CN111624146A - Method for quantitatively characterizing full-scale pore size distribution characteristics of compact reservoir - Google Patents
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Abstract
The application provides a method for quantitatively characterizing full-scale pore size distribution characteristics of a compact reservoir, which comprises the following steps: s1, obtaining a relaxation time cumulative distribution curve of the compact reservoir sample by a nuclear magnetic resonance method; s2, acquiring a pore diameter cumulative distribution curve of the reservoir sample by a field emission scanning electron microscope full-scale pore diameter quantitative characterization technology; and S3, acquiring the target function relation between the relaxation time and the pore size distribution of the reservoir sample by utilizing the relaxation time cumulative distribution curve and the pore size cumulative distribution curve and combining a linear regression method. By the method, the conversion from the relaxation time distribution of compact reservoir gaps to the pore size distribution can be realized, and the full-scale characterization of the compact reservoir pore size is quantitatively characterized.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a method for quantitatively characterizing full-scale pore size distribution characteristics of a compact reservoir.
Background
Modern unconventional oil and gas exploration practices reveal that oil and gas accumulation and distribution in tight sandstone reservoirs are very different from oil and gas occurrence in conventional sandstone reservoirs. In tight sandstone reservoirs, hydrocarbons are mainly collected and distributed in micro-nano pore throat network systems therein. Compared with a millimeter-micron pore throat network system of a conventional sandstone reservoir, the micron-nanometer pore throat network system has the advantages of more complex structure, smaller pore diameter and stronger heterogeneity, and particularly, the full-scale pore diameter distribution characteristics are difficult to describe and need to be realized by establishing a targeted method system.
The existing compact sandstone reservoir pore diameter full-scale quantitative characterization method mostly adopts a method of combining a nuclear magnetic resonance technology and a high-pressure mercury injection technology. In the method, the main measurement range of the nuclear magnetic resonance technology is 5nm to 100 mu m, the detection range of the high-pressure mercury injection technology is 10nm to 100 mu m, and the method combines the throat distribution result obtained in the high-pressure mercury injection experiment with the T of the nuclear magnetic resonance2And carrying out comparative analysis on the relaxation time result, and converting the relaxation time of nuclear magnetic resonance into the pore size distribution of the compact reservoir through formula deduction so as to realize the quantitative characterization of the pore size. However, the method has a more obvious problem that the result tested by the high-pressure mercury intrusion test is the throat distribution characteristic of the compact reservoir, the result tested by the nuclear magnetic resonance test is the pore development characteristic of the compact reservoir, the two are two main components in the microstructure of the reservoir respectively, and the two should not be confused and mutually converted.
Through patent and literature search, the currently disclosed aperture full-scale characterization method is greatly different from the technology provided by the invention, and the technical categories related to the aperture full-scale characterization method provided by the invention and the differences of the technical categories and the technology provided by the invention are described as follows.
The shale pore size distribution test method with the publication number of CN105486621B is mainly characterized in that a gas adsorption test is adopted to represent the pore size distribution characteristics of 2nm-200nm, a high-pressure mercury intrusion test result is adopted, and a Washburn equation is utilized to calculate the residual pore size distribution characteristics. The method is a pore size characterization method designed for shale reservoirs, and various test results are spliced. Shale reservoir and methodThe pore size distribution of the tight sandstone reservoir is obviously different, the invention aims at the pore size characterization of the tight sandstone, adopts the conversion based on the relaxation time of the nuclear magnetic resonance T2, and directly converts T2The spectrum is converted into a pore size distribution curve, and the problem of splicing does not exist. Literature nuclear magnetic resonance T2The research of the spectrum conversion pore radius distribution method is mainly realized by comparing nuclear magnetic resonance T2The pore radius is converted by the spectrum and the pore throat distribution curve measured by a high-pressure mercury intrusion method, and the pore radius is mixed with the throat radius. According to the invention, the nuclear magnetic resonance and scanning electron microscope image processing are both characterized by the pore radius, so that the problem that the pore and the throat do not correspond is effectively avoided.
Therefore, a calculation method for the quantitative distribution characterization of the pore diameter under the full scale of the tight sandstone reservoir is needed to be provided, so that the problem that the distribution of the pore diameter in the tight reservoir is not clear under the full scale due to strong heterogeneity is solved, and the understanding of the quantitative characterization of the pore diameter in the tight reservoir can be solved.
