Rock permeability prediction method based on computed tomography image

Abstract

According to the method for predicting the rock permeability based on the computed tomography image, the computed tomography image of the rock core is obtained by using the computed tomography device, and then the digital rock core is established through the steps of binaryzation, threshold segmentation and the like on the computed tomography image; performing connectivity measurement, pore throat segmentation and the like on the digital rock core to obtain a pore network model, and extracting pore throat structure parameters such as porosity, average throat section equivalent circle radius, average throat section shape factor, coordination number and the like of the rock core through the pore network model; and directly utilizing a prediction formula to obtain the permeability of the rock through the pore throat structure parameters on the premise that a seepage experiment or numerical simulation does not need to be carried out. The error of the prediction method provided by the invention and the error of the measured value are within 7%. The technical effects that the prediction device is low in manufacturing cost, the prediction steps are simple, the requirements for experiment and simulation conditions are low, the application range is wide, and the prediction precision is high are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock permeability measurement, in particular to a rock permeability prediction method based on computer tomography images. BACKGROUND

[0002] In the field of oil and gas engineering, it is of great significance to accurately predict the permeability of reservoir rocks. For example, in the process of transforming buried hill oil reservoirs into gas storage reservoirs, the permeability of reservoir and cap rock plays a crucial role in the evaluation of the sealing performance of the gas storage reservoir, the prediction of gas storage capacity, and the design of well pattern. Currently, the existing methods for determining rock permeability mainly include direct measurement and indirect measurement. Direct measurement methods mainly include seepage experiment, mercury injection method, etc., which are intuitive but have limitations such as long sample measurement period and complex operation, while indirect measurement methods, although using digital cores for flow field simulation and combining Darcy's law to calculate the equivalent permeability of the core, often require a large amount of computing resources. SUMMARY

[0003] The present application provides a rock permeability prediction method based on computer tomography images, aiming to solve the problem of long sample measurement time and complex measurement method in the existing methods for determining rock permeability.

[0004] The present application provides a rock permeability prediction method based on computer tomography images, which comprises:

[0005] Obtaining a scanning image of a sample core using a computer tomography device;

[0006] Processing the scanning image and establishing a digital core;

[0007] Measuring the connectivity and segmenting the pore throat of the digital core to obtain a pore network model;

[0008] Obtaining a plurality of pore throat structure parameters of the digital core through the pore network model;

[0009] Obtaining the permeability of the rock by using a prediction formula based on the plurality of pore throat structure parameters;

[0010] Wherein, the pore throat structure parameters include porosity, average throat cross-section equivalent circle radius, average throat cross-section shape factor and coordination number.

[0011] Optionally, the prediction formula of the permeability is:

[0012]

[0013] Wherein, K is the permeability, is the porosity, rt wherein R is the average throat section equivalent circle radius, G is the average throat section shape factor, and C is the coordination number.

[0014] Optionally, in the step of processing the scanned image and establishing a digital core, the prediction method comprises:

[0015] The scanned image is denoised to obtain a denoised scanned image;

[0016] The denoised scanned image is threshold segmented to obtain a digital pore space image and a digital rock skeleton image of the core;

[0017] The digital pore space image and the digital rock skeleton image are superimposed to obtain a three-dimensional digital core.

[0018] Optionally, in the step of measuring the connectivity of the digital core and segmenting the pore throat to obtain a pore network model, the prediction method comprises:

[0019] The connectivity of the digital core is measured, and independent pore spaces in the digital core are removed;

[0020] The pore spaces connected in the digital core are segmented by pore throat to obtain a pore network model.

[0021] Optionally, the denoising process uses a filtering algorithm.

[0022] Optionally, the filtering algorithm comprises a Gaussian filtering algorithm, a median filtering algorithm and a mean filtering algorithm.

[0023] Optionally, in the threshold segmentation, the threshold is adjusted to just cover the black area of the scanned image.

[0024] Optionally, the connectivity measurement uses a burning algorithm.

[0025] Optionally, the pore throat segmentation method for establishing a pore network model comprises a center axis algorithm and a maximum sphere algorithm.

