are respectively, and the dimensions are not increased;
Gα(l, m) is a two-dimensional Gaussian smoothing kernel with standard deviation of α, dimensionless.
In a possible implementation manner, the porosity-based image segmentation method, which processes the filtered tomographic data by using a binarization algorithm to obtain two sets of sub-tomographic data after binarization processing, includes:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the method is an optimal segmentation threshold without dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) is a voxel with a gray value of i, with no dimension;
IMAXis the maximum gray level of the image.
In one possible implementation manner, the acquiring acoustic response characteristic data of the core based on the tomography data of the core by simulating an acoustic response process in a finite element analysis manner includes:
establishing a finite element model as a relation 4 and a relation 5;
pt＝p+pbrelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure during excitation, Pa;
pbis the boundary pressure, Pa;
obtaining equivalent pore parameters and equivalent skeleton parameters of the rock;
acquiring an incident boundary condition;
and adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
In one aspect, an apparatus for obtaining acoustic response of a core is provided, the apparatus comprising:
the core acquisition module is used for taking out a core to be detected from the underground carbonate rock reservoir section;
the parameter acquisition module is used for acquiring basic information parameters of the rock core;
the scanning module is used for scanning the rock core by adopting a computer tomography technology to obtain tomography data of the rock core;
the data processing module is used for processing the fault scanning data of the rock core by adopting a non-local mean filtering algorithm to obtain filtered fault scanning data;
the data processing module is also used for processing the filtered tomography data by adopting a binarization algorithm based on a porosity image segmentation method to obtain two groups of sub-tomography data after binarization processing;
the image acquisition module is used for respectively acquiring a first space reconstruction graph and a second space reconstruction graph corresponding to the rock core based on the two sets of sub-tomography data after binarization processing, wherein the first space reconstruction graph comprises the form of at least part of pores in the rock core, and the second space reconstruction graph comprises the form of at least part of cracks in the rock core;
the acoustic wave simulation module is used for simulating an acoustic wave response process based on the fault scanning data of the core in a finite element analysis mode to obtain acoustic wave response characteristic data of the core;
and the relation acquisition module is used for acquiring the corresponding relation among the pores, the cracks and the acoustic response characteristics in the rock core based on the first spatial reconstruction map, the second spatial reconstruction map and the acoustic response characteristic data of the rock core.
In one possible implementation, the basic information parameter includes: length, diameter, and density.
In one possible implementation, the data processing module is configured to:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and has no dimension;
c (i) is a normalization factor, dimensionless;
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in omega rangeiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe method is a positive direction area with i as the center and side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension;
i (i) the gray values of the ith pixel point before processing are respectively, and the dimensions are not increased;
Gα(l, m) is a two-dimensional gaussian smoothing kernel with standard deviation of α, dimensionless.
In one possible implementation, the data processing module is configured to:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the method is an optimal segmentation threshold without dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) is a voxel with a gray value of i, with no dimension;
IMAXis the maximum gray level of the image.
In one possible implementation, the acoustic wave simulation module is configured to:
establishing a finite element model as a relation 4 and a relation 5;
pt＝p+pbrelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure during excitation, Pa;
pbis the boundary pressure, Pa;
obtaining equivalent pore parameters and equivalent skeleton parameters of the rock;
acquiring an incident boundary condition;
and adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
According to the technical scheme provided by the embodiment of the application, the core is scanned by adopting a computer tomography technology to obtain the tomography data of the core, the data not only can reflect the form information of pores and fractures in the core, but also can provide a data base for response calculation of a simulated sound wave process, specifically, the data can be denoised by filtering the data, two groups of sub-tomography data after binarization processing can be obtained by performing binarization processing on the filtered data, and a first space reconstruction graph comprising a pore structure of the core and a second space reconstruction graph comprising a fracture structure of the core are drawn according to the two groups of sub-tomography data; and based on the tomography data, a digital core can be established, the acoustic response process is simulated, and the acoustic response characteristic data of the core is obtained. Based on the two space reconstruction images and the sound wave response characteristic data, the corresponding relation among the pores, the cracks and the sound wave response characteristics in the rock core can be obtained.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for obtaining a core acoustic response according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a method for obtaining a core acoustic response according to an embodiment of the present disclosure;
fig. 3 is a first spatially reconstructed image provided by an embodiment of the present application;
fig. 4 is a second spatially reconstructed image provided by an embodiment of the present application;
fig. 5 is a diagram of a digital core grid model structure provided in an embodiment of the present application;
FIG. 6 is a graph of displacement as a function of incident light according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram of finite element simulation loading for a digital core according to an embodiment of the present disclosure;
fig. 8 is a waveform diagram obtained by finite element simulation of a digital core according to an embodiment of the present disclosure;
FIG. 9 is a graph of the intersection between fracture porosity and shear wave velocity provided by an embodiment of the present application;
FIG. 10 is a schematic structural diagram of an apparatus for obtaining acoustic response of a core according to an embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In this application, unless expressly stated or limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can include, for example, fixed connections, removable connections, or integral connections; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Fig. 1 is a flowchart of a method for acquiring a core acoustic response according to an embodiment of the present application, where the method may be applied to a computer device, please refer to fig. 1, where the method includes:
101. and taking out the core to be tested from the underground carbonate rock reservoir interval.
