Microscopic oil displacement experiment image quality evaluation method
By introducing topological characteristic parameters and optimizing device matching, the problem of image stitching time difference in micro-displacement experiments was solved, improving the accuracy of quantitative analysis and the reliability of experimental design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DAQING OILFIELD CO LTD
- Filing Date
- 2024-10-29
- Publication Date
- 2026-05-01
AI Technical Summary
In microscopic oil displacement experiments, time difference issues exist when stitching images, which increases the difficulty of stitching, affects the quantitative analysis results, and lacks quantitative evaluation standards.
By introducing topological feature parameters, the residual oil occurrence morphology is analyzed, the relevant parameters of the target and the original experimental equipment are calculated, the experimental equipment is optimized to improve the matching degree, and the image quality is quantified.
It improves the accuracy of quantitative analysis in microscopic oil displacement experiments, provides basic image data, ensures the reliability and stability of experimental design, reduces image blurring issues, and optimizes experimental design.
Smart Images
Figure CN121961969A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of computer image technology, and in particular to a method for evaluating the image quality of microscopic oil displacement experiments. Background Technology
[0002] Currently, in the development of water-driven oil reservoirs, it is necessary to conduct micro-displacement experiments using glass etching models. Generally, by recording images and videos, the types and proportions of various residual oils are analyzed manually or by computer throughout the entire experimental process. However, the evaluation of the generated images in this field currently relies solely on visual identification, lacking quantitative understanding of images taken by digital cameras. The evaluation is only vaguely based on whether the images are usable, without a specific evaluation standard system.
[0003] Microscopic seepage is seepage at the pore level, allowing direct observation and study of the distribution, flow details, and regularities of various fluids within porous media. A glass etching model reflecting the pore structure of rocks is fabricated. During the physical simulation of displacement, photographic and video recordings are used to capture images of the process and results. The characteristics of oil displacement and seepage under specific injection pressures and velocities with different oil-displacing agents, as well as the distribution of residual oil, are observed. Image analysis software is then used to quantitatively identify and statistically analyze the microscopic residual oil. Due to the lack of quantitative standards, the model width and microscope field of view are mismatched in the experimental design. An excessively large field of view results in blurry images, while a too small field of view provides clear images but does not allow for a complete view of the process. Image stitching is necessary for overall consistency, increasing the difficulty and time required. Therefore, a quantitative evaluation of the experimental images is needed to ensure the reliability, stability, accuracy, and convenience of the experimental design and analysis.
[0004] In practical applications, the different permeability levels of actual oil reservoirs are mainly reflected in the width of the throat. For high-permeability glass-etched models, full-field microscopy, including photography and video recording, is generally sufficient to meet the requirements for quantitative analysis. However, in models with medium to low permeability, blurring occurs. To achieve clarity, microscopic magnification is often used. However, due to the small field of view, it is impossible to capture images of the entire experimental domain in a single image. Therefore, image stitching is required to achieve quantitative analysis of the entire model. However, image stitching suffers from time lag, increasing the difficulty of stitching and affecting the quantitative analysis results. Therefore, to address these shortcomings, a method for evaluating the image quality of microscopic oil displacement experiments is proposed. Summary of the Invention
[0005] (a) Technical problems to be solved
[0006] This invention provides a method for evaluating the image quality of microscopic oil displacement experiments, which overcomes the problems in the existing technology of using stitched images to perform quantitative analysis of the entire model. These problems are caused by time difference issues, which increase the difficulty of stitching and affect the quantitative analysis results.
[0007] (II) Technical Solution
[0008] To address the above problems, this invention provides a method for evaluating the image quality of microscopic oil displacement experiments, comprising:
[0009] Step S1: Determine the experimental object, the original experimental equipment and the target experimental equipment; design and fabricate a porous network glass etching model for the experimental object; conduct a micro-displacement experiment on the porous network glass etching model; and acquire the target experimental image of the micro-displacement experiment through the target experimental equipment.