Disclosure of Invention
Aiming at the problems in the prior art, the nuclear magnetic resonance technology and the field emission scanning electron microscope are discovered to have relatively consistent detection ranges through comparative analysis, the main measurement ranges are all about 5nm to 100 mu m, and the pore spaces in all scales of the compact sandstone reservoir can be basically covered, so that the nuclear magnetic resonance technology and the field emission scanning electron microscope are in contact, and the quantitative conversion representation of the relaxation time distribution to the pore size distribution of the nuclear magnetic resonance is realized.
The method for quantitatively characterizing the full-scale pore size distribution characteristics of the compact reservoir provided by the invention comprises the following steps: s1, obtaining a relaxation time cumulative distribution curve of the compact reservoir sample by a nuclear magnetic resonance method; s2, acquiring a pore diameter cumulative distribution curve of the reservoir sample by a field emission scanning electron microscope full-scale pore diameter quantitative characterization technology; and S3, acquiring the target function relation between the relaxation time and the pore size distribution of the reservoir sample by utilizing the relaxation time cumulative distribution curve and the pore size cumulative distribution curve and combining a linear regression method. By the method, the conversion from the relaxation time distribution of compact reservoir gaps to the pore size distribution can be realized, and the full-scale characterization of the compact reservoir pore size is quantitatively characterized.
In a preferred embodiment, step S2 includes: s21, obtaining a target continuous image representing full-scale reservoir pore development characteristics by a plurality of sub-images through an image splicing method; s22, separating the target continuous image by a threshold segmentation method to obtain pore binary images of different types of pores; s23, quantitatively counting the reservoir pore parameters in the pore binary image to obtain the pore diameter cumulative distribution curve. Through the implementation mode, the distribution characteristics of different types of pores of the compact reservoir under the full pore size can be identified, so that the understanding of the pore-throat structure development in the compact reservoir is increased, and the area percentages and the pore size distribution of different sizes and different types of pores are extracted from the two-dimensional image.
In a preferred embodiment, step S21 includes: s11, determining a target view capable of effectively representing full-scale reservoir pore development characteristics through a field emission scanning electron microscope; s12, dividing the target view into a plurality of sub-view grids; s13, respectively carrying out high-resolution imaging on each sub-view grid in the sub-view grids to obtain a plurality of sub-images; and S14, splicing the multiple sub-images in sequence under the premise of ensuring the integrity to obtain the target continuous image.
In a preferred embodiment, step S22 includes: s221, determining a gray threshold; s222, segmenting pixels of which the gray values are greater than or equal to the gray threshold value in the target continuous image into backgrounds; s223, dividing pixels with the gray value smaller than the gray threshold value in the target continuous image into pores to obtain a pore binary image; and S224, repeating the steps S221 to S223 to obtain the pore binary images of different types of pores.
In a preferred embodiment, in step S3, the objective function relationship is expressed by the following equation:
wherein r iscPore size, T, of the reservoir sample2For the relaxation time of the reservoir sample, n and C are the power exponent and constant, respectively, to be determined.
In a preferred embodiment, step S3 includes: s31, determining a relaxation time value and an aperture value corresponding to the same ordinate value in the relaxation time cumulative distribution curve and the aperture cumulative distribution curve; s32, determining the relaxation time value and the aperture value as an abscissa and an ordinate respectively to obtain a target value pair; s33, repeating the steps S31-S32 to obtain at least one target value pair; and S34, substituting the at least one target value pair into the target function relational expression, and solving n and C by using a linear regression method.
In a preferred embodiment, the gray threshold is 30.
In a preferred embodiment, the reservoir pore parameters include pore size, number of pores, and areal porosity.
In a preferred embodiment, the target field of view has a dimension greater than 10 times the reservoir pore size.
In a preferred embodiment, the size of the target field of view is greater than 1 mm.
In a preferred embodiment, the high resolution is 8.0 × 105ppi。
The method for quantitatively characterizing the full-scale pore size distribution of the compact reservoir provided by the invention can convert the nuclear magnetic resonance relaxation time distribution of the pores of the reservoir into the pore size distribution, realize the quantitative characterization of the pore size distribution of the compact reservoir under the full scale, solve the problem of ambiguous distribution of the pore size in the compact reservoir under the full scale due to strong heterogeneity, and also solve the recognition of the quantitative characterization of the pore size in the compact reservoir.