102. And acquiring basic information parameters of the rock core.
103. And scanning the core by adopting a computed tomography technology to obtain tomography data of the core.
104. And processing the fault scanning data of the rock core by adopting a non-local mean filtering algorithm to obtain filtered fault scanning data.
105. And processing the filtered tomography data by adopting a binarization algorithm based on a porosity image segmentation method to obtain two groups of sub-tomography data after binarization processing.
106. And respectively acquiring a first spatial reconstruction graph and a second spatial reconstruction graph corresponding to the core based on the two sets of sub-tomography data after binarization processing, wherein the first spatial reconstruction graph comprises the form of at least part of pores in the core, and the second spatial reconstruction graph comprises the form of at least part of cracks in the core.
107. And simulating an acoustic response process by adopting a finite element analysis mode based on the fault scanning data of the core, and acquiring acoustic response characteristic data of the core.
108. And acquiring corresponding relations among pores, cracks and acoustic wave response characteristics in the core based on the first spatial reconstruction map, the second spatial reconstruction map and the acoustic wave response characteristic data of the core.
The method comprises the steps of scanning a rock core by adopting a computer tomography technology to obtain tomography data of the rock core, wherein the data can reflect form information of pores and fractures in the rock core and can also provide a data base for response calculation of a sound wave simulation process; and based on the tomography data, a digital core can be established, the acoustic response process is simulated, and the acoustic response characteristic data of the core is obtained. Based on the two space reconstruction images and the sound wave response characteristic data, the corresponding relation among the pores, the cracks and the sound wave response characteristics in the rock core can be obtained.
In one possible implementation, the basic information parameter includes: length, diameter, and density.
In one possible implementation manner, the processing the tomographic data of the core by using a non-local mean filtering algorithm to obtain filtered tomographic data includes:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and has no dimension;
c (i) is a normalization factor, dimensionless;
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in the range of omegaiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe method is a positive direction area with i as the center and side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension;
i (i) the gray values of the ith pixel point before processing are respectively, and the dimensions are not increased;
Gα(l, m) is a two-dimensional gaussian smoothing kernel with standard deviation of α, dimensionless.
In a possible implementation manner, the porosity-based image segmentation method, which processes the filtered tomographic data by using a binarization algorithm to obtain two sets of sub-tomographic data after binarization processing, includes:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the method is an optimal segmentation threshold without dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) is a voxel with a gray value of i, with no dimension;
IMAXis the maximum gray level of the image.
In one possible implementation manner, the acquiring acoustic response characteristic data of the core based on the tomography data of the core by simulating an acoustic response process in a finite element analysis manner includes:
establishing a finite element model as a relation 4 and a relation 5;
pt＝p+pbrelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure during excitation, Pa;
pbis the boundary pressure, Pa;
obtaining equivalent pore parameters and equivalent skeleton parameters of the rock;
acquiring an incident boundary condition;
and adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
Fig. 2 is a flowchart of a method for obtaining a core acoustic response according to an embodiment of the present disclosure. The method may be applied to a computer device, see fig. 2, the embodiment comprising:
201. and taking out the core to be tested from the underground carbonate rock reservoir interval.
