Method, device and storage medium for determining reservoir type

Abstract

The present application discloses a method, device and storage medium for determining reservoir types, which belong to the field of oil and gas exploitation. The method includes: obtaining multiple CT images of the target core; determining the corrected binarization threshold corresponding to each CT image in the multiple CT images; determining the actual binarization threshold based on the corrected binarization threshold corresponding to the multiple CT images; binarizing the multiple CT images based on the actual binarization threshold to obtain multiple binarized images; and determining the reservoir type of the oil and gas reservoir to be exploited based on the multiple binarized images. The method for determining the reservoir type provided in the present application can determine the reservoir type of the oil and gas reservoir to be exploited through the actual binarization threshold corresponding to the multiple CT images, and then can predict the oil and gas reserves and formulate oil and gas exploitation strategies according to the reservoir type of the oil and gas reservoir, solving the problem that there is no better method for quickly determining the reservoir type in the prior art.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploitation, and in particular to a method, device and storage medium for determining a reservoir type. Background Art

[0002] Generally, the prediction methods of oil and gas reserves of different types of oil and gas reservoirs are different. In order to predict the size of oil and gas reserves of oil and gas reservoirs and reasonably formulate oil and gas production countermeasures, it is usually necessary to first determine the reservoir type of the oil and gas reservoir. However, there is currently no good method for quickly determining the reservoir type. Based on this, a reservoir type determination method is urgently needed to determine the reservoir type of the oil and gas reservoir, so as to facilitate the prediction of oil and gas reserves of the oil and gas reservoir according to the reservoir type, and formulate oil and gas production countermeasures. Summary of the invention

[0003] The present application provides a method, device and storage medium for determining reservoir types, which can solve the problem of difficulty in determining reservoir types of oil and gas reservoirs. The technical solution is as follows:

[0004] In one aspect, a method for determining a reservoir type is provided, the method comprising:

[0005] Acquire multiple CT images of a target core, wherein the target core refers to a core obtained from a reservoir of an oil and gas reservoir to be mined, and the multiple CT images are obtained by scanning core cross sections of the target core at multiple locations of different thicknesses;

[0006] Determining a modified binarization threshold corresponding to each CT image in the plurality of CT images;

[0007] Determining an actual binarization threshold based on the modified binarization thresholds corresponding to the plurality of CT images;

[0008] Binarization processing is performed on the plurality of CT images based on the actual binarization threshold value to obtain a plurality of binarized images;

[0009] Based on the multiple binary images, the reservoir type of the oil and gas reservoir to be produced is determined.

[0010] Optionally, determining a modified binarization threshold corresponding to each of the multiple CT images includes:

[0011] For a first CT image among the multiple CT images, determine a binarization threshold of the first CT image by a binarization algorithm, and use the determined binarization threshold as a simulated binarization threshold, wherein the first CT image is any one of the multiple CT images;

[0012] displaying a binarized image after the first CT image is processed by the simulated binarization threshold;

[0013] When a correction operation is detected based on the displayed binary image, obtaining a binary correction value of the first CT image;

[0014] The sum of the simulated binarization threshold value and the binarization correction value of the first CT image is determined as the corrected binarization threshold value of the first CT image.

[0015] Optionally, before determining the binarization threshold of the first CT image by a binarization algorithm, the method further includes:

[0016] Acquire the position coordinates and grayscale value of each pixel point included in the first CT image;

[0017] Determining a core boundary of the first CT image according to grayscale values ​​of two adjacent pixels in the first CT image;

[0018] The first CT image is cropped according to the core boundary to remove the area outside the core boundary in the first CT image.

[0019] Optionally, determining the actual binarization threshold based on the modified binarization thresholds corresponding to the multiple CT images includes:

[0020] Obtaining weights corresponding to N CT images among the plurality of CT images, where N is less than or equal to the total number of the plurality of CT images;

[0021] The actual binarization threshold is determined based on the modified binarization thresholds and weights corresponding to the N CT images.

[0022] Optionally, determining the reservoir type of the oil and gas reservoir to be produced based on the multiple binary images includes:

[0023] Based on each of the plurality of binary images, determining the perimeter and area of ​​each connected region on the core cross section corresponding to the corresponding binary image;

[0024] Obtaining the area of ​​the core cross section corresponding to each binary image in the plurality of binary images, and the distance between two adjacent core cross sections;

[0025] Determine multiple reservoir parameters of the target core based on the perimeter and area of ​​each connected region on the core cross section corresponding to each binary image, the area of ​​the core cross section corresponding to each binary image, and the distance between two adjacent core cross sections, the multiple reservoir parameters including the porosity, the number of holes and the number of fractures of the target core, and the diameter of each hole on the core cross section corresponding to the multiple CT images;

[0026] Based on the multiple reservoir parameters, the reservoir type of the oil and gas reservoir to be produced is determined.

[0027] In another aspect, a device for determining a reservoir type is provided, the device comprising:

[0028] An acquisition module, used to acquire multiple CT images of a target core, wherein the target core refers to a core obtained from a reservoir of an oil and gas reservoir to be mined, and the multiple CT images are obtained by scanning the core cross-section of the target core at multiple positions of different thicknesses;

[0029] A first determination module is used to determine a modified binarization threshold corresponding to each CT image in the plurality of CT images;

[0030] A second determination module is used to determine an actual binarization threshold based on the modified binarization thresholds corresponding to the plurality of CT images;

[0031] A processing module, configured to perform binarization processing on the plurality of CT images based on the actual binarization threshold value to obtain a plurality of binarized images;

[0032] The third determination module is used to determine the reservoir type of the oil and gas reservoir to be exploited based on the multiple binary images.

[0033] Optionally, the first determining module includes:

[0034] a first determining unit, configured to determine a binarization threshold of a first CT image among the plurality of CT images by a binarization algorithm, and use the determined binarization threshold as a simulated binarization threshold, wherein the first CT image is any one of the plurality of CT images;

[0035] A display unit, used for displaying a binarized image after the first CT image is processed by the simulated binarization threshold;

[0036] A first acquisition unit, configured to acquire a binarized correction value of the first CT image when a correction operation is detected based on the displayed binarized image;

[0037] The second determining unit is used to determine the sum of the simulated binarization threshold value and the binarization correction value of the first CT image as the corrected binarization threshold value of the first CT image.

