CN111626975A - Method for quantitatively representing full-scale reservoir pores - Google Patents
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Abstract
The application provides a method for quantitatively characterizing full-scale reservoir pores, which comprises the following steps: obtaining a target continuous image representing full-scale reservoir pore development characteristics from a plurality of sub-images by an image splicing method; separating the target continuous image by adopting a threshold segmentation method to obtain pore binary images of different types of pores; and quantitatively counting the pore parameters of the reservoir in the pore binary image. By the method, 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, the area percentages and the pore size distribution of different sizes and different types of pores are extracted from the two-dimensional image, and the tight combination of qualitative description and quantitative evaluation research on the pores of the reservoir is realized.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration, and more particularly to a method for quantitatively characterizing full-scale reservoir pores.
Background
Compact reservoirs have various pore types, the pore diameters span multiple scales, and are distributed from nano-scale to micron-scale, so that the distribution rule of the whole pore space is difficult to show comprehensively. Therefore, it is necessary to study and establish a full-scale pore size distribution test method for the compact rock sample.
The existing dense reservoir micro-pore structure characterization technology includes a two-dimensional field emission Scanning electron Microscope, a three-dimensional CT scan, a Focused Ion beam electron Microscope (FIB-SEM) and the like, direct observation is mainly used as a means, resolution is a key of technical distinction, and the technology mainly studies the size, shape and distribution characteristics of pores. Two-dimensional field emission scanning electron microscopy, three-dimensional CT scanning, focused ion beam electron microscopy and the like (FIB-SEM) have ultrahigh resolution, but the resolution and the characterization scale are usually a pair of spears, the higher the resolution is, the smaller the research scale is, and vice versa. When the micro pore structure of the compact reservoir is described, the macroscopic reservoir scale characteristics are required to be solved, and if only the local pore structure is characterized, the pore structure characteristics of the sample cannot be truly and comprehensively reflected. Generally, the smaller the field of view observed, the higher the resolution and the greater the homogeneity, the poorer the corresponding representativeness. Different types of samples, influenced by mineral composition, pore size, and representative dimensions also differ. Typically, the representative dimension should be greater than 10 times the rock sample size and pore size. Through observation and analysis, the size of the pores in the research area is 50 nm-100 mu m, and the size of the large visual field of the tight sandstone is more than 1 mm.
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.
According to the shale pore quantitative characterization method based on the three-dimensional FIB-SEM image with the publication number of CN107067379A, inorganic substances, organic substances and pores in shale are subjected to high-precision three-dimensional imaging with micron and nanometer scales by using an FIB-SEM to obtain a three-dimensional FIB-SEM image; aligning each two-dimensional image of the three-dimensional FIB-SEM image, correcting the brightness nonuniformity of the image, and filtering to remove the noise of the image; dividing the preprocessed image into a pore area, an organic area and an inorganic area; marking each pore in the pore area of the image, and dividing the pore into an organic pore, an inorganic pore and a boundary pore according to the distribution condition of an organic matter area and an inorganic matter area around each pore; and respectively analyzing the organic pores, the inorganic pores and the boundary pores to obtain various characterization parameter values of the three-dimensional pores. The three-dimensional FIB-SEM image adopted by the method is high in acquisition cost and long in time, and the sample specification is micron-sized, so that the detection range is small, and the method cannot be applied to the compact sandstone with commonly developed micron-sized pores.
Therefore, there is a need to develop a method that can qualitatively describe and quantitatively characterize the pore size of a full-scale reservoir.
Disclosure of Invention
Aiming at the problems in the prior art, the method for quantitatively characterizing the pores of the full-scale reservoir is provided, the problem of contradiction between the scale and the resolution is effectively solved, the parameters such as the sizes, the number, the surface porosity and the like of different types of pores can be quantitatively counted, and therefore the close combination of the qualitative description and the quantitative evaluation research of the pores is realized, and the understanding of the pore-throat structure development in the compact reservoir is increased.
The method for quantitatively characterizing the full-scale reservoir pores comprises the following steps: s1, obtaining a target continuous image representing full-scale reservoir pore development characteristics by a plurality of sub-images through an image splicing method; s2, separating the target continuous image by adopting a threshold segmentation method to obtain pore binary images of different types of pores; and S3, quantitatively counting the reservoir pore parameters in the pore binary image. By the method, 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 S1 includes: s11, determining a target view capable of effectively representing full-scale reservoir pore development characteristics through a 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.
In a preferred embodiment, step S2 includes: s21, determining a gray threshold; s22, segmenting pixels with the gray value larger than or equal to the gray threshold value in the target continuous image into backgrounds; s23, segmenting pixels with the gray values smaller than the gray threshold value in the target continuous image into pores to obtain a pore binary image; and S24, repeating the steps S21-S23 to obtain the pore binary images of different types of pores.
In a preferred embodiment, the gray scale 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 good priorityIn a preferred embodiment, the high resolution is 8.0 × 105ppi。
In a preferred embodiment, a plurality of sub-images are obtained by the sensor.
In a preferred embodiment, the plurality of sub-field grids are 625 sub-field grids each having 25 horizontal and vertical sides.
Compared with the prior art, the method provided by the invention can effectively solve the problem of contradiction between the dimension and the resolution, and can quantitatively count parameters such as the size, the number, the surface porosity and the like of different types of pores, thereby realizing the tight combination of the qualitative description and the quantitative evaluation research of the pores and increasing the understanding of the pore-throat structure development in a 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 of quantitatively characterizing full-scale reservoir pores according to an embodiment of the present invention;
FIG. 2 shows a schematic flow chart of a method of acquiring successive images of a target according to an embodiment of the invention;
FIG. 3 shows a schematic diagram of a plurality of sub-field meshing according to an embodiment of the invention;
FIGS. 4 and 5 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. 6 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.
