Oil-water two-phase flow simulation analysis method and system based on shale oil reservoir pore network model

Abstract

The invention discloses an oil-water two-phase flow simulation analysis method and system based on a shale oil reservoir pore network model, and the method comprises the following steps: obtaining a rock of a shale reservoir, and carrying out the slice scanning of the rock to obtain a slice image; selecting a region in which pores are developed in a concentrated manner in the slice image, and segmenting the region to obtain a segmented image; preprocessing the segmented image to obtain a preprocessed image; performing connectivity threshold segmentation on the preprocessed image to obtain a final image; based on the final image, establishing a pore network model through a maximum internally tangent ball method; and shale oil-water two-phase flow simulation is carried out through the pore network model, and a simulation result is obtained. The method does not depend on traditional well logging, well drilling and production dynamic data any more, information is directly extracted from the images of the rock samples, then oil-water two-phase flow simulation is carried out, and therefore errors and uncertainty possibly introduced by a traditional method are avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum geology and oil and gas field development engineering, and in particular relates to an oil-water two-phase flow simulation analysis method and system based on a shale oil reservoir pore network model. Background Art

[0002] As an important component of unconventional oil and gas resources, shale oil occupies an increasingly important position in the global energy structure. With the gradual reduction of conventional oil and gas resources, the development and utilization of shale oil has become the key to ensuring national energy security and promoting energy transformation. As a major energy consumer, China's exploration and development of shale oil resources is of great significance to meeting the country's growing energy demand and reducing dependence on foreign energy. However, shale oil reservoirs are usually characterized by low porosity and low permeability, which brings challenges to oil and gas extraction.

[0003] In order to deeply understand the pore structure and seepage characteristics of shale oil reservoirs, oil-water two-phase simulation technology is widely used. However, existing technologies mostly rely on traditional logging, drilling and production dynamic data, which easily introduces errors and increases uncertainty. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an oil-water two-phase flow simulation analysis method and system based on a shale oil reservoir pore network model to solve the problems existing in the above prior art.

[0005] To achieve the above object, the present invention provides an oil-water two-phase flow simulation analysis method based on a shale reservoir pore network model, comprising the following steps:

[0006] Acquire rocks from a shale reservoir, and perform slice scanning on the rocks to obtain slice images;

[0007] Selecting an area in the slice image where pores are concentrated for segmentation to obtain a segmented image;

[0008] Preprocessing the segmented image to obtain a preprocessed image;

[0009] Performing connectivity threshold segmentation on the preprocessed image to obtain a final image;

[0010] Based on the final image, a pore network model is established by a maximum inscribed sphere method;

[0011] The pore network model is used to simulate the oil-water two-phase flow in shale to obtain simulation results.

[0012] Preferably, the method for obtaining a slice image comprises:

[0013] A representative flat core section with well-developed rock pores in the shale reservoir is selected as the observation surface, and the observation surface is sliced ​​and scanned by a focused ion beam scanning electron microscope system to obtain a slice image.

[0014] Preferably, before performing the slice scanning, the observation surface is subjected to argon ion polishing to obtain a polished observation surface.

[0015] Preferably, the method for obtaining the preprocessed image comprises:

[0016] Performing noise reduction processing on the segmented image to obtain a noise-reduced image;

[0017] The denoised image is filtered using a Gaussian filtering method to obtain a filtered image.

[0018] Preferably, the method for obtaining the final image comprises:

[0019] Determine a threshold of a preprocessed image by adaptive Otsu threshold segmentation, perform segmentation based on the threshold of the preprocessed image to obtain a segmentation result, and process the segmentation result by a morphological dilation operation to obtain a dilation operation image;

[0020] Performing an opening operation on the dilation operation image to obtain an opening operation image, performing a bottom-hat operation on the opening operation image to obtain a result of the bottom-hat operation, and performing image superposition processing on the opening operation image and the result of the bottom-hat operation to obtain a superimposed image;

[0021] Performing a top-hat operation on the superimposed image to obtain a top-hat image, and performing a difference operation between the superimposed image and the top-hat image to obtain an edge detection image;

[0022] The edge detection image is subjected to edge detection through Canny edge calculation, and the required seed points are extracted from the edge detection results for regional growth to obtain the final image.

