A method and device for determining REV of digital core permeation characteristics

Abstract

The application discloses a REV determination method and device for digital core seepage characteristic analysis. The method comprises the following steps: scanning a core to obtain a core image, and segmenting a pore structure and a skeleton structure in the core image; selecting a plurality of calculation units with required pore throat structure connectivity from the core image; determining an average pore radius and an average throat radius of each calculation unit respectively, and determining an average pore throat radius based on the average pore radius and the average throat radius; determining an error between the average pore throat radius of each calculation unit and a pore throat radius corresponding to a permeability contribution peak value of the core measured by experiment, and selecting a calculation unit with an error not greater than a set threshold as a REV. The REV can be accurately and quickly selected, and unnecessary simulation calculation is not needed.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method and apparatus for determining REV in digital core seepage characteristic analysis. Background Technology

[0002] In the field of oil and gas exploration technology, the study of rock pore structure has always been a research hotspot. In order to accurately characterize the pore structure of rocks, physical imaging technology can be used to establish three-dimensional digital cores. Through digital cores, the permeability and other properties of rocks can be simulated and studied.

[0003] When using digital cores to study rock-related properties, it is essential to identify the representative element volume (REV, also known as the representative basic volume or characterizing unit). The REV is the smallest volume of soil when rock-related properties tend to be basically stable. Obtaining the REV of rocks with strong heterogeneity can fully characterize the pore structure of the rocks.

[0004] Currently, the study of digital core permeability characteristics involves randomly selecting multiple computational units to calculate single-phase seepage characteristics. Finally, the unit that is close to the experimental permeability results is selected as the representative computational unit from multiple sets of calculation results. This method requires multiple unnecessary simulation calculations on unrepresentative computational units. Only after multiple sets of calculations can the REV selection be completed. This is not only time-consuming and laborious, but also has a large degree of randomness, making it difficult to meet the actual requirements for conducting core seepage mechanism analysis. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method and apparatus for determining REV in digital core seepage characteristics analysis to overcome or at least partially solve the above problems.

[0006] This invention provides a method for determining REV in digital core seepage characteristic analysis, comprising:

[0007] The core was scanned to obtain a core image, and the pore structure and skeleton structure in the core image were segmented.

[0008] Select multiple computational units from the core image whose pore throat structure connectivity meets the requirements;

[0009] The average pore radius and average throat radius of each calculation unit are determined respectively, and the average pore-throat radius is determined based on the average pore radius and average throat radius;

[0010] The error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally is determined, and the calculation unit with an error not greater than a set threshold is selected as the representative volume unit REV.

[0011] In some optional embodiments, the step of scanning the core to obtain a core image and segmenting the pore structure and skeleton structure in the core image includes:

[0012] The core was scanned to obtain a set of core images with voxels of the first set size;

[0013] The core image was denoised using a median filter;

[0014] The core image is segmented based on a set segmentation threshold to separate the pore structure and skeleton structure in the core image.

[0015] In some optional embodiments, the segmentation of the core image based on a set segmentation threshold to segment the pore structure and skeleton structure in the core image includes:

[0016] Based on the set segmentation threshold, regions in the core image with pixel values ​​less than the segmentation threshold are identified as pore structures, and regions with pixel values ​​not less than the segmentation threshold are identified as skeleton structures. The grayscale value of the pore structure is set to a first value, and the grayscale value of the skeleton structure is set to a second value, thus obtaining a binarized core image.

[0017] In some optional embodiments, the step of selecting multiple computational units from the core image whose pore throat structure connectivity meets the requirements includes:

[0018] The pore structure is then subjected to continuity treatment;

[0019] Multiple computational units of a second predetermined size are selected from the core image, and computational units whose pore throat structure connectivity meets the requirements are selected.

[0020] In some optional embodiments, selecting multiple computational units of a second predetermined size from the core image includes:

[0021] Multiple computational units of a second set size are randomly selected from the core image. Based on the gray values ​​of the pore structure and skeleton structure in the computational unit, the porosity is calculated. The calculated porosity is compared with the experimentally measured porosity, and multiple computational units whose porosity error is within the set error range are selected.

[0022] In some optional embodiments, the average pore radius and average throat radius of each computing unit are determined separately, and the average pore-throat radius is determined based on the average pore radius and average throat radius, including:

[0023] Using the maximum sphere algorithm, the pore radius of each pore and the throat radius of each throat are determined. The obtained pore radii are weighted and calculated to obtain the average pore radius. The obtained throat radii are weighted and calculated to obtain the average throat radius.

[0024] The average pore radius and the average throat radius are weighted and averaged to obtain the average pore-throat radius.

[0025] In some optional embodiments, the error between the average pore throat radius of each computational unit and the pore throat radius corresponding to the experimentally measured permeability contribution peak of the core is determined, including:

[0026] The average pore radius and average throat radius of each calculated unit are compared with the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally.

[0027] Once the dimensions of the distribution intervals of the determined average pore radius, average throat radius, and corresponding pore throat radius are the same, for each calculation unit, the radius difference between its average pore throat radius and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment is calculated, and the ratio of the radius difference to the corresponding pore throat radius is taken as the error.

[0028] In some optional embodiments, the above also includes: simulating the seepage characteristics of each computational unit using a selected fluid medium, and verifying the representativeness of the selected REV based on the simulation results.

