A microcosmic residual oil type conversion quantitative analysis method based on CT technology

Abstract

The application discloses a microcosmic residual oil type conversion quantitative analysis method based on CT technology. The method comprises the following steps: collecting a core sample of a to-be-detected area, simulating a displacement process of the core sample, and collecting two-dimensional CT scanning images corresponding to the core sample under at least two different displacement states in the displacement process; for the two-dimensional CT scanning images under each displacement state, an oil phase three-dimensional model under the displacement state is obtained by recombination respectively; according to a three-dimensional shape factor G of a residual oil cluster in the oil phase three-dimensional model and an Euler number E of a pore space N , a microcosmic occurrence type of the residual oil cluster is divided; according to a conversion relationship of the residual oil cluster, a microcosmic residual oil type conversion three-dimensional model is obtained. The microcosmic residual oil type conversion three-dimensional model is applied to calculate a residual oil cluster conversion rate and an actual production proportion. The application is more accurate in residual oil type division and quantitative analysis, and is beneficial to improve the production degree of microcosmic residual oil.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of microcosmic residual oil type conversion quantitative analysis, and particularly relates to a microcosmic residual oil type conversion quantitative analysis method based on CT technology. BACKGROUND

[0002] In the current exploitation work, it is difficult to mobilize microcosmic residual oil, especially for the microcosmic residual oil in the reservoirs with medium and high permeability, one of the main reasons is that the residual oil type is not accurately classified, and there is a lack of accurate microcosmic residual oil type conversion quantitative analysis method. At present, there are two methods for analyzing microcosmic residual oil. One is a direct method, that is, based on imaging technology to characterize the distribution law of residual oil at different development stages, such as microcosmic glass etching model, fluorescence analysis method, nuclear magnetic resonance and the like. The other is an indirect method, that is, a mathematical simulation model is established, and the model is described by a computer. The advantage of this method is that it is not disturbed by external conditions, such as geological complexity, experimental conditions, sample representativeness and the like, and can present the three-dimensional space residual oil evolution law. SUMMARY

[0003] The inventors found that in the prior art, the direct method has the disadvantage that it can only simulate the occurrence state and distribution law of residual oil in two-dimensional plane, and cannot present the mobilization ratio of crude oil and the distribution characteristics of residual oil in three-dimensional space. The disadvantage of the indirect method is that the seepage law of oil-water two-phase in the reservoir is complex, and it is difficult to accurately establish a mathematical model. Whether the direct method or the indirect method, the current microcosmic residual oil type conversion problem is less involved, the accuracy of the microcosmic residual oil type classification is insufficient, there is a lack of a method that can clearly show the conversion process between different types of microcosmic residual oil at different stages of reservoir development, the quantitative analysis of the conversion is not accurate enough, it is difficult to clarify the mobilization ability of different displacement media on various types of microcosmic residual oil, and there is a lack of accurate quantitative analysis method for the occurrence state, mobilization degree and type conversion of microcosmic residual oil at different exploitation stages (water injection stage, chemical flooding stage and subsequent water flooding stage), which leads to the problem of low mobilization degree of microcosmic residual oil. In order to at least partially solve the technical problems existing in the prior art, the inventors make the present application, and provide a microcosmic residual oil type conversion quantitative analysis method and device based on CT technology through specific embodiments.

[0004] In a first aspect, the embodiments of the present application provide a method for generating a microcosmic residual oil type conversion three-dimensional model, comprising:

[0005] Collecting a core sample of a to-be-tested region, simulating a displacement process for the core sample, and collecting a two-dimensional CT scan image corresponding to the core sample under at least two different displacement states in the displacement process;

[0006] Respectively recombining the two-dimensional CT scan image under each displacement state to obtain an oil phase three-dimensional model under the displacement state.

[0007] According to the three-dimensional shape factor G of the remaining oil cluster in the oil phase three-dimensional model and the Euler number E of the pore space N , the micro-occurrence morphology type of the remaining oil cluster is divided, and the pixel points of the remaining oil cluster under various micro-occurrence morphology types are distinguished and marked;

[0008] According to the conversion relationship of the remaining oil cluster of the preset micro-occurrence morphology type and the remaining oil cluster of all types under two different displacement states, the pixel points corresponding to the conversion part, the net unproduced part, and the actually produced part in the remaining oil cluster pixel points of the preset micro-occurrence morphology type are determined and distinguished and marked, and a micro-remaining oil type conversion three-dimensional model is obtained.

[0009] In some optional embodiments, the displacement state includes a pre-displacement state, a displacement process state, and a post-displacement end state; and the pre-displacement state is a saturated oil state.

[0010] In some optional embodiments, after collecting the two-dimensional CT scan images corresponding to the core samples under at least two different displacement states during the displacement process, the two-dimensional CT scan images are preprocessed; the preprocessing includes segmentation processing, noise reduction, and gray value binarization; the segmentation processing refers to dividing different substances on the two-dimensional CT scan images, first segmenting the pores, then segmenting the pores into oil, and then segmenting the pores swept by the displacement agent.

[0011] In some optional embodiments, for the two-dimensional CT scan image under each displacement state, the oil-water segmentation is performed on the two-dimensional CT scan image, the oil phase two-dimensional model is extracted from the two-dimensional CT scan image after oil-water segmentation, and the oil phase three-dimensional model under the displacement state is reorganized according to the oil phase two-dimensional model.

[0012] In some optional embodiments, the oil-water segmentation includes using a threshold segmentation method to divide the pixel value intervals of oil and water, respectively.

[0013] In some optional embodiments, the three-dimensional shape factor G of the remaining oil cluster and the Euler number E of the pore space are calculated N , according to G and E N , the micro-occurrence morphology of the remaining oil cluster is divided into different types according to different intervals, and the pixel points of the remaining oil cluster of various micro-occurrence morphology types are marked and named.

