CN109345625B - Rock core image self-adaptive partition three-dimensional reconstruction method - Google Patents
Rock core image self-adaptive partition three-dimensional reconstruction method Download PDFInfo
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Abstract
The invention discloses a core image self-adaptive partitioning three-dimensional reconstruction method, which divides an image into regions by using the size and distribution characteristics of pores in a core image. The inside of each region after being partitioned has homogeneity, and the local pore information can be used for representing the pore characteristics of a larger region, so that the calculation amount and the reconstruction time of reconstruction are greatly reduced. By the aid of the method and the device for the partitioned reconstruction of the rock core image, the problem that the reconstruction size is limited by computer memory and reconstruction time can be solved, and three-dimensional structure information of a rock sample with a larger size can be acquired.
Description
Technical Field
The invention relates to a partitioned reconstruction method, in particular to a core image self-adaptive partitioned three-dimensional reconstruction method, and belongs to the technical field of three-dimensional image reconstruction.
Background
The core porous medium is one of the most important storage spaces of oil and gas energy in nature, and the form (geometric shape and connectivity) of the pore space strongly influences the storage and migration of oil and gas in a reservoir. Therefore, the research on the microstructure of the core pores is becoming the focus of attention.
At present, two methods for obtaining the pore structure of the core are mainly used: firstly, a real three-dimensional digital core is directly constructed through equipment such as a Computer Tomography (CT) and a focused ion beam scanning electron microscope (FIB-SEM); and secondly, performing three-dimensional mathematical modeling by using the two-dimensional image. The first method has not been commonly used because the scanning equipment is expensive. In addition, since the high-resolution image can reveal important details about pore networks and pore connectivity, the method has great significance for core characteristic research, but is limited by an imaging mechanism of a scanning device, the resolution of the scanned image is contradictory to the image size, and it is expensive and time-consuming to acquire a clear image of a large-view 3D pore structure (such as dense rock like shale) in micron or nanometer. While 2D high resolution images can be acquired at lower cost and higher efficiency on a larger scale. For this reason, three-dimensional reconstruction of cores based on two-dimensional images remains an important research direction.
Most of the existing three-dimensional reconstruction algorithms are based on the assumption that the three-dimensional structure is homogeneous and isotropic, but in practical application, most of the core microstructure is heterogeneous and anisotropic. To obtain a more complete pore distribution of the rock sample, a high-resolution image with a large view field is required, and such an image generally exhibits strong heterogeneity as a whole and has a large image size, for example, a 1cm × 1cm rock sample, the image size is as high as 10000 × 10000 in the case of a resolution of 1 μm/pixel, and the image size obtained by scanning is larger in the case of a higher nm-level resolution. The larger the image size is, the higher the requirements on reconstruction time and computer memory are, and the direct three-dimensional reconstruction of the images is still a big difficulty in three-dimensional reconstruction research, and the related research is less.
Disclosure of Invention
The invention aims to solve the problems that a large-vision-field core image is difficult to directly carry out three-dimensional reconstruction and complete structure information of a rock sample is difficult to obtain, and provides a core image self-adaptive partitioning three-dimensional reconstruction method for respectively reconstructing and integrating various kinds of structure information contained in the rock sample.
The invention realizes the purpose through the following technical scheme:
1. the invention discloses a core image self-adaptive partition three-dimensional reconstruction method which comprises the following steps:
(1) calculating the optimal local porosity template size of the region to be divided;
(2) calculating the corresponding local porosity of all pixel points in the region to be divided by using the optimal template;
(3) dividing pixel points with local porosity larger than p into one region according to the porosity p of the region to be divided, and classifying the rest pixel points into another region;
(4) judging whether the existing regions need to be divided or not according to the area of each region and the maximum and minimum local porosity difference;
(5) repeating the steps (1) to (4) for the areas which need to be divided until no area needs to be divided;
(6) calculating the porosity of each region and the area ratio of the region to the whole image, and extracting a typical representative unit body REV of each region by taking the porosity as a basis;
(7) performing three-dimensional reconstruction on all REVs by using a traditional three-dimensional reconstruction algorithm;
(8) and (4) calculating pore throat parameters of all the reconstructed three-dimensional pore structures, and integrating the parameters according to the area ratio calculated in the step (6), thereby obtaining the three-dimensional structure parameters of the whole rock sample.
