A method for predicting ground crude oil density based on fluorescence image processing

Abstract

The present invention discloses a method for predicting ground crude oil density based on fluorescence image processing, comprising the following steps: step 1, extracting a fluorescence main-tone color image by color extraction from a fluorescent color image of an oil-bearing wall core of a reservoir; step 2, converting the fluorescent main-tone color image into a grayscale image under the same color mode; step 3, extracting grayscale values ​​from the grayscale image; step 4, obtaining ground crude oil density data of crude oil samples collected from the oil-bearing wall core of a reservoir at the same reservoir depth; step 5, establishing a regression equation between ground crude oil density data and grayscale values; and step 6, predicting ground crude oil density by using the regression equation. Compared with the prior art, the present invention realizes quantitative prediction of ground crude oil density data at the same depth position by using the fluorescence image of the oil-bearing wall core.

Description

Technical Field

[0001] The invention belongs to the technical field of oil field development, and in particular relates to a ground crude oil density prediction method based on fluorescent image processing. Background Art

[0002] The surface crude oil density is one of the important parameters for evaluating the properties of crude oil fluid. At present, in the exploration and reserve evaluation stages, most structures evaluate the properties of reservoir crude oil fluid by collecting crude oil samples and obtaining surface crude oil density data through indoor testing. However, crude oil sampling involves a certain sampling cost, so for oil companies, not every exploration well or evaluation well will collect crude oil samples. How to achieve quantitative evaluation of reservoir crude oil fluid properties without obtaining crude oil fluid samples has become a production problem.

[0003] During the on-site drilling process, oil-bearing core samples are generally taken at the depth indicated by oil and gas logging. Fluorescent color images of the oil-bearing core samples will be collected in the laboratory. How to make full use of the fluorescent image data of the oil-bearing core, mine valuable color parameters from the image, establish the correlation between color parameters and ground density of crude oil, and achieve quantitative evaluation is a relatively difficult research direction. Summary of the invention

[0004] In order to overcome the technical problems existing in the prior art, the present invention proposes a method for predicting ground crude oil density based on fluorescence image processing. By processing the color of the fluorescence image of the oil-bearing wall core of the reservoir, a regression formula between the grayscale value of the grayscale image and the ground density of crude oil is established, thereby realizing the quantitative prediction of the ground density data of crude oil at the same depth position through the fluorescence image of the oil-bearing wall core.

[0005] The present invention is achieved by utilizing the following technical solutions:

[0006] A method for predicting ground crude oil density based on fluorescence image processing, the method comprising the following steps:

[0007] Step 1, extracting the main color tone of the fluorescent color image of the oil-bearing wall core of the reservoir;

[0008] Step 2, converting the fluorescent main tone color image into a grayscale image under the same color mode;

[0009] Step 3, extracting grayscale values ​​from the grayscale image;

[0010] Step 4, obtaining surface crude oil density data of crude oil samples collected from the oil-bearing wall core of the reservoir at the same reservoir depth;

[0011] Step 5: Establish a regression equation between ground crude oil density data and grayscale value. The expression is as follows:

[0012] p=a 1 ×M+a 2 (1)

[0013] Among them, p is the ground density of crude oil, M is the gray value, a 1 、a 2 All are regression coefficients;

[0014] Step 6: Predict the surface crude oil density through the regression equation.

[0015] In step 5, linear regression is used for the reservoir oil-bearing wall core sample point data to obtain the regression coefficient a 1 、a 2 .

[0016] In step 6, if a fluorescence image of the oil-bearing wall core is obtained at a certain point of the reservoir, the grayscale value of the grayscale image of the oil-bearing wall core can be obtained through steps 1 to 3, and the grayscale value is substituted into formula (1) to calculate the crude oil surface density data at the sampling depth of the oil-bearing wall core.

[0017] In the step 1, the fluorescent color image of the oil-bearing wall core of the reservoir is obtained by collecting images of a certain end face formed in the original state of the oil-bearing wall core at a certain reservoir depth position.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects:

[0019] 1) The coefficient is relatively high, which enables the quantitative prediction of the surface density data of crude oil at the same depth through the fluorescence image of the oil-bearing wall core;

[0020] 2) It can be used as a quality control method for the accuracy of ground density test data of crude oil samples;

[0021] 3) Reservoir researchers can also use this invented technology to conduct quantitative evaluation of the fluid properties of reservoir crude oil even without obtaining crude oil samples on site.