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.
The FIB-SEM is a double-beam system integrating the imaging function of a Scanning Electron Microscope (SEM) and the cutting function of a Focused Ion Beam (FIB), and is mainly used for carrying out high-precision three-dimensional imaging on minerals, organic matters and pores in rocks in micron and nanometer scales. The imaging principle is as follows: the surface of the sample is vertical to the ion beam; etching the surface of the sample by using FIB ion beams to expose an observation surface, scanning the observation surface by using SEM electron beams, and collecting secondary electrons or backscattered electrons to obtain high-resolution imaging; and then setting an ion beam energy parameter to denude the observation surface according to the requirement of denude thickness, imaging a new observation surface by using an electron beam after denude, repeating the step until the imaging is finished, and finally obtaining a series of images.
The images obtained by FIB-SEM are a series of two-dimensional images, and the three-dimensional images are obtained by stacking the two-dimensional images at different heights in space.
And obtaining the pores and throats as low as 5-10 nm in the compact sandstone by using an FIB-SEM double-beam scanning electron microscope.
It should be understood by those skilled in the art that when the micro pore structure of the tight reservoir is described, the macroscopic reservoir scale features are required to be solved, and if only the local pore structure is characterized, the obtained information cannot truly and comprehensively reflect the pore structure features of the sample. Different types of samples, influenced by mineral composition, pore size, and representative dimensions also differ. Typically, the representative dimension should be greater than 10 times the rock sample size and pore size. Through observation and analysis, the pore size of the tight sandstone is 50 nm-100 μm, and the size of the large visual field of the tight sandstone is more than 1 mm.
Fig. 1 is a schematic flow diagram of a method 100 for quantitatively characterizing full-scale reservoir pores provided by the present invention. As shown in fig. 1, the method 100 includes the steps of:
s110, obtaining a target continuous image representing full-scale reservoir pore development characteristics from a plurality of sub-images by an image splicing method;
s120, separating the target continuous image by adopting a threshold segmentation method to obtain pore binary images of different types of pores;
and S130, quantitatively counting reservoir pore parameters in the pore binary image.
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. 2, in step S110, a target continuous image capable of characterizing pore development of a pore size full-scale reservoir is obtained, wherein:
in S111, a target view that can effectively characterize 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. 3, in S112, the target view selected in S111 is divided into a plurality of sub-view grids under 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, high resolution imaging is performed on each of the plurality of sub-field meshes in S113. 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 S114, the multiple sub-images are sequentially re-spliced by the MAPS1.0 software to obtain a target continuous image, that is, multiple images with overlapping regions are spliced 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 visually identified. 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 through S120, that is, a pore binary image of different types of pores is obtained by separating from the target continuous image through a threshold segmentation method. Specifically, step S120 includes the steps of:
and S121, determining a gray threshold.
S122, dividing pixels with the gray value larger than or equal to a gray threshold value in the target continuous image into backgrounds;
s123, 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. 4) 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. 5.
And S124, repeating the steps from S21 to S123 to obtain the pore binary images of different types of pores.
And S130, after obtaining the pore binary images of different types of pores of the compact sandstone sample through S120, 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.
In the embodiment of the invention, the tight sandstone is taken as an example to describe the quantitative characterization of the full-scale reservoir pores, however, it should be understood that the method not only uses the tight sandstone, but also can be applied to other rock formations, such as shale, tight limestone and other unconventional reservoirs.
The invention realizes the digital imaging of full aperture and high resolution aiming at the unconventional reservoir with low porosity, low permeability and strong heterogeneity, can further identify different types of pores under the full aperture and quantitatively count the parameters such as the size, the number, the face porosity and the like of the pores, and realizes the application and popularization.
Examples
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
By the method, the distribution characteristics of different types of pores of the compact reservoir under the full pore diameter can be identified, so that the understanding of the pore-throat structure development in the compact reservoir is increased, the area percentages and the pore size distribution of different sizes and different types of pores are extracted from the two-dimensional image, and the tight combination of qualitative analysis and quantitative research on the reservoir characteristics is realized.
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 features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (10)
1. A method for quantitatively characterizing full-scale reservoir pores, comprising the steps of:
s1, obtaining a target continuous image representing full-scale reservoir pore development characteristics by a plurality of sub-images through an image splicing method;
s2, separating the target continuous image by adopting a threshold segmentation method to obtain pore binary images of different types of pores;
and S3, quantitatively counting the reservoir pore parameters in the pore binary image.
2. The method according to claim 1, wherein step S1 includes:
s11, determining a target view capable of effectively representing full-scale reservoir pore development characteristics through a 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.
3. The method according to claim 1, wherein step S2 includes:
s21, determining a gray threshold;
s22, segmenting pixels with the gray value larger than or equal to the gray threshold value in the target continuous image into backgrounds;
s23, segmenting pixels with the gray values smaller than the gray threshold value in the target continuous image into pores to obtain a pore binary image;
and S24, repeating the steps S21-S23 to obtain the pore binary images of different types of pores.
4. The method of claim 3, wherein the grayscale threshold is 30.
5. The method of claim 1, wherein the reservoir pore parameters comprise pore size, pore number, and areal porosity.
6. The method of claim 2, wherein the target field of view has a size greater than 10 times a reservoir pore size.
7. The method of claim 6, wherein the size of the target field of view is greater than 1 mm.
8. The method of claim 2, wherein the high resolution is 8.0 × 105ppi。
9. The method of claim 1, wherein the plurality of sub-images are obtained by a sensor.
10. The method of claim 2, wherein the plurality of sub-field grids are 625 sub-field grids with 25 horizontal and vertical dimensions.
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