[0029] In some optional embodiments, the step of simulating the seepage characteristics of each computational unit using a selected fluid medium and verifying the representativeness of the selected REV based on the simulation results includes:

[0030] The seepage characteristics of each computational unit are simulated using the selected fluid medium, and the permeability of each computational unit in each direction and / or the average permeability in each direction are obtained respectively.

[0031] For each calculation unit, the permeability in each direction and / or the average permeability in each direction is obtained, and compared with the permeability in each direction and / or the average permeability in each direction obtained by the fluid medium experiment test, to obtain the permeability error in each direction and / or the average permeability error in each direction.

[0032] Based on the permeability error in each direction of each calculation unit and / or the average permeability error in each direction, verify whether the selected REV is representative.

[0033] This invention also provides a device for determining REV (Recovery Volume) in digital core seepage characteristic analysis, comprising:

[0034] The image acquisition module is used to scan the rock core to obtain a rock core image and segment the pore structure and skeleton structure in the rock core image;

[0035] The unit selection module is used to select multiple computational units from the core image whose pore throat structure connectivity meets the requirements.

[0036] The parameter calculation module is used to determine the average pore radius and average throat radius of each calculation unit, and to determine the average pore-throat radius based on the average pore radius and average throat radius.

[0037] The analysis selection module is used to determine the error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally, and select the calculation unit with an error not greater than a set threshold as the representative volume unit REV.

[0038] In some optional embodiments, the image acquisition module is used to scan the core to obtain a core image and segment the pore structure and skeleton structure in the core image, including:

[0039] The core was scanned to obtain a set of core images with voxels of the first set size;

[0040] The core image was denoised using a median filter;

[0041] The core image is segmented based on a set segmentation threshold to separate the pore structure and skeleton structure in the core image.

[0042] In some optional embodiments, the image acquisition module is used to segment the core image based on a set segmentation threshold, segmenting the pore structure and skeleton structure in the core image, including:

[0043] Based on the set segmentation threshold, regions in the core image with pixel values ​​less than the segmentation threshold are identified as pore structures, and regions with pixel values ​​not less than the segmentation threshold are identified as skeleton structures. The grayscale value of the pore structure is set to a first value, and the grayscale value of the skeleton structure is set to a second value, thus obtaining a binarized core image.

[0044] In some optional embodiments, the unit selection module is used to select multiple computational units from the core image whose pore throat structure connectivity meets the requirements, including:

[0045] The pore structure is then subjected to continuity treatment;

[0046] Multiple computational units of a second predetermined size are selected from the core image, and computational units whose pore throat structure connectivity meets the requirements are selected.

[0047] In some optional embodiments, the unit selection module is used to select a plurality of computational units of a second predetermined size from the core image, including:

[0048] Multiple computational units of a second set size are randomly selected from the core image. Based on the gray values ​​of the pore structure and skeleton structure in the computational unit, the porosity is calculated. The calculated porosity is compared with the experimentally measured porosity, and multiple computational units whose porosity error is within the set error range are selected.

[0049] In some optional embodiments, the parameter calculation module is used to determine the average pore radius and average throat radius of each calculation unit, and to determine the average pore-throat radius based on the average pore radius and average throat radius, including:

[0050] Using the maximum sphere algorithm, the pore radius of each pore and the throat radius of each throat are determined. The obtained pore radii are weighted and calculated to obtain the average pore radius. The obtained throat radii are weighted and calculated to obtain the average throat radius.

[0051] The average pore radius and the average throat radius are weighted and averaged to obtain the average pore-throat radius.

[0052] In some optional embodiments, the analysis selection module is used to determine the error between the average pore throat radius of each computational unit and the pore throat radius corresponding to the experimentally measured permeability contribution peak of the core, including:

[0053] The average pore radius and average throat radius of each calculated unit are compared with the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally.

[0054] Once the dimensions of the distribution intervals of the determined average pore radius, average throat radius, and corresponding pore throat radius are the same, for each calculation unit, the radius difference between its average pore throat radius and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment is calculated, and the ratio of the radius difference to the corresponding pore throat radius is taken as the error.

[0055] In some alternative embodiments, the above also includes:

[0056] The simulation verification module is used to simulate the seepage characteristics of each computational unit using the selected fluid medium, and to verify whether the selected REV is representative based on the simulation results.

[0057] In some optional embodiments, the simulation verification module is specifically used for:

[0058] The seepage characteristics of each computational unit are simulated using the selected fluid medium, and the permeability of each computational unit in each direction and / or the average permeability in each direction are obtained respectively.

[0059] For each calculation unit, the permeability in each direction and / or the average permeability in each direction is obtained, and compared with the permeability in each direction and / or the average permeability in each direction obtained by the fluid medium experimental test, to obtain the permeability error in each direction and / or the permeability error in the average permeability in each direction; based on the permeability error in each direction and / or the permeability error in the average permeability in each direction of each calculation unit, the representativeness of the selected REV is verified.

[0060] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for determining the REV of digital core seepage characteristics analysis.

[0061] This invention also provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for determining the REV of digital core seepage characteristics analysis.