[0022] 4) The technology is simple and practical to operate, which can save a lot of crude oil sampling costs for oil companies and generate greater economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is an overall flow chart of a method for predicting ground crude oil density based on fluorescent image processing according to the present invention;

[0024] Figure 2 Schematic diagram of fluorescent color image of oil-bearing wall core in a mining area according to an embodiment of the present invention; (a) 20°C - ground density: 830.5 kg / m 3 , (b) 20℃-ground density: 840.4kg / m 3, (c) 20℃-ground density: 848.3kg / m 3 , (d) 20℃-ground density: 854.0kg / m 3 , (e) 20℃-ground density: 895.3kg / m 3 , (f) 20℃-ground density: 901.7kg / m 3 , (g) 20℃-ground density: 941.8kg / m 3 , (h) 20℃-ground density: 952.9kg / m 3 , (i) 20℃-ground density: 960.1kg / m 3 , (j) 20℃-ground density: 966.1kg / m 3 , (k) 20℃-ground density: 977.9kg / m 3 ;

[0025] Figure 3 This is a schematic diagram of the main tone image display of the fluorescent color image of the oil-bearing wall core of the mining area according to an embodiment of the present invention;

[0026] Figure 4 It is a schematic diagram of grayscale image display of a fluorescent color image of an oil-bearing wall core of a mining area according to an embodiment of the present invention;

[0027] Figure 5 This is a linear regression diagram of ground crude oil density and gray value according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0029] like Figure 1 As shown, Figure 1 The flowchart of a method for predicting ground crude oil density based on fluorescence image processing is as follows:

[0030] Step 1, extracting the fluorescence main color image by coloring the fluorescence color image of the oil-bearing wall core of the reservoir;

[0031] Step 2, decolorizing the fluorescent main color image and converting it into a grayscale image under the same color mode;

[0032] Step 3, extracting grayscale values ​​from the grayscale image;

[0033] Step 4, performing chemical analysis on the crude oil sample collected at the same reservoir depth as the oil-bearing wall core of the reservoir to obtain surface crude oil density data;

[0034] Step 5: Establish a regression equation between ground crude oil density data and grayscale value. The expression is as follows:

[0035] p=a 1 ×M+a 2 (1)

[0036] Where p is the surface density of crude oil (unit: kg / m 3 ), M is the gray value (dimensionless), a 1 、a 2 All are regression coefficients (dimensionless);

[0037] Step 6: Predict the surface crude oil density through the regression equation.

[0038] In step 5, linear regression is used for the reservoir oil-bearing wall core sample point data to obtain the regression coefficient a 1 、a 2 In step 6, if a fluorescence image of the oil-bearing wall core is obtained at a certain point of the reservoir, the grayscale value of the grayscale image of the oil-bearing wall core can be obtained through steps 1 to 3, and the grayscale value is substituted into formula (1) to calculate the crude oil surface density data at the sampling depth of the oil-bearing wall core.

[0039] The embodiment of the present invention takes the fluorescent color image of the oil-bearing wall core at a certain depth position of a certain oil reservoir in a mining area as an example:

[0040] In the step 1, the fluorescent color image of the oil-bearing wall core is obtained by collecting an image of a certain end face of the oil-bearing wall core in its original state. The color extraction process of the fluorescent color image of the oil-bearing wall core of the reservoir can be achieved by using the color extraction function of image processing software including but not limited to Photoshop, extracting the main color tone of the fluorescence, and filling it into the newly created document to obtain a color image of the main color tone of the fluorescence;

