Method for testing fracture permeability of carbonate reservoir

Abstract

The invention discloses a carbonate reservoir fracture permeability test method, and relates to the technical field of ore exploration, the test steps are as follows: S1, blending a tracer agent: preparing iohexol, iopamidol and barium sulfate suspension, and blending to obtain the tracer agent; s2, injecting the prepared tracer agent into the rock stratum, and waiting for diffusion of the tracer agent; s3, a CT scanning device is used for scanning the rock stratum, the flowing path of the tracer agent on the rock stratum is tracked, image data are collected and obtained, and the collected data image is preprocessed; and S4, constructing a three-dimensional model of the crack network by using a three-dimensional imaging technology, displaying a three-dimensional flow path of the tracer agent in the crack, analyzing the acquired CT image data, and measuring to obtain the concentration distribution of the tracer agent at different positions. By means of the tracer agent injection method, the rock stratum fracture permeability closer to the actual situation is obtained, and a more reliable basis is provided for development and evaluation of carbonate rock reservoirs.

Description

Technical Field

[0001] The present invention relates to the technical field of ore exploration, and in particular to a method for testing the permeability of carbonate reservoir fractures. Background Art

[0002] Carbonate reservoirs occupy a significant position in global oil and gas resources. Fractures, as important seepage pathways, significantly affect reservoir permeability and oil and gas recovery efficiency. In oil and gas exploration and development, carbonate reservoirs are one of the most important reservoir types. The fracture system in carbonate reservoirs has a significant impact on oil and gas seepage and reserve assessment. Accurately measuring the permeability of carbonate reservoir fractures is crucial for the rational development of oil and gas resources. Existing permeability testing methods have many limitations when applied to carbonate reservoir fractures. For testing the permeability of carbonate reservoir fractures, traditional laboratory core testing methods can simulate reservoir conditions to a certain extent. However, due to the limitations of core sample selection, it is difficult to fully represent the overall fracture distribution characteristics of the reservoir. Moreover, during the experiment, the processing and treatment of the core will change the original state of the fractures, resulting in deviations between the test results and the actual reservoir conditions, leading to errors in the calculation and analysis of the test permeability. To this end, we propose a method for testing the permeability of carbonate reservoir fractures. Summary of the Invention

[0003] In order to solve the above technical problems, a method for testing the permeability of carbonate reservoir fractures is provided. This technical solution solves the problem that the above test results deviate from the actual reservoir and cause errors.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a method for testing the permeability of carbonate reservoir fractures, wherein the testing steps are as follows: S1. preparing a tracer by preparing iohexol, iopamidol, and a barium sulfate suspension, and mixing them to obtain a tracer; S2. Inject the prepared tracer into the rock formation and wait for the tracer to diffuse; S3. Scan the rock formation using a CT scanner, track the flow path of the tracer on the rock formation, collect image data, and pre-process the collected data images; S4. Using 3D imaging technology, a three-dimensional model of the fracture network is constructed to display the three-dimensional flow path of the tracer in the fracture. The collected CT image data is analyzed to measure the concentration distribution of the tracer at different locations, and the flow rate and migration time of the tracer are calculated. S5. Based on the migration time, flow path and concentration change value of the tracer, the fracture permeability of the current rock formation is comprehensively calculated.

[0005] Preferably, the tracer preparation step in step S1 is: A1. Prepare the following materials: 10-500 parts of iohexol, 20-550 parts of iopamidol, and 40-560 parts of barium sulfate suspension; A2. Weigh the materials in proportion and pour them into a beaker. Insert a glass rod into the beaker and stir at a constant speed in a clockwise direction to mix. A3. Continue stirring for 5-10 minutes until the mixture is homogeneous without stratification or precipitation. The mixing is complete.

[0006] Preferably, in step S2, a geological compass and a total station are used in advance to determine the position of the tracer injection point on the rock surface, perform drilling operations, insert the injection needle into the borehole and seal it, connect the injection pump pipeline, inject the tracer into the borehole, wait for the injection to be completed, pull out the injection needle, and record the injection time and location information.

