Method, device and equipment for identifying heterogeneous characteristics of reservoir holes and medium
By taking microscopic photographs, processing images, and performing fractal calculations on thin sections of carbonate rock formations, a heterogeneity evaluation chart was established, which solved the problems of low efficiency and low accuracy in reservoir heterogeneity evaluation in existing technologies, and achieved efficient and accurate quantitative evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-10-23
- Publication Date
- 2026-04-24
AI Technical Summary
The lack of quantitative methods in existing technologies to evaluate reservoir heterogeneity leads to low evaluation efficiency and accuracy, and large discrepancies in human observation.
A quantitative method was used to photograph thin sections of carbonate rock formations under a microscope, acquire images, classify heterogeneity levels, extract fracture and cavity information, perform unifractal and multifractal calculations, establish a heterogeneity evaluation chart, and conduct quantitative evaluation using BX and BY parameters.
It achieves high-precision and high-efficiency evaluation of reservoir heterogeneity, reduces human error, and improves the accuracy and efficiency of evaluation.
Smart Images
Figure CN121918211A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of geological exploration technology, and in particular to a method, apparatus, equipment and medium for identifying the heterogeneous characteristics of reservoir pores. Background Technology
[0002] Existing technologies for evaluating reservoir heterogeneity using thin sections are generally qualitative methods, lacking quantitative evaluation parameters and efficiency.
[0003] Methods for evaluating reservoir heterogeneity include those based on core permeability, using parameters such as coefficient of variation and grade difference; those based on CT images and mercury intrusion curves; and new methods based on surface area extraction from cast thin section images, using surface area to propose a heterogeneity coefficient U to quantitatively characterize microscopic pore heterogeneity. Multifractal methods are used to evaluate reservoir pore structure, but there is a lack of applications of integrated fractal and multifractal methods.
[0004] Current methods for evaluating reservoir heterogeneity using cast thin sections have the following problems: 1) Relying solely on human observation to evaluate reservoir heterogeneity is inefficient; 2) Different people have different experiences, leading to different conclusions and affecting the accuracy of the evaluation.
[0005] In summary, there is an urgent need for a technical solution based on quantitative identification of reservoir porosity heterogeneity distribution characteristics using cast thin sections, in order to improve the efficiency of thin section heterogeneity evaluation. Summary of the Invention
[0006] To address the aforementioned issues, this disclosure provides a method, apparatus, equipment, and medium for identifying reservoir porosity heterogeneity. This technical solution eliminates qualitative factors and uses quantitative charts to evaluate reservoir heterogeneity with high precision and efficiency. It is also simple to operate and has a high identification accuracy.
[0007] In a first aspect, a method for identifying reservoir porosity heterogeneity features, the method comprising:
[0008] Several images of thin sections of cast bodies in the Dengying Formation of carbonate rock strata were obtained by microscopic photography.
[0009] For several thin-section images of cast bodies, different levels of heterogeneity are classified;
[0010] Extract seams from several thin-section images of cast bodies and obtain binary images of seam information;
[0011] Perform unifractal and multifractal calculations on binary images to obtain the fractal dimension D, multifractal spectral asymmetry parameter B, peak value FMAX, and spectral width W.
[0012] Based on the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX, and the spectral width W, the first parameter BX and the second parameter BY are calculated for each thin-slice image of the casting; where BY = (B*B+W) / B; BX = D*FMAX / 2;
[0013] With BX as the x-axis and BY as the y-axis, a thin section point is obtained for each thin section image of the casting based on BX and BY; a heterogeneity evaluation chart is established based on the thin section points.
[0014] Based on different levels of heterogeneity, the thin-slice points of different heterogeneity levels are divided into different data regions on the heterogeneity evaluation chart;
[0015] Calculate BX and BY of the cast sheet to be evaluated, and evaluate the heterogeneity level based on the different data regions where BX and BY of the cast sheet to be evaluated are located.
[0016] Furthermore, several images of the cast thin sections were obtained by microscopic photography, including:
[0017] Epoxy resin was cast into the thin film of the lamp shadow assembly, and a color RGB image of the thin film was obtained by taking a picture under a microscope.
[0018] Furthermore, for several thin-section images of the cast body, different levels of heterogeneity were classified, including:
[0019] Based on the thin section images of the cast body, and according to geological understanding, the thin section images of the cast body are divided into different levels of heterogeneity;
[0020] Heterogeneity levels include: strong heterogeneity, strong heterogeneity, moderate heterogeneity, and weak heterogeneity.