[0014] In some optional embodiments, for a preset micro-occurrence mode type, pixel points of remaining oil clusters of the preset micro-occurrence mode type in the post-flood state intersect with pixel points of remaining oil clusters of all types except the preset micro-occurrence mode type in the pre-flood state, to obtain pixel points corresponding to the remaining oil clusters of the preset micro-occurrence mode type being converted into the remaining oil clusters of the all types except the preset micro-occurrence mode type from the pre-flood state to the post-flood state;

[0015] The pixel points of the remaining oil clusters of the preset micro-occurrence mode type in the post-flood state are subtracted by the pixel points corresponding to the remaining oil clusters of the preset micro-occurrence mode type being converted into the remaining oil clusters of the all types except the preset micro-occurrence mode type from the pre-flood state to the post-flood state, to obtain pixel points corresponding to the net non-producing part of the remaining oil clusters of the preset micro-occurrence mode type in the post-flood state;

[0016] The pixel points of the remaining oil clusters of the preset micro-occurrence mode type in the pre-flood state respectively intersect with the pixel points of the remaining oil clusters of all types in the post-flood state, to obtain pixel points corresponding to the remaining oil clusters of the preset micro-occurrence mode type being converted into the remaining oil clusters of the all types from the pre-flood state to the post-flood state;

[0017] The pixel points of the remaining oil clusters of the preset micro-occurrence mode type in the pre-flood state are subtracted by the pixel points of the net non-producing part of the remaining oil clusters of the preset micro-occurrence mode type in the post-flood state and the pixel points corresponding to the remaining oil clusters of the preset micro-occurrence mode type being converted into the remaining oil clusters of the all types from the pre-flood state to the post-flood state, to obtain pixel points corresponding to the actual producing part of the remaining oil clusters of the preset micro-occurrence mode type from the pre-flood state to the post-flood state.

[0018] In a second aspect, an embodiment of the present application provides a micro-remaining oil type conversion quantitative analysis method, comprising:

[0019] establishing a micro-remaining oil type conversion three-dimensional model;

[0020] The micro-remaining oil type conversion three-dimensional model is obtained by the micro-remaining oil type conversion three-dimensional model generation method;

[0021] The micro-remaining oil type conversion three-dimensional model is applied to calculate a conversion rate and an actual producing proportion of a preset micro-occurrence mode type.

[0022] In some optional embodiments, the pixel points corresponding to the actual producing part of the preset microscopic occurrence morphology type of the remaining oil cluster from the pre-displacement state to the post-displacement state are counted; the pixel points corresponding to the actual producing part of the preset microcosmic occurrence morphology type of the remaining oil cluster from the pre-displacement state to the post-displacement state are divided by the pixel points of the preset microcosmic occurrence morphology type of the remaining oil cluster in the pre-displacement state, to obtain the actual producing proportion of the preset microcosmic occurrence morphology type of the remaining oil cluster in the pre-displacement state.

[0023] In some optional embodiments, the pixel points corresponding to the actual producing part of the preset microcosmic occurrence morphology type of the remaining oil cluster from the pre-displacement state to the post-displacement state are counted; the pixel points corresponding to the actual producing part of the preset microcosmic occurrence morphology type of the remaining oil cluster from the pre-displacement state to the post-displacement state are divided by the pixel points of the preset microcosmic occurrence morphology type of the remaining oil cluster in the pre-displacement state, to obtain the actual producing proportion of the preset microcosmic occurrence morphology type of the remaining oil cluster in the pre-displacement state.

[0024] In a third aspect, an embodiment of the present application provides a device for generating a microcosmic remaining oil type conversion three-dimensional model, comprising:

[0025] An oil phase three-dimensional model establishing module is configured to collect a core sample of a to-be-tested region, simulate a displacement process of the core sample, and collect two-dimensional CT scan images corresponding to the core sample in two different displacement states during the displacement process; and for the two-dimensional CT scan images in each displacement state, the oil phase three-dimensional model in the displacement state is reorganized respectively.

[0026] A remaining oil cluster type marking module is configured to mark the microcosmic occurrence morphology type of the remaining oil cluster according to the three-dimensional shape factor G of the remaining oil cluster and the Euler number E of the pore space in the oil phase three-dimensional model in the displacement state. N The microcosmic occurrence morphology type of the remaining oil cluster is divided, and the pixel points of the remaining oil cluster in each microcosmic occurrence morphology type are marked differently.

[0027] A conversion relationship determining module is configured to determine the pixel points corresponding to the conversion part, the net unproduced part, and the actual producing part of the remaining oil cluster in the preset microcosmic occurrence morphology type according to the conversion relationship between the remaining oil cluster in the preset microcosmic occurrence morphology type and the remaining oil cluster in all other types in two different displacement states, and mark the pixel points differently, to obtain a microcosmic remaining oil conversion three-dimensional model.

[0028] In a fourth aspect, an embodiment of the present application provides a micro residual oil type conversion quantitative analysis device, comprising:

[0029] A conversion three-dimensional model establishing module is configured to establish a micro residual oil type conversion three-dimensional model, wherein the micro residual oil type conversion three-dimensional model is obtained by using the method for generating a micro residual oil type conversion three-dimensional model.

[0030] A quantitative analysis module is configured to calculate a residual oil cluster conversion rate and an actual production ratio of a preset micro occurrence morphology type by using the micro residual oil type conversion three-dimensional model.

[0031] In a fifth aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for generating a micro residual oil type conversion three-dimensional model or the method for micro residual oil type conversion quantitative analysis when executing the program.

[0032] In a sixth aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the method for generating a micro residual oil type conversion three-dimensional model or the method for micro residual oil type conversion quantitative analysis.

[0033] The above technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0034] The method for generating a micro residual oil type conversion three-dimensional model provided by the embodiments of the present application can pre-process a CT scanning image, including segmentation processing, noise reduction, and gray scale binarization, so that the division of different substances in the image is more accurate, the influence of image noise and other non-characteristic factors is removed, the image is more accurate, the automation degree of image processing is improved by combining ImageJ with Avizo, the image processing is more accurate and fast, and the quantitative analysis based on the above is more accurate. N The combination of the three-dimensional shape factor G and the Euler number E of the pore space as the basis for dividing the micro residual oil makes the division of the micro residual oil type more accurate. N The combination of the three-dimensional shape factor G and the Euler number E of the pore space as the basis for dividing the micro residual oil makes the division of the micro residual oil type more accurate. According to the needs of the quantitative analysis accuracy, the core samples can be collected multiple times at different times during the displacement process, so as to analyze the type conversion and production of the micro residual oil under the action of different types of displacement agents at different times, and accordingly, the displacement agent can be reasonably adjusted on site, a specific type of displacement agent is selected at a specific time, and the production degree of the micro residual oil is improved.

[0035] The embodiment two of the present application provides a micro residual oil type conversion quantitative analysis method, residual oil conversion process can be quantitatively analyzed accurately and intuitively by constructing a three-dimensional model, marking the area of a certain type of residual oil under different displacement conditions and calculating pixel points, various parameters such as residual oil conversion rate and actual utilization ratio can be calculated, reference can be provided for field staff, and the displacement agent can be reasonably adjusted, so that the utilization degree of residual oil is improved. Since only the core sample is simulated displacement, compared with the displacement experiment in the actual mining process, the cost is very low. And the two-dimensional image processing, three-dimensional image recombination, image segmentation and pixel point calculation process of the present application are all completed by computer software, and the automation degree is high, accurate, efficient, fast and intuitive, which further reduces the cost.