[0023] Preferably, the method for establishing a pore network model comprises:

[0024] Extracting and segmenting pores from the final image to obtain a three-dimensional model of pores and rock skeleton;

[0025] The pores and throats in the three-dimensional model are organized by the maximum inscribed sphere method to establish a pore network model.

[0026] Preferably, the method for simulating shale oil-water two-phase flow comprises:

[0027] Set different displacement pressures to simulate oil-water two-phase flow and obtain the changes of flow rate and displacement efficiency over time under different displacement pressure conditions;

[0028] By setting different wall wettability, oil-water two-phase flow simulation was carried out to obtain the change of production degree over time under different wall wettability conditions.

[0029] The present invention also proposes an oil-water two-phase flow simulation and analysis system based on a shale reservoir pore network model, comprising:

[0030] An image acquisition module is used to acquire rocks of a shale reservoir, and slice and scan the rocks to obtain slice images; select an area with concentrated pores in the slice image for segmentation to obtain a segmented image;

[0031] An image denoising module, connected to the image acquisition module, for performing denoising preprocessing on the segmented image to obtain a preprocessed image;

[0032] A threshold segmentation module, connected to the image denoising module, for performing connectivity threshold segmentation on the preprocessed image to obtain a final image;

[0033] Establishing a model module, connected with the threshold segmentation module, and establishing a pore network model based on the final image by a maximum inscribed sphere method;

[0034] The flow simulation module is connected to the model building module, and performs shale oil-water two-phase flow simulation through the pore network model to obtain simulation results.

[0035] Preferably, the threshold segmentation module includes: an adaptive Otsu threshold segmentation unit, a morphological processing unit, a Canny edge calculation unit, and a region growing unit:

[0036] The adaptive Otsu threshold segmentation unit is used to determine the threshold of the preprocessed image through adaptive Otsu threshold segmentation;

[0037] The morphological processing unit is used to perform image enhancement processing on the image through the structural element to obtain an edge detection image;

[0038] The Canny edge calculation unit is used to detect edges in the obtained edge detection image;

[0039] The region growing unit is used to merge pixels with similar attributes into regions.

[0040] Preferably, the flow simulation module comprises a displacement pressure changing unit and a wettability changing unit:

[0041] The displacement pressure changing unit is used to set different displacement pressures, perform oil-water two-phase flow simulation, and obtain the changes of flow rate and displacement efficiency over time under different displacement pressure conditions;

[0042] The wettability changing unit is used to set different wall wettabilities, perform oil-water two-phase flow simulation, and obtain the change of the production degree over time under different wall wettability conditions.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] The present invention adopts an innovative method to improve the identification of pore structure of shale reservoirs and the subsequent oil-water two-phase microscopic flow simulation. Scanning images of rock samples are obtained through scanning technology. After noise reduction, filtering and other processing, noise is effectively removed from the image, and important morphological features are retained. Then, connectivity threshold segmentation is performed, which effectively solves the problems of unclear fracture extraction, poor terminal extraction effect and disappearance of connection features. It also significantly reduces the interference of noise, making the fracture features more prominent, thereby greatly improving the accuracy and completeness of fracture identification; the subsequent flow simulation research results are of guiding significance for the study of oil-water two-phase flow of shale oil under microscopic conditions. This method no longer relies on traditional logging, drilling and production dynamic data, but directly extracts information from the image of the rock sample, and then performs oil-water two-phase flow simulation, thereby avoiding the errors and uncertainties that may be introduced by these traditional methods.

[0045] This method can not only accurately identify and quantify microscale pore structures, but also study the flow laws of fluids at the microscale, providing more accurate and efficient technical support for the study of seepage characteristics of shale oil reservoirs and the formulation of development strategies. This image analysis-based technology can better adapt to complex geological conditions and provide strong support for the exploration and development of oil and gas resources. In addition, the application of this method can also be extended to the analysis of other types of rock reservoirs, which has broad application prospects and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0048] Figure 2 This is a diagram of the connectivity threshold segmentation structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0049] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0051] Embodiment 1

[0052] like Figure 1 As shown, this embodiment provides an oil-water two-phase flow simulation analysis method based on a shale reservoir pore network model, comprising the following steps:

[0053] Obtain rocks from a shale reservoir, and perform slice scanning on the rocks to obtain slice images;

[0054] Select the area where pores are concentrated in the slice image for segmentation to obtain a segmented image;

[0055] Preprocessing the segmented image to obtain a preprocessed image; performing connectivity threshold segmentation on the preprocessed image to obtain a final image;

[0056] Based on the final image, the pore network model was established by the maximum inscribed sphere method;

[0057] The pore network model is used to simulate the oil-water two-phase flow in shale and obtain the simulation results.