[0038] Optionally, the first determining module further includes:

[0039] A second acquisition unit, used to acquire the position coordinates and grayscale value of each pixel point included in the first CT image;

[0040] A third determining unit, configured to determine a core boundary of the first CT image according to gray values ​​of two adjacent pixels in the first CT image;

[0041] A cropping unit is used to crop the first CT image according to the core boundary to remove the area outside the core boundary in the first CT image.

[0042] Optionally, the second determining module includes:

[0043] A third acquisition unit is used to acquire weights corresponding to N CT images among the multiple CT images, where N is less than or equal to the total number of the multiple CT images;

[0044] The fourth determining unit is used to determine the actual binarization threshold based on the modified binarization threshold and the weight corresponding to the N CT images.

[0045] Optionally, the third determining module includes:

[0046] a fifth determining unit, configured to determine, based on each of the plurality of binary images, a perimeter and an area of ​​each connected region on the core cross section corresponding to the corresponding binary image;

[0047] A fourth acquisition unit, used to acquire the area of ​​the core cross section corresponding to each binary image in the plurality of binary images, and the distance between two adjacent core cross sections;

[0048] a sixth determination unit, configured to determine a plurality of reservoir parameters of the target core based on the perimeter and area of ​​each connected region on the core cross section corresponding to each binary image, the area of ​​the core cross section corresponding to each binary image, and the distance between two adjacent core cross sections, the plurality of reservoir parameters including the porosity, the number of holes and the number of fractures of the target core, and the diameter of each hole on the core cross section corresponding to the plurality of CT images;

[0049] The seventh determination unit is used to determine the reservoir type of the oil and gas reservoir to be produced based on the multiple reservoir parameters.

[0050] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, any step in the above-mentioned method for determining the reservoir type is implemented.

[0051] In another aspect, a device for determining a reservoir type is provided, the device comprising:

[0052] processor;

[0053] a memory for storing processor-executable instructions;

[0054] The processor is configured to execute any step in the reservoir type determination method provided above.

[0055] In yet another aspect, a computer program product comprising instructions is provided. When the computer program product is run on a computer, the computer is caused to perform any step in the reservoir type determination method provided above.

[0056] The beneficial effects brought by the technical solution provided by the embodiments of the present application may at least include:

[0057] In the embodiment of the present application, firstly, multiple CT images of the target core can be obtained, and then the corrected binarization threshold corresponding to each CT image in the multiple CT images can be determined. Next, the actual binarization threshold can be obtained based on the corrected binarization threshold corresponding to the multiple CT images, and then the reservoir type of the oil and gas reservoir to be mined can be determined based on the actual binarization threshold. It can be seen that in the embodiment of the present application, the reservoir type of the oil and gas reservoir to be mined is determined based on the actual binarization threshold, rather than by the corrected binarization threshold. Therefore, it is closer to the actual situation, and the reservoir type of the determined oil and gas reservoir is more accurate. Finally, the oil and gas reserves can be predicted and oil and gas exploitation strategies can be formulated based on the reservoir type of the oil and gas reservoir, which solves the problem that there is no better method for quickly determining the reservoir type in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0059] Figure 1 is a flow chart of a method for determining a reservoir type provided in an embodiment of the present application;

[0060] Figure 2 is a schematic structural diagram of a reservoir type determination device provided in an embodiment of the present application;

[0061] Figure 3 It is a schematic diagram of the structure of another reservoir type determination device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0063] Before explaining the embodiments of the present application in detail, the application scenarios involved in the embodiments of the present application are first explained.

[0064] Different types of oil and gas reservoirs have different oil production and oil production rates. In order to predict whether an oil and gas reservoir can produce oil, it is usually necessary to determine the reservoir type of the oil and gas reservoir. However, there is currently no good method for determining the reservoir type of an oil and gas reservoir, which makes it extremely difficult to predict oil and gas reserves and formulate oil and gas production strategies. Based on this scenario, an embodiment of the present application provides a method for determining the reservoir type.

[0065] Figure 1 is a flow chart of another method for determining reservoir type provided in an embodiment of the present application. The method is applied to a device for determining reservoir type, and the device for determining reservoir type can be integrated in a terminal. Figure 1 , the method comprising:

[0066] Step 101: Acquire multiple CT images of the target core.

[0067] In some embodiments, the terminal can display an insertion window for a CT image of a target core. Accordingly, a technician can insert multiple CT images of the target core into the terminal through the insertion window for the CT image of the target core displayed by the terminal. After that, the terminal will acquire the multiple CT images of the target core.

[0068] It should be noted that the terminal can also obtain multiple CT images of the target core in other ways, which are not specifically limited in the embodiments of the present application. For example, the terminal can be connected to a storage device storing multiple CT images, so that the terminal can directly obtain the multiple CT images from the storage device.

[0069] The target core is the core obtained from the oil and gas reservoir to be mined. For example, if the technician needs to determine the oil and gas reservoir type of reservoir A, then reservoir A is the oil and gas reservoir to be mined, and the target core is the core drilled from reservoir A.

[0070] In addition, the above-mentioned multiple CT images are obtained by scanning the cross-section of the target core at multiple different thickness positions. For example, assuming that the shape of the target core is a cylinder with a thickness of 10 cm, 1000 CT images can be obtained by scanning every 0.1 mm along the axial direction of the cylinder, that is, 1000 CT images of the target core are obtained. Among them, each CT image can be a grayscale image.

[0071] It should be noted that when the target core is scanned every 0.1 mm, each CT image obtained by scanning can correspond to the core cross section at the corresponding position. In addition, during scanning, it is inevitable to scan areas outside the core cross section, thereby obtaining a CT image containing the background.

[0072] Step 102: Determine a modified binarization threshold corresponding to each CT image in the plurality of CT images.

[0073] In some embodiments, for each of the multiple CT images, since the method for determining the modified binarization threshold corresponding to each CT image can be the same, the method for determining the modified binarization threshold corresponding to the first CT image is described in detail through steps 1021-1027 by taking the first CT image as an example. The first CT image is any CT image among the multiple CT images.