[0062] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0063] This method identifies pores and skeletal structures in core images, calculates the average pore radius and average throat radius for selected computational units, and obtains the average pore throat radius. The method selects a REV (Representative Vessel Value) based on the error between the calculated average pore throat radius and the pore throat radius corresponding to the experimentally measured peak permeability contribution of the core. This allows for the pre-selection of representative REVs, eliminating the need for multiple unnecessary simulations on unrepresentative computational units, significantly reducing the computational load, saving time and effort. The selected REVs effectively meet the requirements for core seepage characteristics and mechanism analysis, enabling more efficient and rapid use of digital core technology to study seepage characteristics, accurately obtain the seepage characteristics and mechanisms of the core, and better analyze the seepage characteristics and mechanisms of the core at the microscopic scale.

[0064] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0065] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0066] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0067] Figure 1 This is a flowchart of the REV determination method for digital core seepage characteristic analysis in Embodiment 1 of the present invention;

[0068] Figure 2 This is a flowchart of the REV determination method for digital core seepage characteristic analysis in Embodiment 2 of the present invention;

[0069] Figure 3 This is a schematic diagram of the original core image obtained by scanning in Embodiment 2 of the present invention;

[0070] Figure 4 This is a schematic diagram of the binarized core image after threshold segmentation in Embodiment 2 of the present invention;

[0071] Figure 5 This is an example diagram of selecting computational units on a core image in Embodiment 2 of the present invention;

[0072] Figure 6 This is a probability distribution diagram of pore radius in Embodiment 2 of the present invention;

[0073] Figure 7 This is a probability distribution diagram of the throat radius in Embodiment 2 of the present invention;

[0074] Figure 8 This is an example diagram showing the measurement results of the mercury intrusion porosimetry experiment in Embodiment 2 of the present invention;

[0075] Figure 9 This is a schematic diagram of the REV determination device for digital core seepage characteristic analysis in an embodiment of the present invention. Detailed Implementation

[0076] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0077] To address the problem in existing digital core permeability characteristic studies that require multiple unnecessary simulations on unrepresentative computational units to select the REV (Receptor Volume), which is time-consuming, labor-intensive, and fails to meet the practical requirements of core seepage mechanism analysis, this invention provides a method for determining the REV in digital core seepage characteristic analysis. This method selects the REV based on the error between the average pore throat radius of the computational unit in the digital core and the pore throat radius corresponding to the experimentally measured permeability contribution peak of the core. This method enables accurate and rapid REV selection without the need for unnecessary simulations. A detailed description of specific embodiments follows.

[0078] Example 1

[0079] Embodiment 1 of the present invention provides a method for determining REV in digital core seepage characteristic analysis, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0080] Step S101: Scan the core to obtain a core image, and segment the pore structure and skeleton structure in the core image.

[0081] In this step, the core is scanned to obtain a set of core images with a first predetermined voxel size. A median filter is then used to reduce noise in the core images. Next, the core images are segmented based on a predetermined segmentation threshold to separate the pore structure and skeleton structure. Specifically, after segmenting the original scanned core image, different grayscale values ​​can be assigned to the pore structure and skeleton structure, transforming the original core image into a binarized core image.

[0082] Step S102: Select multiple computational units from the core image whose pore throat structure connectivity meets the requirements.

[0083] In this step, after obtaining the binarized core image, minor imperfections in the continuity of the pore structure can be processed before selecting multiple computational units to perform subsequent steps. Specifically, the continuity of the pore structure is processed, multiple computational units of a second predetermined size are selected from the core image, and computational units whose pore throat connectivity meets the requirements are selected.

[0084] Step S103: Determine the average pore radius and average throat radius of each calculation unit, and determine the average pore-throat radius based on the average pore radius and average throat radius.

[0085] In this step, the average pore radius and average throat radius of each selected computational unit are determined. This can be done using the maximum sphere algorithm, which in turn determines the average pore-throat radius for each computational unit.

[0086] Using the maximum sphere algorithm, the pore radius of each pore and the throat radius of each throat are determined. The obtained pore radii are weighted to obtain the average pore radius. The obtained throat radii are weighted to obtain the average throat radius. The average pore radius and the average throat radius are weighted and averaged to obtain the average pore-throat radius.

[0087] Step S104: Determine the error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment, and select the calculation unit with an error not greater than the set threshold as REV.

[0088] In this step, the average pore radius and average throat radius of each calculation unit are compared with the pore throat radius corresponding to the peak permeability contribution of the experimentally measured core sample. Once the dimensions of the distribution intervals of the determined average pore radius, average throat radius, and the pore throat radius corresponding to the peak permeability contribution of the experimentally measured core sample are found to be the same, the difference between the average pore throat radius and the pore throat radius corresponding to the peak permeability contribution of the experimentally measured core sample is calculated for each calculation unit. The ratio of this radius difference to the pore throat radius corresponding to the peak permeability contribution of the experimentally measured core sample is taken as the error. Based on the determined error value, calculation units with error values ​​not far from the set threshold are selected as representative calculation units.

