Method for predicting mechanical parameters of oil sand rock through digital core

Abstract

The invention discloses a method for predicting oil sand rock mechanical parameters through a digital core. The method comprises the following steps: 1, acquiring a core grayscale image of oil sand by using an environmental scanning electron microscope; 2, denoising processing is carried out on the core gray level image of the oil sand; and step 3, carrying out binarization processing on the denoised rock core grayscale image to obtain a binary image of oil sand. And 4, vectorizing the binary image of the oil sand to obtain a vector diagram of the oil sand. And 5, calculating the oil sand porosity by using Matlab software. And 6, carrying out a digital rock core deformation simulation experiment by using the COMSOL. According to the technical scheme, the rock mechanical parameters of the oil sand can be rapidly and accurately predicted, and a large amount of manpower, material resources and time are saved.

Description

Technical Field

[0001] The invention belongs to the field of rock mechanics, and in particular relates to a method for predicting oil sand rock mechanics parameters through digital cores. Background Art

[0002] Oil sand refers to sedimentary sand rich in natural asphalt, which is essentially a mixture of asphalt, water, sand and rich clay. Among them, the asphalt content is 10-12%, water accounts for 3-5%, and minerals such as sand and clay account for 80-85%. The mineral composition of oil sands in different regions is different. For example, in the "oil sand" deposits in Western Canada, the content of natural asphalt may dominate in some lithologies such as siltstone and carbonate.

[0003] However, there are few studies on the effect of oil sand porosity on oil sand rock mechanical parameters. There is no good theoretical relationship between oil sand rock mechanical parameters and porosity, and most studies focus on the determination of oil sand rock mechanical parameters. How to establish a relationship between oil sand rock mechanical parameters measured by digital core and oil sand porosity has become a difficult problem. Therefore, it is urgent to develop a method to predict oil sand rock mechanical parameters through digital core. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting oil sand rock mechanical parameters through digital core.

[0005] The technical solution adopted by the present invention is:

[0006] A method for predicting oil sand rock mechanical parameters by digital core, characterized by comprising the following steps:

[0007] Step 1: Use environmental scanning electron microscope to obtain grayscale image of oil sand core.

[0008] Step 2: De-noise the grayscale image of the oil sand core.

[0009] Step 3: Binarize the denoised core grayscale image to obtain a binary image of the oil sand.

[0010] Step 4: Vectorize the binary image of the oil sand to obtain a vector image of the oil sand.

[0011] Step 5: Calculate the porosity of oil sands using Matlab software.

[0012] Step 6: Use COMSOL to conduct digital core deformation simulation experiments

[0013] In the step 1, a field emission scanning electron microscope FEIQuanta 200F is mainly used to complete the process.

[0014] In the step 1, the sample is cut from a 25mm×50mm standard core with an ultra-thin slicer to form a tiny block with a length and width of 5mm×5mm and a thickness of 2-3mm. The lower end is ground flat and the upper end facing the electron beam is a fresh cut surface broken open by tweezers. The sample is then fixed on an aluminum sample holder and placed in a low vacuum environment wellbore.

[0015] In the step 1, the sample is observed in a Quanta 200F field emission scanning electron microscope at magnifications of 60 times, 75 times, 90 times and 120 times, thereby obtaining a corresponding microstructure electron map.

[0016] In the step 2, a median filter algorithm is selected to perform denoising on the digital core grayscale image.

[0017] In step 2, the basic principle of the median filtering method is: select the pixel values ​​of the pixel points in the digital image and the pixel values ​​of the surrounding pixels (A1, A2, ... A n ), take the window length m (m is an odd number), then sort the pixel values, and then select the pixel value in the middle to replace the pixel value of the current pixel as the filter output, and so on to all the pixels of the digital image, so that the surrounding pixel values ​​are closer to the true value, thereby removing the noise. The expression is as follows:

[0018]

[0019] In the step 2, the built-in function medfilt2 of Matlab software is used to perform median filtering to denoise the image.

[0020] In the step three, the denoised rock grayscale image is binarized using the Otsu method.

[0021] In the step 3, an optimal gray level (segmentation threshold) is selected by using the Otsu method, i.e., the maximum inter-class variance method, to divide the gray image with 256 gray levels into two parts according to the selected threshold, i.e., the background part with a gray value less than the threshold and the foreground part with a gray value greater than the threshold. The pixels of the background are set to 0 (black) and the pixels of the foreground are set to 255 (white) to achieve image binarization.

[0022] In step 3, assume that the image threshold is R, the proportion of foreground to the entire image is W0, the average gray value is μ0, the proportion of background to the entire image is W1, and the average gray value is μ1.