[0026] Beneficial effects:

[0027] The rock permeability prediction method of the computer tomography image in the application, using a computer tomography device to obtain a computer tomography image of a core, and then through steps such as binarization and threshold segmentation of the computer tomography image to establish a digital core, and through connectivity measurement and pore throat segmentation of the digital core to obtain a pore network model; through the pore network model to extract pore structure parameters of the core such as porosity, average throat cross-section equivalent circle radius, average throat cross-section shape factor and coordination number, and then through the pore structure parameters to directly use a prediction formula to obtain the rock permeability without performing a percolation experiment or numerical simulation, the error of the prediction method provided in the application is within 7% of the measured value. The application achieves the technical effects of low cost of the prediction device, simple prediction steps, low requirements for experimental and simulation conditions, wide application range, and high prediction accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the application. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0029] Figure 1 is a flow chart of a rock permeability prediction method based on a computer tomography image according to an embodiment of the application;

[0030] Figure 2 is a pore throat structure schematic diagram of a rock permeability prediction method based on a computer tomography image according to an embodiment of the application;

[0031] Figure 3 is a computer tomography image of a rock permeability prediction method based on a computer tomography image according to an embodiment of the application;

[0032] Figure 4 is a threshold segmentation schematic diagram of a rock permeability prediction method based on a computer tomography image according to an embodiment of the application;

[0033] Figure 5 is a digital core model diagram of a rock permeability prediction method based on a computer tomography image according to an embodiment of the application;

[0034] Figure 6 is a pore network model diagram of a rock permeability prediction method based on a computer tomography image according to an embodiment of the application; DETAILED DESCRIPTION

[0035] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of the present application.

[0036] As shown in Figure 1 A method for predicting rock permeability based on a computer tomography image, the prediction method comprising:

[0037] Step S1: obtaining a scanning tomography image of a sample core by using a computer tomography device.

[0038] Specifically, a rock sample to be predicted and with a proper size is placed in a scanning area of the computer tomography device, and initial scanning images of the sample core in multiple two-dimensional slices are obtained by using the computer tomography device, and a three-dimensional image of the sample core can be obtained by sequentially stacking each initial scanning image.

[0039] The computer tomography is referred to as Computed Tomography, and the computer tomography is a technology for displaying multiple cross-sectional images with the aid of a computer by using ionizing radiation as energy. The computer tomography scans a specific area by using one or more transverse X-rays to obtain a set of transverse and longitudinal tomographic images, and combines the images into a complete three-dimensional image.

[0040] It can be understood that the sample core is placed in the scanning area, the computer tomography device rotates around the sample core, and X-ray beams are emitted through the sample core. These X-ray beams scan the sample core at various angles, and a detector captures X-ray data transmitted through the sample core.

[0041] The detector records and measures the X-ray intensity in the transmission process, and the data is referred to as projection data. According to the projection data, a two-dimensional projection image of the sample core at the shooting angle can be generated, that is, an initial scanning image.

[0042] The computer in the computer tomography device stacks the two-dimensional projection images together to obtain a three-dimensional image of the sample core.

[0043] In the embodiments of the present application, the sample core is rock at a specific location of a buried hill oil reservoir reconstructed as a gas storage, and dolomitic rock is taken as the research object. The sample is from No. 303 oil well, the sample diameter is 1 mm, the computer tomography device is Versa510 of ZEISS Company in Germany, the scanning resolution is 0.5 μm, and the initial scanning image is as shown in Figure 3The total number of 970 images is shown, and the three-dimensional image of the dolomitic rock sample can be obtained by stacking the 970 initial scanning images.

[0044] Step S2: processing the computer tomography image and establishing a digital core.

[0045] Specifically, in step S2, the prediction method comprises:

[0046] Step S21: denoising the initial scanning image to obtain a denoised scanning image.

[0047] Specifically, the denoising of the scanning image can use a filtering algorithm.

[0048] The filtering algorithm includes a Gaussian filtering algorithm, a median filtering algorithm, and a mean filtering algorithm or other algorithms.

[0049] The filtering algorithm is a technology for extracting useful signals from original disturbed measurement data. The filtering algorithm is a theory and method for estimating the state of a system based on the measurement of the observable signal of the system according to certain filtering criteria. In image filtering algorithms, it includes mean filtering, median filtering, Gaussian filtering, bilateral filtering, etc.

[0050] Mean filtering is mainly used for image smoothing and denoising. The basic idea of mean filtering is to replace the original gray value of each pixel point in the image with the average value of the gray values of all pixel points in its neighborhood.