[0074] It should be noted that, based on the above description, the multiple CT images are obtained by scanning the core cross sections of the target core at multiple different thickness positions. It can be understood that the number of multiple CT images is often large at this time. In some embodiments, in order to reduce the number of CT images so that the amount of calculation is small when the corrected binarization threshold corresponding to the CT image is determined later, in a possible implementation method, M CT images can be selected from the multiple CT images, and then the corrected binarization threshold corresponding to each image in the M CT images is determined. Obviously, this will reduce the amount of calculation.

[0075] Of course, the modified binarization threshold corresponding to each CT image in the multiple CT images may also be directly determined, and the embodiment of the present application does not make any specific limitation on this.

[0076] Step 1021: Obtain the position coordinates and grayscale value of each pixel point included in the first CT image.

[0077] Since each image is composed of multiple pixels, each pixel corresponds to a position coordinate and a grayscale value, so the terminal device can obtain the position coordinate and grayscale value corresponding to each pixel included in the first CT image.

[0078] It should be noted that the position coordinates of each pixel point included in each CT image can be obtained based on the same plane rectangular coordinate system. Of course, the position coordinates of each pixel point included in each CT image can also be obtained based on a three-dimensional rectangular coordinate system. The embodiments of the present application do not limit this.

[0079] Since each scanned CT image has a background part, the pixel values ​​of the pixels corresponding to the background part will not affect the corrected binarization threshold of the CT image, that is, the pixels corresponding to the background part can be defaulted to invalid pixels. Therefore, the area corresponding to the background part of each CT image can be removed according to the following steps 1022-1023.

[0080] Step 1022: Determine the core boundary of the first CT image according to the grayscale values ​​of two adjacent pixels in the first CT image.

[0081] Generally, since the grayscale values ​​of the pixels corresponding to the background part and the grayscale values ​​of the pixels corresponding to the core part on the first CT image are greatly different, the core boundary of the first CT image can be determined by the size relationship between the difference between the grayscale values ​​of two adjacent pixels and the grayscale threshold, wherein the grayscale threshold can be set manually.

[0082] For example, in some embodiments, the core boundary of the first CT image can be determined by scanning. In a possible implementation, the terminal can first perform a horizontal scan from left to right, determine the grayscale values ​​of two adjacent pixels in turn, and make a difference between the grayscale values ​​of the two adjacent pixels to obtain a difference. When the difference is greater than the grayscale threshold, the pixel with the smaller grayscale value among the current two pixels can be determined as a boundary pixel. After determining multiple boundary pixels, the multiple boundary pixels can constitute the first boundary of the first CT image. Next, scan from right to left again, and use the same method to determine the second boundary of the first CT image, and then determine the core boundary of the first CT image by combining the first boundary and the second boundary.

[0083] The minimum boundary enclosed by the first boundary and the second boundary may be determined as the core boundary of the first CT image, and the maximum boundary enclosed by the first boundary and the second boundary may be determined as the core boundary of the first CT image.

[0084] Of course, the terminal may also determine the core boundary of the first CT image in other ways, and the embodiment of the present application does not specifically limit the method for determining the core boundary of the first image.

[0085] Step 1023: cropping the first CT image according to the core boundary to remove the area outside the core boundary in the first CT image.

[0086] For the first CT image, the area outside the core boundary is the background part. It can be understood that the background part is an invalid part. Therefore, the first CT image can be cropped according to the core boundary determined in step 1022 to remove the area outside the core boundary in the first CT image. That is, the background part in the first CT image is removed to reduce the total number of pixels in the first CT image.

[0087] Step 1024: Determine a binarization threshold of the first CT image by using a binarization algorithm, and use the determined binarization threshold as a simulated binarization threshold.

[0088] In some embodiments, the terminal may perform binarization processing on the first CT image by using the Otsu method in the prior art to obtain a binarization threshold of the first CT image. In some embodiments, the terminal may also display and store the binarization threshold, and display and store the determined binarization threshold as a simulated binarization threshold in the terminal.

[0089] It should be noted that the terminal may also determine the binarization threshold of the first CT image through other binarization algorithms. For details, reference may be made to related technologies, and the embodiments of the present application do not make specific limitations on this.

[0090] It should be noted that, based on the above description, the first CT image in step 1024 is a CT image after cropping the area outside the core boundary in the CT image, and in some embodiments, the first CT image may not be cropped, that is, without going through steps 1021-1023, the binarization threshold of the first CT image is directly determined by a binarization algorithm, and the determined binarization threshold is used as the simulated binarization threshold, that is, steps 1021-1023 are optional steps, and the embodiments of the present application are not limited to this.

[0091] Step 1025: Displaying a binarized image after the first CT image is processed by a simulated binarization threshold.

[0092] After the first CT image is processed by simulating the binarization threshold, the terminal can display the binarized image after the first CT image is processed by simulating the binarization threshold, so that the technician can observe the processing effect in real time according to the binarized image displayed by the terminal.

[0093] Step 1026: When a correction operation is detected based on the displayed binarized image, a binarized correction value of the first CT image is obtained.

[0094] In actual application scenarios, since the simulated binarization threshold of the first CT image determined by binarization algorithms such as the maximum inter-class variance method often has deviations, the binarized image determined by the simulated binarization threshold cannot well reflect the core reservoir parameters corresponding to the first CT image.

[0095] Therefore, in some embodiments, the technician can also make a correction operation based on the binarized image displayed by the terminal. The correction operation can be that the terminal displays a correction window based on the binarized image. When the terminal detects the adjustment of the analog binarization threshold based on the correction window, the binarized image is adjusted according to the adjusted analog binarization threshold. When the terminal detects a determination instruction based on the adjusted analog binarization threshold, the terminal can determine the adjustment amount of the analog binarization threshold as the binarization correction value.

[0096] Step 1027: Determine the sum of the simulated binarization threshold value and the binarization correction value of the first CT image as the corrected binarization threshold value of the first CT image.

[0097] In some embodiments, the terminal may determine the sum of the simulated binarization threshold value and the binarization correction value of the first CT image as the corrected binarization threshold value of the first CT image. Of course, based on different usage scenarios, the corrected binarization threshold value of the first CT image may also be determined by other methods, and the embodiments of the present application do not specifically limit this.