The basic principle of the method is as follows:
in practical application, although the overall heterogeneity of the core image is strong, the pore sizes and the distribution in different areas of the image are uniform, and the core image has homogeneity. The size and distribution of the pores can be critical for zone division. The local porosity distribution curve is one of important methods for judging the homogeneity characteristic of the rock core image, and the method comprises the steps of scanning the whole training image by using a fixed template, calculating the porosity in the template, namely the porosity of a local area of the image, and counting the occurrence probability and the distribution condition of the local porosity so as to obtain the homogeneity or the non-homogeneity characteristic of the image. In general, the narrower the local porosity distribution range of the image, the stronger the homogeneity of the core. Therefore, the local porosity may reflect the size and distribution of the pores to some extent. The porosity of a local area with each pixel point as the center is calculated, so that the porosity characteristics around the pixel point can be represented, the pixel points with similar local porosity are divided into the same area, and the distribution range of the local porosity curve of the area is narrow, so that the homogeneity inside the area is indicated. And considering that the shapes of the divided regions are irregular, the regular typical representative unit bodies REV of each region can be obtained according to the porosity of the regions, the representative unit bodies REV are respectively reconstructed as training images of the regions, finally, the reconstructed three-dimensional structure related parameters are integrated according to the area occupation ratio of each region, and the integrated result is used as the related parameters of the whole image corresponding to the three-dimensional structure.
Specifically, in the step (1), the local porosity is defined as follows:
the local porosity is calculated by first setting a measuring unit
The measuring unit is a square with the side length L in the two-dimensional image, and is a cube with the side length L in the three-dimensional image, wherein N (G) represents the proportion of the G phase in the measuring unit, N (G) represents the area of the G phase for the two-dimensional image, N (G) represents the volume of the G phase for the three-dimensional image, m is the number of the measuring units, and delta (x) is a Dirac function;
the side length of the optimal local porosity template is determined according to the pore size and distribution of the region to be divided, and the maximum value of the chord length of the pore and the background in the x and y directions in the image is used as the optimal template size m _ size, so that the condition that the template is totally a pore point or totally a background point cannot occur in the process of traversing the image, and the distribution condition of the pore is better reflected;
in the step (2), the optimal template is used for calculating the local porosity p _ local of each pixel point of the region to be divided, and the value is obtained by superposing the center of the template and the pixel point and calculating the porosity in the template;
in the step (3), the porosity p _ area of the whole region to be divided is taken as a threshold, if the p _ local of the pixel points in the region to be divided is less than p _ area, the pixel points meeting the condition are divided into one region, and the pixel points meeting the p _ local > p _ area are automatically classified into another region;
in the step (4), whether the areas of the existing regions are large enough is judged, for the regions with large enough areas, the difference between the maximum local porosity and the minimum local porosity of the pixel points in the region is calculated, if the difference is large, the region is proved to have heterogeneity, and the division needs to be continued;
in the step (5), for the region which needs to be further divided, since the pore size and the distribution of the region are different from those of the previous region and the optimal template size is also changed, the optimal local porosity template size of each region needs to be recalculated, and the steps (1) to (4) are repeated until no region needs to be divided;
in the step (6), the area ratio of the divided regions to the whole image needs to be calculated, regular typical representative unit bodies REV need to be selected from the regions because the pore sizes and the distribution of the regions are uniform but the shapes of the regions are irregular, and the porosity of the regions is selected as a standard to enable the REV to be more representative so that the REV is close to the porosity of the regions represented by the REV;
in the step (7), the REV of each region is used as a training image, and the training image is three-dimensionally reconstructed by using the existing three-dimensional reconstruction algorithm, such as a simulated annealing algorithm;
in the step (8), pore throat parameters of all the reconstructed three-dimensional structures are calculated, the parameters are integrated according to the area ratio of the representative region of the parameters, and the integrated result is used as the structural parameters of the three-dimensional structure corresponding to the whole image.