[0036] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and obtained by the structure particularly pointed out in the written description, claims, and drawings.

[0037] The technical solutions of the present application will be further described in detail below by means of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0038] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:

[0039] Figure 1 The flow chart of the generation method of the micro residual oil type conversion three-dimensional model in an embodiment of the present application;

[0040] Figure 2 The original two-dimensional CT scanning image in an embodiment of the present application;

[0041] Figure 3 The two-dimensional CT scanning image after preprocessing in an embodiment of the present application;

[0042] Figure 4 The two-dimensional scanning image before oil-water segmentation in an embodiment of the present application;

[0043] Figure 5 The gray scale image of the two-dimensional scanning image after oil-water segmentation in an embodiment of the present application;

[0044] Figure 6 The three-dimensional image of the two-dimensional CT processing image recombination in an embodiment of the present application;

[0045] Figure 7 The micro occurrence form classification marking diagram of residual oil in an embodiment of the present application;

[0046] Figure 8 A three-dimensional image of a digital core after each set is marked in an embodiment of the application;

[0047] Figure 9 A schematic diagram of the source of cluster-shaped residual oil in the water drive process in an embodiment of the application;

[0048] Figure 10 A flowchart of a micro residual oil type conversion quantitative analysis method in an embodiment of the application;

[0049] Figure 11 A three-dimensional model of micro residual oil type conversion in an embodiment of the application is applied to a schematic diagram;

[0050] Figure 12 A block diagram of a micro residual oil type conversion quantitative analysis device in an embodiment of the application;

[0051] Figure 13 A block diagram of a micro residual oil type conversion quantitative analysis device in an embodiment of the application; DETAILED DESCRIPTION

[0052] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0053] To solve the problems of insufficient accuracy in the classification of micro residual oil types in the prior art, the lack of a method that can clearly show the conversion process between different types of micro residual oil at different stages of reservoir development, the lack of accurate quantitative analysis of conversion, the difficulty in clarifying the producing capacity of different displacement media for various types of micro residual oil, the lack of accurate quantitative analysis of the occurrence state, producing degree and type conversion of micro residual oil at different stages of development (water injection stage, chemical flooding stage and subsequent water flooding stage), and the low producing degree of micro residual oil, the present application is described in detail in the following embodiments.

[0054] Embodiment one

[0055] Embodiment one of the present application provides a method for generating a three-dimensional model of micro residual oil type conversion, as shown in Figure 1 The method comprises the following steps:

[0056] Step S1, collect the core sample of the area to be tested, simulate the displacement process of the core sample, and collect the two-dimensional CT scan images corresponding to the core sample under at least two different displacement states during the displacement process.

[0057] Because of using digital core technology, only core sample is simulated displacement, so the cost is very low compared with displacement experiment of actual exploitation process.

[0058] The displacement state includes a pre-displacement state, a displacement process state and a post-displacement state; the pre-displacement state is a saturated oil state.

[0059] The displacement process state can include multiple states according to one or more parameters, for example, according to the displacement time parameter, the state after N minutes or hours of displacement, the state after 2N minutes or hours of displacement, the state after N+2 minutes or hours of displacement, etc., and for example, according to the volume of oil displaced, the state of displacing M milliliters of oil, the state of displacing 2M milliliters of oil, etc., and for example, the state after N minutes or hours after displacing M milliliters of oil, etc. The end of displacement refers to when the water cut of the produced fluid reaches 95% or more, the displacement ends.

[0060] The displacement method includes water flooding, polymer flooding or ternary composite flooding, etc.

[0061] The core sample is photographed or scanned by experimental instruments (such as high-power optical microscope or X-ray CT scanner, etc.) to obtain a large number of core two-dimensional CT scan images, and then the two-dimensional pictures are stacked and reconstructed into three-dimensional digital core by modeling program or software, mainly including sequential slice imaging method, laser scanning confocal microscope method and X-ray CT scanning method. Referring to Figure 2 shown, Figure 2 is the original two-dimensional CT scan image.

[0062] The two-dimensional CT scan image is pre-processed. The pre-processing includes segmentation processing, noise reduction and gray scale binarization, etc.

[0063] The segmentation processing refers to dividing different substances on the two-dimensional CT scan image. First, the pores are segmented, then the pores into oil are segmented, and then the pores swept by the displacing agent are segmented. The core projection image directly obtained by the CT scan expresses the change of the core composition substance density through the change of the gray value, and cannot directly distinguish the pores and the skeleton in the core. Therefore, the different components in the two-dimensional CT scan image need to be divided for further use of the image. The gray value change interval of two different substances has an overlapping interval, and the pore position can be identified by the naked eye, but some boundary areas cannot be divided by the naked eye, such as the boundary part of the pores and the skeleton, which is relatively blurred and difficult to distinguish. Therefore, the image needs to be segmented to reasonably divide the pores and the skeleton. For example, the liquid in the core includes water and oil. After adding potassium iodide to the water, the part with water appears bright white, so that the water can be identified. If a pore is small, the water entering the pore is relatively small, and the brightness of the pore is relatively dark. If a pore is relatively large and contains a lot of oil, the image gray value is large due to the large density of the oil, and the image color is close to black.

[0064] Image denoising is a process of removing pixel noise points from an image. Currently, there are various denoising algorithms to choose from. Through appropriate denoising processing, the original information integrity (i.e., the main features) can be maintained as much as possible while removing the useless information in the image.

[0065] Gray scale binarization refers to obtaining a binarized image that can still reflect the overall or local features of the image through appropriate threshold selection of the gray scale image, that is, the entire image presents a clear black and white effect, so that the differentiation of different substances on the image is more obvious.

[0066] In one embodiment, ImageJ software can be selected to assist in segmenting, denoising, and gray scale binarizing the two-dimensional CT scan image. In addition to basic image operations such as coloring, adjusting the gray scale, and the like, ImageJ can create statistical information according to user-defined parameters, such as image area and pixel statistics, interval, angle calculation, columnar chart and profile creation, Fourier transform, and the like. Since ImageJ is a development structure software, corresponding plug-ins or macros can be designed according to the specific needs of processing the two-dimensional CT scan image to improve the automation degree of image processing, making the image processing more accurate and fast. Therefore, with the assistance of ImageJ software, the two-dimensional CT scan image can be processed more quickly and accurately. Referring to Figure 3 As shown in the formula, Figure 3 is the preprocessed two-dimensional CT scan image.