[0098] Exemplarily, referring to Table 1, assuming that the simulated binarization threshold of the first CT image determined by the terminal through the maximum inter-class variance method is 0.2863, and the binarization correction value input by the technician to the terminal through the correction window is 0.1664, then it can be seen that the corrected binarization threshold of the first CT image is 0.4527.

[0099] Table 1

[0100] Simulate Binarization Threshold Binarization correction value Corrected binarization threshold 0.2863 0.1664 0.4527

[0101] After the terminal determines the corrected binarization threshold corresponding to the first CT image, the same method can be used to determine the corrected binarization threshold corresponding to each CT image remaining in the multiple CT images except the first CT image. The method for determining the corrected binarization threshold corresponding to each CT image remaining in the multiple CT images except the first CT image is not described in detail in the embodiment of the present application.

[0102] Step 103: Determine an actual binarization threshold based on the modified binarization thresholds corresponding to the multiple CT images.

[0103] In some embodiments, the terminal may determine the actual binarization threshold value based on the modified binarization threshold values ​​corresponding to the plurality of CT images in the following manner:

[0104] The terminal may obtain weights corresponding to N CT images among the multiple CT images, and then determine an actual binarization threshold based on the modified binarization thresholds and weights corresponding to the three CT images.

[0105] Wherein, N is less than or equal to the total number of the multiple CT images.

[0106] To facilitate understanding, an example will be given below.

[0107] Continuing with the above example, assuming that N is 3, the terminal can obtain the weights corresponding to the three CT images.

[0108] In some embodiments, the terminal can display a weight input window. At this time, the technician can input the weights corresponding to the three CT images into the terminal device through the weight input window. Next, when the terminal detects the weights corresponding to the three CT images based on the weight input window, the terminal can store and display the weights. When the terminal detects a determination instruction based on the weights, the terminal can obtain the weights corresponding to the three CT images.

[0109] It should be noted that the number of N CT images may also be other numbers, and 3 CT images are just an exemplary embodiment shown in the present application. The embodiments of the present application do not limit the number of N CT images.

[0110] The weights of the three CT images may be determined by the technician according to the importance of the three CT images in the entire 1000 CT images.

[0111] Continuing with the above example, referring to Table 2, it is assumed that the three CT images are the first CT image, the second CT image and the third CT image, and the modified binarization threshold corresponding to the first CT image is 0.4527 and the corresponding weight is 0.3, the modified binarization threshold corresponding to the second CT image is 0.5315 and the corresponding weight is 0.3, and the modified binarization threshold corresponding to the third CT image is 0.4957 and the corresponding weight is 0.4, see Table 2.

[0112] Table 2

[0113] Serial number Corrected binarization threshold Weight First CT image 0.4527 0.3 Second CT image 0.5315 0.3 The third CT image 0.4957 0.4

[0114] Then, in some embodiments, the actual binarization threshold may be determined by the following first formula.

[0115] First formula:

[0116] Where: t gm Refers to the actual binarization threshold; t i It refers to the modified binarization threshold corresponding to the i-th CT image; h irIt refers to the weight corresponding to the i-th CT image among N CT images, a dimensionless quantity.

[0117] For example, assuming that t1 = 0.3, t2 = 0.5315, t3 = 0.4957, the corresponding weights are: h 1r =0.3,h 2r =0.3,h 3r =0.4, then, according to the first formula:

[0118]

[0119] That is, the actual binarization threshold is 0.4953.

[0120] Step 104: Binarization processing is performed on the multiple CT images based on the actual binarization threshold value to obtain multiple binary images.

[0121] After determining the actual binarization threshold, the terminal can perform binarization processing on the multiple CT images based on the actual binarization threshold to obtain multiple binarized images after binarization.

[0122] Exemplarily, continuing with the above embodiment, assuming that the number of the plurality of CT images is 1000 and the actual binarization threshold is 0.4953, the terminal can binarize the 1000 CT images according to the actual binarization threshold of 0.4953 to obtain 1000 binarized images after binarization.

[0123] Step 105: Determine the reservoir type of the oil and gas reservoir to be produced based on the multiple binary images.

[0124] In the embodiment of the present application, this step can be implemented by the following steps 1051-1054:

[0125] Step 1051: Based on each binary image in the plurality of binary images, determine the perimeter and area of ​​each connected region on the core cross section corresponding to the corresponding binary image.

[0126] Since the method for determining the perimeter and area of ​​each connected region on the core cross section corresponding to each binary image in the plurality of binary images can be the same, the following takes determining the perimeter and area of ​​the first connected region on the core cross section corresponding to the first binary image as an example to explain in detail the determination of the perimeter and area of ​​the first connected region. The first binary image is any binary image in the plurality of binary images, and the first connected region refers to any connected region on the core cross section corresponding to the first binary image.

[0127] In some embodiments, first, the terminal can determine the perimeter and area of ​​the first connected area through the 8-connectivity judgment and identification method. In a possible implementation, the terminal can identify and search from 8 directions, namely, top, bottom, left, right, upper left, lower left, upper right, and lower right, to see whether a pixel point having the same grayscale value as the target pixel point in the first connected area is connected to it. If connected, it can be determined that the pixel point is located in the first connected area where the target pixel point is located.

[0128] After all the pixels connected to the target pixel are determined, the perimeter and area of ​​the first connected region can be determined according to the coordinates of all the pixels in the connected region where the target pixel is located.

[0129] After the terminal determines the perimeter and area of ​​the first connected region, the same method can be used to determine the perimeter and area of ​​each connected region on the core cross section corresponding to multiple binary images, which will not be described in detail in the embodiments of the present application.

[0130] Assuming that in the above binary image, the first connected region is composed of pixels with a gray value of 0, then the above target pixel is any pixel among the pixels with a gray value of 0 in the first connected region.

[0131] Of course, the first connected area may also be composed of pixels with a grayscale value of 255. When the first connected area is composed of pixels with a grayscale value of 255, the target pixel is any pixel in the first connected area with a grayscale value of 255. The embodiment of the present application does not limit the grayscale values ​​of the pixels constituting the connected area.

[0132] Step 1052: Obtain the area of ​​the core cross section corresponding to each binary image in the plurality of binary images, and the distance between two adjacent core cross sections.