[0010] Step S2: Analyze the target experimental image determined in Step S1 using experimental image analysis software, determine the type of residual oil in the pore network glass etching model in Step S1 based on the analysis results, introduce multiple topological feature parameters, and analyze the residual oil occurrence morphology of each type of residual oil.
[0011] Step S3: Based on the residual oil occurrence morphology of each residual oil type in Step S2, calculate multiple target-related parameters of the target experimental image captured in Step S2, and analyze the correlation between the magnification of the target experimental equipment and each target-related parameter.
[0012] Step S4: Determine the type of target parameter of the target experimental equipment based on the relevance of step S3. Acquire the original experimental image of the microscopic displacement experiment using the original experimental equipment of step S1, determine the type of the original parameter of the corresponding target parameter, and calculate the target parameter value and the original parameter value respectively.
[0013] Step S5: Obtain the comparison results of the target parameter values and the original parameter values in step S4. Based on the comparison results, analyze the matching degree between the target experimental equipment and the porous network glass etching model in step S1. Optimize and design subsequent experiments based on the matching degree analysis results.
[0014] Preferably, in step S1, the experimental objects include etched transparent glass, oil, and water, and the etched transparent glass is used to create a porous network glass etching model.
[0015] Preferably, in step S1, the target experimental device and the original experimental device are microscopes of different models and their equipped digital cameras. The criteria for judging the target experimental device and the original experimental device are the years since the equipment was purchased and the equipment performance. The equipment performance criteria include resolution and field of view width. The field of view width is the range captured by the digital camera equipped with the microscope. The equipment with a long purchase period, low resolution, and low field of view width is the original experimental device.
[0016] Preferably, in step S2, the experimental image analysis software is a microscopic residual oil occurrence and flow state quantitative analysis system. The experimental images obtained from the microscopic displacement experiment video of the porous network glass etching model and the numerical simulation water-driven oil experiment video images are processed by the microscopic residual oil occurrence and flow state quantitative analysis system to calculate the pore throat parameters of the porous network glass etching model.
[0017] Preferably, in step S2, the type of residual oil includes film flow, droplet flow, columnar flow, porous flow, and cluster flow; the topological characteristic parameters include shape factor, Euler number, and contact ratio; and the residual oil occurrence morphology includes the flow morphology of the residual oil, the occurrence location of the residual oil, and the oil-water contact relationship of the residual oil.
[0018] Preferably, in step S3, the target-related parameters include the width of the pore network glass etching model, the throat width of the pore network glass etching model, and the minimum resolution of the target experimental image.
[0019] Preferably, in step S3, the formula for calculating the correlation between the target experimental equipment and each target-related parameter is as follows:
[0020] Rmin=Mw / (Th / 3) (1)
[0021] Where Rmin is the minimum resolution of the experimental image; Mw is the width of the porous network glass etching model; and Th is the throat width of the porous network glass etching model.
[0022] Preferably, in step S4, the minimum resolution of the experimental image is independent of the magnification of the target experimental device. When the width of the porous network glass etching model is determined, the magnification of the target experimental device is strongly correlated with the throat width of the porous network glass etching model.
[0023] Preferably, in step S4, the type of the target parameter of the target experimental device and the type of the original parameter of the original experimental device are both the throat width of the pore network glass etching model.
[0024] Preferably, in step S4, the formulas for calculating the target parameter value and the original parameter value are as follows:
[0025] Th=3*V / R (2)
[0026] Where R is the resolution of the experimental equipment; V is the field of view of the experimental equipment; and Th is the throat width of the porous network glass etching model.
[0027] Preferably, in step S5, the feasibility of the target experimental equipment is evaluated based on the matching degree result. If the evaluation result is not feasible, the target experimental equipment is reselected or the various accessories of the selected target experimental equipment are adjusted, and steps S1 to S5 are repeated until the evaluation result is feasible. When the evaluation result is feasible, subsequent experiments are carried out based on the selected target experimental equipment.
[0028] Preferably, in step S5, if the target parameter value is less than the original parameter value, it means that the compatibility between the target experimental device and the porous network glass etching model is less than that between the original experimental device and the porous network glass etching model, and the target experimental device is not feasible; if the target parameter value is greater than the original parameter value, it means that the compatibility between the target experimental device and the porous network glass etching model is greater than that between the original experimental device and the porous network glass etching model, and the target experimental device is feasible.