[0021] Furthermore, seams are extracted from several images of thin-film castings to obtain binary images of the seam information, including:
[0022] When injecting epoxy resin into a cast thin sheet, the epoxy resin can only be injected into areas where cavities have developed.
[0023] Within the thin-film image, the color corresponding to the epoxy resin represents the seam. The area corresponding to the epoxy resin color is extracted and converted into a binary image.
[0024] Furthermore, it is converted into a binary image, including:
[0025] The values in a binary image are 0 or 1, where 0 is black, representing a hole, and 1 is white, representing the background value.
[0026] Furthermore, on the heterogeneity evaluation chart, thin-film points of different heterogeneity levels are divided into different data regions, including:
[0027] The thin-slice points with different levels of heterogeneity were divided into different data regions by drawing lines.
[0028] Furthermore, lines are used to divide the thin-patch points of different heterogeneity levels into different data regions, including:
[0029] The least squares method is used to maximize the distance between different data areas in order to determine the position of the line.
[0030] In a second aspect, an apparatus for identifying the heterogeneous characteristics of reservoir pores includes: a cast thin section image acquisition unit, a heterogeneity level classification unit, a binary image acquisition unit, a parameter calculation unit, a heterogeneity evaluation chart establishment unit, and an evaluation unit.
[0031] The thin section image acquisition unit is used to take microscopic photographs of thin sections of cast bodies in the Dengying Formation of carbonate rock strata to obtain several thin section images.
[0032] The heterogeneity level unit is used to divide several thin-section images of castings into different heterogeneity levels.
[0033] The binary image acquisition unit is used to extract seams from several thin-film images of castings and obtain a binary image of seam information.
[0034] The parameter calculation unit is used to perform unifractal and multifractal calculations on binary images to obtain the fractal dimension D, multifractal spectrum asymmetry parameter B, peak value FMAX, and spectral width W.
[0035] The parameter calculation unit is also used to calculate the first parameter BX and the second parameter BY for each cast thin-slice image based on the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX and the spectral width W; where BY=(B*B+W) / B; BX=D*FMAX / 2;
[0036] The heterogeneity evaluation chart establishment unit is used to obtain a slice point for each slice image of the casting with BX as the horizontal axis and BY as the vertical axis; and to establish a heterogeneity evaluation chart based on the slice points.
[0037] The heterogeneity evaluation chart establishment unit is also used to divide the thin-slice points of different heterogeneity levels into different data areas on the heterogeneity evaluation chart according to different heterogeneity levels.
[0038] The evaluation unit is used to calculate the BX and BY of the casting sheet to be evaluated, and to evaluate the heterogeneity level based on the different data areas where the BX and BY of the casting sheet to be evaluated are located.
[0039] Thirdly, an electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;
[0040] Memory, which stores computer programs;
[0041] When a processor executes a computer program stored in memory, it implements the above-described method for identifying reservoir porosity heterogeneity.
[0042] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying reservoir porosity heterogeneity.
[0043] This disclosure has at least the following beneficial effects:
[0044] This disclosure selects cast thin-section images when evaluating reservoir heterogeneity. Digital image processing can extract fracture and void information and obtain the distribution characteristics of fractures and voids in a plane. Qualitative analysis involves observing the distribution of fractures and voids in a two-dimensional plane, while quantitative analysis is based on the parametric characterization of the planar distribution of fractures and voids. Fractal dimension has been used to characterize reservoir heterogeneity and connectivity, and multifractal spectrum parameters are also widely used to characterize reservoir heterogeneity. Therefore, combining the two will yield better results in evaluating reservoir heterogeneity.
[0045] This disclosure applies to any microbial carbonate formation, provided that the above-mentioned natural gamma logging curves, FMI electrical imaging logging images, and sufficient core data from cored wells are available.
[0046] The most significant features of the technical solution disclosed herein are high precision and high efficiency, which are advantages of quantitative methods. This method can meet production requirements.
[0047] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description
[0048] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0049] Figure 1 This is a schematic diagram of the identification method flow according to an embodiment of the present disclosure;
[0050] Figure 2 This is a schematic diagram of the identification device structure according to an embodiment of the present disclosure;
[0051] Figure 3 This is a schematic diagram of the electronic device structure according to an embodiment of the present disclosure;
[0052] Figure 4 Extract the binary image of the seam hole from the thin sheet of the casting in Case 1;
[0053] Figure 5 This is a chart for the quantitative evaluation of the heterogeneity of the Dengying Formation reservoir in Case Study 1.