[0007] Preferably, in step S3, a CT scanning device is started to scan, and X-rays are emitted to penetrate the rock formation. The X-rays interact with different parts containing the tracer, and attenuation signals of different intensities are received and recorded by the detector. According to the preset time interval, the same fracture area is scanned and imaged regularly. In the initial stage, an image is collected every 10 minutes. As the diffusion speed slows down, the collection time is extended to 30 minutes. The imaging device parameters are kept consistent each time an image is collected.

[0008] Preferably, the preprocessing in step S3 includes image enhancement and image noise reduction. The image enhancement is performed by grayscale transformation. The image is read and converted into a grayscale image. The grayscale histogram of the image is calculated. According to the histogram distribution, the grayscale range of the image is increased by linear transformation. The image noise reduction is based on median filtering. The median filter window size is selected, and the window is slid on the image. The window pixel values are sorted according to the grayscale size, the median of the window pixel values is taken, and the pixel value in the center of the window is replaced to perform noise reduction.

[0009] Preferably, in step S4, the three-dimensional model construction step adopts a threshold segmentation algorithm, sets a threshold based on the grayscale difference between the crack and the surrounding rock on the CT image, segments the crack from the background, and outlines the crack boundary based on the Canny algorithm; extracts a two-dimensional crack image sequence, uses the marching cubes algorithm to construct the isosurface between two adjacent layers of images, and generates a three-dimensional model.

[0010] Preferably, let I(x, y) be the grayscale value of the CT image at the coordinate (x, y), set the threshold to T, and after threshold segmentation, the binary image B(x, y) is expressed as: , Where 1 represents the crack area and 0 represents the background area; The process of obtaining edge image E(x, y) after processing B(x, y) is: , in It is the Canny operation process; The extracted two-dimensional crack image sequence is represented as (E n (x, y)) where n = 1, 2, ... m, n is the sequence number of the image, and m is the total number of image frames; The three-dimensional model construction is expressed as follows: , in Indicates the process of algorithm processing, receiving three-dimensional volume data V (x, y, z) and isosurface value V iso As input, the output is a set of facets F, V that constitute the surface of the three-dimensional model iso is the value of the preset isosurface.

[0011] Preferably, the data analysis in step S4 is performed by converting the CT value of each pixel in the image according to the CT value to obtain the tracer concentration value; by comparing the position change of the tracer at different time points in the path segment, combined with the size information of the voxel unit, the average flow velocity of the tracer in the path segment is calculated; based on the flow velocity information and the length of the flow path, the velocity is divided by the distance to calculate the migration time of the tracer on the path.

[0012] Preferably, the concentration value calculation formula is: , Where C(x, y) is the tracer concentration value at the pixel, k is the conversion coefficient, and CT(x, y) is the CT value of the pixel at coordinate (x, y) in the image; The average flow rate is calculated as follows: , Where A is the calculated average flow velocity value, It is expressed as the modulus of the position difference, s is the size of the voxel unit, t2 and t1 are the second monitoring time point and the first monitoring time point of the reagent; The calculation formula for migration time is: , Where G is the migration time of the tracer on the path, H is the length of the flow path, and A is the average flow velocity.

[0013] Preferably, the permeability calculation in step S5 is performed based on Darcy's theorem formula, and the permeability of the fracture is comprehensively calculated by obtaining the migration time, flow path and concentration change value in combination with the known tracer viscosity value.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses a tracer injection method and imaging technology to observe their diffusion in the fractures, thereby obtaining the influence of the fracture wall properties on the permeability. The three-dimensional imaging technology is used to construct a three-dimensional model of the fracture network, intuitively showing the three-dimensional flow path of the tracer in the fractures. The combined influence of the spatial distribution and connectivity of the fractures on the permeability can be accurately measured. By integrating and analyzing the multi-dimensional data, the permeability of the rock formation fractures that is closer to the actual situation can be obtained, providing a more reliable basis for the development and evaluation of carbonate reservoirs, and avoiding the situation in which the processing of the core in traditional testing methods changes the original state of the fractures, resulting in deviations between the test results and the actual reservoir conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flow chart of the test steps of the present invention. DETAILED DESCRIPTION