The rock in the carbonate rock reservoir section becomes carbonate rock, and the pore structure of the carbonate rock fracture-cave response is complex and strong in heterogeneity, so that the acoustic wave response characteristic is particularly complex.
In this step, sampling may be performed according to existing core sampling specifications, and a corresponding core is obtained from each interval, and the sizes of the cores may be identical, so as to ensure that data obtained in subsequent experiments can have contrast.
In this step, a plurality of cores may be taken out, and in the subsequent step, cores with typical characteristics are selected according to the characteristics of the cores to continue the test.
202. And acquiring basic information parameters of the rock core.
In this step, basic information of all cores to be measured is measured, and based on the obtained basic information parameters, qualitative evaluation can be performed on the cores so as to screen the cores and obtain cores with typical characteristics, and subsequent steps are performed.
In one possible implementation, the basic information parameter includes: length, diameter and density, the above-mentioned basic information parameter is relatively easy to obtain, have higher reference value again. For example, a fracture-cave carbonate rock core is taken and tested to have a density of 2.79kg/m3。
203. And carrying out multi-frequency acoustic measurement on the rock core to obtain acoustic data of the rock core.
In the step, the core is tested by using a multi-frequency acoustic wave measuring instrument, and acoustic wave data of the rock sample, such as a wave form diagram, an acoustic wave time difference and the like, are obtained. Therefore, the lithology can be evaluated and screened based on the obtained acoustic data, and the fracture-cavity carbonate rock core with better physical property is selected for subsequent steps, so that a model obtained subsequently is more typical and easier to observe and deduce. For example, a fracture-cave carbonate core was taken and tested to have a porosity of 4.6% and a longitudinal wave velocity of 6174.58 m/s.
204. And scanning the core by adopting a computed tomography technology to obtain tomography data of the core.
Computed Tomography (CT) refers to obtaining tomographic data of a core by performing tomographic scanning on the core using precisely collimated rays or ultrasonic waves. The CT technology can be used for effectively extracting the pore structure of the carbonate rock core and well keeping the fine characteristics of the pore space.
In this step, a corresponding three-dimensional image may also be acquired based on the acquired tomography data to visually observe the features of the core, which is not limited in this embodiment.
In order to perform three-dimensional spatial reconstruction on the core based on the tomography data, the acquired data needs to be processed to reduce or eliminate noise, and meanwhile, detail features of an image corresponding to the data can be maintained. The data processing procedure will be described below with reference to step 205 and step 206.
205. And processing the fault scanning data of the rock core by adopting a non-local mean filtering algorithm to obtain filtered fault scanning data.
The non-local mean filtering algorithm can fully utilize redundant information in the image based on the non-correlation characteristic of noise, and maintain the detail characteristics of the image to the maximum extent while denoising.
In one possible implementation, the steps include:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and the NL (i) is dimensionless;
c (i) is a normalization factor, dimensionless; the parameter normalization is used for normalizing the weight, and the weight omega (i, j) is processed to be between [ 01 ];
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in the range of omegaiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe method is a positive direction area with i as the center and side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension; the larger the h is, the larger the influence of the neighborhood point set on the point to be processed is;
i (i) the gray values of the ith pixel point before processing are respectively, and the dimensions are not increased;
Gα(l, m) is a two-dimensional gaussian smoothing kernel with standard deviation of α, dimensionless.
Through the process, the acquired tomography data of the rock core are input into the relational expression 1 and the relational expression 2, and the filtered tomography data are obtained.
206. And processing the filtered tomography data by adopting a binarization algorithm based on a porosity image segmentation method to obtain two groups of sub-tomography data after binarization processing.
In the step, the filtered image is subjected to binarization processing by using an image segmentation method based on porosity, so that two groups of sub-tomography data after binarization processing corresponding to the pore structure and the fracture structure of the carbonate rock sample respectively can be extracted.
In a possible implementation manner, the porosity-based image segmentation method, which processes the filtered tomographic data by using a binarization algorithm to obtain two sets of sub-tomographic data after binarization processing, includes:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the method is an optimal segmentation threshold without dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) is a voxel with a gray value of i, with no dimension;
IMAXis the maximum gray level of the image.