[0067] Step S2, for each displacement state of the two-dimensional CT scan image, the oil phase three-dimensional model under the displacement state is reorganized respectively. The detailed steps are as follows:

[0068] For each displacement state of the two-dimensional CT scan image, the oil-water segmentation is performed on the two-dimensional CT scan image respectively, the oil phase two-dimensional model is extracted from the oil-water segmented two-dimensional CT scan image, and the oil phase three-dimensional model under the displacement state is reorganized according to the oil phase two-dimensional model.

[0069] Wherein the oil-water segmentation refers to dividing the pixel value interval of oil and water respectively by using threshold segmentation method, so as to achieve the purpose of oil-water segmentation, wherein the segmentation effect can be made more obvious by coloring processing, for example, referring to Figure 4 and Figure 5 , Figure 4 is the two-dimensional scan image before oil-water segmentation, Figure 5 is the gray image of the two-dimensional scan image after oil-water segmentation, in the colored oil-water segmented two-dimensional scan image, the image area where the oil is located is set to red, the image area where the water is located is set to blue, and the image area where the skeleton is located is set to black.

[0070] Reorganization refers to reorganizing the two-dimensional image after segmentation processing into a three-dimensional image. For example, in this embodiment, 1440 two-dimensional CT processing images after processing are reorganized into a three-dimensional image, referring to the three-dimensional image shown in Figure 6 to achieve the purpose of extracting three-dimensional pore or oil-water distribution.

[0071] In the segmentation and reorganization process, ImageJ is combined with Avizo to improve the automation degree of image processing, so that the image processing is more accurate and fast. Avizo is a software for scientific and industrial data visualization and analysis, and its functions mainly include three-dimensional reconstruction, three-dimensional image data rendering, display of flow simulation results inside three-dimensional model through advanced vector field visualization, calculation and quantification of density, distance, area, volume and other data through statistical module, generation of three-dimensional model based on individual pixel allocation to distinguish different structures, and further data analysis. Therefore, through the Avizo software, three-dimensional reorganization can be realized, the conversion process of micro residual oil can be displayed, different substances such as pores, skeletons, different types of micro residual oil can be marked, and the density, distance, area, volume, self-defined unit and other data of different substances such as pores, skeletons, different types of micro residual oil can be calculated and quantitatively analyzed through the statistical module. Therefore, with the aid of Avizo software, the automation degree of image processing and data analysis is improved, and the quantitative analysis of micro residual oil conversion is more accurate and fast.

[0072] The accuracy of oil-water segmentation is directly related to image preprocessing, and pixel noise has a huge impact on oil-water segmentation.

[0073] A two-dimensional model of the oil phase is extracted from the two-dimensional image after oil-water segmentation, and the two-dimensional models of the oil phase corresponding to different displacement states are reorganized into a three-dimensional model of the oil phase. N The micro-occurrence morphology of the oil in the three-dimensional model of the oil phase is classified, and different colors are marked for the pixel points of the oil of different micro-occurrence morphology types, and the same color is marked for the pixel points of the oil of the same micro-occurrence morphology type.

[0074] The two-dimensional CT processed images after oil-water segmentation under different displacement states are extracted to establish a two-dimensional model of the oil phase. For example, the part other than the oil phase in the two-dimensional image is set to be colorless or white, and the obtained image is the extracted two-dimensional image of the oil phase, and a two-dimensional model of the oil phase is established from these extracted two-dimensional images of the oil phase. Then, a three-dimensional image of the oil phase is reorganized according to the two-dimensional model of the oil phase. As shown in Figure 5 , Figure 5 is a three-dimensional image of the oil phase reorganized from the two-dimensional image of the oil phase.

[0075] Step S3, for the three-dimensional model of the oil phase under the displacement state, according to the three-dimensional shape factor G of the remaining oil cluster in the three-dimensional model of the oil phase and the Euler number E of the pore space N , the micro-occurrence morphology type of the remaining oil cluster is classified, and the pixel points of the remaining oil cluster under various micro-occurrence morphology types are distinguished and marked;

[0076] Affected by the type of micro-reservoir rock pore space structure, the remaining oil enrichment mode on the pore scale is various. Combined with the three-dimensional shape factor G and the Euler number E of the pore space N , the micro-occurrence morphology of the remaining oil can be quantitatively distinguished. The three-dimensional shape factor represents the similarity of the oil droplet to the sphere, and the greater the shape factor, the higher the similarity. For a sphere, the shape factor reaches a maximum value of 1, and the calculation formula is as follows:

[0077]

[0078] In the formula, G is the three-dimensional shape factor; π is the constant of the circle, V is the volume of the remaining oil cluster, unit: cubic meter; S is the surface area of the remaining oil cluster, unit: square meter. The remaining oil cluster is the aggregation form of the remaining oil in space.

[0079] Image topology parameters are a general term for various characteristic parameters used for shape matching and object recognition in the field of image processing. Euler number is a spatial topology description method, also known as Euler-Poincare characteristic, which is divided into three-dimensional surface and three-dimensional body. It remains unchanged after image translation, rotation and other operations, and is widely used in image processing such as geological sandstone analysis and shadow detection. Its formula is:

[0080] E N =b0-b1+b2

[0081] In the formula, E N is the Euler number of pore space; b0 is the number of connected bodies; b1 is the number of hole bodies, which refers to the maximum cutting number without fracture, also known as tunnel; and b2 represents the number of cavities, also known as cavity.

[0082] Calculate the three-dimensional shape factor G of the remaining oil cluster and the Euler number E of the pore space N , and divide the micro-occurrence form of the remaining oil cluster into different types according to the different intervals of G and E N . Mark and name the pixel points of the remaining oil cluster of various micro-occurrence form types.

[0083] In this patent, the three-dimensional shape factor G and the Euler number E of the pore space N can quantitatively distinguish the micro-occurrence form of the remaining oil into five types, including cluster, column, porous, membrane and isolated. Among them, the determination basis of cluster is G < 0.1; the determination basis of column is 0.1 ≤ G < 0.3; the determination basis of porous is 0.3 ≤ G < 0.7 and E N < 1; the determination basis of membrane is 0.3 ≤ G < 0.7 and E N ≥ 1; and the determination basis of isolated is G ≥ 0.7.