[0058] Furthermore, the method for obtaining the slice image includes:

[0059] A representative flat core section with well-developed rock pores in the shale reservoir was selected as the observation surface, and the observation surface was sliced ​​and scanned by a focused ion beam scanning electron microscope system to obtain a slice image.

[0060] Specifically, the rock of the shale reservoir is obtained, and a flat core section with good pore development and representativeness is selected as the observation surface. After the section is polished by argon ions, a morphological scanner is used to slice and scan the core sample to obtain a preliminary slice image.

[0061] Furthermore, for the slice image, the area with concentrated pore development is selected for segmentation. The process of obtaining the segmented image includes: selecting the area with concentrated pore development for segmentation to reduce the calculation and reconstruction time. The core grid size is 220×220×220, and the actual size is 2.2×2.2×2.2μm.

[0062] Furthermore, the method for obtaining the preprocessed image includes:

[0063] The scanned slice images are subjected to noise reduction processing to eliminate random noise in the images. Noise reduction is to improve image quality and ensure the accuracy of 3D reconstruction.

[0064] Then, the image after preliminary noise reduction is filtered using a Gaussian filtering method to obtain a filtered image, including: using a Gaussian filtering method to filter the image after preliminary noise reduction to further optimize the image, filter out high-frequency noise, while retaining the basic shape and integrity of the pores and throats, and effectively improving the image signal-to-noise ratio.

[0065] Furthermore, the filtered image is subjected to connectivity threshold segmentation. After threshold segmentation, the software will automatically construct a preliminary three-dimensional model of pores and rock skeleton, including:

[0066] Connectivity threshold segmentation is performed. Connectivity threshold segmentation is a segmentation method based on the connection relationship of pixels in an image. This method aggregates similar pixels into one region according to the similarity between pixels, and identifies specific targets by analyzing the connectivity of the aggregated region. Figure 2 In the paper, the connectivity threshold segmentation is divided into four modules: adaptive Otsu threshold segmentation module, morphological processing module, Canny edge calculation module, and region growing module;

[0067] Adaptive Otsu threshold segmentation is a method that automatically determines the optimal threshold to segment the foreground and background of an image. First, the image is divided into multiple sub-regions, and the threshold is calculated independently for each sub-region. Next, the optimal threshold for each sub-region is automatically determined by maximizing the inter-class variance. The sub-regions are then segmented using the calculated threshold, and the results are merged to reconstruct the complete segmented image. The adaptive Otsu threshold segmentation method is applied to segment the image, decomposing it into multiple small regions, each of which contains N pixels. These pixels are divided into foreground and background by the set threshold T, where the proportion of foreground pixels is P0 and the proportion of background pixels is P1. The number of pixels with grayscale values ​​below the threshold T is recorded as N0, and the number of pixels with grayscale values ​​above the threshold T is recorded as N1:

[0068]

[0069] The average gray value in the image is: μ=P0μ0+P1μ1

[0070] Where μ0 and μ1 are the mean grayscale values ​​of the foreground and background, respectively.

[0071] The class variance of the foreground area and the background area is: 2 =P0(μ0-μ) 2 +P1(μ1-μ) 2

[0072] When σ reaches its maximum value, T is the optimal threshold. After determining the optimal thresholds for each sub-region of the image, these thresholds are used to segment each sub-region of the original image. In each sub-region, pixels are divided into foreground and background according to their respective optimal thresholds, and then all segmentation results are summarized to form a complete segmentation map. This method fully considers the local characteristics of the image and calculates the most suitable threshold for each sub-region;

[0073] By applying morphological operations, images are processed using specific structural elements with the goal of highlighting specific morphological features in the image. These operations include erosion and dilation, which can remove tiny noise points in the image and close small holes or cracks in the cracks, making the crack boundaries clearer and more coherent. The dilation operation is particularly helpful in enlarging the crack area in the image, while expanding and smoothing its edges:

[0074]

[0075] Where: I1 is the image after expansion; I is the image after preprocessing; is the expansion operation; S is the structural element;

[0076] Morphological dilation can effectively close tiny holes and cracks in rock fissures, making the fissure outline more continuous and clear. However, this process may also enhance image noise and cause the fissure area to over-expand. To solve this problem, an opening operation is usually used for image processing. This operation first corrodes the image and then performs dilation. The purpose is to eliminate noise while maintaining the structural features of the main fissures, making the fissure outline clearer, and separating over-connected fissures and eliminating tiny protrusions:

[0077]

[0078] Where: I2 is the image after opening operation; It is the morphological opening operation; is the erosion operation; x, y are the coordinates of the pixel row and column in the image respectively; (x+i, y+j) are the coordinates defined on the image I; (i, j) are the coordinates defined on the structural element S.

[0079] The opening operation performs well in smoothing crack edges, removing image noise, and separating adjacent cracks, but it is not effective in identifying cracks in dark areas. To compensate for this deficiency, this study introduces a bottom-hat operation to enhance the display of cracks in images, especially those located in dark areas. The bottom-hat operation helps to reveal the details and crack features in dark areas through a process of dilation followed by corrosion. Through this processing, the dark areas of the image are better highlighted, the contrast is enhanced, and the crack features are more obvious and complete in the image:

[0080]

[0081] Where: I3 is the image after superimposing the bottom hat operation.

[0082] To extract the image details that were removed during the opening operation, the image with the bottom hat operation superimposed is subtracted from the image with the top hat operation:

[0083]

[0084] Where: I4 is the image extracted after differential top-hat operation.

[0085] Canny edge detection is a popular edge detection algorithm used to detect edges in images. It uses a multi-stage algorithm, including filtering, calculating gradients, non-maximum suppression, and hysteresis thresholds, to accurately detect crack edges. It is also used to provide seed points. The output of the Canny algorithm is used in the region growing algorithm to provide initial seed points.

[0086] The region growing algorithm is an image segmentation technique that relies on seed points to divide image regions. Starting from the seed points obtained by Canny edge detection, this method gradually gathers surrounding pixels with similar grayscale characteristics; according to the preset merging criteria (for example, the grayscale difference does not exceed a specific threshold), select appropriate pixels to add to the region; this process is repeated until all pixels that meet the conditions are included in the corresponding region, and finally achieve the purpose of accurately dividing the gap. The growth process is as follows:

[0087]

[0088]

[0089] Using the software, the maximum inscribed sphere method was used to organize the pores and throats to establish the final pore network model.

[0090] The combined use of these technologies makes the identification of cracks more accurate and efficient, improves the quality of crack feature extraction, and provides a solid foundation for subsequent analysis and processing.

[0091] Furthermore, the established shale pore network model is used to simulate the oil-water two-phase flow in shale, and the process of obtaining the simulation results includes:

[0092] The displacement pressure unit is used to set different displacement pressures. The basic parameters are set as follows: the water density is 1000kg / m 3 , viscosity is 1mPa·s, and the density of oil is 860kg / m 3, viscosity is 10mPa·s, the wall is water-wet, the wetting angle is 60°, and the displacement pressure is set to 2.00MPa, 5.00MPa and 8.00MPa respectively, and the oil-water two-phase flow simulation is carried out. The changes of flow rate and displacement efficiency over time under different displacement pressure conditions are obtained. The results show that as the injection pressure increases, the oil recovery rate of the reservoir also increases. However, after further increasing the injection pressure, the production increase effect gradually weakens, which indicates that the effect of pressure on improving the recovery rate is decreasing.

[0093] Different wall wettability was set. Based on the established pore network model, the displacement pressure was set to 5.00MPa. The wall wettability was water-wet (wetting angle 30°), neutral-wet (wetting angle 90°), and oil-wet (wetting angle 150°), respectively, and the oil-water two-phase flow simulation was performed. The change of the degree of recovery over time under different wall wettability conditions was obtained. The results show that under water-wet conditions, the model shows the highest crude oil recovery rate; relatively speaking, neutral-wet and oil-wet conditions are less likely to lead to the formation of residual oil. When the rock surface presents characteristics close to water-wet, the best oil recovery effect can be achieved.