[0029] (III) Beneficial Effects
[0030] The method for evaluating the quality of microscopic oil displacement experimental images provided by this invention effectively quantifies multiple indicators of microscopic oil displacement experimental image acquisition. Based on the acquired multiple indicator parameters, it predicts experimental images of pore network glass etching models with different throat widths, makes targeted judgments on image blurring problems that occur during analysis, and provides timely improvement measures based on the judgment results. This not only improves the accuracy of quantitative analysis but also provides basic image data for intelligent analysis software, further ensuring the standardization of basic data. Attached Figure Description
[0031] Figure 1 This is a flowchart of a method for evaluating the image quality of a microscopic oil displacement experiment according to an embodiment of the present invention;
[0032] Figure 2 This is a rock skeleton diagram of an embodiment of the present invention;
[0033] Figure 3 This is a three-valued grayscale image of an embodiment of the present invention;
[0034] Figure 4 This is a classification and distribution diagram of microscopic residual oil in an embodiment of the present invention;
[0035] Figure 5 This is a diagram of a porous network glass etching model according to an embodiment of the present invention. Detailed Implementation
[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0037] Figure 1 This is a flowchart of the method for evaluating the image quality of microscopic oil displacement experiments according to an embodiment of the present invention, as shown below. Figure 1 As shown, this invention provides a method for evaluating the image quality of microscopic oil displacement experiments, comprising:
[0038] Step S1: Determine the experimental object, the original experimental equipment and the target experimental equipment; design and fabricate a porous network glass etching model for the experimental object; conduct a micro-displacement experiment on the porous network glass etching model; and acquire the target experimental image of the micro-displacement experiment through the target experimental equipment.
[0039] Step S2: Analyze the target experimental image determined in Step S1 using experimental image analysis software, determine the type of residual oil in the pore network glass etching model in Step S1 based on the analysis results, introduce multiple topological feature parameters, and analyze the residual oil occurrence morphology of each type of residual oil.
[0040] Step S3: Based on the residual oil occurrence morphology of each residual oil type in Step S2, calculate multiple target-related parameters of the target experimental image captured in Step S2, and analyze the correlation between the magnification of the target experimental equipment and each target-related parameter.
[0041] Step S4: Determine the type of target parameter of the target experimental equipment based on the relevance of step S3. Acquire the original experimental image of the microscopic displacement experiment using the original experimental equipment of step S1, determine the type of the original parameter of the corresponding target parameter, and calculate the target parameter value and the original parameter value respectively.
[0042] Step S5: Obtain the comparison results of the target parameter values and the original parameter values in step S4. Based on the comparison results, analyze the matching degree between the target experimental equipment and the porous network glass etching model in step S1. Optimize and design subsequent experiments based on the matching degree analysis results.
[0043] In this evaluation method, step S1 involves etched transparent glass, oil, and water as experimental subjects, and a porous network glass etching model is created from the etched transparent glass. Furthermore, in practical applications, the target experimental equipment and the original experimental equipment are different models of microscopes and their equipped digital cameras. The criteria for distinguishing between the target and original experimental equipment are the equipment's purchase age and performance. Equipment performance standards include resolution and field of view width. The field of view width refers to the range captured by the digital camera equipped with the microscope. Equipment with a longer purchase age, lower resolution, and a narrower field of view width is considered the original experimental equipment.
[0044] In practical applications, in step S2, the experimental image analysis software is a microscopic residual oil occurrence and flow state quantitative analysis system. The experimental images obtained from the microscopic displacement experiment video of the porous network glass etching model and the numerical simulation water-driven oil experiment video images are processed by the microscopic residual oil occurrence and flow state quantitative analysis system to calculate the pore throat parameters of the porous network glass etching model.