Through the process, the acquired tomography data of the rock core are input into the relational expression 3, and two groups of sub-tomography data after binarization processing are obtained.
207. And respectively acquiring a first spatial reconstruction graph and a second spatial reconstruction graph corresponding to the core based on the two sets of sub-tomography data after binarization processing, wherein the first spatial reconstruction graph comprises the form of at least part of pores in the core, and the second spatial reconstruction graph comprises the form of at least part of cracks in the core.
Fig. 3 is a first spatial reconstruction diagram provided in an embodiment of the present application, and fig. 4 is a second spatial reconstruction diagram provided in an embodiment of the present application, please refer to fig. 3 and fig. 4, a form of at least a part of the pores in the core can be visually identified from fig. 3, and a form of at least a part of the fractures in the core can be visually identified from fig. 4, which facilitates visual observation by a tester, and also facilitates subsequent acquisition of correspondence between the pores, fractures and acoustic wave response characteristics based on the positions of the pores and fractures shown in the drawing.
208. And simulating an acoustic response process by adopting a finite element analysis mode based on the fault scanning data of the core, and acquiring acoustic response characteristic data of the core.
In this step, a digital core grid model structure diagram as shown in fig. 5 may be drawn according to the tomography data of the heart, please refer to fig. 5, and the digital core grid model structure diagram can intuitively reflect various parameters of the core. The method comprises the steps of bridging a finite element model by constructing a tetrahedral mesh of a carbonate rock sample pore space structure, constructing a pressure acoustic finite element model based on a Helmholtz equation (Helmholtz equation, which is an elliptic partial differential equation for describing electromagnetic waves and is named under the name Helmholtz of German physicist), setting corresponding boundary conditions and calculating to obtain the corresponding sound wave velocity. By testing the sound wave velocity of the carbonate rock core sample in different directions, the anisotropy of the sample can be contrastively analyzed.
In this step, the acoustic response characteristic data of the core can reflect specific acoustic response data corresponding to various positions in the core, including pores and fractures.
In one possible implementation, the steps include:
2081. finite element models like relation 4 and relation 5 are established.
pt＝p+pbRelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure at excitation, Pa;
pbis the boundary pressure, Pa.
2082. And acquiring equivalent pore parameters and equivalent skeleton parameters of the rock.
This data may be used in the calculation, for example, it may be material property parameter data as shown in table 1.
TABLE 1
| Kind of material | Equivalent density kg/m3 | Equivalent sound velocity m/s |
| Pores of | 1.29 | 340 |
| Rock framework | 2710 | 6500 |
2083. An incident boundary condition is obtained.
In this step, for the digital core mesh model structure of the core, one of the end faces is selected as an entrance, and a sound source of a time-dependent displacement function is added to emit an energy wave. The entry boundary condition is exemplified by the incidence of a sine wave, the frequency may be preset to 25kHz, and the functional expression may be the following relational expression 6, and the functional image is shown in fig. 6.
P＝P0Sin (2 π ft) relation 6
Wherein, P is elastic wave sound pressure Pa;
P0initial elastic wave sound pressure, Pa;
f is frequency, kHz;
t-time, s.
In this step, other boundaries are set as boundary conditions considering acoustic impedance. Acoustic impedance is the resistance that a medium needs to overcome to displace when conducting sound waves, i.e. the complex ratio of the sound pressure over an area to the sound flux through the area. Sound waves reflect, refract, and transmit when they encounter media with different acoustic impedances during propagation. Fig. 7 is a schematic diagram of finite element simulation loading of a digital core according to an embodiment of the present disclosure, where a probe is added to the ending boundary to detect the change of the average sound pressure with time, so as to obtain the incident and ending boundaries shown in fig. 7.
2084. And adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
When solving the oscillogram, a transient solver is needed to be added for solving the transient problem, that is, the partial differential equation contains a partial differential over time, in this embodiment, the time interval to be solved is set to [0,10] us, and the step length is set to 0.01 us. Fig. 8 is a waveform diagram obtained by finite element simulation of a digital core according to an embodiment of the present disclosure, please refer to fig. 8, where a sound wave is emitted from an incident interface, a fluctuation point appearing on the waveform diagram is a sound wave initial point after the sound wave reaches a receiving interface, and a longitudinal sound velocity obtained by finite element simulation is about 3425 m/s.