[0023] W0+W1=1 (1)

[0024] The average gray value μ of the image is:

[0025] μ=W0μ0+W1μ1 (2)

[0026] The intra-class variance g is defined as:

[0027] g=W0(μ0-μ) 2 +W1(μ1-μ) 2 (3)

[0028] Definition of between-class variance:

[0029] g=W0 W1(μ0-μ1) 2 =[μ . ω(R)-μ(R)] 2 / ω(R)[1-ω(R)] (4)

[0030] Change R from 0 to 255. When g in equation (4) takes the maximum value, R is the desired threshold.

[0031] In the step three, the image is binarized using the imbinarize function in Matlab.

[0032] In the step 4, the binary image is vectorized using CorelDRAW software, that is, the binary image is imported, and the image is outlined using a spline curve to generate a vector image.

[0033] In the step 5, Matlab is used to calculate the black pixel area (i.e., pore area) in the binary image, and the ratio of the calculated value to the total pixels of the image is the porosity of the oil sand.

[0034] In step 6, firstly, the microscopic deformation simulation of the oil sand uses the solid mechanics physical field interface and a steady-state solver, and then the oil sand vector map generated by CorelDRAW is imported into COMSOL.

[0035] In step 6, it is assumed that the oil sand consists of only a skeleton phase and a pore phase, wherein the skeleton phase is mainly composed of quartz and the pore phase is filled with asphalt. The skeleton phase and pore phase regions of the oil sand are selected and the corresponding materials are added for assignment.

[0036] In step six, the digital core deformation simulation experiment adopts a plane strain model, imposes fixed constraints on the bottom boundary of the digital core, applies a strain of about 10% downward in the Y direction on the top of the digital core, and constrains the top to not displace in the X direction.

[0037] In the step six, the stress field, strain field and displacement field distribution diagrams of the oil sand under different applied strains are obtained through simulation calculation.

[0038] In step 6, the stress-strain curve of the oil sand can be obtained by calculating the reaction force at the top of the digital core, and then the elastic modulus of the oil sand can be obtained. The Poisson's ratio of the oil sand can be obtained by the strain values ​​at the left and right ends of the digital core.

[0039] In the step six, the influence of porosity on the elastic modulus and Poisson's ratio of the oil sands is analyzed through the curves of the elastic modulus and Poisson's ratio of the oil sands with the porosity.

[0040] The beneficial effects of the present invention are as follows: by conducting digital core experiments to predict mechanical parameters of oil sands according to the technical solution of the present invention, the rock mechanical parameters of oil sands can be predicted quickly and accurately, and a large amount of manpower, material resources and time can be saved. For a long time, the determination of rock mechanical parameters has been measured by triaxial compression mechanical experiments on cores. Although this method has high precision, the work is complicated and costly, and this problem is particularly prominent for petroleum engineering. The present method uses a digital core test method to evaluate the mechanical properties of oil sands, thereby establishing the relationship between the porosity of oil sands and rock mechanical parameters. Only a simple digital core experiment is required to quickly predict the change law of rock mechanical parameters of oil sands. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of a method for predicting oil sand rock mechanical parameters by digital core;

[0042] Figure 2 This is the image after oil sands are binarized;

[0043] Figure 3 This is the vectorized map of oil sands;

[0044] Figure 4 This is the stress distribution diagram of the digital core at the end of loading;

[0045] Figure 5 This is the strain distribution diagram of the digital core at the end of loading;

[0046] Figure 6 This is the displacement field distribution diagram at the end of digital core loading;

[0047] Figure 7 is the stress-strain curve of oil sands;

[0048] Figure 8 is the relationship between elastic modulus and porosity of oil sands;

[0049] Fig. 9 This is the relationship between Poisson's ratio and porosity of oil sands;

[0050] Fig.10 For code diagram;

[0051] Fig.11 For code diagram;

[0052] Fig.12 For code diagram. DETAILED DESCRIPTION

[0053] The present invention is further described below in conjunction with embodiments and drawings.

[0054] Embodiment 1:

[0055] like Figure 1 As shown, a method for predicting oil sand rock mechanical parameters by digital core includes the following steps:

[0056] Step 1: Use environmental scanning electron microscope to obtain grayscale image of oil sand core.

[0057] Step 2: De-noise the grayscale image of the oil sand core.

[0058] Step 3: Binarize the denoised core grayscale image to obtain a binary image of the oil sand.

[0059] Step 4: Vectorize the binary image of the oil sand to obtain a vector image of the oil sand.

[0060] Step 5: Calculate the porosity of oil sands using Matlab software.

[0061] Step 6: Use COMSOL to conduct digital core deformation simulation experiments

[0062] In the step 1, a field emission scanning electron microscope FEIQuanta 200F is mainly used to complete the process.