[0094] Embodiment 2

[0095] This embodiment provides an oil-water two-phase flow simulation and analysis system based on a shale reservoir pore network model, including:

[0096] The image acquisition module is used to acquire rocks from the shale reservoir and perform slice scanning on the rocks to obtain slice images; the area with concentrated pores in the slice image is selected for segmentation to obtain a segmented image;

[0097] An image denoising module, connected to the image acquisition module, is used to perform denoising preprocessing on the segmented image to obtain a preprocessed image;

[0098] A threshold segmentation module, connected to the image denoising module, is used to perform connectivity threshold segmentation on the preprocessed image to obtain the final image;

[0099] A model module is established, connected with the threshold segmentation module, and a pore network model is established based on the final image by the maximum inscribed sphere method;

[0100] The flow simulation module is connected with the model building module, and performs oil-water two-phase flow simulation in shale through the pore network model to obtain simulation results.

[0101] Furthermore, the image acquisition module includes:

[0102] Scanning the obtained rock to obtain a slice image of the rock;

[0103] For the preliminary slice image, the area where pores are concentrated is selected for segmentation to obtain the segmented image;

[0104] Furthermore, the image denoising module includes:

[0105] Image denoising is used to perform noise reduction processing on the segmented image to obtain a preliminary denoised image;

[0106] Filtering: Using Gaussian filtering method to filter the image after preliminary noise reduction to obtain a filtered image;

[0107] Furthermore, the threshold segmentation module includes:

[0108] Connectivity threshold segmentation is a segmentation method based on the connection relationship of pixels in an image. This method aggregates similar pixels into one region based on the similarity between pixels, and identifies specific targets by analyzing the connectivity of the aggregated region.

[0109] Adaptive Otsu threshold segmentation is used to automatically determine the optimal threshold to segment the foreground and background. By calculating the inter-class variance under different thresholds, the threshold that maximizes the inter-class variance is selected;

[0110] Morphological processing uses specific structural elements to process images to highlight crack features and remove unnecessary details;

[0111] Canny edge computing is a popular edge detection algorithm used to detect edges in images;

[0112] Region growing is a seed point-based image segmentation technique used to merge pixels with similar attributes into regions.

[0113] Furthermore, the model building module includes:

[0114] The maximum inscribed sphere method was used to organize the pores and throats to establish the final pore network model.

[0115] Furthermore, the flow simulation module includes:

[0116] By changing the displacement pressure, setting different displacement pressures, and conducting oil-water two-phase flow simulations, we can obtain the changes in flow rate and displacement efficiency over time under different displacement pressure conditions. The results show that as the displacement pressure increases, the recovery rate increases, but as the displacement pressure increases, the increase in the recovery rate decreases, indicating that the effect of pressure on displacement efficiency decreases at this time. As the injection pressure increases, the oil recovery rate of the reservoir also increases. However, after further increasing the injection pressure, the production increase effect gradually weakens, indicating that the effect of pressure on improving recovery is decreasing.

[0117] By changing the wettability and setting different wall wettability, the oil-water two-phase flow simulation was performed to obtain the change of the recovery degree over time under different wall wettability conditions. The results show that under water-wet conditions, the model shows the highest crude oil recovery rate; relatively speaking, neutral wetting and oil-wet conditions are less likely to lead to the formation of residual oil. When the rock surface presents characteristics close to water-wet, the best oil recovery effect can be achieved.

[0118] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for simulating and analyzing oil-water two-phase flow based on a shale reservoir pore network model, characterized in that: The following steps are involved: Acquire rocks from a shale reservoir, and perform slice scanning on the rocks to obtain slice images; Selecting an area in the slice image where pores are concentrated for segmentation to obtain a segmented image; Preprocessing the segmented image to obtain a preprocessed image; Performing connectivity threshold segmentation on the preprocessed image to obtain a final image; Based on the final image, a pore network model is established by a maximum inscribed sphere method; The pore network model is used to simulate the oil-water two-phase flow in shale to obtain simulation results.

2. The oil-water two-phase flow simulation analysis method based on the shale reservoir pore network model according to claim 1 is characterized in that: The method for obtaining a slice image comprises: A representative flat core section with well-developed rock pores in the shale reservoir is selected as the observation surface, and the observation surface is sliced ​​and scanned by a focused ion beam scanning electron microscope system to obtain a slice image.