209. And acquiring corresponding relations among pores, cracks and acoustic wave response characteristics in the core based on the first spatial reconstruction map, the second spatial reconstruction map and the acoustic wave response characteristic data of the core.
Intersection analysis is carried out based on the characteristics of the pore space and the fracture shown in the CT image, such as the form and the inclination angle, and the carbonate rock sound wave velocity characteristics obtained by finite element numerical simulation experiments, so that the influence of the carbonate rock pore structure on the sound wave velocity propagation rule can be analyzed.
In this step, the inclination angle of the core is obtained through the first spatial reconstruction map and the second spatial reconstruction map, and the porosity of the core is obtained by dividing the pore volume by the total volume of the core, so that a cross plot between the absolute fracture porosity and the shear wave velocity can be drawn as shown in fig. 9, where a circle point is a carbonate sample of a mainly developing low-inclination-angle (less than 30 degrees) fracture, and a square point is a carbonate sample of a mainly developing high-inclination-angle (more than 30 degrees) fracture. It can be seen from fig. 9 that there is a good functional relationship between shear wave velocity and low dip fractures. On the oscillogram, the first fluctuation point is the time required by the elastic wave from the incident interface to the receiving interface, and the wave speed can be obtained by dividing the core length by the time. Wherein the wave velocity of the elastic wave in the low-dip fracture direction is about 3400m/s, and the wave velocity of the elastic wave in the high-dip fracture direction is about 3600 m/s. It can be seen that the direction of the fracture dip angle can affect the elastic wave velocity, and the elastic wave velocity gradually increases along with the gradual increase of the fracture dip angle of the carbonate rock sample.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
The method provided by the embodiment of the application is combined with a digital core pore space reconstruction technology, the structural characteristics of the carbonate fracture-cave carbonate rock and the basic physical property response relation are determined, the influence of the carbonate rock pore structure on the acoustic properties of the carbonate rock is researched by using a numerical simulation means, and a new research idea is provided.
The method provided by the embodiment of the application can evaluate the response of the longitudinal wave velocity of the fracture-cave carbonate rock based on the digital core. According to the method, the digital core is constructed through CT scanning, the cracks and the holes in the digital core are extracted and quantified and characterized, the longitudinal wave velocity response of the digital core is analyzed through longitudinal wave propagation numerical simulation, and the method for evaluating the longitudinal wave velocity response of the cracks and the holes in the fracture-cave carbonate rock based on the digital core is established. The method provides a new method for evaluating the response of the longitudinal wave velocity of the rock with the complex structure, and provides a basic experimental method for geophysical technologies such as seismic exploration and petroleum logging of the complex stratum.
Fig. 10 is a schematic structural diagram of an apparatus for acquiring an acoustic response of a core according to an embodiment of the present application, where the apparatus includes:
the core acquiring module 901 is used for taking out a core to be detected from the underground carbonate reservoir interval;
a parameter obtaining module 902, configured to obtain a basic information parameter of the core;
the scanning module 903 is used for scanning the core by adopting a computer tomography technology to obtain tomography data of the core;
a data processing module 904, configured to process the tomography data of the core by using a non-local mean filtering algorithm, and obtain filtered tomography data;
the data processing module 905 is further configured to perform processing on the filtered tomographic data by using a binarization algorithm based on a porosity image segmentation method to obtain two sets of sub-tomographic data after binarization processing;
an image obtaining module 906, configured to obtain a first spatial reconstruction map and a second spatial reconstruction map corresponding to the core based on the two sets of sub-tomography data after the binarization processing, where the first spatial reconstruction map includes a morphology of at least part of pores in the core, and the second spatial reconstruction map includes a morphology of at least part of fractures in the core;
the acoustic wave simulation module 907 is used for simulating an acoustic wave response process based on the fault scanning data of the core in a finite element analysis mode to obtain acoustic wave response characteristic data of the core;
a relationship obtaining module 908, configured to obtain a corresponding relationship between pores, fractures, and acoustic response characteristics in the core based on the first spatial reconstruction map, the second spatial reconstruction map, and the acoustic response characteristic data of the core.