[0063] In the step 1, the sample is cut from a 25mm×50mm standard core with an ultra-thin slicer to form a tiny block with a length and width of 5mm×5mm and a thickness of 2-3mm. The lower end is ground flat and the upper end facing the electron beam is a fresh cut surface broken open by tweezers. The sample is then fixed on an aluminum sample holder and placed in a low vacuum environment wellbore.

[0064] In the step 1, the sample is observed in a Quanta 200F field emission scanning electron microscope at magnifications of 60 times, 75 times, 90 times and 120 times, thereby obtaining a corresponding microstructure electron map.

[0065] In step 2, the basic principle of the median filtering method is: select the pixel values ​​of the pixel points in the digital image and the pixel values ​​of the surrounding pixels (A1, A2, ... A n ), take the window length m (m is an odd number), then sort the pixel values, and then select the pixel value in the middle to replace the pixel value of the current pixel as the filter output, and so on to all the pixels of the digital image, so that the surrounding pixel values ​​are closer to the true value, thereby removing the noise. The expression is as follows:

[0066]

[0067] In step 2, the built-in function medfilt2 of Matlab software is used to perform median filtering to denoise the image. The code is as follows Fig.10 As shown:

[0068] In the step three, the denoised rock grayscale image is binarized using the Otsu method.

[0069] In the step 3, an optimal gray level (segmentation threshold) is selected by using the Otsu method, i.e., the maximum inter-class variance method, to divide the gray image with 256 gray levels into two parts according to the selected threshold, i.e., the background part with a gray value less than the threshold and the foreground part with a gray value greater than the threshold. The pixels of the background are set to 0 (black) and the pixels of the foreground are set to 255 (white) to achieve image binarization.

[0070] In step 3, assume that the image threshold is R, the proportion of foreground to the entire image is W0, the average gray value is μ0, the proportion of background to the entire image is W1, and the average gray value is μ1.

[0071] W0+W1=1 (1)

[0072] The average gray value μ of the image is:

[0073] μ=W0μ0+W1μ1 (2)

[0074] The intra-class variance g is defined as:

[0075] g=W0(μ0-μ) 2 +W1(μ1-μ) 2 (3)

[0076] Definition of between-class variance:

[0077] g=W0 W1(μ0-μ1) 2

[0078] g=W0 W1(μ0-μ1) 2 =[μ . ω(R)-μ(R)] 2 / ω(R)[1-ω(R)] (4)

[0079] Change R from 0 to 255. When g in equation (4) takes the maximum value, R is the desired threshold.

[0080] In step 3, the image is binarized using the imbinarize function in Matlab. The code is as follows: Fig.11 The generated binary image is shown as Figure 2 shown.

[0081] In step 4, the binary image is vectorized using CorelDRAW software. That is, the binary image is imported, and the image is outlined using a spline curve to generate a vector image. Figure 3 shown.

[0082] In step 5, Matlab is used to calculate the black pixel area (i.e., pore area) in the binary image, and the ratio of the calculated value to the total pixels of the image is the porosity of the oil sand. The Matlab porosity calculation code is as follows: Fig.12 As shown:

[0083] In step 6, firstly, the microscopic deformation simulation of the oil sand uses the solid mechanics physical field interface and a steady-state solver, and then the oil sand vector map generated by CorelDRAW is imported into COMSOL.

[0084] In step 6, it is assumed that the oil sand consists of only a skeleton phase and a pore phase, wherein the skeleton phase is mainly composed of quartz and the pore phase is filled with asphalt. The skeleton phase and pore phase regions of the oil sand are selected and the corresponding materials are added for assignment.

[0085] In step six, the digital core deformation simulation experiment adopts a plane strain model, imposes fixed constraints on the bottom boundary of the digital core, applies a strain of about 10% downward in the Y direction on the top of the digital core, and constrains the top to not displace in the X direction.

[0086] In step 6, the stress field of the oil sand under different strains is obtained by simulation calculation ( Figure 4 ), strain field( Figure 5 ) and displacement field distribution diagram ( Figure 6 ).

[0087] In step 6, the stress-strain curve of the oil sand can be obtained by calculating the reaction force at the top of the digital core ( Figure 7 ), and then the elastic modulus of the oil sand is obtained. The Poisson's ratio of the oil sand can be obtained by the strain values ​​at the left and right ends of the digital core.

[0088] In step 6, the elastic modulus of oil sand ( Figure 8 ) and Poisson's ratio ( Fig. 9 ) is used to analyze the influence of porosity on the elastic modulus and Poisson's ratio of oil sands.