[0051] Median filtering is a nonlinear spatial filtering method. The basic idea of median filtering is to replace the value of a point in a digital image or digital sequence with the median value of the points in its neighborhood, thereby eliminating isolated noise points.

[0052] Gaussian filtering is a linear smoothing filter. Gaussian filtering is suitable for eliminating Gaussian noise and can effectively smooth the image while preserving the edges and details of the image.

[0053] Bilateral filtering is a nonlinear filtering method. The basic principle of bilateral filtering is to consider both the spatial proximity and the pixel value similarity of the image to achieve the purpose of edge-preserving denoising.

[0054] When using different computer tomography scanning devices and processing different cores, different filtering algorithms are used according to the situation to adapt to different use conditions.

[0055] Through the filtering algorithm, the noise in the image can be effectively removed, the edges and details of the image can be enhanced, and the quality of the image can be improved.

[0056] Step S22: threshold segmentation is performed on the denoised scanning image to obtain a digital pore space image and a digital rock skeleton image of the core.

[0057] Specifically, when threshold segmentation is performed on the denoised scanning image, the threshold is adjusted to just cover the black area of the scanning image (for example, the black area in FIG. 4B), and the threshold is adjusted to just cover the black area of the two-dimensional gray image (for example, the black area in FIG. 4C). The part below the threshold is the pore space, and the area above the threshold is the rock skeleton, so as to distinguish the pore space and the rock skeleton. Figure 4 As shown in FIG. 4D, Figure 4 wherein a is a two-dimensional gray image after denoising, Figure 4 wherein b is an image in which the threshold is adjusted to just cover the black area of the two-dimensional gray image), the part below the threshold is the pore space, and the area above the threshold is the rock skeleton, so as to distinguish the pore space and the rock skeleton.

[0058] The threshold segmentation is a region-based image segmentation technique, which divides the image pixels into several categories to achieve image segmentation. In the computed tomography image, the threshold segmentation is used to distinguish different structures, so as to distinguish the pore space and the rock skeleton of the sample core.

[0059] Step S23: superimposing the digital pore space image and the digital rock skeleton image to obtain a three-dimensional digital core.

[0060] Specifically, in the embodiment of the present application, the two-dimensional image after threshold segmentation is sequentially superimposed by the computer in the computed tomography device, so that the real three-dimensional pore image inside the rock, that is, the digital core, can be reconstructed, as shown in FIG. 4D. Figure 5 As shown in FIG. 4D.

[0061] Step S3: performing connectivity measurement and pore throat segmentation on the digital core to obtain a pore network model.

[0062] Specifically, in step S3, the prediction method comprises:

[0063] Step S31: performing connectivity measurement on the digital core and removing independent pore spaces in the digital core.

[0064] Specifically, the connectivity measurement is performed by a burning algorithm to measure the permeability of the digital core, and the independent pore spaces in the digital core are removed to obtain the pore spaces that can affect the permeability.

[0065] In the embodiment of the present application, based on the three-dimensional digital core of the dolomitic rock sample, a burning algorithm is used to detect the connectivity in different directions of the selected calculation area and remove isolated pores.

[0066] The burning algorithm is an algorithm used in image processing, computer graphics, and certain physical simulations. In image processing, especially in digital core analysis and pore network modeling, the burning algorithm is often used to detect and remove isolated pixels or regions in an image to more accurately reconstruct the three-dimensional pore structure.

[0067] Specifically, first, one or more starting points (i.e., "seeds") are selected, and these points are pore space points that are known not to belong to isolated regions.

[0068] From the starting points, the algorithm "burns" the adjacent pixels or regions, which is done by checking the neighborhood of each pixel. If at least one pixel in the neighborhood of a pixel has already been "burned" and the pixel meets the burning condition, i.e., it is a pore space pixel, it will also be marked as burned.

[0069] When there are no more pixels to burn, the algorithm terminates, and all the pore space regions connected to the starting points have been processed, while the isolated pore space regions remain unburned.

[0070] In digital core analysis and pore network modeling, the burning algorithm is used to detect and remove isolated pores in an image. With appropriate starting points and burning conditions, only regions connected to the main pore structure are retained, while isolated, disconnected small pores are removed, accurately reconstructing the pore structure inside the rock and extracting meaningful pore networks.