In one possible implementation, the basic information parameter includes: length, diameter, and density.
In one possible implementation, the data processing module is configured to:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and has no dimension;
c (i) is a normalization factor, dimensionless;
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in omega rangeiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe method is a positive direction area with i as the center and side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension;
i (i) the gray values of the ith pixel points before processing are respectively the gray values without factors;
Gα(l, m) is a two-dimensional gaussian smoothing kernel with standard deviation of α, dimensionless.
In one possible implementation, the data processing module is configured to:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the method is an optimal segmentation threshold without dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) is a voxel with a gray value of i, with no dimension;
IMAXis the maximum gray level of the image.
In one possible implementation, the acoustic wave simulation module is configured to:
establishing a finite element model as a relation 4 and a relation 5;
pt＝p+pbrelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure during excitation, Pa;
pbis the boundary pressure, Pa;
obtaining equivalent pore parameters and equivalent skeleton parameters of the rock;
acquiring an incident boundary condition;
and adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
According to the device provided by the embodiment of the application, the core is scanned by adopting a computer tomography technology to obtain the tomography data of the core, the data can reflect the form information of pores and fractures in the core and can provide a data base for response calculation of a simulated sound wave process, specifically, the data can be denoised by filtering, two groups of sub-tomography data after binarization processing can be obtained by performing binarization processing on the filtered data, and a first space reconstruction graph comprising a pore structure of the core and a second space reconstruction graph comprising a fracture structure of the core are drawn according to the two groups of sub-tomography data; and moreover, based on the tomography data, a digital core can be established, the acoustic response process can be simulated, and the acoustic response characteristic data of the core can be obtained. Based on the two space reconstruction images and the sound wave response characteristic data, the corresponding relation among the pores, the cracks and the sound wave response characteristics in the rock core can be obtained.
It should be noted that: the apparatus for triggering an intelligent network service provided in the foregoing embodiment is only illustrated by dividing the functional modules when triggering an intelligent network service, and in practical applications, the function distribution may be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus for triggering an intelligent network service and the method for triggering an intelligent network service provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.
Fig. 11 is a schematic structural diagram of a computer device according to an embodiment of the present application, where the computer device 1100 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 1101 and one or more memories 1102, where the memory 1102 stores at least one program code, and the at least one program code is loaded and executed by the processors 1101 to implement the methods provided by the method embodiments. Certainly, the computer device may further have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input and output, and the computer device may further include other components for implementing the functions of the device, which is not described herein again.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, including program code, which is executable by a processor in a computer device to perform the method of core acoustic response acquisition of the above embodiments is also provided. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by hardware associated with program code, and that the above programs may be stored in a computer readable storage medium, and the above mentioned storage medium may be read only memory, magnetic or optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for obtaining a core acoustic response, the method comprising:
taking out a core to be tested from the underground carbonate rock reservoir section;
acquiring basic information parameters of the rock core;
scanning the rock core by adopting a computer tomography technology to obtain tomography data of the rock core;
processing the fault scanning data of the rock core by adopting a non-local mean filtering algorithm to obtain filtered fault scanning data;
processing the filtered tomography data by adopting a binarization algorithm based on a porosity image segmentation method to obtain two groups of sub-tomography data after binarization processing;
respectively acquiring a first spatial reconstruction graph and a second spatial reconstruction graph corresponding to the rock core based on the two sets of sub-tomography data after binarization processing, wherein the first spatial reconstruction graph comprises the form of at least part of pores in the rock core, and the second spatial reconstruction graph comprises the form of at least part of cracks in the rock core;
simulating an acoustic response process by adopting a finite element analysis mode based on the fault scanning data of the core, and acquiring acoustic response characteristic data of the core;
and acquiring corresponding relations among pores, cracks and acoustic wave response characteristics in the rock core based on the first spatial reconstruction map, the second spatial reconstruction map and the acoustic wave response characteristic data of the rock core.