[0089] In the method described in this embodiment, by identifying the pores and skeleton structure in the core image, the average pore radius and average throat radius of the selected computational unit are calculated to obtain the average pore throat radius. The REV is selected based on the error between the calculated average pore throat radius and the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally. This allows for the pre-selection of representative REVs, eliminating the need for multiple unnecessary simulations on unrepresentative computational units, greatly reducing the amount of simulation computation, saving time and effort. The selected REVs can well meet the requirements of core seepage characteristics and mechanism analysis, enabling more efficient and rapid use of digital core technology to conduct seepage characteristic research, accurately obtain the seepage characteristics and mechanism of the core, and better analyze the core seepage characteristics and mechanism at the microscale.

[0090] Example 2

[0091] Embodiment 2 of the present invention provides a specific implementation process for determining the REV (Recovery Volume) in digital core seepage characteristic analysis, the process of which is as follows: Figure 2 As shown, it includes the following steps:

[0092] Step S201: Scan the core to obtain a set of core images with voxels of the first set size.

[0093] High-precision Micro-CT scanners or other scanning instruments can be used to scan the core to obtain a set of volumetric stacked images with voxels of 1024×1024×1024. Of course, the voxel size of the core image can also be other sizes, such as 2048×2048×2048. The specific voxel size can be selected according to the needs.

[0094] Taking a low-permeability Bailey core (5.6 mD) as an example, Bailey sandstone is a sandstone recommended by the Application Programming Interface (API) for verifying perforation depth and flow efficiency. Its porosity is 19-21%, and the core of this sandstone is called a Bailey core. Figure 3 The image shown is an example of a raw core image obtained based on high-precision micro-CT scanning.

[0095] Step S202: Use a median filter to denoise the core image.

[0096] The image can be imported into ImageJ© software or other processing software, and noise reduction can be performed using a median filter to ensure the clarity of the core image.

[0097] Step S203: Segment the core image based on the set segmentation threshold to segment the pore structure and skeleton structure in the core image.

[0098] In this step, based on a set segmentation threshold, regions in the core image with pixel values ​​less than the segmentation threshold can be identified as pore structures, and regions with pixel values ​​not less than the segmentation threshold can be identified as skeleton structures. The grayscale value of the pore structure is set as the first value, and the grayscale value of the skeleton structure is set as the second value, thus obtaining a binarized core image.

[0099] Specifically, a manual thresholding method can be used to distinguish between pores and skeleton in the core sample. During segmentation, a reasonable selection of the segmentation threshold can ensure that the core porosity matches the experimental porosity results. The original grayscale image is then processed into a binary core image. For example, a grayscale value of '0' can represent pores, and a grayscale value of '255' can represent the skeleton. The original grayscale image is reconstructed by reconstructing pixels with grayscale values ​​less than the segmentation threshold as '0', and pixels with grayscale values ​​not less than the segmentation threshold as '255', resulting in the reconstructed binary image. Figure 4 The image shown is an example of a binarized core image after threshold segmentation.

[0100] Step S204: Perform continuity treatment on the pore structure.

[0101] The binarized core images are imported into Matlab© software or other processing software to reprocess minor imperfections in the continuity of the pore structure, making the image features more accurate.

[0102] Step S205: Select multiple calculation units of a second set size from the core image using voxels.

[0103] In this step, multiple calculation units of a second set size are randomly selected from the core image. Based on the gray values ​​of the pore structure and skeleton structure in the calculation unit, the porosity is calculated. The calculated porosity is compared with the experimentally measured porosity, and multiple calculation units whose porosity error is within the set error range are selected.

[0104] Optionally, the processed binarized core image can be imported into ImageJ© software or other similar processing software. A 200×200×200 voxel computational unit can be randomly selected on the binarized core image. The voxel size can be selected as needed. The porosity of the computational unit can be initially calculated using the gray values ​​of the pixels in the binarized image. This ensures that the porosity value of the computational unit is within a set range from the experimentally measured porosity, for example, within 10%. That is, the absolute value of the difference between the calculated porosity value and the experimentally measured porosity value is divided by the experimentally measured porosity, and the percentage obtained is within 10%. In this way, computational units with porosity that meet the requirements can be selected to continue the subsequent steps.

[0105] An example of selecting a computational unit on a core image is as follows: Figure 5 As shown, the three white boxes represent the three selected computational units.

[0106] Step S206: Select the computational units whose pore throat structure connectivity meets the requirements.

[0107] The software is used to visualize the three-dimensional structure of the core sample. Calculation cells with satisfactory connectivity are selected by observation and output in a specified file type. Alternatively, the 3Dviewer function of ImageJ© or other similar processing software can be used to visualize the three-dimensional structure of the core sample. The pores and throats of the selected calculation cells can be observed to see if they can penetrate the volume covered by the selected calculation cells in all directions. Calculation cells with good connectivity are then selected and output as raw files, which are image files in an unprocessed, raw data format.

[0108] Step S207: Determine the average pore radius and average throat radius of each calculation unit.

[0109] In this step, the maximum sphere algorithm can be used to determine the pore radius of each pore and the throat radius of each throat. Then, the obtained pore radii are weighted to obtain the average pore radius; similarly, the obtained throat radii are weighted to obtain the average throat radius. Since each calculation unit contains multiple pores and throats, weighting coefficients can be set according to the actual situation when calculating the average radius. For example, different weighting coefficients can be set for pores of different radii when calculating the average pore radius, thus obtaining the average pore radius through a weighted average. The calculation of the average throat radius is similar.