[0045] In this evaluation method, in step S2, the type of residual oil includes film flow, droplet flow, columnar flow, porous flow, and cluster flow. The topological characteristic parameters include shape factor, Euler number, and contact ratio. The residual oil occurrence morphology includes the flow morphology of the residual oil, the occurrence location of the residual oil, and the oil-water contact relationship of the residual oil.
[0046] In practical applications, in step S3, the target-related parameters include the width of the porous network glass etching model, the throat width of the porous network glass etching model, and the minimum resolution of the target experimental image. The formula for calculating the correlation between the target experimental equipment and each target-related parameter is as follows:
[0047] Rmin=Mw / (Th / 3) (1)
[0048] Where Rmin is the minimum resolution of the experimental image; Mw is the width of the porous network glass etching model; and Th is the throat width of the porous network glass etching model.
[0049] In this evaluation method, in step S4, the minimum resolution of the experimental image is independent of the magnification of the target experimental device. When the width of the porous network glass etching model is determined, the magnification of the target experimental device is strongly correlated with the throat width of the porous network glass etching model. It should be noted that the type of the target parameter of the target experimental device and the type of the original parameter of the original experimental device are both the throat width of the porous network glass etching model.
[0050] In practical applications, the formulas for calculating the target parameter value and the original parameter value in step S4 are as follows:
[0051] Th=3*V / R (2)
[0052] Where R is the resolution of the experimental equipment; V is the field of view of the experimental equipment; and Th is the throat width of the porous network glass etching model.
[0053] It should be noted that formula (2) is derived from formula (1) and can be used for model design, especially in cases where the throat is relatively small in low-to-medium infiltration. The minimum throat width of the model is calculated based on the equipment parameters of the laboratory microscope, such as the digital camera resolution and field of view. If it is lower than this value, the captured images will not be able to fully express the experimental information, that is, the image will be blurry, which will affect the analysis and summary of the experiment.
[0054] In this evaluation method, in step S5, the feasibility of the target experimental equipment is evaluated based on the matching degree result. If the evaluation result is not feasible, the target experimental equipment is reselected or the various accessories of the selected target experimental equipment are adjusted, and steps S1 to S5 are repeated until the evaluation result is feasible. When the evaluation result is feasible, subsequent experiments are carried out based on the selected target experimental equipment.
[0055] In practical applications, in step S5, if the target parameter value is less than the original parameter value, it means that the compatibility between the target experimental equipment and the porous network glass etching model is less than that between the original experimental equipment and the porous network glass etching model, and the target experimental equipment is not feasible; if the target parameter value is greater than the original parameter value, it means that the compatibility between the target experimental equipment and the porous network glass etching model is greater than that between the original experimental equipment and the porous network glass etching model, and the target experimental equipment is feasible.
[0056] It's important to note that, according to computer graphics principles, the number of pixels determines the quality of an image. The more pixels per unit area, the higher the image quality and the higher the resolution.
[0057] In this evaluation method, the matching degree is defined as the experimental images corresponding to the throat widths of different porous network glass etching models observed within the field of view and minimum resolution of a fixed experimental device. If all experimental images are clearly visible, the matching degree between the experimental device and the porous network glass etching model is high; if some experimental images are clear or all are blurry, the matching degree between the experimental device and the porous network glass etching model is low.
[0058] In practical applications, this embodiment quantifies the minimum resolution of microscopic oil displacement images captured by the model. Based on the actual model width and throat width, the minimum resolution is calculated to obtain a standard-compliant digital image. At the same time, it provides a way to design reasonable model sizes based on throat widths under different permeability, avoiding the inconvenience of observation and increased analysis difficulty caused by size issues, further optimizing the experimental design, and achieving matching between model design and experimental equipment.
[0059] This invention provides a method for evaluating the image quality of microscopic oil displacement experiments. It evaluates whether the experimental results images meet the requirements for quantitative analysis of the remaining oil type and proportion by quantifying the minimum image resolution required for capturing images using models at different permeability levels. The working principle of this method for evaluating the image quality of microscopic oil displacement experiments is described in detail below:
[0060] Step 1: Determine the experimental object, the original experimental equipment, and the target experimental equipment. Design and fabricate a porous network glass etching model for the experimental object. Conduct a microscopic displacement experiment on the porous network glass etching model. Acquire the target experimental image of the microscopic displacement experiment using the target experimental equipment.