[0084] The three-dimensional shape factor G and the Euler number E of the pore space N are combined as the basis for dividing the micro-occurrence form of oil in the three-dimensional model, i.e. dividing the types of micro-remaining oil, which makes the division of micro-remaining oil types more accurate. The three-dimensional shape factor G and the Euler number E of the pore space N are combined as the basis for division, which is more reasonable and accurate than relying solely on the three-dimensional shape factor G as the basis for division. The pixel points of oil of different micro-occurrence form types are marked with different colors, and the pixel points of oil of the same micro-occurrence form type are marked with the same color.

[0085] For example, refer to Figure 7As shown, the quantitative distinguishing basis of the micro-occurrence morphology of the remaining oil is input in the Avizo software or other software with similar functions, and the remaining oil is classified according to the rules and marked with different colors for distinguishing. Figure 7 In the color version, different types of remaining oil clusters are presented in different colors. Figure 7 In the color version, different types of remaining oil clusters are presented in different colors.

[0086] Step S4, according to the conversion relationship of the remaining oil cluster of the preset micro-occurrence morphology type and the remaining oil cluster of all other types under two different displacement states, the pixel points corresponding to the converted part, the net unproduced part and the actually produced part in the pixel points of the remaining oil cluster of the preset micro-occurrence morphology type are determined and distinguished, and a micro-remaining oil type conversion three-dimensional model is obtained.

[0087] The process of determining the pixel points corresponding to the converted part, the net unproduced part and the actually produced part in the pixel points of the remaining oil cluster of the preset micro-occurrence morphology type includes:

[0088] For the preset micro-occurrence morphology type, the pixel points of the remaining oil cluster of the preset micro-occurrence morphology type under the later displacement state intersect with the pixel points of the remaining oil cluster of all other types except the preset micro-occurrence morphology type under the former displacement state, to obtain the pixel points corresponding to the conversion of the remaining oil cluster of all other types from the former displacement state to the later displacement state into the remaining oil cluster of the preset micro-occurrence morphology type.

[0089] The pixel points of the remaining oil cluster of the preset micro-occurrence morphology type under the later displacement state are subtracted from the pixel points corresponding to the conversion of the remaining oil cluster of all other types from the former displacement state to the later displacement state into the remaining oil cluster of the preset micro-occurrence morphology type, to obtain the pixel points corresponding to the net unproduced part of the remaining oil cluster of the preset micro-occurrence morphology type under the later displacement state.

[0090] The pixel points of the remaining oil cluster of the preset micro-occurrence morphology type under the former displacement state intersect with the pixel points of the remaining oil cluster of all other types under the later displacement state, to obtain the pixel points corresponding to the conversion of the remaining oil cluster of the preset micro-occurrence morphology type from the former displacement state to the later displacement state into the remaining oil cluster of all other types.

[0091] The pixel point of the remaining oil cluster of the preset micro-occurrence form type in the pre-displacement state is subtracted by the pixel point corresponding to the net unproduced part of the remaining oil cluster of the preset micro-occurrence form type in the post-displacement state, subtracted by the pixel point corresponding to the conversion of the remaining oil cluster of the preset micro-occurrence form type from the pre-displacement state to the post-displacement state into the remaining oil cluster of the rest type, to obtain the pixel point corresponding to the actual produced part of the remaining oil cluster of the preset micro-occurrence form type from the pre-displacement state to the post-displacement state.

[0092] In order to make the process of determining the pixel points corresponding to the parts such as the converted part, the net unproduced part and the actual produced part more clear, the preset micro-occurrence form type is taken as an example of cluster, and the displacement mode is taken as an example of water drive. The premise of the mutual conversion of the cluster remaining oil type is first that the cluster remaining oil in different stages (i.e. different displacement states) is directed to the produced part and the unproduced part, the produced part includes the produced part and the type conversion (cluster to other types) part, and the unproduced part includes the net unproduced part and the type conversion (other types to cluster) part. The cluster remaining oil in the displacement process can be divided into two directions of direction and source. Among them, in the direction of direction, the cluster remaining oil can be divided into the produced remaining oil and the converted remaining oil of other types, and in the direction of source, the cluster remaining oil can be divided into the net unproduced remaining oil and the converted remaining oil of other types.

[0093] For the unproduced part of the cluster remaining oil, the intersection of the cluster remaining oil in the water drive state (which can be the state in the displacement process or the state after the displacement ends) and the remaining four types of remaining oil except the cluster remaining oil in the saturated oil state is obtained, and this part of the cluster remaining oil is converted from the remaining four types of remaining oil in the water drive process, as shown in the following formula,

[0094] A (drive non-cluster to cluster) = A (drive cluster) ∩ A (saturated non-cluster)

[0095] Wherein A (drive cluster) is the cluster remaining oil set in the water drive state (which can be the intermediate state in the displacement or the state after the displacement ends), A (saturated non-cluster) is the remaining four types of remaining oil set except the cluster remaining oil in the saturated oil state, and A (drive non-cluster to cluster) is the cluster remaining oil set converted from the remaining four types of remaining oil in the water drive state.

[0096] The cluster remaining oil obtained by subtracting the part of the remaining oil from the cluster remaining oil in the water drive state is the net unproduced part of the actual cluster remaining oil, as shown in the following formula,

[0097] A (drive cluster net unproduced) = A (drive cluster) - A (drive non-cluster to cluster)

[0098] Wherein, A(displaced clusters) is the collection of clustered residual oil in the water-drive state (which can be the intermediate state of displacement or the end of displacement), A(displaced non-cluster to cluster) is the collection of clustered residual oil converted from the other four types of residual oil in the water-drive state, and A(displaced clusters net unused) is the collection of the net unused portion of the actual clustered residual oil in the water-drive state.

[0099] The utilized portion of clustered residual oil can be further divided into the actually utilized portion and the residual oil conversion portion. The clustered residual oil in the saturated oil state is intersected with the other four types of residual oil in the water-drive state. The resulting residual oil is the portion converted from clustered residual oil to the other four types of residual oil during the water-drive process, i.e., the clustered residual oil conversion portion. This is illustrated in the following formula.

[0100] A(cluster-driving to non-cluster) = A(saturated cluster) ∩ A(non-cluster-driving)

[0101] Wherein, A(saturated cluster) is the set of clustered residual oil in the saturated oil state, A(displaced non-cluster) is the set of the other four types of residual oil in the water-drive state except for clustered residual oil, and A(displaced cluster to non-cluster) is the set of the portion of residual oil that is transformed from clustered residual oil to the other four types of residual oil in the water-drive state.

[0102] The actual utilization capacity of the remaining oil in water injection development, i.e., the actual utilization portion of the remaining clustered oil, is calculated by subtracting the net unutilized portion and the converted portion of the remaining clustered oil under water-drive conditions from the remaining clustered oil under saturated oil conditions; as shown in the following formula.