[0071] Step S32: Perform pore throat segmentation on the connected pore network space in the digital core to obtain a pore network model.

[0072] Specifically, the pore network model is segmented based on the maximum sphere algorithm to establish a simplified dolomite sample pore network model with equivalent pore space topology and effectively containing pore geometric characteristics, as shown in Figure 6 .

[0073] The method for establishing the pore network model through pore throat segmentation can use the centerline algorithm, maximum sphere algorithm, or other algorithms.

[0074] Pore throat segmentation is used to distinguish pores and throats in the pore space in three-dimensional space, extract parameters related to pores and throats, such as pore cavity equivalent diameter l p (a parameter of the size of the pore cavity inside the rock), throat equivalent diameter l t (a parameter quantifying the size of the throat), from the three-dimensional digital core model, and then obtain the pore network model.

[0075] The center axis algorithm is an algorithm commonly used in computer graphics, image processing and computational geometry, and is used to extract a pore space center line or skeleton. The center axis algorithm finds all the centers of the largest inscribed circles in a shape and connects the centers to form a "center axis" of the shape. The connection of the "center axes" forms a pore network model.

[0076] The maximum sphere algorithm is an algorithm that finds one or more largest inscribed spheres (or maximum spheres) in a given three-dimensional space by iteration. These spheres can fill or cover the target area (such as a pore space) as much as possible, thereby dividing the pore space into different pore throat regions and establishing a simplified pore network model that effectively contains the geometric characteristics of the pore space.

[0077] Step S4: Obtain a plurality of pore throat structure parameters of the digital core through the pore network model.

[0078] Specifically, the pore throat structure parameters include porosity, average throat cross-section equivalent circle radius, average throat cross-section shape factor and coordination number.

[0079] Specifically, the porosity is the ratio of the sum of the pore volume in the rock to the total volume of the rock. The porosity represents the proportion of space in the rock that can be occupied by fluid. Under the condition of constant, the greater the porosity, the more the pore space in the rock, and the more the flow channels of fluid in the rock, thereby the higher the permeability.

[0080] The average throat cross-section equivalent circle radius is the radius of the average cross-section equivalent circle of the throat (i.e. the narrow channel between pores) in the rock. The average throat cross-section equivalent circle radius reflects the size and connectivity of the throat. The throat is the main channel for fluid flow in the rock. The larger the throat, the smaller the resistance to fluid flow, and the higher the permeability.

[0081] The average throat cross-section shape factor is a parameter that describes the complexity of the throat cross-section shape. The average throat cross-section shape factor is the ratio of the area of the throat cross-section to the square of the perimeter of the cross-section, which reflects the regularity of the throat cross-section. The more regular the throat cross-section shape (i.e. the larger the shape factor), the smoother the fluid flow in the throat, and the higher the permeability. Conversely, if the throat cross-section shape is complex, fluid flow is easily hindered, and permeability is reduced.

[0082] The coordination number represents the number of connections of each pore to its adjacent pores in the rock. The coordination number reflects the connectivity and complexity of the pore network. The larger the coordination number, the better the connectivity of the pore network, the easier the fluid flow in the rock, and the higher the permeability. Conversely, if the coordination number is small, the connectivity of the pore network is poor, fluid flow is easily hindered, and permeability is reduced.

[0083] In the embodiment of the present application, the pore network model of the dolomitic rock sample is used to obtain the following pore throat structure parameters:

[0084] The porosity is 0.11;

[0085] The average throat cross-section equivalent circle radius is 2.68 μm;

[0086] The average throat cross-section shape factor is 0.29;

[0087] The coordination number is 0.29.

[0088] Step S5: obtaining the permeability of the rock by using the prediction formula based on the plurality of pore throat structure parameters.

[0089] Specifically, the prediction formula of the permeability is as follows:

[0090]

[0091] wherein K is the permeability, φ is the porosity, r t rt is the average throat cross-section equivalent circle radius, G is the average throat cross-section shape factor, and C is the coordination number.

[0092] The permeability of the core can be obtained by using the prediction formula.

[0093] In the embodiment of the present application, the relevant parameters in step S4 are substituted into the prediction formula to obtain the permeability of the dolomitic rock sample.