[0110] Optionally, each selected calculation unit can be imported into Spicore© software or other software with relevant functions. Click the built-in "Apply Maximum Sphere Algorithm" button in the software and generate an analysis report to obtain the average pore radius and average throat radius of the selected calculation unit. The pore and throat size parameters are the decisive parameters that determine the single-phase seepage characteristics of the core.

[0111] For example, the probability distribution of pore radius obtained from the analysis is as follows: Figure 6 As shown, the horizontal axis represents the size of the pore radius in μm, and the vertical axis represents the probability distribution of pores with different pore radii. Figure 6 It can be seen that the pores with a radius of 2 μm have the highest probability of distribution. The probability distribution of the throat radius obtained from the analysis is as follows. Figure 7 As shown, the horizontal axis represents the size of the larynx radius in μm, and the vertical axis represents the probability distribution of larynx radii with different larynx radii. Figure 7 It can be seen that the throat with a radius of 1μm has the highest probability of distribution.

[0112] Taking the three selected calculation units as examples, Table 1 below shows the calculation results of the average pore radius, average throat radius, and average pore throat radius inside the core of each calculation unit.

[0113] Table 1

[0114] Number of pores Average pore radius (μm) Average throat radius (μm) Average pore throat radius (μm) Calculation Unit 1 510 2.51 1.32 1.95 Calculation Unit 2 627 2.31 1.15 1.73 Calculation Unit 3 613 2.24 1.11 1.68

[0115] Step S208: Determine the average pore-throat radius based on the average pore radius and the average throat radius.

[0116] For the selected computational unit, the average pore radius and the average throat radius are weighted and averaged to obtain the average pore-throat radius.

[0117] Step S209: Determine the error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment, and select the calculation unit with an error not greater than the set threshold as REV.

[0118] In this step, the average pore radius and average throat radius of each calculation unit are compared with the pore throat radius corresponding to the permeability contribution peak value of the core measured in the experiment. When the dimensions of the distribution intervals of the determined average pore radius, average throat radius and the pore throat radius corresponding to the permeability contribution peak value of the core measured in the experiment are the same, for each calculation unit, the radius difference between its average pore throat radius and the pore throat radius corresponding to the permeability contribution peak value of the core measured in the experiment is calculated, and the ratio of the radius difference to the pore throat radius corresponding to the permeability contribution peak value of the core measured in the experiment is used as the error.

[0119] like Figure 8 The figure shows the permeability measurement results obtained from mercury intrusion porosimetry (MIP) experiments on the core sample. The horizontal axis represents different pore sizes (μm), the left vertical axis represents the permeability contribution, and the right vertical axis represents the pore throat distribution frequency. Figure 8 The medium-sized bar graphs represent the permeability contribution corresponding to different pore sizes, and the dotted lines represent the frequency of pore throat distribution in different pore sizes. From... Figure 8 As can be seen, the permeability contribution and pore throat distribution frequency reach their peak when the pore size is around 1.7400.

[0120] Following the example above, when analyzing each computational unit, we first compare the pore and throat radius ranges obtained from core image processing with the permeability contribution size pore-throat size ranges obtained from core mercury intrusion porosimetry to determine that the pore-throat size distribution ranges have the same dimensions. Table 2 shows an example of the comparison results between the pore radius and throat radius ranges of each computational unit and the size ranges of the permeability contribution size pore-throat in the mercury intrusion porosimetry results. As can be seen from the table, the dimensions of the three ranges are the same.

[0121] Table 2

[0122] Pore ​​radius size range (μm) Throat radius size range (μm) Mercury porosimetry permeability contribution to pore throat size range (μm) Calculation Unit 1 1-5 1-4 0.9-3.9 Calculation Unit 2 1-5 1.2-4 0.9-3.9 Calculation Unit 3 1-5 1-3 0.9-3.9

[0123] The average pore-throat radius is then obtained by calculating the arithmetic mean of the average pore radius and the average throat radius. This average pore-throat radius is compared with the pore-throat radius corresponding to the peak permeability contribution measured by mercury intrusion porosimetry to determine the error range of each calculation unit. Table 3 shows the comparison between the calculated average pore-throat radius and the pore-throat radius corresponding to the peak permeability contribution obtained by mercury intrusion porosimetry, along with the corresponding error ranges. Calculation units 2 and 3, with the closest errors within 5%, can be selected as the representative volume (REV).

[0124] Table 3

[0125] Average pore throat radius (μm) Peak permeability contribution pore throat radius (μm) Error range (%) Calculation Unit 1 1.95 1.74 12.07 Calculation Unit 2 1.73 1.74 0.57 Calculation Unit 3 1.68 1.74 3.45

[0126] Step S210: Simulate the seepage characteristics of each computational unit using the selected fluid medium, and verify the representativeness of the selected REV based on the simulation results.

[0127] In this step, the seepage characteristics of each computational unit are simulated using a selected fluid medium, yielding the permeability and / or average permeability in each direction for each unit. For each computational unit, the permeability and / or average permeability in each direction are compared with the permeability and / or average permeability obtained from experimental testing using the fluid medium, resulting in the permeability error in each direction and / or the error in the average permeability in each direction. Based on the permeability error in each direction and / or the error in the average permeability in each direction for each computational unit, the representativeness of the selected REV is verified. The fluid medium can be water, gas, or other fluid media.