3. The oil-water two-phase flow simulation analysis method based on the shale reservoir pore network model according to claim 2 is characterized in that: Before performing slice scanning, the observation surface is subjected to argon ion polishing to obtain a polished observation surface.

4. The oil-water two-phase flow simulation analysis method based on the shale reservoir pore network model according to claim 1 is characterized in that: The method for obtaining the preprocessed image comprises: Performing noise reduction processing on the segmented image to obtain a noise-reduced image; The denoised image is filtered using a Gaussian filtering method to obtain a filtered image.

5. The oil-water two-phase flow simulation analysis method based on the shale reservoir pore network model according to claim 1 is characterized in that: The method for obtaining the final image comprises: Determine a threshold of a preprocessed image by adaptive Otsu threshold segmentation, perform segmentation based on the threshold of the preprocessed image to obtain a segmentation result, and process the segmentation result by a morphological dilation operation to obtain a dilation operation image; Performing an opening operation on the dilation operation image to obtain an opening operation image, performing a bottom-hat operation on the opening operation image to obtain a result of the bottom-hat operation, and performing image superposition processing on the opening operation image and the result of the bottom-hat operation to obtain a superimposed image; Performing a top-hat operation on the superimposed image to obtain a top-hat image, and performing a difference operation between the superimposed image and the top-hat image to obtain an edge detection image; The edge detection image is subjected to edge detection through Canny edge calculation, and the required seed points are extracted from the edge detection results for regional growth to obtain the final image.

6. The oil-water two-phase flow simulation analysis method based on the shale reservoir pore network model according to claim 1 is characterized in that: The method for establishing a pore network model comprises: Extracting and segmenting pores from the final image to obtain a three-dimensional model of pores and rock skeleton; The pores and throats in the three-dimensional model are organized by the maximum inscribed sphere method to establish a pore network model.

7. The oil-water two-phase flow simulation analysis method based on the shale reservoir pore network model according to claim 1 is characterized in that: The method for simulating shale oil-water two-phase flow comprises: Set different displacement pressures to simulate oil-water two-phase flow and obtain the changes of flow rate and displacement efficiency over time under different displacement pressure conditions; By setting different wall wettability, oil-water two-phase flow simulation was carried out to obtain the change of production degree over time under different wall wettability conditions.

8. An oil-water two-phase flow simulation and analysis system based on a shale reservoir pore network model, characterized in that: include: An image acquisition module is used to acquire rocks of a shale reservoir and perform slice scanning on the rocks to obtain slice images; Selecting an area in the slice image where pores are concentrated for segmentation to obtain a segmented image; An image denoising module, connected to the image acquisition module, for performing denoising preprocessing on the segmented image to obtain a preprocessed image; A threshold segmentation module, connected to the image denoising module, for performing connectivity threshold segmentation on the preprocessed image to obtain a final image; Establishing a model module, connected with the threshold segmentation module, and establishing a pore network model based on the final image by a maximum inscribed sphere method; The flow simulation module is connected to the model building module, and performs shale oil-water two-phase flow simulation through the pore network model to obtain simulation results.

9. The oil-water two-phase flow simulation and analysis system based on the shale reservoir pore network model according to claim 8, characterized in that: The threshold segmentation module includes: an adaptive Otsu threshold segmentation unit, a morphological processing unit, a Canny edge calculation unit, and a region growing unit: The adaptive Otsu threshold segmentation unit is used to determine the threshold of the preprocessed image through adaptive Otsu threshold segmentation; The morphological processing unit is used to perform image enhancement processing on the image through the structural element to obtain an edge detection image; The Canny edge calculation unit is used to detect edges in the obtained edge detection image; The region growing unit is used to merge pixels with similar attributes into regions.

10. The oil-water two-phase flow simulation and analysis system based on the shale oil reservoir pore network model according to claim 8, characterized in that: The flow simulation module includes a displacement pressure change unit and a wettability change unit: The displacement pressure changing unit is used to set different displacement pressures, perform oil-water two-phase flow simulation, and obtain the changes of flow rate and displacement efficiency over time under different displacement pressure conditions; The wettability changing unit is used to set different wall wettabilities, perform oil-water two-phase flow simulation, and obtain the change of the production degree over time under different wall wettability conditions.

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