[0061] Step 2: Analyze the target experimental image using experimental image analysis software, determine the type of residual oil in the porous network glass etching model based on the analysis results, introduce multiple topological feature parameters, and analyze the residual oil occurrence morphology of each type of residual oil.
[0062] Step 3: Based on the residual oil occurrence morphology of each residual oil type, calculate multiple target-related parameters of the target experimental image captured in step S2, and analyze the magnification of the target experimental equipment and the correlation between each target-related parameter;
[0063] Step 4: Determine the type of target parameter of the target experimental equipment according to the degree of relevance. Collect the original experimental images of the micro-displacement experiment through the original experimental equipment, determine the type of the original parameter of the corresponding target parameter, and calculate the target parameter value and the original parameter value respectively.
[0064] Step 5: Obtain the comparison results between the target parameter values and the original parameter values. Based on the comparison results, analyze the matching degree between the target experimental equipment and the porous network glass etching model. Optimize and design subsequent experiments based on the matching degree analysis results.
[0065] In practical applications, Table 1 provides a summary of different pore throat structure types and related parameters. As shown in Table 1, by comparing with standard data, a pore network glass etching model was designed and fabricated for the experimental object. Figure 5 As shown, the medium is inside the etched glass pore throat. The narrow channel is the throat, which is generally tens of micrometers wide. At the narrowest throat, there may be five basic combinations of oil, water, oil-water, oil-water-oil, and water-oil-water. Therefore, we believe that at least 3 pixels are needed to represent the vertical throat to better reflect the realism of the experimental image.
[0066] Table 1. Overview of different pore-throat structure types and related parameters
[0067] Classification A type Category II Three categories Category Four Five categories <![CDATA[Permeability classification (x10 -3 μm 2 )]]> ≥2000 800-2000 100-800 10-100 <10 <![CDATA[Average permeability (x10 -3 um 2 )]]> 3955.25 1298.3 410.02 54.39 3.58 Porosity (%) 31.2 30.7 28.29 25.24 20.15 Maximum throat radius (µm) 48.57 45.73 34.73 27.58 9.41 Average pore throat radius (µm) 42.52 14.19 14.19 9.19 2.51
[0068] In this embodiment, as Figure 2As shown, taking a water-driven image as an example: After saturating the porous network glass etching model with oil, a threshold segmentation method is used to obtain the rock skeleton, with black representing oil and white representing the skeleton; as shown... Figure 3 As shown, after removing the rock skeleton from the post-waterflood image, three-color grayscale images (black, white, and gray) can be obtained through ternary processing. Image analysis software can then be used to classify and statistically analyze the microscopic residual oil from the waterflooding process. Figure 4 As shown, there are five types of microscopic residual oil, including film flow, droplet flow, columnar flow, porous flow, and cluster flow.
[0069] In practical applications, it is not simply a matter of processing the microscopic residual oil occurrence and flow quantitative analysis system software to obtain digital residual oil results. Instead, it is about judging whether the captured images are usable by the magnification of the target experimental equipment and the correlation between various target-related parameters, and then by comparing the target parameter values with the original parameter values.
[0070] In this embodiment, according to formula (1), Table 2 shows the minimum resolution for different permeability levels of the 4cm model, which is also different throat widths. As shown in Table 2, the minimum resolution images corresponding to different permeability levels, which is also different throat widths, were calculated. From Table 2, it can be seen that for the high permeability model, that is, the throat width is greater than 100um, we can use a digital camera with a resolution of about 1024*1024, that is, 1 million to 2 million pixels and above, to shoot a 4cm field of view, which meets the requirements of the megapixel digital camera we used before. In the early stage of the experiment, we did not encounter the problem of unclear images.
[0071] Table 24 shows the lowest resolution for different permeability levels (i.e., different throat widths) of the 24cm model.
[0072]
[0073] It should be noted that with the increasing demand for low-to-medium permeability model experiments, image problems are becoming more and more common, reflecting the mismatch between model design and experimental equipment.
