Transparent characterization method for large-scale fracture cavity

Abstract

The invention discloses a transparent characterization method for a large-scale fracture cavity. The method comprises the following steps: carrying out CT scanning on a large rock sample; establishing a three-dimensional reconstruction model for the large rock sample; binarization processing is carried out according to the three-dimensional reconstruction model, pores in the image are extracted through a gray threshold, and the space proportion of the sum of the pore volumes is calculated; calculating the diameter of each pore according to the three-dimensional model of each pore; and slicing the three-dimensional reconstruction model for multiple times, and respectively calculating the surface porosity of the pores of each slice. According to the method, under the condition that the rock core is not damaged, the number, the size, the orientation and the three-dimensional space distribution of the caves, the holes and the cracks of different sizes in the ultra-large fracture-vuggy carbonate rock can be accurately recognized based on CT scanning, and quantitative evaluation of the caves, the holes, the cracks of different sizes and fillers in the caves, the holes and the cracks is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field development, and particularly relates to a method for transparently characterizing large-scale fractures and caves. Background Art

[0002] At present, the seismic fracture and cave body carving method based on seismic attribute volumes and seismic inversion volumes, combined with geological modeling technology, has been very mature. However, limited by the accuracy of seismic data, only fracture and cave bodies with a size of more than 20 m can be carved and described through seismic attribute volumes or inversion data volumes. For fractures, caves and their internal fillers with a size smaller than 20 m, due to sampling conditions and size limitations, it is difficult to obtain caves and large holes in full-diameter cores. The cores obtained mainly contain matrix pores and microfractures with a diameter of less than 0.1 mm. The identification of these pores and fractures mainly uses conventional means such as cast thin sections, scanning electron microscopy, and mercury injection. To obtain the reservoir porosity using cast thin section images, professional technical personnel are required for interpretation, which highly depends on the technical level and experience of the operators. Differences in the thickness and flatness of the thin sections will directly affect the quality of the identification results. In actual work, due to differences in operators, camera parameter settings, etc., the analysis results may vary, and the representativeness of the identified images is poor. Since the scale of the thin sections is too small, the magnified field of view is very small. Due to the presence of fractures, caves and their internal fillers, the heterogeneity of fracture-vuggy carbonate rocks is very strong, making it difficult to make a correct evaluation of them. In addition, during the process of injecting colored polymethyl methacrylate or epoxy resin into the pores and fractures of the rock for cast thin sections, the original pores and the clay minerals inside will be damaged, resulting in artificially induced pores and fractures, which cannot reflect the true pore structure characteristics of the rock. During the existing mercury injection experiment, the injection process of mercury will damage the original pores and the clay minerals inside, etc., and cannot reflect the true multi-scale pore structure characteristics of the rock. The higher the injection pressure, the greater this effect. In short, these methods reflect the quantitative characterization of the three-dimensional characteristics of multi-scale fractures, caves and their internal fillers in fracture-vuggy carbonate rocks.

[0003] The patent application with the publication number CN115718056A and the name of "A method for measuring reservoir porosity in mottled carbonate rocks" includes the following steps: drilling a core sample of mottled carbonate rock and cutting a part of the sample to prepare a casting thin section; calculating the area ratios of the reservoir area and the non-reservoir area respectively according to the microscopic images of the obtained casting thin sections; measuring the outer volume and the rock skeleton volume of the obtained core sample respectively; performing a CT scan on the obtained core sample, and calculating the volume ratios of the reservoir area and the non-reservoir area in the core sample respectively in combination with the area ratios of the reservoir area and the non-reservoir area in the obtained casting thin section; calculating the porosity of the reservoir in the core sample according to the obtained results. Although this patent application can obtain the porosity, this patent is aimed at pore-type carbonate rock reservoirs. The scale of the thin section is too small, and the magnified field of view is very small. However, there are multi-scale fractures and cavities in the fracture-cavity carbonate rock reservoir, and their scales are in the micrometer level, millimeter level, centimeter level, or even larger. The heterogeneity of the reservoir rock is very strong. According to the method provided by the patent CN115718056A, it is impossible to make a quantitative evaluation of large-scale fractures and cavities without damaging the core. At the same time, it is also very difficult for this patent to identify the composition and content of the fillers in the fractures and cavities. Summary of the Invention

[0004] In order to overcome the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for transparent characterization of large-scale fractures and cavities. By collecting millimeter CT images of super-large samples of fracture-cavity carbonate rock outcrops, through denoising, filtering, three-dimensional reconstruction, and three-dimensional transparent characterization, pores and fractures can be quantitatively identified. The present invention can accurately analyze the size and three-dimensional distribution of multi-scale fractures, cavities, porosity, and pore throats without damaging the core, providing favorable conditions for the development of fracture-cavity carbonate rock reservoirs.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A method for transparent characterization of large-scale fractures and cavities, including the following steps:

[0007] S1: Perform a CT scan on a large rock sample;

[0008] S2: Establish a three-dimensional reconstruction model for the large rock sample;

[0009] S3: Perform binarization processing according to the three-dimensional reconstruction model, extract the pores in the image through a gray threshold, and calculate the spatial proportion of the sum of the pore volumes;

[0010] S4: Calculate the diameter of each pore according to the three-dimensional model of each pore;

[0011] S5: Perform multiple slicing on the three-dimensional reconstruction model, and calculate the pore surface area ratio of each slice respectively.

[0012] Optionally, before step S2, denoising and filtering processing are performed on the image.

[0013] Optionally, the diameter of the large rock sample is 300 mm to 500 mm.

[0014] Optionally, the pores include caves, holes, and fractures.

[0015] Optionally, in step S3, the matrix and the filler in the three-dimensional reconstruction model are extracted according to the threshold and morphological methods.

[0016] Optionally, the calculation method of the spatial proportion of the sum of pores in step S3 is: (cave volume + hole volume + fracture volume) / total volume of the large rock sample.

[0017] Optionally, in step S3, on the basis of extracting pores in the image by the gray threshold, morphological analysis is combined to divide the pores in the image.

[0018] Optionally, in step S4, the surface porosity is the ratio of the pore area to the total area of the slice.

[0019] Optionally, in step S4, the bwboundaries method is used to extract the boundary information and distribution information of the pores.

[0020] Optionally, in step S1, a high-energy CT is used to scan the large rock sample.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention can accurately identify the number, volume, orientation, and three-dimensional spatial distribution of caves, holes, and fractures of different scales inside the super-large fracture-vuggy carbonate rock without damaging the core based on CT scanning, obtain quantitative evaluations of caves, holes, fractures of different scales and the fillers inside them, and solve the problems of identification, precise characterization, and transparent representation of multi-scale, super-large fractures, vugs, and their fillers in strongly heterogeneous carbonate rocks such as fracture-vuggy carbonate rock reservoirs. It is of great significance for the reserve calculation, improvement of drilling success rate, and oil reservoir recovery rate of such reservoirs.

[0023] Based on the technical solution of the present invention, it can well solve the three-dimensional quantitative description of multi-scale caves, holes, and fractures in strongly heterogeneous fracture-vuggy carbonate rock oil and gas reservoirs, and is of great significance for the precise characterization and identification of multi-scale fractures and vugs in carbonate rocks, reserve calculation, improvement of drilling success rate, and oil reservoir recovery rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure of the present invention in any way. Additionally, the shapes and proportional dimensions of the various components in the drawings are only schematic and are used to assist in understanding the present invention, rather than specifically defining the shapes and proportional dimensions of the various components of the present invention. In the accompanying drawings:

[0025] Figure 1 is the flowchart of the steps of the present invention;

[0026] Figure 2 is the physical diagram during the implementation process of the present invention;

[0027] Figure 3 is the result diagram of the image binarization processing of the present invention;

[0028] Figure 4 is the distribution frequency diagram of caves, holes and cracks of different scales of the present invention;

[0029] Figure 5 is the two-dimensional diagram of the large-scale image segmentation result of the present invention;

[0030] Figure 6 is the three-dimensional diagram of the image segmentation result of the filling material in the fracture-cave of the present invention;

[0031] Figure 7 is the processing result diagram of the fracture-cave boundary of the present invention;

[0032] Figure 8 is the size and three-dimensional distribution diagram of caves and holes of different scales of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art of the present technology to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0035] The present invention will be described in detail below with reference to the accompanying drawings.

[0036] As Figure 1 andFigure 2 As shown in the figure, a method for transparent characterization of large-scale fractures and caves of the present invention includes the following steps:

[0037] Collect large rock samples in the outcrop in the field; according to the horizons and characteristics of the reservoir to be studied, preferably select the corresponding outcrop areas in the field and the outcrop sections of the target formation group, and ensure that there are multi-scale caves and fractures in the large samples according to the sampling requirements, and collect multi-scale large rock samples with a scale of cubic rock samples with a side length of 300-500 mm.

[0038] Perform CT scanning on the large rock samples by a high-energy CT machine.