[0103] A(cluster active) = A(saturated cluster) - A(displaced clusters not moved) - A(displaced clusters converted to non-clusters)

[0104] Among them, A(cluster actual action) is the set of cluster residual oil actually used in the water drive state, A(saturated cluster) is the set of cluster residual oil in the saturated oil state, A(displaced cluster net unused) is the set of actual cluster residual oil net unused in the water drive state, and A(displaced cluster to non-cluster) is the set of cluster residual oil converted into the other four types of residual oil in the water drive state.

[0105] By labeling each of the above sets, a three-dimensional model of microscopic residual oil type transformation is obtained. (Refer to...) Figure 8 As shown, Figure 8 This is a digital core 3D image after labeling each set. For example, in Avizo software or other software with similar capabilities, set operations can be performed on the above parts, and the pixels corresponding to each set can be labeled with different colors to distinguish them, thus obtaining a 3D model of microscopic residual oil type conversion. (Refer to...) Figure 9 As shown, a, b, and c represent the fate of clustered residual oil during water flooding, while d, e, and f represent its sources. Figure 9In the color version of the figure, the various types of remaining oil are shown in different colors.

[0106] According to the need for quantitative analysis of the degree of detail, the core samples can be collected multiple times in time division during the displacement process, so as to analyze the type conversion and production of micro remaining oil under the action of different types of displacement agents at different times during the displacement process. According to the analysis, the displacement agent can be reasonably adjusted on site, and a specific type of displacement agent can be selected at a specific time, which is beneficial to improve the production degree of micro remaining oil.

[0107] Embodiment two

[0108] Embodiment two of the present application provides a micro remaining oil type conversion quantitative analysis method, referring to Figure 10 As shown in the figure, the method comprises the following steps:

[0109] Step S5, establishing a micro remaining oil type conversion three-dimensional model;

[0110] The specific steps of establishing the micro remaining oil type conversion three-dimensional model can refer to embodiment one.

[0111] Step S6, applying the micro remaining oil type conversion three-dimensional model to calculate the conversion rate and actual production proportion of the remaining oil cluster of the preset micro occurrence morphology type. Referring to Figure 11 As shown in the figure, the process of calculating the conversion rate of the remaining oil cluster of the preset micro occurrence morphology type is as follows:

[0112] Counting the number of pixel points corresponding to the conversion of the remaining oil cluster of the preset micro occurrence morphology type from the preceding displacement state to the subsequent displacement state into the remaining remaining oil cluster;

[0113] Counting the number of pixel points of the remaining oil cluster of the preset micro occurrence morphology type in the preceding displacement state;

[0114] The number of pixel points corresponding to the conversion of the remaining oil cluster of the preset micro occurrence morphology type from the preceding displacement state to the subsequent displacement state into the remaining remaining oil cluster is divided by the number of pixel points of the remaining oil cluster of the preset micro occurrence morphology type in the preceding displacement state, to obtain the proportion of the conversion of the remaining oil cluster of the preset micro occurrence morphology type into the remaining remaining oil, that is, the conversion rate of the remaining oil cluster of the preset micro occurrence morphology type;

[0115] Taking the conversion rate of cluster remaining oil as an example, the calculation formula of the conversion rate of cluster remaining oil is,

[0116]

[0117] The process of calculating the actual production proportion of the remaining oil cluster of the preset micro occurrence morphology type is as follows:

[0118] counting the pixel points corresponding to the actually produced part of the remaining oil cluster of the preset micro-occurrence form type from the pre-displacement state to the post-displacement state;

[0119] counting the pixel points corresponding to the actually produced part of the remaining oil cluster of the preset micro-occurrence form type from the pre-displacement state to the post-displacement state, and dividing the pixel points of the remaining oil cluster of the preset micro-occurrence form type in the pre-displacement state to obtain the actually produced proportion of the remaining oil cluster of the preset micro-occurrence form type in the pre-displacement state.

[0120] The calculation formula of the actually produced proportion of the cluster-shaped remaining oil is

[0121]

[0122] Similarly, other parameters can be designed and results can be calculated according to the pixel point number of the set marked by the foregoing steps. For example, the produced part of the remaining oil of a certain micro-occurrence form is calculated, and the calculation formula of the produced part of the cluster-shaped remaining oil is taken as an example, which is

[0123] A (drive cluster production) = A (saturated cluster) - A (drive cluster net non-movement) - A (drive cluster to non-cluster)

[0124] Wherein, A (drive cluster production) is the produced part of the cluster-shaped remaining oil in the water drive state, and other consistent meanings are the same as the foregoing description.

[0125] By applying the micro-remaining oil conversion three-dimensional model, the area where a certain type of remaining oil is located in different displacement states is found, and the pixel points are calculated, so that the conversion process of the remaining oil can be accurately and intuitively quantitatively analyzed, various parameters such as the conversion rate of the remaining oil and the actually produced proportion are calculated, the reference for the field staff is provided, and the degree of utilization of the remaining oil is improved. Since only the core sample is simulated and displaced, the cost is very low compared with the displacement experiment in the actual mining process. Moreover, the two-dimensional image processing, three-dimensional image reconstruction, image segmentation and pixel point calculation processes of the present application are all completed by computer software, and the degree of automation is high, which is accurate, efficient, fast and intuitive, and further reduces the cost.

[0126] Example three

[0127] The embodiment three of the present application provides a micro-remaining oil type conversion three-dimensional model generation device, and the flow thereof is as shown in Figure 12 The device comprises:

[0128] The oil phase three-dimensional model establishing module 101 is used for collecting a core sample of a region to be measured, simulating a displacement process on the core sample, and collecting two-dimensional CT scan images corresponding to the core sample in two different displacement states during the displacement process; for the two-dimensional CT scan images in each displacement state, the oil phase three-dimensional model in the displacement state is respectively recombined;

[0129] The residual oil cluster type marking module 102 is used for marking the micro-occurrence morphology type of the residual oil cluster according to the three-dimensional shape factor G of the residual oil cluster in the oil phase three-dimensional model and the Euler number E of the pore space in the oil phase three-dimensional model N , and different pixel points of the residual oil cluster in various micro-occurrence morphology types are marked.

[0130] The conversion relationship determining module 103 is used for determining pixel points corresponding to a conversion part, a net non-producing part and an actually producing part in the residual oil cluster pixel points of a preset micro-occurrence morphology type according to a conversion relationship of the residual oil cluster of the preset micro-occurrence morphology type and residual oil clusters of all other types in two different displacement states, and marking the pixel points, to obtain a micro residual oil conversion three-dimensional model.