[0133] For each of the multiple binary images, the method by which the terminal obtains the area of ​​the core cross section corresponding to each of the multiple binary images and the thickness of the core corresponding to each binary image can be the same. Therefore, taking the first binary image as an example, the method by which the terminal obtains the area of ​​the core cross section corresponding to the first binary image and the thickness of the core corresponding to the first binary image is described in detail.

[0134] First, the terminal may obtain the coordinates of the pixel points of the core section corresponding to the first binary image, and then determine the area of ​​the core section corresponding to the first binary image according to the coordinates of the pixel points of the core section corresponding to the first binary image.

[0135] Next, the terminal may obtain the distance between two adjacent core sections corresponding to the first binary image, wherein the distance between two adjacent core sections corresponding to the first binary image is: the distance between the core section corresponding to the first binary image and the adjacent core section.

[0136] For example, assuming that the shape of the target core is a cylinder with a thickness of 10 cm, and scanning is performed every 1 mm along the axial direction of the cylinder, then the distance between the core section corresponding to the first binary image and the adjacent core section is 1 mm.

[0137] After the terminal obtains the area of ​​the core cross-section corresponding to the first binary image and the distance between two adjacent core cross-sections corresponding to the first binary image, the same method can be used to obtain the area of ​​the core cross-section corresponding to each binary image remaining except the first binary image in multiple binary images, as well as the distance between two adjacent core cross-sections corresponding to each binary image remaining except the first binary image, which will not be repeated in this application.

[0138] Step 1053: Based on the perimeter and area of ​​each connected region on the core section corresponding to each binary image, the area of ​​the core section corresponding to each binary image, and the distance between two adjacent core sections, determine multiple reservoir parameters of the target core, the multiple reservoir parameters including the porosity, the number of holes and the number of fractures of the target core, and the diameter of each hole on the core section corresponding to the multiple CT images.

[0139] 1. Determine the porosity of the target core.

[0140] In some embodiments, the terminal can determine the porosity of the target core by the following second formula based on the area of ​​each connected region on the core section corresponding to each binary image, the area of ​​the core section corresponding to each binary image, and the distance between two adjacent core sections.

[0141] Second formula:

[0142] in: refers to the porosity of the target core, a dimensionless quantity; n refers to the total number of binary images, a dimensionless quantity; A ic It refers to the sum of the areas of all connected regions on the core section corresponding to the i-th binary image, cm 2 ; A i Refers to the area of ​​the core cross section corresponding to the i-th binary image, cm 2 ;h i It refers to the distance between two adjacent core sections corresponding to the core section of the i-th CT image, cm; h refers to the thickness of the target core, cm.

[0143] 2. Determine the diameter of each hole on the core section corresponding to multiple CT images.

[0144] In some embodiments, since the method for determining the diameter of each hole on the core cross section corresponding to multiple CT images can be the same, the method for determining the diameter of the first hole on the core cross section corresponding to the first CT image is taken as an example, and the method for determining the diameter of the first hole is described in detail through the following steps (1)-(3), wherein the first CT image is any one of the multiple CT images, and the first hole is any one of the multiple holes on the core cross section corresponding to the first CT image.

[0145] Step (1): The terminal can determine the shape factor of the first connected area through the perimeter and area of ​​the first connected area on the core section corresponding to the first binary image through the following third formula, where the first binary image corresponds to the first CT image.

[0146] The third formula:

[0147] Where: F is the shape factor of the first connected region (hole, cavity, crack), a dimensionless quantity; S is the area of ​​the first connected region, cm 2 ; L is the perimeter of the first connected area, cm.

[0148] After the terminal determines the shape factor of the first connected area, in some embodiments, the terminal may store the corresponding relationship between the first connected area and the shape factor.

[0149] Step (2): Determine the shape of the first connected region on the core cross section corresponding to the first binary image according to the shape factor of the first connected region on the core cross section corresponding to the first binary image.

[0150] In some embodiments, when F>0.15, the terminal can determine that the first connected area is a hole; when F≤0.15, the terminal can determine that the first connected area is a crack. Of course, based on actual application scenarios, F can also be other values ​​to determine that the first connected area is a hole or a crack, and the embodiments of the present application do not make specific limitations on this.

[0151] After the terminal determines the shape of the first connected region according to the shape factor of the first connected region, in some embodiments, the terminal may store a corresponding relationship between the first connected region and the shape.

[0152] Step (3): Determine the diameter of the first hole on the core cross section corresponding to the first CT image according to the shape of the first connected area on the core cross section corresponding to the first binary image.

[0153] Since the terminal can determine the shape of the first connected area on the core cross section corresponding to the first binary image, the terminal can use the same method to determine the shapes of multiple connected areas on the core cross section corresponding to the first binary image. After that, the terminal can filter out the first hole with a hole shape from the multiple connected areas corresponding to the first binary image.

[0154] After the terminal screens out the first hole, in one embodiment, the terminal may determine the diameter of the first hole on the core cross section corresponding to the first CT image by the following fourth formula, wherein the first hole is any connected area in the connected areas having a hole shape;

[0155] The fourth formula:

[0156] Where d is the diameter of the first hole, S1 is the area of ​​the first hole, cm 2 .

[0157] After the terminal determines the diameter of the first hole, the same method can be used to determine the diameter of each hole on the core cross section corresponding to multiple CT images, which will not be described in detail in the embodiments of the present application.

[0158] 3. Determine the number of fractures in the target core.

[0159] First, the terminal can determine the number of fractures on the core cross section corresponding to the multiple binary images.

[0160] Since the method for determining the number of cracks on the core section corresponding to multiple binary images can be the same as the method for determining the number of cracks on the core section corresponding to the first binary image, the following is a detailed description taking the method of determining the number of cracks on the core section corresponding to the first binary image as an example.

[0161] Since the terminal can determine the shapes of multiple connected areas on the core section corresponding to the first binary image, the terminal can count the number of cracks on the core section corresponding to the first binary image based on the shapes of multiple connected areas on the core section corresponding to the first binary image.

[0162] Next, the terminal can use the same method to determine the number of cracks on the core cross section corresponding to the multiple binary images, which will not be described in detail in the embodiment of the present application.