[0016] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0017] Reference Figure 1 As shown in FIG, a method for testing the permeability of carbonate reservoir fractures, the testing steps are as follows: S1. preparing a tracer by preparing iohexol, iopamidol, and a barium sulfate suspension, and mixing them to obtain a tracer; S2. Inject the prepared tracer into the rock formation and wait for the tracer to diffuse; S3. Scan the rock formation using a CT scanner, track the flow path of the tracer on the rock formation, collect image data, and pre-process the collected data images; S4. Using 3D imaging technology, a three-dimensional model of the fracture network is constructed to display the three-dimensional flow path of the tracer in the fracture. The collected CT image data is analyzed to measure the concentration distribution of the tracer at different locations, and the flow rate and migration time of the tracer are calculated. S5. Based on the migration time, flow path and concentration change value of the tracer, the fracture permeability of the current rock formation is comprehensively calculated.

[0018] The tracer preparation steps in step S1 are: A1. Prepare the following materials: 10-500 parts of iohexol, 20-550 parts of iopamidol, and 40-560 parts of barium sulfate suspension; A2. Weigh the materials in proportion and pour them into a beaker. Insert a glass rod into the beaker and stir at a constant speed in a clockwise direction to mix. A3. Continue stirring for 5-10 minutes until the mixture is homogeneous without stratification or precipitation. The mixing is complete.

[0019] This application uses iohexol, iopamidol, and barium sulfate suspension for formulation. Iohexol and iopamidol are commonly used contrast agents with good water solubility and biocompatibility. They can exist stably in rock formation fluids without causing chemical contamination to the rock formation or changing its physical properties. The barium sulfate suspension helps enhance the contrast of the tracer in imaging. By rationally blending these substances, the tracer can be smoothly diffused in the rock formation and clearly identified in subsequent testing, laying the foundation for accurately tracking its flow path and concentration changes. CT scanning equipment can perform comprehensive, high-resolution scans of rock formations. Compared to other traditional detection methods, it can clearly identify the internal structure of the rock formation, including fractures of varying sizes and the distribution of tracers within them. By analyzing the scan data, it can precisely track the flow path of the tracer within the complex fracture network, which is the key to accurately understanding the seepage characteristics of the rock formation. Perform pre-processing on the acquired data images, such as denoising and contrast enhancement. Denoising can remove random noise interference generated during the scanning process, improve image clarity and accuracy, and make the flow path of the tracer more clearly discernible. Using 3D imaging technology to construct a three-dimensional model of the fracture network can intuitively demonstrate the spatial distribution, morphology, and interconnectedness of the fractures. This helps researchers grasp the overall structure of the fracture system and is crucial for understanding the flow patterns of fluids within it. By displaying the three-dimensional flow path of the tracer within the fracture, the migration of the tracer in different directions and locations can be clearly seen, providing a more comprehensive perspective for analyzing the permeability of the fracture. A comprehensive calculation based on the tracer's migration time, flow path, and concentration change value can fully account for multiple factors affecting permeability in the fracture system. The migration time reflects the overall flow velocity of the fluid in the fracture, the flow path reflects the connectivity and complexity of the fracture, and the concentration change value contains information about the tracer's diffusion and convection processes.

[0020] In step S2, a geological compass and a total station are used in advance to determine the location of the tracer injection point on the rock surface, and drilling operations are performed. The injection needle is inserted into the borehole and sealed. The injection pump pipe is connected, and the tracer is injected into the borehole. After the injection is completed, the injection needle is pulled out, and the injection time and location information are recorded.

[0021] Based on these precise coordinates, the present application can more accurately track the flow path of the tracer within the rock formation, providing a basis for constructing an accurate fracture network model. Drilling at a determined injection point can provide a controllable channel for the tracer to enter the rock formation. Accurately recording the injection time is key to the subsequent calculation of the tracer migration time. The tracer migration time is one of the important parameters for calculating the fracture permeability. Through precise time recording, the flow rate of the tracer between different locations can be accurately calculated.