[0131] Embodiment four

[0132] Embodiment four of the present application provides a micro residual oil type conversion quantitative analysis device, and a flow thereof is as shown in the figure Figure 13 , and the flow thereof comprises:

[0133] The conversion three-dimensional model establishing module 111 is used for establishing a micro residual oil type conversion three-dimensional model; the micro residual oil type conversion three-dimensional model is obtained by the method for generating the micro residual oil type conversion three-dimensional model.

[0134] The quantitative analysis module 112 is used for applying the micro residual oil type conversion three-dimensional model to calculate a conversion rate and an actually producing proportion of a residual oil cluster of a preset micro-occurrence morphology type.

[0135] The embodiment of the present application provides an electronic device, characterized in that the electronic device comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor; when the processor executes the program, the method for generating the micro residual oil type conversion three-dimensional model or the micro residual oil type conversion quantitative analysis method is realized.

[0136] The embodiment of the present application provides a computer storage medium, characterized in that the computer storage medium stores computer executable instructions; when the computer executable instructions are executed by a processor, the method for generating the micro residual oil conversion three-dimensional model or the micro residual oil type conversion quantitative analysis method is realized.

[0137] Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, and the like, can refer to an action or process of one or more processing or computing systems, or similar devices, that manipulate or transform data represented as physical (e.g., electronic) quantities within the systems' registers or memories into other data similarly represented as physical quantities within the systems' memories, registers or other such information storage, transmission or display devices. The terms "information," "data," "instructions," “command,” “signal,” “bit,” “symbol,” and the like refer to physical quantities presumed to represent a pertinent physical reality.

[0138] It should be understood that the particular order in which the steps in the disclosed processes have been presented is exemplary. Based on design preferences, it is understood that the particular order of steps in the processes can be rearranged without departing from the scope of the disclosure. The accompanying method claims present elements of the various steps in exemplary order and are not meant to be limited to the specific order or hierarchy presented.

[0139] In the above detailed description, various features are grouped together in single embodiments for the purpose of streamlining the disclosure. This disclosed approach is not to be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are expressly recited in each claim. Rather, as the claims below reflect, inventive subject matter lies in fewer than all features of the disclosed single embodiments. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment.

[0140] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0141] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0142] For a software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

[0143] The above description includes one or more examples of the embodiments. Of course, not all possible combinations of components or methods described above can be claimed as embodiments. One of ordinary skill in the art can recognize that modifications and variations of the described embodiments can be made without departing from the scope of the present disclosure. It is therefore intended that the embodiments described herein be considered in all respects as illustrative and not restrictive, particularly as numerous modifications and further embodiments can become apparent to those skilled in the art. Accordingly, the scope of the present disclosure is intended to be defined by the following claims rather than the description. Moreover, the use of the terms "first", "second", etc. do not denote any order or importance, but rather the terms are used to distinguish one element from another. Furthermore, the use of the terms "including", "containing", etc. are meant to encompass the terms "consisting of" and / or "consisting essentially of". Moreover, the use of the term "or" is meant to encompass "and / or", unless otherwise indicated.

Claims

1. A method for generating a three-dimensional model of microscopic residual oil type conversion, characterized in that, include: Core samples were collected from the area to be tested, the displacement process was simulated on the core samples, and two-dimensional CT scan images of the core samples under at least two different displacement states were collected during the displacement process. For each displacement state, the two-dimensional CT scan image is reconstructed to obtain the three-dimensional oil phase model under the displacement state; For the three-dimensional model of the oil phase under displacement, based on the three-dimensional shape factor G of the remaining oil clusters and the Euler number E of the pore space in the three-dimensional model of the oil phase... N The microscopic occurrence morphology of the remaining oil clusters is classified, and the pixels of the remaining oil clusters under various microscopic occurrence morphology types are distinguished and marked. Based on the transformation relationship between the remaining oil clusters of the preset micro-occurrence morphology type and all other types of remaining oil clusters under two different displacement states, the pixels of the remaining oil clusters of the preset micro-occurrence morphology type that belong to the transformed part, the net unused part, and the actual used part are identified and marked to obtain a three-dimensional model of micro-remaining oil type transformation. The process of determining a pixel includes: For a preset microstructure type, the pixels of the remaining oil clusters of the preset microstructure type in the post-displacement state are intersected with the pixels of all remaining oil clusters of all types except the preset microstructure type in the pre-displacement state, to obtain the pixels of all remaining oil clusters of all types transformed from the pre-displacement state to the post-displacement state into the pixels corresponding to the remaining oil clusters of the preset microstructure type. The pixels corresponding to the remaining oil clusters of the preset micro-attribution type in the post-displacement state are obtained by subtracting the pixels corresponding to the remaining oil clusters of the preset micro-attribution type from the remaining oil clusters of all other types from the pre-displacement state to the post-displacement state. The pixels of the remaining oil clusters of the preset micro-occurrence morphology type in the pre-displacement state are intersected with the pixels of all remaining oil clusters of the remaining types in the post-displacement state to obtain the pixels corresponding to the remaining oil clusters of the preset micro-occurrence morphology type from the pre-displacement state to the post-displacement state. The pixels corresponding to the net unused portion of the remaining oil clusters of the preset microstructure type in the pre-displacement state are subtracted from the pixels corresponding to the remaining oil clusters of the preset microstructure type in the post-displacement state, and the pixels corresponding to the remaining oil clusters of the preset microstructure type converted from the pre-displacement state to the post-displacement state are subtracted from the pixels corresponding to the remaining oil clusters of all other types. The actual used portion of the remaining oil clusters of the preset microstructure type in the pre-displacement state from the post-displacement state is obtained.

2. The method as described in claim 1, characterized in that, The displacement state includes: The states are defined as follows: pre-displacement state, during-displacement state, and post-displacement state; the pre-displacement state is a saturated oil state.

3. The method as described in claim 1, characterized in that, After collecting two-dimensional CT scan images of the core samples corresponding to at least two different displacement states during the displacement process, the method further includes: Preprocessing of 2D CT scan images; The preprocessing includes: segmentation, noise reduction, and grayscale binarization; The segmentation process refers to dividing different substances on a two-dimensional CT scan image. First, the pores are segmented, then the pores that enter the oil are segmented, and then the pores affected by the displacing agent are segmented.