[0163] In some embodiments, the terminal may also store the corresponding relationship between the first binary image and the number of cracks in the terminal.

[0164] After the terminal determines the number of cracks on the core cross section corresponding to the multiple binary images, in some embodiments, the terminal can determine the number of cracks with the largest number of cracks in the multiple core cross sections corresponding to the multiple binary images as the number of cracks in the target core. For example, there are three binary images, namely the first binary image, the second binary image and the third binary image, the number of cracks in the core cross section corresponding to the first binary image is 10, the number of cracks in the core cross section corresponding to the second binary image is 11, and the number of cracks in the core cross section corresponding to the third binary image is 20, then the number of cracks 20 in the core cross section corresponding to the third binary image can be determined as the number of cracks in the target core.

[0165] 4. Determine the number of holes in the target core.

[0166] First, the terminal can determine the number of holes on the core cross section corresponding to multiple binary images.

[0167] Since the method for determining the number of holes on the core section corresponding to multiple binary images can be the same as the method for determining the number of holes on the core section corresponding to the first binary image, the following is a detailed description using the method of determining the number of holes on the core section corresponding to the first binary image as an example.

[0168] Since the terminal can determine the shapes of all connected areas on the core section corresponding to the first binary image, the terminal can count the number of holes on the core section corresponding to the first binary image based on the shapes of all connected areas on the core section corresponding to the first binary image.

[0169] Next, the terminal can use the same method to determine the number of holes on the core cross section corresponding to the multiple binary images, which will not be described in detail in the embodiment of the present application.

[0170] In some embodiments, the terminal may further store the corresponding relationship between the first binary image and the number of holes in the terminal.

[0171] Afterwards, the terminal may determine the average area of ​​the holes on the core section corresponding to each binary image according to the total area of ​​the holes on the core section corresponding to each binary image and the number of holes through the following fifth formula.

[0172] The fifth formula:

[0173] in, Refers to the average area of ​​holes on the core section corresponding to the i-th binary image, cm 2 ; S i Refers to the total area of ​​holes on the core section corresponding to the i-th binary image, cm 2 ; counti It refers to the number of holes on the core cross section corresponding to the i-th binary image, a dimensionless quantity.

[0174] After the terminal determines the average area of ​​the holes on the corresponding core section, in some embodiments, the terminal can also determine the probability of hole repetition through the average area of ​​the holes on the core section corresponding to each binary image and the number of holes through the following sixth formula.

[0175] Sixth formula:

[0176] Among them, RP is the hole repetition probability number, a dimensionless quantity; Refers to the average area of ​​holes on the core section corresponding to the i-th binary image, cm 2 ; count i It refers to the number of holes on the core cross section corresponding to the i-th binary image, a dimensionless quantity.

[0177] Next, after the terminal determines the number of hole repetition probabilities on the corresponding core cross section, the number of holes in the target core can be determined by the following seventh formula according to the number of hole repetition probabilities and the number of holes on the core cross section corresponding to each binary image.

[0178] Seventh formula:

[0179] Where M refers to the number of holes in the target core, a dimensionless quantity; m i It refers to the number of holes on the core section corresponding to the i-th binary image, n refers to the total number of binary images, a dimensionless quantity; RP refers to the probability of hole repetition, a dimensionless quantity.

[0180] In summary, through the sixth formula and the seventh formula, the terminal can determine the number of holes in the target core.

[0181] Step 1054: Determine the reservoir type of the oil and gas reservoir to be produced based on multiple reservoir parameters.

[0182] In some embodiments, the terminal can divide the diameters of all holes on the core cross section corresponding to the multiple CT images into intervals. It is assumed that the hole diameters on the core cross section corresponding to the multiple CT images are divided into five intervals, and the hole diameters in the five intervals range from: <200μm, 200-1000μm, 1000μm-1500μm, 1500μm-2000μm and >2000μm. In some embodiments, when the number of holes with a diameter of less than 200μm on the core cross section accounts for the largest proportion in the five intervals, it can be considered that the oil and gas reservoir where the target core is located is a microporous reservoir; when the number of holes with a diameter of 200μm-1000μm accounts for the largest proportion in the five intervals, it can be considered that the oil and gas reservoir where the target core is located is a small pore reservoir; when the number of holes with a diameter of 1000μm-1500μm on the core cross section accounts for the largest proportion in the five intervals, it can be considered that the oil and gas reservoir where the target core is located is a small pore reservoir. When the proportion of the number of holes with a diameter of 1500μm-2000μm on the core cross section is the largest in the five intervals, the oil and gas reservoir where the target core is located can be considered to be a medium-pore reservoir; when the proportion of the number of holes with a diameter of >2000μm on the core cross section is the largest in the five intervals, the oil and gas reservoir where the target core is located can be considered to be a large-pore reservoir; when the proportion of the number of holes with a diameter of >2000μm on the core cross section is the largest in the five intervals, the oil and gas reservoir where the target core is located can be considered to be a vuggy reservoir.

[0183] Of course, the ranges of the hole diameters in the above five intervals may also be other numerical ranges, and the embodiments of the present application do not make specific limitations on this.

[0184] In addition, in some embodiments, when the porosity of the target core is greater than or equal to the porosity threshold, the oil and gas reservoir where the target core is located can be considered to be an oil-rich oil and gas reservoir; when the porosity of the target core is less than the porosity threshold, the oil and gas reservoir where the target core is located can be considered to be an oil-poor oil and gas reservoir or a tight oil and gas reservoir.

[0185] In some embodiments, the porosity threshold may be 3%. Of course, the porosity threshold may also be other values, which are not specifically limited in the embodiments of the present application.

[0186] In addition, in some embodiments, when there are cracks on the core cross section corresponding to any one of the multiple CT images of the target core, the oil and gas reservoir where the target core is located can be considered to be a fractured reservoir. Of course, in other embodiments, when the number of cracks on the core cross section corresponding to any one of the multiple CT images of the target core is greater than the crack threshold, the oil and gas reservoir where the target core is located can be considered to be a fractured reservoir. For example, in some embodiments, the crack threshold can be 3. Of course, the crack threshold can also be other values, and the embodiments of the present application do not specifically limit this.