[0022] In step S3, the CT scanning equipment is started to scan, emitting X-rays to penetrate the rock formation. The X-rays interact with different parts containing tracers, and attenuation signals of different intensities are received and recorded by the detector. At preset time intervals, the same fracture area is scanned and imaged regularly. In the initial stage, an image is collected every 10 minutes. As the diffusion rate slows down, the collection time is extended to 30 minutes. The imaging equipment parameters are kept consistent each time an image is collected.

[0023] This application transmits X-rays to penetrate the rock formation and can conduct in-depth detection inside the rock formation. Compared with other surface detection methods, CT scanning is not limited by the surface conditions of the rock formation and can obtain complete structural information inside the rock formation; regularly scanning and imaging the same fracture area at preset time intervals can achieve dynamic monitoring of the tracer diffusion process; maintaining consistent imaging equipment parameters each time an image is collected is the key to obtaining reliable data. Changes in imaging equipment parameters may cause changes in the brightness and contrast characteristics of the collected image, thereby affecting the judgment of the tracer position and concentration.

[0024] The preprocessing in step S3 includes image enhancement and image denoising. Image enhancement is performed by grayscale transformation. The image is read and converted into a grayscale image. The grayscale histogram of the image is calculated. According to the histogram distribution, the grayscale range of the image is increased by linear transformation. Image denoising is based on median filtering. The median filter window size is selected and the window is slid on the image. The pixel values of the window are sorted according to the grayscale size. The median of the window pixel values is taken and the pixel value in the center of the window is replaced to perform denoising.

[0025] After converting a color image into a grayscale image, this application calculates a grayscale histogram to understand the grayscale distribution of the image. A linear transformation is then used to increase the grayscale range of the image, effectively enhancing the contrast between the tracer and the rock formation background. In the original image, the tracer signal may be difficult to distinguish due to its close grayscale to the rock formation. However, after the grayscale range is expanded, the tracer portion becomes brighter or darker, making the difference between it and the surrounding rock formation more obvious. This helps to more accurately identify the position and distribution range of the tracer in the rock formation, providing clearer visual information for subsequent tracking of the tracer's flow path.

[0026] In step S4, the three-dimensional model construction step uses a threshold segmentation algorithm to set a threshold based on the grayscale difference between the cracks and the surrounding rocks in the CT image, segmenting the cracks from the background and outlining the crack boundaries based on the Canny algorithm; extracting a two-dimensional crack image sequence, using the marching cubes algorithm to construct the isosurface between two adjacent image layers to generate a three-dimensional model.

[0027] This application sets the threshold based on the grayscale difference between the cracks and the surrounding rocks in the CT image. This is a simple and effective method. The grayscale of cracks and rocks in CT images is usually different. By setting the threshold reasonably, the pixels representing the cracks can be distinguished from the pixels representing the rock background. This precise separation can highlight the crack part, provide a clear target object for subsequent model construction and analysis, and avoid the interference of background rock information on crack feature extraction.

[0028] Let I(x, y) be the grayscale value of the CT image at the coordinate (x, y), set the threshold to T, and the binary image B(x, y) obtained after threshold segmentation is expressed as: , Where 1 represents the crack area and 0 represents the background area; The process of obtaining edge image E(x, y) after processing B(x, y) is: , in It is the Canny operation process; The extracted two-dimensional crack image sequence is represented as (E n (x, y)) where n = 1, 2, ... m, n is the sequence number of the image, and m is the total number of image frames; The three-dimensional model construction is expressed as follows: , in Indicates the process of algorithm processing, receiving three-dimensional volume data V (x, y, z) and isosurface value V iso As input, the output is a set of facets F, V that constitute the surface of the three-dimensional model iso is the value of the preset isosurface.