[0094] The porosity φ is 0.11;

[0095] The average throat cross-section equivalent circle radius rt is 2.68 μm;

[0096] The average throat cross-section shape factor G is 0.29;

[0097] The coordination number C is 0.29.

[0098] The calculated permeability K of the core is 0.27 mD, and the error between the calculated permeability K and the permeability K0 measured by the experiment and numerical calculation is only about 7%.

[0099] By the rock permeability prediction method based on computer tomography image provided in the application, the computer tomography image of the core is obtained by using the computer tomography device, and then the digital core is established by performing binarization, threshold segmentation and other steps on the computer tomography image, and the pore network model is obtained by performing connectivity measurement, pore throat segmentation and other steps on the digital core; the pore structure parameters of the core, such as porosity, average throat cross-section equivalent circle radius, average throat cross-section shape factor and coordination number, are extracted through the pore network model, and then the permeability of the rock is directly obtained by using the prediction formula without performing the percolation experiment or numerical simulation, and the error of the prediction method provided in the application is within 7% of the measured value. The prediction method provided in the application has the technical effects of low cost of the prediction device, simple prediction steps, low requirement for experimental and simulation conditions, wide application range and high prediction accuracy.

[0100] It should be noted that each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts between each embodiment can be referred to each other.

[0101] It should also be noted that in this paper, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In addition, relationship terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations, and cannot be understood as indicating or implying relative importance. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or terminal device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or terminal device including the element.

[0102] The technical solutions provided by the present application are described in detail above, and the principles and implementation manners of the present application are described by using specific examples. The above description of the examples is only used to help understand the present application, and the content of the description should not be understood as a limitation on the present application. Meanwhile, for those skilled in the art, according to the present application, there will be different forms of changes in the specific implementation manners and application ranges, which do not need and cannot be exhausted here, and the obvious changes or changes derived therefrom are still within the protection scope of the present application.

Claims

1. A method of predicting rock permeability based on computed tomography images, characterized by, The prediction method comprises: obtaining a scanning image of a sample core by using a computer tomography device; processing the scanning image and establishing a digital core; conducting connectivity measurement and pore throat segmentation on the digital core to obtain a pore network model; obtaining a plurality of pore throat structure parameters of the digital core through the pore network model; obtaining the permeability of the rock by using a prediction formula through the plurality of pore throat structure parameters; wherein the pore throat structure parameters comprise porosity, average throat cross-section equivalent circle radius, average throat cross-section shape factor and coordination number.

2. The prediction method of rock permeability based on a computer tomography image according to claim 1, characterized in that: the prediction formula of the permeability is: where K is the permeability, is the porosity, r t is the average throat cross-sectional equivalent circle radius, G is the average throat cross-sectional shape factor, and C is the coordination number.

3. The method of predicting rock permeability based on computed tomography images according to claim 1, wherein, in the step of processing the scanning image and establishing a digital core, the prediction method comprises: conducting noise reduction processing on the scanning image to obtain a noise reduction scanning image; conducting threshold segmentation on the noise reduction scanning image to obtain a digital pore space image and a digital rock skeleton image of the core; superimposing the digital pore space image and the digital rock skeleton image to obtain a three-dimensional digital core.

4. The method of predicting rock permeability based on computed tomography images according to claim 1, wherein, in the step of conducting connectivity measurement and pore throat segmentation on the digital core to obtain a pore network model, the prediction method comprises: conducting connectivity measurement on the digital core and removing independent pore spaces in the digital core; conducting pore throat segmentation on the connected pore spaces in the digital core to obtain a pore network model.

5. The method of predicting rock permeability from computed tomography images of claim 3, wherein, The noise reduction processing adopts a filtering algorithm.

6. The method of predicting rock permeability from computed tomography images of claim 5, wherein, The filtering algorithm comprises a Gaussian filtering algorithm, a median filtering algorithm and a mean filtering algorithm.

7. The method of predicting rock permeability based on computed tomography images according to claim 3, wherein, The threshold in the threshold segmentation is adjusted to just cover the black area of the noise reduction scanning image.

8. The method of predicting rock permeability based on computed tomography images according to claim 4, wherein, The connectivity measurement adopts a burning algorithm.

9. The method of predicting rock permeability based on computed tomography images according to claim 4, wherein, The method for establishing the pore network model by the pore throat segmentation comprises a center axis algorithm and a maximum sphere algorithm.

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