The features mentioned above can be combined in various suitable ways or replaced by equivalent features as long as the object of the invention is achieved.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 shows a schematic flow diagram of a method for quantitative characterization of tight reservoir full-scale pore size distribution according to an embodiment of the present invention;
FIG. 2 shows a schematic flow diagram of a full-scale aperture quantitative characterization method according to an embodiment of the present invention;
FIG. 3 shows a schematic flow chart of a method of acquiring successive images of a target according to an embodiment of the invention;
FIG. 4 shows a schematic diagram of a plurality of sub-field meshing according to an embodiment of the invention;
FIGS. 5 and 6 show a grayscale image before segmentation of chlorite intergranular pores and a binarized image after segmentation, respectively, according to an embodiment of the present invention;
FIG. 7 shows an image of a two-dimensional target sequential image reflecting a full-scale aperture of a reservoir in accordance with an embodiment of the invention;
FIG. 8 shows a comparison of compact reservoir pore relaxation time cumulative distribution curves and pore size cumulative distribution curves in accordance with an embodiment of the present invention.
In the drawings, like parts are provided with like reference numerals. The drawings are not to scale.
Detailed Description
The invention will be further explained with reference to the drawings.
Fig. 1 is a schematic flow chart 100 of a method for quantitatively characterizing the full-scale pore size of a tight reservoir provided by the present invention. As shown in fig. 1, the method 100 includes:
s110, acquiring a relaxation time cumulative distribution curve of the compact reservoir sample by a nuclear magnetic resonance method;
s120, acquiring a pore diameter cumulative distribution curve of the reservoir sample by a field emission scanning electron microscope full-scale pore diameter quantitative characterization technology;
and S130, acquiring a target function relation between the relaxation time and the pore size distribution of the reservoir sample by utilizing the relaxation time cumulative distribution curve and the pore size cumulative distribution curve and combining a linear regression method.
Specifically, in S110, according to the selected target layer compact sandstone sample, performing nuclear magnetic resonance test on T2Measuring relaxation time, wherein the magnitude of the relaxation time depends on the strength of the acting force of the fluid molecules on the solid surface, and accumulating the relaxation time as an abscissaAnd taking the distribution value as a vertical coordinate to obtain a full-scale pore relaxation time cumulative distribution curve of the compact reservoir sample.
For a pure material sample (such as pure water) in which the surrounding environment and interaction of each hydrogen nucleus are the same, the relaxation of the atomic nuclei in the individual pores can be expressed by a relaxation time through a diffusion surface relaxation model of nuclear magnetic resonance, when T is2Can be expressed as:
the first term on the right side of equation (1) is the bulk relaxation term, the magnitude of which depends on the properties of the saturated fluid. Volume relaxation T2BIt is meant that when a fluid is present in the larger pores (believed to be independent of the pore space), the fluid itself undergoes a decay, also referred to as free relaxation. For a porous medium sample such as reservoir rock, which has a very complex mineral composition and pore structure, a fluid is present in the porous medium, and is divided and surrounded by a plurality of interfaces, the shape and size of the pores are not uniform, and the pore space is not restricted, so that the volume relaxation has no obvious correlation with the pore surface, but is related to the whole test system, such as the temperature, rock wettability, fluid viscosity and the like under the environment where the test system is located, wherein the most important parameters are the properties of the fluid in the pores.
In nmr applications, the relaxation of the fluid is generally negligible, since the relaxation strength of the rock surface is much greater than the free relaxation strength of the fluid.
The third term on the right side of the formula (1) is a diffusion relaxation term, and the diffusion relaxation term can be removed according to an experimental technology of nuclear magnetic resonance diffusion removal measurement. After removing the first term and the third term on the right side of the equation, equation (1) can be simplified as follows:
in the formula, ρ2The surface relaxation strength is controlled by mineral compositionAnd pore surface properties; S/V is the specific surface of an individual pore, the size of which is inversely proportional to the pore radius. It can be seen that when the pore structure is more complex, the pores are smaller and the specific surface area is larger, the influence of the pore surface interaction is stronger, and T2The shorter the time.
As can be seen from formula (2), the observed relaxation time T2Associated with the specific surface of the porous medium. If the pore structure is simplified into a spherical or columnar shape, the relationship between the specific surface area of the pores and the pore diameter is changed to
Then there are:
in the formula, FsIs the shape factor of the pore (for spherical pores, F)s3; for columnar voids, Fs2), dimensionless; r iscPore radius, μm.