The invention has the beneficial effects that:
according to the method, the pore distribution condition of the real core image is fully considered, the whole core image does not need to be directly reconstructed, and the pore characteristics are utilized to divide the image into regions, so that the homogeneity is presented in the regions. Because the pore sizes and the distribution in the same region have similarity, a part of representative regions can be selected for reconstruction, so that the reconstruction data volume is greatly reduced, and the problem that the direct three-dimensional reconstruction is difficult to perform when the size of a core image is overlarge is solved. The reconstruction results of all the regions are integrated according to the area ratio of the regions, so that the pore characteristics of the whole rock core image corresponding to the three-dimensional structure can be reflected to a certain extent.
Drawings
FIG. 1 is a true core image;
FIG. 2 is a pore pattern layer corresponding to the true core image of FIG. 1;
FIG. 3 is a partition of an image after region division;
FIG. 4 is a typical representative unit cell REV selected for each region;
FIG. 5 is a three-dimensional structure of each region after REV reconstruction;
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
(1) FIG. 1 is a raw core image of 5984X 5878 size with a resolution of 2.277 μm/pixel, which is first pore extracted to obtain a binary core pore pattern layer, as shown in FIG. 2.
(2) And calculating the porosity of the region to be divided and the size of the optimal template corresponding to the porosity, and traversing the image by using the optimal template so as to obtain the local porosity corresponding to each pixel point of the image. The first calculation resulted in a porosity of 1.705% for the entire image and a best local porosity template size of 321.
(3) And dividing the pixel points with the local porosity value smaller than the regional porosity in the region to be divided into one region, and taking the rest pixel points as the other region.
(4) And judging whether the divided areas need to be divided or not. Because the image size is large, it is set that when the area of the region is larger than 1000 × 1000 and the maximum and minimum local porosity difference of the pixel points in the region is larger than 20%, the region needs to be further divided.
(5) Repeating the steps (2) to (4) until no area needs to be divided, dividing the whole image into 3 areas, giving different colors to different areas for easy observation, and outputting the colored image, as shown in fig. 3.
(6) The porosities of the different regions and their area ratios with respect to the whole image were calculated and the results are shown in table 1.
TABLE 1
(7) According to the porosity of each region, a regular region with a porosity similar to that of the region is selected from the regular region as a representative unit body REV of each region, namely a training image, the training image selected from each region is shown in FIG. 4, the training images of a light gray region, a middle gray region and a dark gray region are sequentially arranged from left to right, the sizes of the training images are 512 × 512, and the porosities are respectively 0.263%, 1.051% and 3.365%.
(8) These training images were reconstructed three-dimensionally using a phase-stationary graded simulated annealing algorithm, and the results are shown in fig. 5, which are presented in the same order as the training images of fig. 4. Pore throat parameters were calculated for each structure and the results are shown in table 2.
TABLE 2
(9) And (3) integrating the pore throat parameters of the three-dimensional structures of the regions obtained by calculation in the step (8) according to the area ratio of the regions, and obtaining the results shown in table 3.
TABLE 3
By observing the size and distribution of pores in the pore pattern layer of the rock core image and comparing the size and distribution with the situation after self-adaptive partitioning, the method can see that the area with similar pore size and distribution is partitioned into the same area, which shows that the self-adaptive partitioning of the rock core image can be better realized by the method, so that the whole heterogeneous image is provided with homogeneity inside the area after partitioning, thereby local pore information can be used for representing the pore characteristics of a larger area, and the calculation amount and the reconstruction time of reconstruction are greatly reduced.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the technical solutions of the present invention, so long as the technical solutions are realized on the basis of the above embodiments without creative efforts, which should be considered to fall within the protection scope of the patent of the present invention.