[0054] Figure 6 The binary diagram of the fractures and voids in the thin-walled casting to be evaluated in Well A, Case 1;
[0055] Figure 7 This is a schematic diagram showing the location of the thin-film points of the casting in Well A of Case 1 on the drawing.
[0056] Figure 8 This is a chart for the quantitative evaluation of reservoir heterogeneity in Case 2;
[0057] Figure 9 The binary image of the fracture cavity in the thin section to be evaluated in well B of Case 2 is shown.
[0058] Figure 10 This is a schematic diagram showing the location of the thin-film point of the casting in Well B of Case 2 on the plate. Detailed Implementation
[0059] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0060] like Figure 1 As shown, a method for identifying reservoir porosity heterogeneity features, the method comprising:
[0061] S101, Several images of the cast thin sections were obtained by microscopic photography of the Dengying Formation of carbonate rock strata.
[0062] S102, for several thin-section images of cast bodies, classify different levels of heterogeneity;
[0063] S103, extract the seams from several thin-film images of the casting and obtain a binary image of the seam information;
[0064] S104, perform unifractal and multifractal calculations on the binary image to obtain the fractal dimension D, multifractal spectrum asymmetry parameter B, peak value FMAX and spectral width W;
[0065] S105, based on the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX and the spectral width W, the first parameter BX and the second parameter BY are calculated for each thin-film image of the casting; where BY=(B*B+W) / B; BX=D*FMAX / 2;
[0066] S106, with BX as the abscissa and BY as the ordinate, a thin section point is obtained for each thin section image of the casting based on BX and BY; a non-homogeneity evaluation chart is established based on the thin section points.
[0067] S107, based on different heterogeneity levels, divide the thin-film points of different heterogeneity levels into different data areas on the heterogeneity evaluation chart;
[0068] S108, calculate BX and BY of the casting sheet to be evaluated, and evaluate the heterogeneity level based on the different data regions where BX and BY of the casting sheet to be evaluated are located.
[0069] The specific implementation details are as follows:
[0070] This technique establishes new parameters. Under weak heterogeneity, the fractal dimension D is low, and the multifractal parameters are: high asymmetry index B, relatively high spectral width W, and low spectral peak value FMAX. Under strong heterogeneity, the fractal dimension D is high, and the multifractal parameters are: medium or low asymmetry index B, low spectral width W, and high spectral peak value FMAX. The fractal dimension D increases with increasing heterogeneity, while B is independent of porosity. For extremely strong heterogeneity with isolated pores, D is generally high, and B has extremely high values, exhibiting characteristics completely different from other heterogeneities. This is related to the basic principle of fractals; fractals are based on self-similarity. When isolated pores develop, the local-to-global similarity decreases during fractal calculation, leading to abnormal patterns in the calculation results because this type of heterogeneity is treated as a separate region when constructing the plot. While D and B calculations are existing technologies, they lack applications for combined use, especially the construction of evaluation charts. The charts, as an innovation, can comprehensively consider the actual calculation results while also taking into account the principles of the technology, and this has been proven in practical applications.
[0071] In this technical solution, the map identification parameters BX and BY are obtained, where BX is the X-axis parameter and BY is the Y-axis parameter. Different reservoir heterogeneity levels are classified based on thin-section calibration to achieve the purpose of establishing a map and classifying reservoir heterogeneity. The positions of the dividing lines for different heterogeneity levels on the map are determined based on typical cast thin-section data of the study area; that is, the positions of the dividing lines are determined through thin-section calibration.
[0072] The thin section data are cast thin section data. Among them, the thin section identification results of the rock core can be obtained by grinding the rock core into thin sections and observing and identifying them under a microscope.
[0073] Specifically, the following steps are included:
[0074] Color RGB images were obtained by taking microscopic photographs of the cast thin sections.
[0075] The typical cast thin sections in the study area were qualitatively classified into different levels of heterogeneity.
[0076] Extract the seam holes from the RGB image to obtain a binary image of the seam hole information;
[0077] We perform unifractal and multifractal calculations on binary images to obtain the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX, and the spectral width W.
[0078] Establish new parameters BX and BY, and create a heterogeneity evaluation chart using cross plots;
[0079] Based on the evaluation chart, the heterogeneity of other cast thin sections in the study area was evaluated.
[0080] like Figure 2 As shown, an apparatus for identifying the heterogeneous characteristics of reservoir pores includes: a cast thin section image acquisition unit 201, a heterogeneity level classification unit 202, a binary image acquisition unit 203, a parameter calculation unit 204, a heterogeneity evaluation chart establishment unit 205, and an evaluation unit 206.