[0128] In a specific embodiment, the simulation calculation of single-phase flow seepage using digital core technology may include the following steps: reconstructing the voxels with a gray value of '0' in the calculation unit, i.e., the pores and throats through which the fluid flows in the core, to obtain a fluid domain model; then using Matlab© software or other similar software to process the continuity of the fluid flow regions in different directions of the model, removing isolated domains; importing the reconstructed model into FluentMeshing© or other similar software for mesh generation; then using Fluent© or other similar software to set the inlet and outlet boundary conditions as pressure boundary conditions, selecting gas as the working fluid, selecting the SIMPLE calculation method, and carrying out simulation calculations of the single-phase seepage characteristics in the core.

[0129] Using the example above, the average value of the three-dimensional permeability simulation calculation of each calculation unit and the experimental gas permeability and their corresponding error range are shown in Table 4. It can be seen that the error range of each calculation unit is within 30%. The error range shows that calculation unit 2 is closer to the experimental gas permeability than calculation unit 3, indicating that calculation unit 2 is more representative.

[0130] Table 4

[0131] Simulated permeability calculation in the X direction (mD) Simulated permeability calculation in the Y direction (mD) Simulated permeability calculations in the Z-direction (mD) Average three-dimensional permeability (mD) Error range (%) between gas permeability measurement and air measurement Calculation Unit 1 24.1 19.8 6.1 16.67 198 Calculation Unit 2 7.8 6.7 5.4 6.63 19 Calculation Unit 3 4.1 5.9 2.2 4.07 27

[0132] This embodiment proposes a novel and practical method for selecting representative volume units (REVs) for single-phase seepage characteristics calculation in digital cores. Based on the positive correlation between single-phase permeability and pore size in cores, a screening method for representative volumes in digital core calculations is proposed. Image processing is used to obtain the pore throat radius parameters of the core, which are compared with the results of mercury intrusion porosimetry. Based on the comparison results, REVs are selected. This method can accurately select representative calculation units from multiple calculation units for single-phase seepage characteristics research, reducing multiple unnecessary simulation calculations on unrepresentative calculation units. This makes it more convenient, effective, and faster to use digital core technology to conduct computational research and accurately obtain the single-phase seepage characteristics of cores. It allows for the analysis of single-phase seepage characteristics and seepage mechanisms at the microscale, which has important practical significance in geological exploration and oil extraction.

[0133] Compared with the method of determining REV by performing single-phase permeability calculation based on randomly selected computing units, the REV selection efficiency can be improved by 60% when the number of randomly selected computing units is 3. As the number of computing unit pixels increases and the number of randomly selected computing units increases, the efficiency improvement will be more obvious.

[0134] Based on the same inventive concept, embodiments of the present invention also provide a REV determination device for digital core seepage characteristic analysis. This device can be installed in a computer or server with computational analysis capabilities. The structure of the device is as follows: Figure 9 As shown, it includes: image acquisition module 11, unit selection module 12, parameter calculation module 13, and analysis selection module 14.

[0135] Image acquisition module 11 is used to scan the rock core to obtain a rock core image and segment the pore structure and skeleton structure in the rock core image;

[0136] The cell selection module 12 is used to select multiple computational cells from the core image whose pore throat structure connectivity meets the requirements.

[0137] The parameter calculation module 13 is used to determine the average pore radius and average throat radius of each calculation unit, and to determine the average pore-throat radius based on the average pore radius and average throat radius.

[0138] The analysis selection module 14 is used to determine the error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the experimentally measured core, and select the calculation unit with an error not greater than a set threshold as REV.

[0139] Optionally, the image acquisition module 11 is used to scan the core to obtain a core image and segment the pore structure and skeleton structure in the core image, including: scanning the core to obtain a set of core images with a first set voxel size; performing noise reduction processing on the core image using a median filter; and segmenting the core image based on a set segmentation threshold to segment the pore structure and skeleton structure in the core image.

[0140] Optionally, the image acquisition module 11 is used to segment the core image based on a set segmentation threshold, segmenting the pore structure and skeleton structure in the core image, including: based on the set segmentation threshold, identifying regions in the core image with pixel values ​​less than the segmentation threshold as pore structures, identifying regions with pixel values ​​not less than the segmentation threshold as skeleton structures, setting the gray value of the pore structure to a first value, and setting the gray value of the skeleton structure to a second value, thereby obtaining a binarized core image.

[0141] Optionally, the cell selection module 12 is used to select multiple computational cells from the core image whose pore throat structure connectivity meets the requirements, including: performing continuity processing on the pore structure; selecting multiple computational cells with a second set voxel size from the core image, and filtering out the computational cells whose pore throat structure connectivity meets the requirements.

[0142] Optionally, the unit selection module 12 is used to select multiple calculation units with voxels of a second preset size from the core image, including: randomly selecting multiple calculation units with voxels of a second preset size from the core image, calculating porosity based on the gray values ​​of the pore structure and skeleton structure in the calculation unit, comparing the calculated porosity with the experimentally measured porosity, and selecting multiple calculation units whose porosity error is within a preset error range.