[0039] Establish a three-dimensional reconstruction model for the large rock samples through algorithms such as denoising and filtering; perform binarization processing according to the three-dimensional reconstruction model, extract fractures, caves, pores and the fillers in the caves in the image through gray-scale threshold and morphological features, and calculate the spatial occupancy ratio of the sum of the volumes of fractures, caves and pores, thereby obtaining the three-dimensional space transparent characteristics of the binarized image as Figure 2 shown. The image binarization technology uses the graythresh method in image processing technology to calculate the threshold, and calls im2bw to perform binarization processing on the image. A gray-scale image with 256 brightness levels is obtained through appropriate threshold selection to obtain a binarized image that can still reflect the overall and local characteristics of the image. A typical binarized image is shown in Figure 3 , and the image is filtered and sharpened. According to the threshold and morphological analysis technology, holes and fractures are segmented and extracted, and the areas corresponding to the holes and fractures are calculated respectively. Then, the area of the plane image is divided by the area of the plane image to obtain the areal porosity of the corresponding holes and fractures. According to the three-dimensional model of each pore, the diameter of each pore is calculated; the three-dimensional reconstruction model is sliced multiple times, and the areal porosity of the pores in each slice is calculated respectively.

[0040] According to the black-and-white image obtained by the image binarization module, analyze the number of pixels of the matrix, holes and fractures, calculate the distribution frequencies of different-scale caves, holes and fractures, and calculate the various areal porosities according to the definition of the area fraction of porosity. The specific segmentation results are shown in Figure 4 . For fracture-cave bodies, matrix and cave fillers, a binarized image that can still reflect the overall and local characteristics of the image can be obtained by appropriately selecting the threshold according to the gray-scale image, and the image is filtered and sharpened. According to the threshold and morphological analysis technology, the matrix, caves, fractures and their fillers are segmented and extracted, where the fillers include gravel and clay. The specific segmentation results are shown in Figure 5 and Figure 6 .

[0041] Use the bwboundaries method in image processing technology to extract the boundary information of fractures and caves, and analyze the size distribution of pores and holes. The results are shown in Figure 7As shown. According to the sizes of the analyzed pores and throats, the skeleton lines and contour lines of the picture are obtained, and the shortest distance from a point on the skeleton to the contour, that is, the radius of the inscribed circle with each point on the skeleton as the center, is used as the pore diameter. According to the classification method of the core, the frequency distribution diagram of the pore-throat diameter is drawn. Furthermore, the sizes and three-dimensional distributions of caves and pores of different scales in ultra-large fracture-vuggy carbonate rocks can be quickly analyzed, as shown in Figure 8 shown.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Any other modifications or equivalent replacements made by those of ordinary skill in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for transparent characterization of large-scale fractures and caves, characterized in that It includes the following steps: S1: Conduct a CT scan on the large rock sample; S2: Establish a three-dimensional reconstruction model for the large rock sample; S3: Conduct binarization processing based on the three-dimensional reconstruction model, extract the pores in the image through the gray threshold, and calculate the spatial occupancy ratio of the sum of the pore volumes; S4: Calculate the diameter of each pore according to the three-dimensional model of each pore; S5: Conduct multiple slicing on the three-dimensional reconstruction model, and calculate the areal porosity of the pores in each slice respectively.

2. The method for transparently characterizing large-scale fractures and cavities according to claim 1, characterized in that Before step S2, noise reduction processing and filtering processing are first performed on the image.

3. A method for transparent characterization of large-scale fractures and caves according to claim 1, characterized in that The diameter of the large rock sample is 300 mm to 500 mm.

4. A method for transparent characterization of large-scale fractures and cavities according to claim 1, characterized in that, The pores include caves, holes, and cracks.

5. A method for transparent characterization of large-scale fractures and cavities according to claim 1, characterized in that In step S3, the matrix and fillers in the three-dimensional reconstruction model are extracted according to the threshold and morphological methods.

6. The method for transparent characterization of large-scale fractures and caves according to claim 1, wherein The calculation method for the spatial occupancy ratio of the sum of the pores in step S3 is: (cave volume + hole volume + crack volume) / total volume of the large rock sample.

7. A method for transparent characterization of large-scale fractures and caves according to claim 1, characterized in that, In step S3, on the basis of extracting the pores in the image through the gray threshold, morphological analysis is combined to divide the pores in the image.

8. A method for transparent characterization of large-scale fractures and caves according to claim 1, characterized in that In step S4, the areal porosity is the ratio of the pore area to the total slice area.

9. A method for transparent characterization of large-scale fractures and caves according to claim 1, characterized in that, In step S4, the bwboundaries method is used to extract the boundary information and distribution information of the pores.

10. The method for transparently characterizing large-scale fractures and caves according to claim 1, characterized in that, In step S1, a high-energy CT is used to scan the large rock sample.

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