[0081] The thin section image acquisition unit 201 is used to take microscopic photographs of thin sections of cast rocks in the Dengying Formation of carbonate rock strata to obtain several thin section images.
[0082] The heterogeneity level division unit 202 is used to divide several thin-section images of cast bodies into different heterogeneity levels;
[0083] Binary image acquisition unit 203 is used to extract seams from several thin-film images of castings and obtain a binary image of seam information.
[0084] The parameter calculation unit 204 is used to perform unifractal and multifractal calculations on the binary image to obtain the fractal dimension D, multifractal spectrum asymmetry parameter B, peak value FMAX and spectral width W.
[0085] The parameter calculation unit 204 is also used to calculate the first parameter BX and the second parameter BY for each cast thin-slice image based on the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX and the spectral width W; where BY=(B*B+W) / B; BX=D*FMAX / 2;
[0086] The heterogeneity evaluation chart establishment unit 205 is used to obtain a slice point for each slice image of the casting with BX as the horizontal axis and BY as the vertical axis; and to establish a heterogeneity evaluation chart based on the slice points.
[0087] The heterogeneity evaluation chart establishment unit 205 is also used to divide the thin film points of different heterogeneity levels into different data areas on the heterogeneity evaluation chart according to different heterogeneity levels.
[0088] Evaluation unit 206 is used to calculate BX and BY of the casting sheet to be evaluated, and to evaluate the heterogeneity level based on the different data areas where BX and BY of the casting sheet to be evaluated are located.
[0089] like Figure 3 As shown, this disclosure provides an electronic device, including a processor 301, a communication interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304;
[0090] Memory 303 stores computer programs;
[0091] The processor 301 implements the above method when executing a computer program stored in the memory 303.
[0092] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.
[0093] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.
[0094] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0095] To enable those skilled in the art to better understand the present disclosure, the principles of the present disclosure are elaborated below in conjunction with the accompanying drawings:
[0096] Specific Case 1: Taking a certain Dengying Formation as an example, this formation belongs to microbial carbonate rock sedimentation, with developed fractures and pores in the reservoir, and strong reservoir heterogeneity. The reservoir is divided into 4 types: pore type, fracture-vug type, vug type, and fracture type.
[0097] It includes the following steps:
[0098] Taking photos of the casting thin sections under a microscope to obtain color RGB images:
[0099] Obtaining the casting thin sections of the Dengying Formation in the study area, casting them into epoxy resin to obtain the casting thin sections, and taking photos under a microscope. In this case, the epoxy resin in the casting thin sections is stained blue. Therefore, the blue areas in the casting thin sections represent the places where fractures and pores are developed. Taking the core casting thin sections of Wells Gaoshi 16, Gaoshi 18, and Gaoshi 101 as examples, 20 typical casting thin sections representing different heterogeneity levels were carefully selected.
[0100] Qualitatively classifying different heterogeneity levels for the typical casting thin sections in the study area:
[0101] Based on the RGB images of the casting thin sections, according to geological understanding, the RGB images of the typical casting thin sections are classified into different heterogeneity levels, generally divided into 4 types: strong, relatively strong, medium, and weak.
[0102] Extracting fractures and pores from the RGB images to obtain a binary image of fracture and pore information:
[0103] Program in Maltab software to extract blue information. The RGB values of blue are: 0 < R < 100, 0 < G < 140, 120 < B < 200. After extracting the blue information, use the im2bw() function in matlab software to convert it into a binary image, that is, a black-and-white image, with 0 values and 1 values, where 0 values represent fractures and pores, and 1 values represent the background white, as Figure 4 shown;
[0104] Performing single fractal calculation and multifractal calculation on the binary image through software to obtain the fractal dimension D, the asymmetry parameter B of the multifractal spectrum, the peak value FMAX, and the spectrum width W.
[0105] Calculating the fractal dimension D, the asymmetry parameter B of the multifractal spectrum, the peak value FMAX, and the spectrum width W for each thin section image. Establish new parameters according to the following formulas (1) and (2):
[0106] BY = (B * B + W) / B (1)
[0107] BX = D * FMAX / 2 (2)
[0108] Using new parameters, establish an anisotropy evaluation chart through cross plots:
[0109] According to the anisotropy classification, draw lines on the cross plot to separate the thin section point data of different anisotropy levels. Of course, in actual situations, it may not be possible to completely separate them 100%, and it is sufficient to separate most of them. Eventually, an evaluation chart for reservoir anisotropy is formed, as shown in Figure 5 shown.