The actual formation has complicated pore structure, T2The distribution is in a power function relationship with the pore radius:
in the formula: n is a power exponent and is dimensionless.
Because the rho cannot be actually measured by the current equipment method2And FsTherefore, it is impossible to use the formula (4) for converting the nuclear magnetic resonance T2The distribution is transformed with the pore radius distribution curve.
When the core is 100% saturated water, the nuclear magnetic resonance T of the core is utilized2The spectra allow the evaluation of the pore size and its corresponding pore volume distribution. For the pore size distribution characterization of the tight reservoir, the pore size distribution characterization can be obtained by using a field emission scanning electron microscopy, namely, the step S120 provided by the invention: acquiring a pore size cumulative distribution curve of the reservoir sample by using a full-scale quantitative characterization technology of a field emission scanning electron microscope, as shown in fig. 2, the step S120 may specifically include the following steps:
s121, obtaining a target continuous image representing full-scale reservoir pore development characteristics by using a plurality of sub-images through an image splicing method;
s122, separating the target continuous image by adopting a threshold segmentation method to obtain pore binary images of different types of pores;
s123, quantitatively counting reservoir pore parameters in the pore binary image to obtain the pore diameter cumulative distribution curve.
Briefly, pores and throats as low as 5-10 nm in compact sandstone are obtained, and image splicing continuous imaging can be realized through software, namely, a plurality of images with overlapped parts are spliced into a large-scale seamless high-resolution image. After an image capable of effectively standardizing the full pore development characteristics of the reservoir is obtained, the area percentages and pore size distribution of pores of different sizes and different types are extracted from the two-dimensional image.
Herein, the different types of pores include intergranular pores, erosion pores, clay mineral intergranular pores, and the like.
Specifically, as shown in fig. 3, in step S121, a target continuous image capable of characterizing pore development of a pore size full-scale reservoir is obtained, wherein:
in S1211, a target field of view capable of effectively characterizing full-scale reservoir pore development features is determined by scanning electron microscopy. Specifically, under a FIB-SEM double-beam scanning electron microscope, finding a visual field of the tight sandstone sample, wherein as mentioned above, the sizes of pores and throats in the target visual field can be as low as 5-10 nm, and the target visual field of the tight sandstone should be larger than 1mm, so that the tight sandstone sample under the visual field comprises pores of various types and sizes, and the tight sandstone sample can be truly and completely characterized.
As shown in fig. 4, in S1212, the target view selected in S1211 is divided into a plurality of sub-view grids under the FIB-SEM. For example, the number of the sub-view grids is 25 × 25, wherein the sizes of the sub-view grids may be the same or different, and the invention is not limited herein.
Next, each of the plurality of sub-field meshes is subjected to high resolution imaging in S1213. In operation, the FIB-SEM may be focused on a target sub-field grid and then set to a high resolution at which the target sub-field grid is imaged so that the smaller pores therein can also be delineated. It should be understood that during the imaging process, the range of imaging should be larger than the range of the target sub-field grid to enable alignment for complete imaging, avoid omission, and ensure accuracy of the result. And (4) carrying out high-resolution imaging (with consistent resolution) on each sub-view grid one by one according to the steps to obtain a plurality of sub-images reflecting the pore characteristics of the sub-view grids.
It can be seen that the multiple sub-images are small-scale and high-resolution images, and the edges of two adjacent sub-images have an overlapping region. Optionally, the multiple sub-images may also be acquired by other means, such as images obtained at different times, at different viewing angles, or by different sensors.
Preferably, high resolution refers to 8.0 × 105ppi。
In S1214, the multiple sub-images are sequentially re-stitched by the MAPS1.0 software to obtain a target continuous image, that is, multiple images with overlapping regions are stitched into a large seamless high-resolution image, the image can reflect the full-scale pores of the tight sandstone sample at high resolution, and the two-dimensional target continuous image is a grayscale image, and the pores can be identified by naked eyes. It should be understood here that, in the stitching, it should be premised on ensuring the integrity, that is, it needs to be relied on according to the arrangement order of the multiple sub-views in the original target view, so as to ensure the integrity of the finally obtained two-dimensional target continuous image.