[0187] Furthermore, in some embodiments, when the number of holes in the target core is greater than the hole number threshold, the oil and gas reservoir where the target core is located can be considered to be a multi-porous reservoir, and when the number of holes in the target core is not greater than the hole number threshold, the oil and gas reservoir where the target core is located can be considered to be a less porous reservoir. In some embodiments, the hole number threshold can be 500. Of course, the hole number threshold can also be other values, and the embodiments of the present application do not specifically limit this.

[0188] Based on this, in one scenario, assuming that the number of holes with a diameter of 200μm-1000μm on the core cross section accounts for the largest proportion in the five intervals, the porosity of the target core is 2%, the number of fractures in the target core is 10, and the number of holes in the target core is 445, then, based on the above description, it can be seen that the oil and gas reservoir where the target core is located is a small pore + dense + fracture + few pores reservoir.

[0189] In the embodiment of the present application, firstly, multiple CT images of the target core can be obtained, and then the corrected binarization threshold corresponding to each CT image in the multiple CT images can be determined. Next, the actual binarization threshold can be obtained based on the corrected binarization threshold corresponding to the multiple CT images, and then the reservoir type of the oil and gas reservoir to be mined can be determined based on the actual binarization threshold. It can be seen that in the embodiment of the present application, the reservoir type of the oil and gas reservoir to be mined is determined based on the actual binarization threshold, rather than by the corrected binarization threshold. Therefore, it is closer to the actual situation, and the reservoir type of the determined oil and gas reservoir is more accurate. Finally, the oil and gas reserves can be predicted and oil and gas exploitation strategies can be formulated based on the reservoir type of the oil and gas reservoir, which solves the problem that there is no better method for quickly determining the reservoir type in the prior art.

[0190] See also Figure 2 An embodiment of the present application provides a device for determining a reservoir type, the device comprising: an acquisition module 201 , a first determination module 202 , a second determination module 203 , a processing module 204 and a third determination module 205 .

[0191] An acquisition module 201 is used to acquire multiple computer tomography (CT) images of a target core. The target core refers to a core obtained from a reservoir of an oil and gas reservoir to be mined. The multiple CT images are obtained by scanning the cross-section of the target core at multiple locations of different thicknesses.

[0192] A first determination module 202 is used to determine a modified binarization threshold corresponding to each CT image in a plurality of CT images;

[0193] A second determination module 203 is used to determine an actual binarization threshold based on the modified binarization thresholds corresponding to the plurality of CT images;

[0194] A processing module 204 is used to perform binarization processing on the multiple CT images based on the actual binarization threshold value to obtain multiple binarized images;

[0195] The third determination module 205 is used to determine the reservoir type of the oil and gas reservoir to be exploited based on a plurality of binary images.

[0196] Optionally, the first determining module 202 includes:

[0197] a first determining unit, configured to determine a binarization threshold of a first CT image among the plurality of CT images by a binarization algorithm, and use the determined binarization threshold as a simulated binarization threshold, wherein the first CT image is any one of the plurality of CT images;

[0198] A display unit, used for displaying a binarized image after the first CT image is processed by simulating a binarization threshold;

[0199] A first acquisition unit, configured to acquire a binarized correction value of the first CT image when a correction operation is detected based on the displayed binarized image;

[0200] The second determining unit is used to determine the sum of the simulated binarization threshold value and the binarization correction value of the first CT image as the corrected binarization threshold value of the first CT image.

[0201] Optionally, the first determining module 202 further includes:

[0202] A second acquisition unit, used to acquire the position coordinates and grayscale value of each pixel point included in the first CT image;

[0203] A third determining unit, configured to determine a core boundary of the first CT image according to gray values ​​of two adjacent pixels in the first CT image;

[0204] The cropping unit is used to crop the first CT image according to the core boundary to remove the area outside the core boundary in the first CT image.

[0205] Optionally, the second determining module 203 includes:

[0206] A third acquisition unit is used to acquire weights corresponding to N CT images among the multiple CT images, where N is less than or equal to the total number of the multiple CT images;

[0207] The fourth determining unit is used to determine the actual binarization threshold based on the modified binarization threshold and the weight corresponding to the N CT images.

[0208] Optionally, the third determining module 205 includes:

[0209] a fifth determining unit, for determining, based on each binary image of the plurality of binary images, a perimeter and an area of ​​each connected region on the core cross section corresponding to the corresponding binary image;

[0210] A fourth acquisition unit is used to acquire the area of ​​the core cross section corresponding to each binary image in the plurality of binary images, and the distance between two adjacent core cross sections;

[0211] a sixth determination unit, configured to determine a plurality of reservoir parameters of the target core based on the perimeter and area of ​​each connected region on the core cross section corresponding to each binary image, the area of ​​the core cross section corresponding to each binary image, and the distance between two adjacent core cross sections, the plurality of reservoir parameters including the porosity, the number of holes and the number of fractures of the target core, and the diameter of each hole on the core cross section corresponding to the plurality of CT images;

[0212] The seventh determination unit is used to determine the reservoir type of the oil and gas reservoir to be produced based on multiple reservoir parameters.

[0213] In summary, in the embodiment of the present application, first, multiple CT images of the target core can be obtained, and then the corrected binarization threshold corresponding to each CT image in the multiple CT images can be determined. Next, the actual binarization threshold can be obtained based on the corrected binarization threshold corresponding to the multiple CT images, and then the reservoir type of the oil and gas reservoir to be mined can be determined based on the actual binarization threshold. It can be seen that in the embodiment of the present application, the reservoir type of the oil and gas reservoir to be mined is determined according to the actual binarization threshold, rather than by the corrected binarization threshold. Therefore, it is closer to the actual situation, and the reservoir type of the determined oil and gas reservoir is more accurate. Finally, the oil and gas reserves can be predicted and oil and gas mining countermeasures can be formulated according to the reservoir type of the oil and gas reservoir, which solves the problem that there is no better method for quickly determining the reservoir type in the prior art. It should be noted that: when determining the reservoir type, the reservoir type determination device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above function allocation can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the reservoir type determination device provided in the above embodiment and the reservoir type determination method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0214] Figure 3 300 is a schematic diagram of the structure of a reservoir type determination device provided in an embodiment of the present application. The reservoir type determination device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 301 and one or more memories 302, wherein the memory 302 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 301. Of course, the reservoir type determination device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The reservoir type determination device 300 may also include other components for realizing device functions, which will not be described in detail here.