4. The method as described in claim 1, characterized in that, The two-dimensional CT scan images for each displacement state are reconstructed to obtain a three-dimensional oil phase model for that displacement state, including: For each displacement state, the two-dimensional CT scan image is divided into oil and water. The two-dimensional model of the oil phase is extracted from the two-dimensional CT scan image after oil-water division. Based on the two-dimensional model of the oil phase, the three-dimensional model of the oil phase under the displacement state is reconstructed.

5. The method as described in claim 4, characterized in that, The oil-water separation includes: Threshold segmentation was used to divide the pixel value ranges for oil and water separately.

6. The method as described in claim 1, characterized in that, The three-dimensional shape factor G of the remaining oil clusters in the three-dimensional oil phase model and the Euler number E of the pore space are used. N The microscopic occurrence morphology of the remaining oil clusters is classified, including: Calculate the three-dimensional shape factor G and the Euler number E of the pore space of the remaining oil clusters. N According to G and E N The microscopic occurrence of the remaining oil clusters is divided into different types in different intervals, and the pixels of the remaining oil clusters of various microscopic occurrence types are marked and named.

7. A method for quantitative analysis of microscopic residual oil type conversion, characterized in that, include: Establish a three-dimensional model for the transformation of microscopic residual oil types; The three-dimensional model for the transformation of microscopic residual oil types is obtained by the method for generating the three-dimensional model for the transformation of microscopic residual oil types as described in any one of claims 1-6; A three-dimensional model for the transformation of microscopic residual oil types is applied to calculate the conversion rate and actual utilization rate of residual oil clusters of preset microscopic occurrence types.

8. The method as described in claim 7, characterized in that, The application of a three-dimensional model for the transformation of microscopic residual oil types calculates the conversion rate of residual oil clusters of preset microscopic occurrence morphologies, including: The number of pixels corresponding to the preset microscopic occurrence morphology type of remaining oil clusters from the pre-displacement state to the post-displacement state is counted. The number of pixels of the remaining oil clusters of the preset microscopic morphology type in the precursor displacement state is counted. The number of pixels corresponding to the remaining oil clusters of the preset microstructure type from the pre-displacement state to the post-displacement state is divided by the number of pixels of the remaining oil clusters of the preset microstructure type in the pre-displacement state to obtain the percentage of the remaining oil clusters of the preset microstructure type converted into the remaining oil, that is, the conversion rate of the remaining oil clusters of the preset microstructure type.

9. The method as described in claim 7, characterized in that, The three-dimensional model for converting microscopic residual oil types calculates the actual utilization rate of residual oil clusters of preset microscopic occurrence morphology types, including: The number of pixels corresponding to the actual activated portion of the remaining oil clusters of the preset microscopic occurrence type from the pre-displacement state to the post-displacement state is counted. The actual utilization percentage of the remaining oil clusters of the preset microstructure type in the pre-displacement state is obtained by dividing the number of pixels corresponding to the actual utilization portion of the remaining oil clusters of the preset microstructure type in the pre-displacement state by the number of pixels of the remaining oil clusters of the preset microstructure type in the pre-displacement state.

10. A device for generating a three-dimensional model of microscopic residual oil type conversion, characterized in that, include: The oil phase three-dimensional model building module is used to collect core samples from the area to be tested, simulate the displacement process of the core samples, and collect two-dimensional CT scan images of the core samples under two different displacement states during the displacement process; for each displacement state, the two-dimensional CT scan image is reconstructed to obtain the oil phase three-dimensional model under the displacement state. The residual oil cluster type labeling module is used to identify the residual oil cluster type in a three-dimensional oil phase model under displacement conditions, based on the three-dimensional shape factor G and the Euler number E of the pore space in the three-dimensional oil phase model. N The microscopic occurrence morphology of the remaining oil clusters is classified, and the pixels of the remaining oil clusters under various microscopic occurrence morphology types are distinguished and marked. The transformation relationship determination module is used to determine the pixels in the preset micro-occurrence morphology type of residual oil clusters and all other types of residual oil clusters according to the transformation relationship between the residual oil clusters of the preset micro-occurrence morphology type and the residual oil clusters of the remaining types under two different displacement states, and to distinguish and mark the pixels corresponding to the transformed part, net unused part and actual used part of the residual oil clusters, so as to obtain a three-dimensional model of micro-residual oil transformation. The process of determining the pixel includes: for a preset microstructure type, the pixel of the remaining oil cluster of the preset microstructure type in the post-displacement state is intersected with the pixel of the remaining oil cluster of all types except the preset microstructure type in the pre-displacement state, to obtain the pixel corresponding to the remaining oil cluster of the preset microstructure type from the pre-displacement state to the post-displacement state. The pixels corresponding to the remaining oil clusters of the preset micro-attribution type in the post-displacement state are obtained by subtracting the pixels corresponding to the remaining oil clusters of the preset micro-attribution type from the remaining oil clusters of all other types from the pre-displacement state to the post-displacement state. The pixels of the remaining oil clusters of the preset micro-occurrence morphology type in the pre-displacement state are intersected with the pixels of all remaining oil clusters of the remaining types in the post-displacement state to obtain the pixels corresponding to the remaining oil clusters of the preset micro-occurrence morphology type from the pre-displacement state to the post-displacement state. The pixels corresponding to the net unused portion of the remaining oil clusters of the preset microstructure type in the pre-displacement state are subtracted from the pixels corresponding to the remaining oil clusters of the preset microstructure type in the post-displacement state, and the pixels corresponding to the remaining oil clusters of the preset microstructure type converted from the pre-displacement state to the post-displacement state are subtracted from the pixels corresponding to the remaining oil clusters of all other types. The actual used portion of the remaining oil clusters of the preset microstructure type in the pre-displacement state from the post-displacement state is obtained.

11. A device for quantitative analysis of microscopic residual oil type conversion, characterized in that, include: A 3D model building module is used to build a 3D model of microscopic residual oil type conversion; the 3D model of microscopic residual oil type conversion is obtained by the generation method of the 3D model of microscopic residual oil type conversion as described in any one of claims 1-6; The quantitative analysis module is used to apply a three-dimensional model of microscopic residual oil type conversion to calculate the conversion rate and actual utilization rate of residual oil clusters of preset microscopic occurrence types.

12. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for generating a three-dimensional model of microscopic residual oil type conversion as described in any one of claims 1-6 or the method for quantitative analysis of microscopic residual oil type conversion as described in any one of claims 7-9.

13. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the method for generating a three-dimensional model of microscopic residual oil conversion as described in any one of claims 1-6 or the method for quantitative analysis of microscopic residual oil type conversion as described in any one of claims 7-9.

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