After the image capable of effectively representing the full-scale pore development characteristics of the reservoir is obtained, in order to identify and represent different types of pores, a reasonable segmentation algorithm is selected to distinguish the pores from the skeleton, namely, the area percentages and pore size distribution of the pores with different scales and different types are extracted from the gray level image. In the embodiment of the present invention, identification and characterization are performed in S122, that is, a pore binary image of different types of pores is obtained by separating from the target continuous image by using a threshold segmentation method. Specifically, step S120 includes the steps of:
and S1221, determining a gray threshold value.
S1222, dividing the pixels with the gray value larger than or equal to the gray threshold value in the target continuous image into the background;
s1223, dividing pixels with the gray values smaller than the gray threshold value in the target continuous image into pores to obtain a pore binary image;
it is assumed that the gray values of adjacent pixels within the object (aperture) or background (skeleton) in the gray-scale image are similar, while the gray values of the pixels between the object and the background are different. Based on
Professional image processing software, taking a chlorite intercrystalline pore image (fig. 5) as an example, finds that the gray values of the pore regions are mainly distributed in [0, 60 ] through manual adjustment of a segmentation threshold value and visual observation of the pore extraction process]That is, when the division threshold is about 30, the division of substantially all pores can be realized, and the result is shown in fig. 6.
And S1224, repeating the steps S1221 to S1223 to obtain the pore binary images of different types of pores.
And S123, after obtaining the pore binary images of different types of pores of the compact sandstone sample through S122, characterizing the pore parameters. Specifically, based on the binary image, parameters such as the size, the number, the surface porosity and the like of different types of pores can be quantitatively counted by using software, so that the close combination of qualitative pore description and quantitative pore research is realized. And taking the pore radius as a horizontal coordinate and the cumulative distribution value as a vertical coordinate to obtain the full-scale pore radius cumulative distribution curve of the compact reservoir sample.
In the embodiment of the invention, the pore space of the full-scale reservoir is quantitatively represented by taking the tight sandstone as an example, however, the method is not only suitable for the tight sandstone, but also suitable for other rock formations, such as shale, tight limestone and other unconventional reservoirs.
The inventors have made attempts in the practice of a dense oil exploration work using the above method. Taking an Erdos basin H221 well 36# sample as an example, the distribution of chlorite mineral intercrystalline pores is considered, a large-area argon ion polishing sample is selected, an analysis area is divided into 625 grids with 25 parts of transverse and longitudinal directions, the horizontal range of each grid is 50 microns, the minimum pore can be identified to be 58nm, all effective pores can be basically observed, the horizontal range of the whole area is 1.19mm (as shown in figure 6), the lower limit of the large visual field size of the tight sandstone is also exceeded, and the development and distribution characteristics of reservoir pores can be effectively represented.
As shown in table 1 below, a total of 298703 effective pores with an areal porosity of 4.71% were identified by the method of the present invention, with the smallest pores having a pore diameter of 58nm and the largest pores having a pore diameter of 15364 nm. The distribution of the pore diameter is in a multi-modal distribution, and the pore distribution is the largest at 900-1000 nm.
TABLE 1 statistical table for pore characterization of samples H221-36
In S130, the relaxation time cumulative distribution curve obtained in S110 and the pore size cumulative distribution curve obtained in S120 are used in combination with a linear regression method to obtain an objective function relationship between the relaxation time and the pore size distribution of the reservoir sample, so that the nmr cumulative distribution curve and the pore radius cumulative distribution curve can be converted into each other.
Order to
Then there are:
the equation (5) is the objective function relationship between relaxation time and pore radius, where T2As pore relaxation time, rcN and C are the power index and constant, respectively, to be determined for the pore radius.
It can be seen that there is a clear similarity in morphology between the pore radius cumulative distribution curve and the relaxation time cumulative distribution curve, indicating that they are comparable. Specifically, step S130 may include the steps of:
s131, determining a relaxation time value T corresponding to the same ordinate value in the relaxation time cumulative distribution curve and the pore size cumulative distribution curve20And the pore size rc0；
S132, converting the relaxation time value T20And the aperture value rc0Respectively determined as abscissa and ordinate to obtain target value pair (T)20，rc0)；
S133, repeating the steps S131 to S132 to obtain at least one target value pair (T)2，rc)；
S134, the at least one target value pair (T) is processed2，rc) Substituting the obtained product into the objective function relational expression, and solving n and C by using a linear regression method.
Selecting the relaxation time value with the same accumulated content and the corresponding aperture value through the pore radius accumulated distribution curve and the relaxation time accumulated distribution curve, and substituting the values into a formula (5) to obtain the values of C and n, thereby realizing the T of the rock with 100 percent of saturated water2And directly converting the distribution curve into a pore size distribution curve to obtain the quantitative pore size representation of the compact sandstone sample.