2. The method of claim 1, wherein the basic information parameters comprise: length, diameter, and density.
3. The method according to claim 1, wherein the processing the tomographic data of the core using a non-local mean filtering algorithm to obtain filtered tomographic data comprises:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and has no dimension;
c (i) is a normalization factor, dimensionless;
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in omega rangeiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe method is a positive direction area with i as the center and side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension;
i (i) the gray values of the ith pixel point before processing are respectively, and the dimensions are not increased;
Gα(l, m) is a two-dimensional gaussian smoothing kernel with standard deviation of α, dimensionless.
4. The method according to claim 1, wherein the porosity-based image segmentation method is used for processing the filtered tomography data by using a binarization algorithm to obtain two sets of sub-tomography data after binarization processing, and comprises:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the optimal segmentation threshold value is zero dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) voxels with gray value i, dimensionless;
IMAXis the maximum gray level of the image.
5. The method as claimed in claim 1, wherein the obtaining acoustic response characteristic data of the core by simulating an acoustic response process in a finite element analysis manner based on the tomography data of the core comprises:
establishing a finite element model as a relation 4 and a relation 5;
pt＝p+pbrelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure during excitation, Pa;
pbis the boundary pressure, Pa;
obtaining equivalent pore parameters and equivalent skeleton parameters of the rock;
acquiring an incident boundary condition;
and adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
6. An apparatus for obtaining acoustic response of a core, the apparatus comprising:
the core acquisition module is used for taking out a core to be detected from the underground carbonate rock reservoir section;
the parameter acquisition module is used for acquiring basic information parameters of the rock core;
the scanning module is used for scanning the rock core by adopting a computer tomography technology to obtain tomography data of the rock core;
the data processing module is used for processing the tomography data of the rock core by adopting a non-local mean filtering algorithm to obtain filtered tomography data;
the data processing module is also used for processing the filtered tomography data by adopting a binarization algorithm based on a porosity image segmentation method to obtain two groups of sub-tomography data after binarization processing;
an image acquisition module, configured to acquire a first spatial reconstruction map and a second spatial reconstruction map corresponding to the core based on the two sets of sub-tomography data after the binarization processing, respectively, where the first spatial reconstruction map includes a form of at least part of pores in the core, and the second spatial reconstruction map includes a form of at least part of cracks in the core;
the acoustic wave simulation module is used for simulating an acoustic wave response process by adopting a finite element analysis mode based on the fault scanning data of the core and acquiring acoustic wave response characteristic data of the core;
and the relation acquisition module is used for acquiring the corresponding relation among the pores, the cracks and the acoustic response characteristics in the rock core based on the first spatial reconstruction map, the second spatial reconstruction map and the acoustic response characteristic data of the rock core.
7. The apparatus of claim 6, wherein the basic information parameters comprise: length, diameter, and density.
8. The apparatus of claim 6, wherein the data processing module is configured to:
the processing is performed based on the following relational expressions 1 and 2:
in the formula, (i) is a search window with the width of 2t +1 and taking i as the center, and has no dimension;
NL (i) is the gray value of the ith pixel point after processing, and the NL (i) is dimensionless;
c (i) is a normalization factor, dimensionless;
omega is a neighborhood box search area for carrying out neighborhood box weighting on each pixel, and has no factor;
ω (i, j) is the neighborhood N of the current pixeljAnd its comparison block N in omega rangeiThe weight coefficient between the two is dimensionless;
i (j) is a noise distortion image without dimension;
Nithe method is a positive direction area with i as the center and side length of f, and has no dimension;
Njis a positive direction area with j as the center and side length of f, and has no dimension;
alpha is standard deviation and has no dimension;
h is the fading degree of omega (i, j), and has no dimension;
i (i) the gray values of the ith pixel point before processing are respectively, and the dimensions are not increased;
Gα(l, m) is a two-dimensional gaussian smoothing kernel with standard deviation of α, dimensionless.
9. The apparatus of claim 6, wherein the data processing module is configured to:
the processing is performed based on the following relation 3:
wherein f is a binary segmentation result (including pores and cracks);
k*the method is an optimal segmentation threshold without dimension;
k is a preset gray level threshold value and has no dimension;
phi is actually measured porosity of the core, and has no dimension;
IMINthe gray scale is the minimum gray scale of the image, and has no dimension;
p (i) is a voxel with a gray value of i, with no dimension;
IMAXis the maximum gray level of the image.