[0143] Optionally, the parameter calculation module 13 is used to determine the average pore radius and average throat radius of each calculation unit respectively. The average pore-throat radius is determined based on the average pore radius and average throat radius, including: using the maximum sphere algorithm to determine the pore radius of each pore and the throat radius of each throat; performing a weighted calculation on the obtained pore radii to obtain the average pore radius; performing a weighted calculation on the obtained throat radii to obtain the average throat radius; and performing a weighted average of the average pore radius and the average throat radius to obtain the average pore-throat radius.

[0144] Optionally, the analysis selection module 14 is used to determine the error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the permeability contribution peak value of the experimentally measured core. This includes: comparing the determined average pore radius and average throat radius of each calculation unit with the pore throat radius corresponding to the permeability contribution peak value of the experimentally measured core; when the dimensions of the distribution intervals of the determined average pore radius, average throat radius, and pore throat radius corresponding to the permeability contribution peak value of the experimentally measured core are the same, for each calculation unit, the radius difference between its average pore throat radius and the pore throat radius corresponding to the permeability contribution peak value of the experimentally measured core is calculated, and the ratio of the radius difference to the pore throat radius corresponding to the permeability contribution peak value of the experimentally measured core is used as the error.

[0145] Optionally, the above-mentioned device further includes a simulation verification module 15, which is used to simulate the seepage characteristics of each calculation unit using the selected fluid medium, and verify whether the selected REV is representative based on the simulation calculation results.

[0146] The simulation verification module 15 is specifically used to simulate the seepage characteristics of each calculation unit using the selected fluid medium, and obtain the permeability and / or the average permeability in each direction for each calculation unit; for each calculation unit, the permeability and / or the average permeability in each direction is compared with the permeability and / or the average permeability in each direction obtained by experimental testing using the fluid medium, and the permeability error in each direction and / or the error of the average permeability in each direction is obtained; based on the permeability error in each direction and / or the error of the average permeability in each direction for each calculation unit, the representativeness of the selected REV is verified.

[0147] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for determining the REV of digital core seepage characteristics analysis.

[0148] This invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for determining the REV of digital core seepage characteristics analysis.

[0149] Regarding the REV determination device for digital core seepage characteristic analysis in the above embodiments, the specific methods by which each module performs its operation have been described in detail in the embodiments of the relevant method, and will not be elaborated here.

[0150] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0151] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0152] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0153] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0154] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0155] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0156] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for determining REV in digital core seepage characteristic analysis, characterized in that, include: The core was scanned to obtain a core image, and the pore structure and skeleton structure in the core image were segmented. Select multiple computational units from the core image whose pore throat structure connectivity meets the requirements; The average pore radius and average throat radius of each calculation unit are determined respectively, and the average pore-throat radius is determined based on the average pore radius and average throat radius; The error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment is determined, and the calculation unit with an error not greater than a set threshold is selected as the representative volume unit REV. The process of determining the error includes comparing the average pore radius and average throat radius of each calculated unit with the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally. Once the dimensions of the distribution intervals of the determined average pore radius, average throat radius, and corresponding pore throat radius are the same, for each calculation unit, the radius difference between its average pore throat radius and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment is calculated, and the ratio of the radius difference to the corresponding pore throat radius is taken as the error.

2. The method as described in claim 1, characterized in that, The process of scanning the rock core to obtain a core image and segmenting the pore structure and framework structure in the core image includes: The core was scanned to obtain a set of core images with voxels of the first set size; The core image was denoised using a median filter; The core image is segmented based on a set segmentation threshold to separate the pore structure and skeleton structure in the core image.

3. The method as described in claim 2, characterized in that, The segmentation of the core image based on a set segmentation threshold, to segment the pore structure and skeleton structure in the core image, includes: Based on the set segmentation threshold, regions in the core image with pixel values ​​less than the segmentation threshold are identified as pore structures, and regions with pixel values ​​not less than the segmentation threshold are identified as skeleton structures. The grayscale value of the pore structure is set to a first value, and the grayscale value of the skeleton structure is set to a second value, thus obtaining a binarized core image.

4. The method as described in claim 1, characterized in that, The selection of multiple computational units from the core image that meet the connectivity requirements of the pore throat structure includes: The pore structure is then subjected to continuity treatment; Multiple computational units of a second predetermined size are selected from the core image, and computational units whose pore throat structure connectivity meets the requirements are selected.

5. The method as described in claim 4, characterized in that, Multiple computational units of a second predetermined size are selected from the core image, including: Multiple computational units of a second set size are randomly selected from the core image. Based on the gray values ​​of the pore structure and skeleton structure in the computational unit, the porosity is calculated. The calculated porosity is compared with the experimentally measured porosity, and multiple computational units whose porosity error is within the set error range are selected.

6. The method as described in claim 1, characterized in that, The average pore radius and average throat radius of each computational unit are determined separately. Based on these average pore radius and average throat radius, the average pore-throat radius is determined, including: Using the maximum sphere algorithm, the pore radius of each pore and the throat radius of each throat are determined. The obtained pore radii are weighted and calculated to obtain the average pore radius. The obtained throat radii are weighted and calculated to obtain the average throat radius. The average pore radius and the average throat radius are weighted and averaged to obtain the average pore-throat radius.