[0074] Therefore, in this embodiment, the original experimental equipment and the target experimental equipment are determined. The two are distinguished based on the year of purchase. In actual application, the model of the original experimental equipment is XY-KW30 with a resolution of 1024*1024; the model of the target experimental equipment is E3ISPM20000KPA with a resolution of 5440*3680.
[0075] In practical applications, Table 3 shows the minimum throat design width of the original experimental equipment and the target experimental equipment at different magnifications. As shown in Table 3, the minimum throat design width of the original experimental equipment and the target experimental equipment at different magnifications was calculated according to formula (2). Calculating the minimum throat based on the field of view of the microscope is more in line with reality, because no matter how large the magnification of the lens of a digital camera is, that is, how large the field of view is, its resolution is constant, but the number of pixels displayed per unit is different, and the unit clarity is different. The higher the magnification, the higher the clarity and the more pixels per unit.
[0076] Table 3 shows the minimum throat design width for the original experimental equipment and the target experimental equipment at different magnifications.
[0077] category Digital camera resolution Magnification View width um Minimum throat design width (µm) Original experimental equipment 1024 5 2540 7.44 Original experimental equipment 1024 10 1280 3.75 Original experimental equipment 1024 20 631 1.85 Target experimental equipment 5440 0.67 72533 40.00 Target experimental equipment 5440 5 22850 12.6 Target experimental equipment 5440 10 11400 6.29 Target experimental equipment 5440 20 new standards 5700 3.14 Target experimental equipment 5440 20 old standard 5726 3.16 Target experimental equipment 5440 40 new standards 2848 1.57 Target experimental equipment 5440 40 old standard 2863 1.58 Target experimental equipment 5440 100 old standard 1153 0.64 Target experimental equipment 5440 100 new standards 1133 0.63
[0078] In this embodiment, if the throat width is designed to be 30µm, and the target experimental equipment with a field of view of 22850µm is selected according to Table 3, then the model width can be designed within 22850µm for convenient viewing and imaging under a microscope, without the need for model movement or post-processing image stitching. Upgrades to the experimental equipment are also possible, such as increasing the camera resolution or finding lenses with a wider field of view.
[0079] In practical applications, this embodiment quantifies the minimum resolution for capturing images in microscopic oil displacement experiments, effectively preventing image blurring issues that may occur during the analysis of images at different permeability levels. This significantly improves the accuracy of quantitative analysis and provides excellent foundational image data for intelligent analysis software, ensuring standardization based on the fundamental data. Furthermore, it provides a quantitative table for matching model design with experimental equipment, further optimizing the experimental design and facilitating experimental observation and data acquisition.
[0080] The method for evaluating the quality of microscopic oil displacement experimental images provided by this invention effectively quantifies multiple indicators of microscopic oil displacement experimental image acquisition. Based on the acquired multiple indicator parameters, it predicts experimental images of pore network glass etching models with different throat widths, makes targeted judgments on image blurring problems that occur during analysis, and provides timely improvement measures based on the judgment results. This not only improves the accuracy of quantitative analysis but also provides basic image data for intelligent analysis software, further ensuring the standardization of basic data.
[0081] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention, and the patent protection scope of the present invention should be defined by the claims.
Claims
1. A method for evaluating the image quality of a microscopic oil displacement experiment, characterized in that, include: Step S1: Determine the experimental object, the original experimental equipment and the target experimental equipment; design and fabricate a porous network glass etching model for the experimental object; conduct a micro-displacement experiment on the porous network glass etching model; and acquire the target experimental image of the micro-displacement experiment through the target experimental equipment. Step S2: Analyze the target experimental image determined in Step S1 using experimental image analysis software, determine the type of residual oil in the pore network glass etching model in Step S1 based on the analysis results, introduce multiple topological feature parameters, and analyze the residual oil occurrence morphology of each type of residual oil. Step S3: Based on the residual oil occurrence morphology of each residual oil type in Step S2, calculate multiple target-related parameters of the target experimental image captured in Step S2, and analyze the correlation between the magnification of the target experimental equipment and each target-related parameter. Step S4: Determine the type of target parameter of the target experimental equipment based on the relevance of step S3. Acquire the original experimental image of the microscopic displacement experiment using the original experimental equipment of step S1, determine the type of the original parameter of the corresponding target parameter, and calculate the target parameter value and the original parameter value respectively. Step S5: Obtain the comparison results of the target parameter values and the original parameter values in step S4. Based on the comparison results, analyze the matching degree between the target experimental equipment and the porous network glass etching model in step S1. Optimize and design subsequent experiments based on the matching degree analysis results.
2. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 1, characterized in that, In step S1, the experimental objects include etched transparent glass, oil, and water, and a porous network glass etching model is made from the etched transparent glass.
3. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 1, characterized in that, In step S1, the target experimental equipment and the original experimental equipment are microscopes of different models and their equipped digital cameras. The criteria for judging the target experimental equipment and the original experimental equipment are the years since the equipment was purchased and the equipment performance. The equipment performance criteria include resolution and field of view width. The field of view width is the range captured by the digital camera equipped with the microscope. The equipment with a long purchase period, low resolution, and low field of view width is the original experimental equipment.
4. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 1, characterized in that, In step S2, the experimental image analysis software is a microscopic residual oil occurrence and flow state quantitative analysis system. The system processes the experimental images obtained from the microscopic displacement experiment video of the porous network glass etching model and the numerical simulation water-driven oil experiment video images to calculate the pore throat parameters of the porous network glass etching model.
5. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 1, characterized in that, In step S2, the type of residual oil includes film flow, droplet flow, columnar flow, porous flow, and cluster flow; the topological characteristic parameters include shape factor, Euler number, and contact ratio; and the residual oil occurrence morphology includes the flow morphology of the residual oil, the occurrence location of the residual oil, and the oil-water contact relationship of the residual oil.
6. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 1, characterized in that, In step S3, the target-related parameters include the width of the porous network glass etching model, the throat width of the porous network glass etching model, and the minimum resolution of the target experimental image.
7. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 6, characterized in that, In step S3, the formula for calculating the correlation between the target experimental equipment and each target-related parameter is as follows: Rmin=Mw / (Th / 3) (1) Where Rmin is the minimum resolution of the experimental image; Mw is the width of the porous network glass etching model; and Th is the throat width of the porous network glass etching model.
8. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 7, characterized in that, In step S4, the minimum resolution of the experimental image is independent of the magnification of the target experimental device. When the width of the porous network glass etching model is determined, the magnification of the target experimental device is strongly correlated with the throat width of the porous network glass etching model.
9. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 8, characterized in that, In step S4, the type of the target parameter of the target experimental device and the type of the original parameter of the original experimental device are both the throat width of the pore network glass etching model.
10. The method for evaluating the image quality of a microscopic oil displacement experiment according to claim 1, characterized in that, In step S4, the formulas for calculating the target parameter value and the original parameter value are as follows: Th=3*V / R (2) Where R is the resolution of the experimental equipment; V is the field of view of the experimental equipment; and Th is the throat width of the porous network glass etching model.
11. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 1, characterized in that, In step S5, the feasibility of the target experimental equipment is evaluated based on the matching degree result. If the evaluation result is not feasible, the target experimental equipment is reselected or the various accessories of the selected target experimental equipment are adjusted, and steps S1 to S5 are repeated until the evaluation result is feasible. When the evaluation result is feasible, subsequent experiments are carried out based on the selected target experimental equipment.
12. The method for evaluating the image quality of microscopic oil displacement experiments according to claim 11, characterized in that, In step S5, if the target parameter value is less than the original parameter value, it means that the compatibility between the target experimental device and the porous network glass etching model is less than that between the original experimental device and the porous network glass etching model, and the target experimental device is not feasible; if the target parameter value is greater than the original parameter value, it means that the compatibility between the target experimental device and the porous network glass etching model is greater than that between the original experimental device and the porous network glass etching model, and the target experimental device is feasible.