10. The apparatus of claim 6, wherein the acoustic simulation module is configured to:
establishing a finite element model as a relation 4 and a relation 5;
pt＝p+pbrelation 5
In the formula, QmBeing a unipolar domain source, 1/s2；
Rho is density, g/cm3；
c is the longitudinal wave sound velocity, m/s;
pttotal pressure, Pa;
t is time, s;
qdbeing a dipole domain source, 1/s2；
p is sound pressure during excitation, Pa;
pbis the boundary pressure, Pa;
obtaining equivalent pore parameters and equivalent skeleton parameters of the rock;
acquiring an incident boundary condition;
and adding a transient solver, and acquiring acoustic wave response characteristic data based on a preset time interval and step length.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101787884A (en) * | 2010-01-28 | 2010-07-28 | 中国石油集团川庆钻探工程有限公司 | Reservoir fluid type discrimination method based on difference value of acoustic porosity and neutron porosity |
| US20140044315A1 (en) * | 2012-08-10 | 2014-02-13 | Ingrain, Inc. | Method For Improving The Accuracy Of Rock Property Values Derived From Digital Images |
| CN105261068A (en) * | 2015-11-16 | 2016-01-20 | 中国石油大学(华东) | Micro-CT technology-based reservoir core three-dimensional entity model reconstruction method |
| CN105484739A (en) * | 2015-11-26 | 2016-04-13 | 中国科学院武汉岩土力学研究所 | Carbonate rock formation pore pressure testing method and device |
| CN109697752A (en) * | 2018-11-20 | 2019-04-30 | 中国石油天然气集团有限公司 | Based on rock core CT image hole information extraction quantitatively characterizing rock core heterogeneity method |
| CN111833450A (en) * | 2020-07-08 | 2020-10-27 | 重庆邮电大学 | Ultrasonic three-dimensional rapid reconstruction and analysis method fused with finite element analysis method |
| CN112098293A (en) * | 2020-08-03 | 2020-12-18 | 西南石油大学 | Unsteady gas-water two-phase seepage simulation method based on pore fracture dual-medium gas reservoir |
-
2021
- 2021-01-04 CN CN202110002148.9A patent/CN114722648B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101787884A (en) * | 2010-01-28 | 2010-07-28 | 中国石油集团川庆钻探工程有限公司 | Reservoir fluid type discrimination method based on difference value of acoustic porosity and neutron porosity |
| US20140044315A1 (en) * | 2012-08-10 | 2014-02-13 | Ingrain, Inc. | Method For Improving The Accuracy Of Rock Property Values Derived From Digital Images |
| CN105261068A (en) * | 2015-11-16 | 2016-01-20 | 中国石油大学(华东) | Micro-CT technology-based reservoir core three-dimensional entity model reconstruction method |
| CN105484739A (en) * | 2015-11-26 | 2016-04-13 | 中国科学院武汉岩土力学研究所 | Carbonate rock formation pore pressure testing method and device |
| CN109697752A (en) * | 2018-11-20 | 2019-04-30 | 中国石油天然气集团有限公司 | Based on rock core CT image hole information extraction quantitatively characterizing rock core heterogeneity method |
| CN111833450A (en) * | 2020-07-08 | 2020-10-27 | 重庆邮电大学 | Ultrasonic three-dimensional rapid reconstruction and analysis method fused with finite element analysis method |
| CN112098293A (en) * | 2020-08-03 | 2020-12-18 | 西南石油大学 | Unsteady gas-water two-phase seepage simulation method based on pore fracture dual-medium gas reservoir |
Non-Patent Citations (1)
| Title |
|---|
| 廉培庆;高文彬;汤翔;段太忠;王付勇;赵华伟;李宜强;: "基于CT扫描图像的碳酸盐岩油藏孔隙分类方法", 石油与天然气地质, no. 04, 12 August 2020 (2020-08-12) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115434692A (en) * | 2022-08-12 | 2022-12-06 | 四川大学 | Gas well pressure acoustic wave measurement monitoring method and device |
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