7. The method according to any one of claims 1-6, characterized in that, Also includes: The seepage characteristics of each computational unit are simulated using the selected fluid medium, and the representativeness of the selected REV is verified based on the simulation results.

8. The method as described in claim 7, characterized in that, The process involves simulating the seepage characteristics of each computational unit using a selected fluid medium, and verifying the representativeness of the selected REV based on the simulation results, including: The seepage characteristics of each computational unit are simulated using the selected fluid medium, and the permeability of each computational unit in each direction and / or the average permeability in each direction are obtained respectively. For each calculation unit, the permeability in each direction and / or the average permeability in each direction is obtained, and compared with the permeability in each direction and / or the average permeability in each direction obtained by the fluid medium experiment test, to obtain the permeability error in each direction and / or the average permeability error in each direction. Based on the permeability error in each direction of each calculation unit and / or the average permeability error in each direction, verify whether the selected REV is representative.

9. A device for determining REV (Reactive Volume) in digital core seepage characteristic analysis, characterized in that, include: The image acquisition module is used to scan the rock core to obtain a rock core image and segment the pore structure and skeleton structure in the rock core image; The unit selection module is used to select multiple computational units from the core image whose pore throat structure connectivity meets the requirements. The parameter calculation module is used to determine the average pore radius and average throat radius of each calculation unit, and to determine the average pore-throat radius based on the average pore radius and average throat radius. The analysis and selection module is used to determine the error between the average pore throat radius of each calculation unit and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment, and select the calculation unit with an error not greater than a set threshold as the representative volume unit REV. The process of determining the error includes comparing the average pore radius and average throat radius of each calculated unit with the pore throat radius corresponding to the peak permeability contribution of the core measured experimentally. Once the dimensions of the distribution intervals of the determined average pore radius, average throat radius, and corresponding pore throat radius are the same, for each calculation unit, the radius difference between its average pore throat radius and the pore throat radius corresponding to the peak permeability contribution of the core measured in the experiment is calculated, and the ratio of the radius difference to the corresponding pore throat radius is taken as the error.

10. The apparatus as claimed in claim 9, characterized in that, The image acquisition module is used to scan the rock core to obtain a rock core image, and to segment the pore structure and skeleton structure in the rock core image, including: The core was scanned to obtain a set of core images with voxels of the first set size; The core image was denoised using a median filter; The core image is segmented based on a set segmentation threshold to separate the pore structure and skeleton structure in the core image.

11. The apparatus as claimed in claim 10, characterized in that, The image acquisition module is used to segment the core image based on a set segmentation threshold, segmenting the pore structure and skeleton structure in the core image, including: Based on the set segmentation threshold, regions in the core image with pixel values ​​less than the segmentation threshold are identified as pore structures, and regions with pixel values ​​not less than the segmentation threshold are identified as skeleton structures. The grayscale value of the pore structure is set to a first value, and the grayscale value of the skeleton structure is set to a second value, thus obtaining a binarized core image.

12. The apparatus as claimed in claim 9, characterized in that, The unit selection module is used to select multiple computational units from the core image whose pore throat structure connectivity meets the requirements, including: The pore structure is then subjected to continuity treatment; Multiple computational units of a second predetermined size are selected from the core image, and computational units whose pore throat structure connectivity meets the requirements are selected.

13. The apparatus as claimed in claim 12, characterized in that, The unit selection module is used to select multiple calculation units of a second predetermined size from the core image, including: Multiple computational units of a second set size are randomly selected from the core image. Based on the gray values ​​of the pore structure and skeleton structure in the computational unit, the porosity is calculated. The calculated porosity is compared with the experimentally measured porosity, and multiple computational units whose porosity error is within the set error range are selected.

14. The apparatus as claimed in claim 9, characterized in that, The parameter calculation module is used to determine the average pore radius and average throat radius of each calculation unit, and to determine the average pore-throat radius based on the average pore radius and average throat radius, including: Using the maximum sphere algorithm, the pore radius of each pore and the throat radius of each throat are determined. The obtained pore radii are weighted and calculated to obtain the average pore radius. The obtained throat radii are weighted and calculated to obtain the average throat radius. The average pore radius and the average throat radius are weighted and averaged to obtain the average pore-throat radius.

15. The apparatus as described in any one of claims 9-14, characterized in that, Also includes: The simulation verification module is used to simulate the seepage characteristics of each computational unit using the selected fluid medium, and to verify whether the selected REV is representative based on the simulation results.

16. The apparatus as claimed in claim 15, characterized in that, The simulation verification module is specifically used for: The seepage characteristics of each computational unit are simulated using the selected fluid medium, and the permeability of each computational unit in each direction and / or the average permeability in each direction are obtained respectively. For each calculation unit, the permeability in each direction and / or the average permeability in each direction is obtained, and compared with the permeability in each direction and / or the average permeability in each direction obtained by the fluid medium experimental test, to obtain the permeability error in each direction and / or the permeability error in the average permeability in each direction; based on the permeability error in each direction and / or the permeability error in the average permeability in each direction of each calculation unit, the representativeness of the selected REV is verified.

17. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the REV determination method for digital core seepage characteristic analysis as described in any one of claims 1-8.

18. A terminal device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the REV determination method for digital core seepage characteristic analysis as described in any one of claims 1-8.

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