[0110] According to the evaluation chart, evaluate the anisotropy of other cast thin sections in the study area:
[0111] As shown in Figure 6 shown, taking the core cast thin section A of the Dengying Formation in Well A as an example, the pores are very developed, with a large number and uniform distribution. Qualitatively evaluated as weakly anisotropic, use the chart to quantitatively evaluate its anisotropy. Calculate the fractal dimension D = 2.4181 and the multifractal spectrum asymmetry index B = 34.3385 of the cast thin section image, the peak value FMAX = 1.9893 and the spectrum width W = 0.2449, BY = 34.3456, BX = 2.4052. Plot the points on the reservoir anisotropy evaluation chart, and point A is located in the weakly anisotropic interval (as shown in Figure 7 shown), which is consistent with the actual situation.
[0112] Specific case two, taking the slope formation in a certain area as an example, includes the following steps:
[0113] Take microscopic photos of the cast thin sections to obtain color RGB images:
[0114] Obtain the cast thin sections of the Dengying Formation in the study area, cast them into epoxy resin to obtain the cast thin sections, and take photos under the microscope. In this case, the epoxy resin in the cast thin sections is stained red red red red, and 16 typical cast thin sections representing different anisotropy levels are carefully selected.
[0115] Qualitatively divide different anisotropy levels for the typical cast thin sections in the study area:
[0116] Based on the RGB images of the cast thin sections, according to geological understanding, divide the RGB images of the typical cast thin sections into different anisotropy levels, generally classified into 4 categories: strong, relatively strong, medium, and weak.
[0117] Extract the fractures and vugs from the RGB images to obtain the binary images of the fracture and vug information:
[0118] Based on Maltab software programming, extract the red information. The RGB values of red are: 140 < R < 220, 0 < G < 90, 0 < B < 90. After extracting the red information, use the im2bw() function in matlab software to convert it into a binary image, that is, a black and white image, with 0 values and 1 values, where 0 values represent fractures and vugs, and 1 values represent the background white;
[0119] The software performs unifractal and multifractal calculations on binary images to obtain the fractal dimension D and the multifractal spectrum asymmetry parameter B.
[0120] Calculate the fractal dimension D, multifractal spectrum asymmetry parameter B, peak value FMAX, and spectral width W for each slice image. Establish new parameters according to the following formulas (1) and (2):
[0121] BY=(B*B+W) / B (1)
[0122] BX = D*FMAX / 2 (2)
[0123] Using the new parameters, a heterogeneity evaluation chart is constructed through cross plots:
[0124] Based on the classification of heterogeneity, lines were drawn to distinguish thin-section point data of different heterogeneity levels. The Leikoupo reservoir is more complex, and the distinction is not as good as that of the Dengying group, but it can still be roughly divided. This resulted in a reservoir heterogeneity evaluation map, as shown below. Figure 8 As shown.
[0125] Based on the evaluation chart, the heterogeneity of other cast lamellae in the study area was evaluated:
[0126] like Figure 9 As shown, taking the core casting thin section B of the Leikoupo Formation in Well B as an example, the qualitative evaluation indicates strong heterogeneity. Quantitative evaluation of its heterogeneity is performed using a chart. The fractal dimension D = 2.4229, the multifractal spectrum asymmetry index B = 31.8567, the peak value FMAX = 2.0004, and the spectral width W = 0.2613, BX = 31.8649, and BY = 2.4234 of the thin section image are calculated. Point b is located in the moderate heterogeneity range (e.g., ...). Figure 10 As shown in the figure, it matches the actual situation.
[0127] The technical solution provided in this disclosure has a good application effect on the evaluation of the heterogeneity of carbonate reservoirs, which can improve the efficiency of scientific research and production and meet the needs of scientific research and production.
[0128] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.