[0029] During edge detection, the Canny algorithm in this application smoothes the image through a Gaussian filter step, effectively suppressing noise interference that may exist in the CT image and avoiding false edge detection caused by noise. At the same time, the algorithm can retain detailed information about the crack edge, accurately capturing even subtle crack changes, ensuring that the true shape of the crack is fully presented in subsequent 3D reconstruction. These two-dimensional image sequences are the basis for constructing three-dimensional models. Each two-dimensional image contains information about the cracks at a certain level. By analyzing and integrating the images in the sequence, we can obtain the position and morphology of the cracks in three-dimensional space, providing a rich and organized data source for subsequent three-dimensional reconstruction using the marching cubes algorithm.

[0030] In step S4, the data analysis converts the CT value of each pixel in the image according to the CT value to obtain the tracer concentration value; by comparing the position changes of the tracer at different time points in the path segment and combining the size information of the voxel unit, the average flow velocity of the tracer in the path segment is calculated; based on the flow velocity information and the length of the flow path, the velocity is divided by the distance to calculate the migration time of the tracer on the path.

[0031] This application obtains tracer concentration values by converting the CT value of each pixel in the image based on the CT value. This provides a quantitative representation method for the distribution of tracers in rock fractures. Compared with estimating the presence of tracers only through the grayscale of the image or other indirect methods, the concentration value can more accurately describe the distribution of tracers in space. This allows researchers to intuitively understand the enrichment level of tracers at different locations, such as the changes in tracer concentration at the intersection of fractures and narrow channels, thereby providing a more accurate data basis for studying the permeability and fluid flow characteristics of fractures.

[0032] The concentration value calculation formula is: , Where C(x, y) is the tracer concentration value at the pixel, k is the conversion coefficient, and CT(x, y) is the CT value of the pixel at coordinate (x, y) in the image; The average flow rate is calculated as follows: , Where A is the calculated average flow velocity value, It is expressed as the modulus of the position difference, s is the size of the voxel unit, t2 and t1 are the second monitoring time point and the first monitoring time point of the reagent; The calculation formula for migration time is: , Where G is the migration time of the tracer on the path, H is the length of the flow path, and A is the average flow velocity.

[0033] The permeability calculation in step S5 is based on Darcy's theorem. The permeability of the fracture is calculated by combining the migration time, flow path, and concentration change values obtained with the known tracer viscosity value.

[0034] Darcy's theorem, a classic theory of seepage mechanics, has a profound scientific foundation and extensive practical verification. It describes the flow laws of fluids in porous media and provides a reliable theoretical framework for calculating fracture permeability. Calculations based on Darcy's theorem ensure that the calculation of fracture permeability is based on mature physical theories, ensuring the scientific nature and credibility of the calculation results. Accurately calculating fracture permeability is crucial for the development of carbonate reservoirs. The permeability results obtained by comprehensively considering multiple factors can more realistically reflect the seepage characteristics of the reservoir, providing a more reliable basis for numerical simulation of oil reservoirs, design of production plans, and well deployment.

[0035] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A method for testing the permeability of carbonate reservoir fractures, characterized in that: The test steps are: S1. preparing a tracer by preparing iohexol, iopamidol, and a barium sulfate suspension, and mixing them to obtain a tracer; S2. Inject the prepared tracer into the rock formation and wait for the tracer to diffuse; S3. Scan the rock formation using a CT scanner, track the flow path of the tracer on the rock formation, collect image data, and pre-process the collected data images; S4. Using 3D imaging technology, a three-dimensional model of the fracture network is constructed to display the three-dimensional flow path of the tracer in the fracture. The collected CT image data is analyzed to measure the concentration distribution of the tracer at different locations, and the flow rate and migration time of the tracer are calculated. S5. Based on the migration time, flow path and concentration change value of the tracer, the fracture permeability of the current rock formation is comprehensively calculated.

2. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: The tracer preparation steps in step S1 are: A1. Prepare the following materials: 10-500 parts of iohexol, 20-550 parts of iopamidol, and 40-560 parts of barium sulfate suspension; A2. Weigh the materials in proportion and pour them into a beaker. Insert a glass rod into the beaker and stir at a constant speed in a clockwise direction to mix. A3. Continue stirring for 5-10 minutes until the mixture is homogeneous without stratification or precipitation. The mixing is complete.

3. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: In step S2, a geological compass and a total station are used in advance to determine the location of the tracer injection point on the rock surface, and drilling operations are performed. The injection needle is inserted into the borehole and sealed. The injection pump pipe is connected, and the tracer is injected into the borehole. After the injection is completed, the injection needle is pulled out, and the injection time and location information are recorded.

4. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: In step S3, the CT scanning equipment is started to scan, emitting X-rays to penetrate the rock formation. The X-rays interact with different parts containing tracers, and attenuation signals of different intensities are received and recorded by the detector. At preset time intervals, the same fracture area is scanned and imaged regularly. In the initial stage, an image is collected every 10 minutes. As the diffusion rate slows down, the collection time is extended to 30 minutes. The imaging equipment parameters are kept consistent each time an image is collected.

5. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: The preprocessing in step S3 includes image enhancement and image denoising. Image enhancement is performed by grayscale transformation. The image is read and converted into a grayscale image. The grayscale histogram of the image is calculated. According to the histogram distribution, the grayscale range of the image is increased by linear transformation. Image denoising is based on median filtering. The median filter window size is selected and the window is slid on the image. The pixel values of the window are sorted according to the grayscale size. The median of the window pixel values is taken and the pixel value in the center of the window is replaced to perform denoising.

6. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: In step S4, the three-dimensional model construction step uses a threshold segmentation algorithm to set a threshold based on the grayscale difference between the cracks and the surrounding rocks in the CT image, segmenting the cracks from the background and outlining the crack boundaries based on the Canny algorithm; extracting a two-dimensional crack image sequence, using the marching cubes algorithm to construct the isosurface between two adjacent image layers to generate a three-dimensional model.

7. The method for testing the permeability of carbonate reservoir fractures according to claim 6, characterized in that: Let I(x, y) be the grayscale value of the CT image at the coordinate (x, y), set the threshold to T, and the binary image B(x, y) obtained after threshold segmentation is expressed as: ， Where 1 represents the crack area and 0 represents the background area; The process of obtaining edge image E(x, y) after processing B(x, y) is: ， in It is the Canny operation process; The extracted two-dimensional crack image sequence is represented as (E n (x, y)) where n = 1, 2, ... m, n is the sequence number of the image, and m is the total number of image frames; The three-dimensional model construction is expressed as follows: ， in Indicates the process of algorithm processing, receiving three-dimensional volume data V (x, y, z) and isosurface value V iso As input, the output is a set of facets F, V that constitute the surface of the three-dimensional model iso is the value of the preset isosurface.

8. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: In step S4, the data analysis converts the CT value of each pixel in the image according to the CT value to obtain the tracer concentration value; by comparing the position changes of the tracer at different time points in the path segment and combining the size information of the voxel unit, the average flow velocity of the tracer in the path segment is calculated; based on the flow velocity information and the length of the flow path, the velocity is divided by the distance to calculate the migration time of the tracer on the path.

9. The method for testing the permeability of carbonate reservoir fractures according to claim 8, characterized in that: The concentration value calculation formula is: ， Where C(x, y) is the tracer concentration value at the pixel, k is the conversion coefficient, and CT(x, y) is the CT value of the pixel at coordinate (x, y) in the image; The average flow rate is calculated as follows: ， Where A is the calculated average flow velocity value, It is expressed as the modulus of the position difference, s is the size of the voxel unit, t2 and t1 are the second monitoring time point and the first monitoring time point of the reagent; The calculation formula for migration time is: ， Where G is the migration time of the tracer on the path, H is the length of the flow path, and A is the average flow velocity.

10. The method for testing the permeability of carbonate reservoir fractures according to claim 1, wherein: The permeability calculation in step S5 is based on Darcy's theorem. The permeability of the fracture is calculated by combining the migration time, flow path, and concentration change values obtained with the known tracer viscosity value.

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