Claims (2)
1. The self-adaptive partitioned three-dimensional reconstruction method of the rock core image is characterized by comprising the following steps of: the method comprises the following steps:
(1) calculating the optimal template size of the region according to the pore size and distribution of the region to be divided;
(2) calculating the local porosity corresponding to each pixel point of the region to be divided by using the optimal template size;
(3) calculating the porosity of the region to be divided, and dividing pixel points with local porosity larger than a threshold into one region by taking the value as the threshold, and classifying the rest pixel points into another region;
(4) after dividing the regions, judging whether the regions need to be divided according to the area of each region and the distribution range of the local porosity;
(5) repeating the steps (1) to (4) for the areas which need to be divided until no area needs to be divided;
(6) searching respective typical representative unit bodies of the regions by taking the porosity of the regions as a basis, taking the representative unit bodies as reconstructed training images, and calculating the area ratio of the regions in the whole image;
(7) performing three-dimensional reconstruction on all training images by using a reconstruction algorithm;
(8) and (4) calculating relevant parameters capable of reflecting the geometrical characteristics of the three-dimensional structure pore and the throat, namely porosity, average shape factor, average pore diameter, average throat diameter, average pore volume, average throat volume and average pore throat radius ratio, and integrating the calculation results according to the area ratio in the step (6), thereby obtaining the pore characteristics of the whole rock sample.
2. The core image adaptive partition three-dimensional reconstruction method according to claim 1, characterized in that:
in the step (1), the optimal template size is obtained by counting the sizes of the pores and the background chord lengths of the image in the x and y directions, and taking the maximum value of the pores and the background chord lengths in the x and y directions as the optimal template size for calculating the local porosity, so as to ensure that the situation that the template is completely a pore point or a background point does not occur in the process of traversing the image, thereby better reflecting the distribution situation of the pores;
in the step (2), the region to be partitioned is traversed by using the region optimal template, and the local porosity value obtained by calculation is assigned to the pixel point at the central position of the template in the traversing process so as to reflect the pore distribution situation around the pixel point;
in the step (3), the porosity of the region to be divided is used as a dividing threshold, all pixel points are classified according to the size relation between the local porosity of each pixel point in the region and the threshold, the pixel points with the local porosity larger than the threshold jointly form one region, and the rest pixel points form the other divided region;
in the step (4), for the divided region, if the difference between the minimum local porosity and the maximum local porosity of the pixel point of the region is greater than 20%, and the area of the region is greater than 1000 × 1000, it is indicated that the region still has heterogeneity, and the region further needs to be divided;
in the step (6), after the region division is completed, a typical representative unit body of each region, namely a training image to be reconstructed, needs to be selected, wherein the porosity of the region is used as a selection standard, and the area ratio of each region relative to the original core image is counted;
in the step (8), after the three-dimensional structure of the pores in each region is reconstructed, parameters of all the three-dimensional structures are integrated according to the region area ratio, so that geometric characteristic parameters of the pores and the throats of the three-dimensional structures corresponding to the whole core image are obtained.
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| CN110135311A (en) * | 2019-05-06 | 2019-08-16 | 重庆科技学院 | A kind of hole based on three-dimensional Core Scanning Image and pore throat identifying system and method |
| CN112017274B (en) * | 2019-05-29 | 2022-11-11 | 四川大学 | Multi-resolution three-dimensional core pore fusion method based on pattern matching |