Claims
1. A method for identifying reservoir porosity heterogeneity, characterized in that, The method includes: Several images of thin sections of cast bodies in the Dengying Formation of carbonate rock strata were obtained by microscopic photography. For several thin-section images of cast bodies, different levels of heterogeneity are classified; Extract seams from several thin-section images of cast bodies and obtain binary images of seam information; Perform unifractal and multifractal calculations on binary images to obtain the fractal dimension D, multifractal spectral asymmetry parameter B, peak value FMAX, and spectral width W. Based on the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX, and the spectral width W, the first parameter BX and the second parameter BY are calculated for each thin-slice image of the casting; where BY = (B*B+W) / B; BX = D*FMAX / 2; With BX as the x-axis and BY as the y-axis, a thin section point is obtained for each thin section image of the casting based on BX and BY; a heterogeneity evaluation chart is established based on the thin section points. Based on different levels of heterogeneity, the thin-slice points of different heterogeneity levels are divided into different data regions on the heterogeneity evaluation chart; Calculate BX and BY of the cast sheet to be evaluated, and evaluate the heterogeneity level based on the different data regions where BX and BY of the cast sheet to be evaluated are located.
2. The method for identifying reservoir porosity heterogeneity characteristics according to claim 1, characterized in that, Several images of the cast thin sections were obtained by microscopic photography, including: Epoxy resin was cast into the thin film of the lamp shadow assembly, and a color RGB image of the thin film was obtained by taking a picture under a microscope.
3. The method for identifying reservoir porosity heterogeneity characteristics according to claim 1, characterized in that, For several thin-section images of cast bodies, different levels of heterogeneity are classified, including: Based on the thin section images of the cast body, and according to geological understanding, the thin section images of the cast body are divided into different levels of heterogeneity; Heterogeneity levels include: strong heterogeneity, strong heterogeneity, moderate heterogeneity, and weak heterogeneity.
4. The method for identifying reservoir porosity heterogeneity characteristics according to claim 1, characterized in that, Extract seams from several images of thin-film castings to obtain binary images of the seam information, including: When injecting epoxy resin into a cast thin sheet, the epoxy resin can only be injected into areas where cavities have developed. Within the thin-film image, the color corresponding to the epoxy resin represents the seam. The area corresponding to the epoxy resin color is extracted and converted into a binary image.
5. The method for identifying reservoir porosity heterogeneity characteristics according to claim 4, characterized in that, Converting to a binary image includes: The values in a binary image are 0 or 1, where 0 is black, representing a hole, and 1 is white, representing the background value.
6. The method for identifying reservoir porosity heterogeneity characteristics according to claim 1, characterized in that, On the heterogeneity evaluation chart, thin-patch points of different heterogeneity levels are divided into different data regions, including: The thin-slice points with different levels of heterogeneity were divided into different data regions by drawing lines.
7. The method for identifying reservoir porosity heterogeneity characteristics according to claim 6, characterized in that, The thin-patch points of different heterogeneity levels are divided into different data regions by drawing lines, including: The least squares method is used to maximize the distance between different data areas in order to determine the position of the line.
8. A device for identifying the heterogeneous characteristics of reservoir pores, characterized in that, include: The system includes a thin-section casting image acquisition unit, a heterogeneity level classification unit, a binary image acquisition unit, a parameter calculation unit, a heterogeneity evaluation chart establishment unit, and an evaluation unit. The thin section image acquisition unit is used to take microscopic photographs of thin sections of cast bodies in the Dengying Formation of carbonate rock strata to obtain several thin section images. The heterogeneity level unit is used to divide several thin-section images of castings into different heterogeneity levels. The binary image acquisition unit is used to extract seams from several thin-film images of castings and obtain a binary image of seam information. The parameter calculation unit is used to perform unifractal and multifractal calculations on binary images to obtain the fractal dimension D, multifractal spectrum asymmetry parameter B, peak value FMAX, and spectral width W. The parameter calculation unit is also used to calculate the first parameter BX and the second parameter BY for each cast thin-slice image based on the fractal dimension D, the multifractal spectrum asymmetry parameter B, the peak value FMAX and the spectral width W; where BY=(B*B+W) / B; BX=D*FMAX / 2; The heterogeneity evaluation chart establishment unit is used to obtain a slice point for each slice image of the casting with BX as the horizontal axis and BY as the vertical axis; and to establish a heterogeneity evaluation chart based on the slice points. The heterogeneity evaluation chart establishment unit is also used to divide the thin-slice points of different heterogeneity levels into different data areas on the heterogeneity evaluation chart according to different heterogeneity levels. The evaluation unit is used to calculate the BX and BY of the casting sheet to be evaluated, and to evaluate the heterogeneity level based on the different data areas where the BX and BY of the casting sheet to be evaluated are located.
9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, which stores computer programs; A processor, when executing a computer program stored in a memory, implements a method for identifying reservoir porosity heterogeneity features as described in any one of claims 1-7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for identifying reservoir porosity heterogeneity features as described in any one of claims 1-7.