[0089] If there is a sample from the same block, the rock mechanical parameters of the oil sand can be evaluated in theory by testing the porosity of the sample according to the above operation method and then following the above operation steps.

Claims

1. A method for predicting oil sand rock mechanical parameters by digital core, characterized in that: The following steps are involved: Step 1: Use environmental scanning electron microscope to obtain grayscale image of oil sand core: The sample was cut from a 25mm×50mm standard core with an ultra-thin slicer. It was a tiny block with a length and width of 5mm×5mm and a thickness of 2-3mm. The lower end was ground flat, and the upper end facing the electron beam was a fresh cut surface broken by tweezers. The sample was then fixed on an aluminum sample holder and placed in a low vacuum environment wellbore. The sample was observed in a Quanta 200F field emission scanning electron microscope at magnifications of 60 times, 75 times, 90 times and 120 times, and the corresponding microstructure electron map was obtained. Step 2: Select the median filter algorithm to denoise the digital core grayscale image: Select the pixel value of the pixel point and its surrounding pixels in the digital image (A1, A2, ... A n ), take the window length m (m is an odd number), then sort the pixel values, and then select the pixel value in the middle to replace the pixel value of the current pixel as the filter output, and so on to all the pixels of the digital image, so that the surrounding pixel values ​​are closer to the true value, thereby removing the noise. The expression is as follows: Step 3: Use the Otsu method to binarize the denoised rock grayscale image to obtain a binary image of the oil sand; Using the Otsu method, i.e., the maximum inter-class variance method, an optimal grayscale level (segmentation threshold) is selected to divide the grayscale image with 256 grayscale levels into two parts according to the selected threshold, namely, the background part with a grayscale value less than the threshold and the foreground part with a grayscale value greater than the threshold, and the background pixels are set to 0 (black) and the foreground pixels are set to 255 (white) to achieve image binarization; Step 4: Use CorelDRAW software to vectorize the binary image of the oil sand, import the binary image, use spline curves to trace the image, and generate a vector image of the oil sand; Step 5: Use Matlab to calculate the black pixel area (i.e., pore area) in the binary image. The ratio of the calculated value to the total pixels of the image is the porosity of the oil sand. Step 6: First, the microscopic deformation simulation of oil sands uses the solid mechanics physical field interface and a steady-state solver. Then the oil sand vector diagram generated by CorelDRAW is imported into COMSOL. COMSOL is used to perform a digital core deformation simulation experiment. It is assumed that the oil sands are composed of only a skeleton phase and a pore phase, in which the skeleton phase is mainly composed of quartz and the pore phase is filled with asphalt. The skeleton phase and pore phase regions of the oil sands are selected and the corresponding materials are added for assignment. Through simulation calculations, the stress field, strain field and displacement field distribution diagrams of the oil sands under different strains are obtained. The stress-strain curve of the oil sands can be obtained by calculating the reaction force at the top of the digital core, and then the elastic modulus of the oil sands can be obtained. The Poisson's ratio of the oil sands can be obtained through the strain values ​​at the left and right ends of the digital core. The influence of porosity on the elastic modulus and Poisson's ratio of the oil sands is analyzed through the curves of the change of the elastic modulus and Poisson's ratio of the oil sands with porosity.

2. A method for predicting oil sand rock mechanical parameters by digital core according to claim 1, characterized in that: In the step 2, the built-in function medfilt2 of Matlab software is used to perform median filtering to denoise the image.

3. The method for predicting oil sand rock mechanical parameters by digital core according to claim 1, characterized in that: In the step 3, it is assumed that the image threshold is R, the proportion of the foreground number in the whole image is W0, the average gray value is μ0, the proportion of the background number in the whole image is W1, and the average gray value is μ1, then W0+W1=1 (1) The average gray value μ of the image is: μ=W0μ0+W1μ1 (2) The intra-class variance g is defined as: g=W0(μ0-μ) 2 +W1(μ1-μ) 2 (3) Definition of between-class variance: g=W0 W1(μ0-μ1) 2 =[μ . [ω(R)-μ(R)] 2 / ω(R)[1-ω(R)] (4) Change R from 0 to 255. When g in equation (4) takes the maximum value, R is the desired threshold.

4. A method for predicting oil sand rock mechanical parameters by digital core according to claim 1, characterized in that: In the step three, the image is binarized using the imbinarize function in Matlab.

5. The method for predicting oil sand rock mechanical parameters by digital core according to claim 1, characterized in that: In step six, the digital core deformation simulation experiment uses a plane strain model to impose fixed constraints on the bottom boundary of the digital core, apply a strain of about 10% downward in the Y direction of the top of the digital core, and constrain the top to not displace in the X direction.

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