Taking the E.deltoid basin H221 well 35# sample as an example, let T2Converting the spectrum and the pore size distribution into cumulative distribution curves, and selecting a series of T with the same cumulative content2The values and corresponding aperture values (FIG. 8) are substituted into equation (5) and C and n are iteratively solved by regression to achieve T2The spectra are converted to pore size distributions. On the basis of the method, the average value of C of a compact reservoir in the 7 sections of the research area is 181, the average value of n is 0.8248, and then the objective function relation between the pore radius and the relaxation time of the sample is as follows:
rc＝181T2 1.2124
the method for quantitatively characterizing the full-scale pore size distribution of the compact reservoir provided by the invention can convert the nuclear magnetic resonance relaxation time distribution of the pores of the reservoir into the pore size distribution, realize the quantitative characterization of the pore size distribution of the compact reservoir under the full scale, solve the problem of ambiguous distribution of the pore size in the compact reservoir under the full scale due to strong heterogeneity, and also solve the recognition of the quantitative characterization of the pore size in the compact reservoir.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "bottom", "top", "front", "rear", "inner", "outer", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that various dependent claims and features described herein may be combined in ways other than those described in the original claims. It is also to be understood that features described in connection with a single embodiment may be used in other such embodiments.
Claims (10)
1. A method for quantitatively characterizing the full-scale pore size distribution of a tight reservoir is characterized by comprising the following steps:
s1, obtaining a relaxation time cumulative distribution curve of the compact reservoir sample by a nuclear magnetic resonance method;
s2, acquiring a pore diameter cumulative distribution curve of the reservoir sample by a field emission scanning electron microscope full-scale pore diameter quantitative characterization technology;
and S3, acquiring the target function relation between the relaxation time and the pore size distribution of the reservoir sample by utilizing the relaxation time cumulative distribution curve and the pore size cumulative distribution curve and combining a linear regression method.
2. The method according to claim 1, wherein step S2 includes:
s21, obtaining a target continuous image representing full-scale reservoir pore development characteristics by a plurality of sub-images through an image splicing method;
s22, separating the target continuous image by adopting a threshold segmentation method to obtain pore binary images of different types of pores;
s23, quantitatively counting reservoir pore parameters in the pore binary image to obtain the pore diameter cumulative distribution curve.
3. The method according to claim 2, wherein step S21 includes:
s11, determining a target view capable of effectively representing full-scale reservoir pore development characteristics through a field emission scanning electron microscope;
s12, dividing the target visual area into a plurality of sub visual area grids;
s13, respectively carrying out high-resolution imaging on each sub-view grid in the sub-view grids to obtain a plurality of sub-images;
and S14, splicing the sub-images in sequence on the premise of ensuring the completeness to obtain the target continuous image.
4. The method according to claim 2, wherein step S22 includes:
s221, determining a gray threshold;
s222, segmenting pixels of which the gray values are larger than or equal to the gray threshold value in the target continuous image into backgrounds;
s223, dividing pixels with the gray values smaller than the gray threshold value in the target continuous image into pores to obtain a pore binary image;
s224, repeating the steps S21-S23 to obtain the pore binary images of different types of pores.
5. The method according to claim 1, wherein in step S3, the objective function relationship is expressed by the following equation:
wherein r iscIs the pore size, T, of the reservoir sample2N and C are the power exponent and constant, respectively, to be determined for the relaxation time of the reservoir sample.
6. The method of claim 5, wherein step S3 includes:
s31, determining a relaxation time value and an aperture value corresponding to the same ordinate value in the relaxation time cumulative distribution curve and the aperture cumulative distribution curve;
s32, determining the relaxation time value and the aperture value as an abscissa and an ordinate respectively to obtain a target value pair;
s33, repeating the steps S31-S32 to obtain at least one target value pair;
and S34, substituting the at least one target value pair into the target function relational expression, and solving n and C by using a linear regression method.
7. The method of claim 4, wherein the grayscale threshold is 30.
8. The method of claim 2, wherein the reservoir pore parameters comprise pore size, pore number, and areal porosity.
9. The method of claim 3, wherein the target field of view has a size greater than 10 times a reservoir pore size.
10. The method of claim 9, wherein the size of the target field of view is greater than 1 mm.
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