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| CN112669243B (en) * | 2020-12-09 | 2022-10-04 | 山东省科学院海洋仪器仪表研究所 | Rock sampling method based on connectivity and porosity |
| CN113310877B (en) * | 2021-06-07 | 2022-12-06 | 中国石油大学(华东) | Method for constructing multi-scale rock pore network model with differentiated regional structure |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104076046A (en) * | 2013-03-28 | 2014-10-01 | 中国石油化工股份有限公司 | Method for collecting and quantitatively characterizing microcosmic distribution images of remaining oil in porous media |
| CN104101562A (en) * | 2013-04-15 | 2014-10-15 | 中国石油天然气集团公司 | Method for determining reservoir permeability |
| CN105115874A (en) * | 2015-08-18 | 2015-12-02 | 中国石油天然气股份有限公司 | Multi-component three-dimensional digital core construction method based on multi-source information fusion |
| 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 |
| CN105487121A (en) * | 2015-12-03 | 2016-04-13 | 长江大学 | Method for constructing multi-scale digital rock core based on fusion of CT scanned image and electro-imaging image |
| CN106596375A (en) * | 2016-12-19 | 2017-04-26 | 中国石油大学(华东) | Method for recovering porosity of reservoir during geological history |
| RU2621371C1 (en) * | 2016-07-13 | 2017-06-02 | Федеральное государственное бюджетное образовательное учреждение высшего образования "Российский государственный университет нефти и газа (национальный исследовательский университет) имени И.М. Губкина" | Method of investigation of filtration-capacitive properties of mineral rocks |
| CN107146279A (en) * | 2017-04-25 | 2017-09-08 | 四川大学 | A kind of porous media three-dimensional modeling method based on symbiosis correlation function |
| CN107655908A (en) * | 2017-11-07 | 2018-02-02 | 中国石油天然气股份有限公司 | Method and device for constructing digital core |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8170799B2 (en) * | 2008-11-24 | 2012-05-01 | Ingrain, Inc. | Method for determining in-situ relationships between physical properties of a porous medium from a sample thereof |
-
2018
- 2018-08-27 CN CN201810980636.5A patent/CN109345625B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104076046A (en) * | 2013-03-28 | 2014-10-01 | 中国石油化工股份有限公司 | Method for collecting and quantitatively characterizing microcosmic distribution images of remaining oil in porous media |
| CN104101562A (en) * | 2013-04-15 | 2014-10-15 | 中国石油天然气集团公司 | Method for determining reservoir permeability |
| CN105115874A (en) * | 2015-08-18 | 2015-12-02 | 中国石油天然气股份有限公司 | Multi-component three-dimensional digital core construction method based on multi-source information fusion |
| 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 |
| CN105487121A (en) * | 2015-12-03 | 2016-04-13 | 长江大学 | Method for constructing multi-scale digital rock core based on fusion of CT scanned image and electro-imaging image |
| RU2621371C1 (en) * | 2016-07-13 | 2017-06-02 | Федеральное государственное бюджетное образовательное учреждение высшего образования "Российский государственный университет нефти и газа (национальный исследовательский университет) имени И.М. Губкина" | Method of investigation of filtration-capacitive properties of mineral rocks |
| CN106596375A (en) * | 2016-12-19 | 2017-04-26 | 中国石油大学(华东) | Method for recovering porosity of reservoir during geological history |
| CN107146279A (en) * | 2017-04-25 | 2017-09-08 | 四川大学 | A kind of porous media three-dimensional modeling method based on symbiosis correlation function |
| CN107655908A (en) * | 2017-11-07 | 2018-02-02 | 中国石油天然气股份有限公司 | Method and device for constructing digital core |
Non-Patent Citations (8)
| Title |
|---|
| Application of borehole logging, core imaging and tomography to geotechnical exploration;R.Schepers et al.;《International Journal of Rock Mechanics and Mining Sciences》;20010930;第36卷(第6期);第867-876页 * |
| Improved multipoint statistics method for reconstructing three-dimensional porous media from a two-dimensional image via porosity matching;Kai Ding et al.;《PHYSICAL REVIEW E 97》;20180622;第063304-1-10页 * |
| Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling;MingLiang Gao et al.;《PHYSICAL REVIEW E 91》;20150126;第013308-1-11页 * |
| Stable-phase method for hierarchical annealing in the reconstruction of porous media images;DongDong Chen et al.;《PHYSICAL REVIEW E 89》;20140117;第013305-1-10页 * |
| 利用层间相关性的岩心CT图像半自动分割方法;徐永进等;《中国图象图形学报》;20151016;第20卷(第10期);第1340-1345页 * |
| 合理分割岩心微观结构图像的新方法;赵秀才等;《中国石油大学学报(自然科学版)》;20090130;第33卷(第1期);第64-67,72页 * |
| 基于数字岩心的低渗储层微观渗流机理研究;陶鹏;《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅰ辑》;20171115;第B019-261页 * |
| 岩石三维重建图像分辨率对孔隙参数的影响分析;刘燕飞等;《计算机与数字工程》;20140320;第42卷(第3期);第486-490页 * |
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