[0215] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions, and the instructions can be executed by a processor in the above-mentioned computer device to complete the reservoir type determination method in the above-mentioned embodiment. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0216] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0217] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a reservoir type, characterized in that: The method comprises: Acquire multiple computerized tomography (CT) images of a target core, wherein the target core is a core obtained from a reservoir of an oil and gas reservoir to be mined, and the multiple CT images are obtained by scanning core cross sections of the target core at multiple locations of different thicknesses; For a first CT image among the multiple CT images, determine a binarization threshold of the first CT image by a binarization algorithm, and use the determined binarization threshold as a simulated binarization threshold, wherein the first CT image is any one of the multiple CT images; displaying a binarized image after the first CT image is processed by the simulated binarization threshold; When a correction operation is detected based on the displayed binary image, obtaining a binary correction value of the first CT image; Determine the sum of the simulated binarization threshold value and the binarization correction value of the first CT image as the corrected binarization threshold value of the first CT image; Determining an actual binarization threshold based on the modified binarization thresholds corresponding to the plurality of CT images; Binarization processing is performed on the plurality of CT images based on the actual binarization threshold value to obtain a plurality of binarized images; Based on the multiple binary images, the reservoir type of the oil and gas reservoir to be produced is determined.

2. The method according to claim 1, characterized in that Before determining the binarization threshold of the first CT image by a binarization algorithm, the method further includes: Acquire the position coordinates and grayscale value of each pixel point included in the first CT image; Determining a core boundary of the first CT image according to grayscale values ​​of two adjacent pixels in the first CT image; The first CT image is cropped according to the core boundary to remove the area outside the core boundary in the first CT image.

3. The method according to any one of claims 1-2, characterized in that: The determining the actual binarization threshold value based on the modified binarization threshold values ​​corresponding to the plurality of CT images comprises: Obtaining weights corresponding to N CT images among the plurality of CT images, where N is less than or equal to the total number of the plurality of CT images; The actual binarization threshold is determined based on the modified binarization thresholds and weights corresponding to the N CT images.

4. The method according to claim 1, characterized in that The step of determining the reservoir type of the oil and gas reservoir to be produced based on the multiple binary images includes: Based on each of the plurality of binary images, determining the perimeter and area of ​​each connected region on the core cross section corresponding to the corresponding binary image; Obtaining the area of ​​the core cross section corresponding to each binary image in the plurality of binary images, and the distance between two adjacent core cross sections; Determine multiple reservoir parameters of the target core based on the perimeter and area of ​​each connected region on the core cross section corresponding to each binary image, the area of ​​the core cross section corresponding to each binary image, and the distance between two adjacent core cross sections, the multiple reservoir parameters including the porosity, the number of holes and the number of fractures of the target core, and the diameter of each hole on the core cross section corresponding to the multiple CT images; Based on the multiple reservoir parameters, the reservoir type of the oil and gas reservoir to be produced is determined.

5. A device for determining reservoir type, characterized in that: The device comprises: An acquisition module, used to acquire multiple CT images of a target core, wherein the target core refers to a core obtained from a reservoir of an oil and gas reservoir to be mined, and the multiple CT images are obtained by scanning the core cross-section of the target core at multiple positions of different thicknesses; A first determination module is used to determine a modified binarization threshold corresponding to each CT image in the plurality of CT images; A second determination module is used to determine an actual binarization threshold based on the modified binarization thresholds corresponding to the plurality of CT images; A processing module, configured to perform binarization processing on the plurality of CT images based on the actual binarization threshold value to obtain a plurality of binarized images; A third determination module is used to determine the reservoir type of the oil and gas reservoir to be exploited based on the multiple binary images; The first determining module comprises: a first determining unit, configured to determine a binarization threshold of a first CT image among the plurality of CT images by a binarization algorithm, and use the determined binarization threshold as a simulated binarization threshold, wherein the first CT image is any one of the plurality of CT images; A display unit, used for displaying a binarized image after the first CT image is processed by the simulated binarization threshold; A first acquisition unit, configured to acquire a binarized correction value of the first CT image when a correction operation is detected based on the displayed binarized image; The second determining unit is used to determine the sum of the simulated binarization threshold value and the binarization correction value of the first CT image as the corrected binarization threshold value of the first CT image.

6. The reservoir type determination device according to claim 5, characterized in that: The first determining module also includes: A second acquisition unit, used to acquire the position coordinates and grayscale value of each pixel point included in the first CT image; A third determining unit, configured to determine a core boundary of the first CT image according to gray values ​​of two adjacent pixels in the first CT image; A cropping unit is used to crop the first CT image according to the core boundary to remove the area outside the core boundary in the first CT image.

7. The reservoir type determination device according to any one of claims 5-6, characterized in that: The second determining module comprises: A third acquisition unit is used to acquire weights corresponding to N CT images among the multiple CT images, where N is less than or equal to the total number of the multiple CT images; The fourth determining unit is used to determine the actual binarization threshold based on the modified binarization threshold and the weight corresponding to the N CT images.

8. The reservoir type determination device according to claim 5, characterized in that: The third determination module comprises: a fifth determining unit, configured to determine, based on each of the plurality of binary images, a perimeter and an area of ​​each connected region on the core cross section corresponding to the corresponding binary image; A fourth acquisition unit, used to acquire the area of ​​the core cross section corresponding to each binary image in the plurality of binary images, and the distance between two adjacent core cross sections; a sixth determination unit, configured to determine a plurality of reservoir parameters of the target core based on the perimeter and area of ​​each connected region on the core cross section corresponding to each binary image, the area of ​​the core cross section corresponding to each binary image, and the distance between two adjacent core cross sections, the plurality of reservoir parameters including the porosity, the number of holes and the number of fractures of the target core, and the diameter of each hole on the core cross section corresponding to the plurality of CT images; The seventh determination unit is used to determine the reservoir type of the oil and gas reservoir to be produced based on the multiple reservoir parameters.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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