[0041] like Figure 2 The figure is a schematic diagram of the fluorescent color image of the oil-bearing wall core of the mining area according to the embodiment of the present invention; (a) 20℃-ground density: 830.5kg / m 3 , (b) 20℃-ground density: 840.4kg / m 3 , (c) 20℃-ground density: 848.3kg / m 3 , (d) 20℃-ground density: 854.0kg / m 3 , (e) 20℃-ground density: 895.3kg / m 3 , (f) 20℃-ground density: 901.7kg / m 3 , (g) 20℃-ground density: 941.8kg / m 3 , (h) 20℃-ground density: 952.9kg / m 3 , (i) 20℃-ground density: 960.1kg / m 3 , (j) 20℃-ground density: 966.1kg / m 3, (k) 20℃-ground density: 977.9kg / m 3 ; Color picking is achieved through the color picking function of image processing software including but not limited to Photoshop. Taking Photoshop software as an example, import the oil-containing wall core fluorescent color image, use the eyedropper tool, select the sampling size of the eyedropper tool as 3×3 average, and use the eyedropper tool to absorb the main color tone of fluorescence on the oil-containing wall core fluorescent color image. The main color tone should be selected from the area with continuous and stable distribution of fluorescence in the field of view. You can select several times and select the main color tone of the oil-containing wall core fluorescent color image after careful comparison. After the eyedropper tool absorbs the color, the software defaults to the foreground color. Create a new document (shortcut key CTRL+N), fill the foreground color just absorbed into the new document (shortcut key ALT+DELETE), and obtain a fluorescent main color tone color image. In RGB color mode, the R value is 176, the G value is 149, and the B value is 108. Figure 3 , which is a schematic diagram of the main tone image display of the fluorescent color image of the oil-bearing wall core of the mining area according to an embodiment of the present invention.

[0042] In step 2, the decolorization process of the fluorescent main color image can be achieved by using the decolorization function of image processing software including but not limited to Photoshop. Taking Photoshop software as an example, the fluorescent main color image is converted into a grayscale image in the same color mode (shortcut key CTRL+SHIFT+U); Figure 4 , which is a schematic diagram of grayscale image display of a fluorescent color image of an oil-bearing wall core in a mining area according to an embodiment of the present invention.

[0043] In step 3, the grayscale value of the grayscale image is extracted. Taking Photoshop software as an example, in the RGB color mode, the values ​​of R, G and B of the grayscale image are the same, all 142, and this value is taken as the grayscale value of the grayscale image. In step 4, the crude oil sample and the oil-bearing wall core sample of the reservoir must be obtained at the same depth of the reservoir. The crude oil sample is tested and the ground density data of 895.3 kg / m 3 ; At this time, the gray value of the fluorescent gray image of the oil-bearing wall core at this point establishes a corresponding relationship with the ground density data;

[0044] In step 5, all the oil-bearing wall core samples and crude oil samples with corresponding relationships within the mining area are summarized and sorted according to steps 1 to 4 to obtain the mining area sample point data shown in Table 1. The data includes ground density, fluorescent main color image parameters (i.e., R, G, B values), and grayscale image parameters, i.e., grayscale values.

[0045] Table 1

[0046]

[0047]

[0048]

[0049] The regression relationship between the ground crude oil density data and the gray value of the mining area is established, and the characterization equation is as follows:

[0050] p=-0.9843×M+1045.5

[0051] Where p is the surface density of crude oil (unit: kg / m 3 ), M is the gray value (dimensionless); the regression coefficients are a 1 =-0.9843, a 2 =1045.5;

[0052] like Figure 5 FIG. 4 is a linear regression diagram of ground crude oil density and gray value according to an embodiment of the present invention.

[0053] In step 6, if a fluorescence image of the oil-bearing wall core is obtained at a certain point of the reservoir, the grayscale value of the grayscale image of the oil-bearing wall core can be obtained through steps 1 to 3, and the grayscale value can be substituted into formula (1) to calculate the ground density data of the crude oil at the sampling depth of the oil-bearing wall core.

[0054] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting ground crude oil density based on fluorescence image processing, It is characterized in that The method comprises the following steps: Step 1, extracting the main color tone of the fluorescent color image of the oil-bearing wall core of the reservoir; Step 2, converting the fluorescent main tone color image into a grayscale image under the same color mode; Step 3, extracting grayscale values ​​from the grayscale image; Step 4, obtaining surface crude oil density data of crude oil samples collected from the oil-bearing wall core of the reservoir at the same reservoir depth; Step 5: Establish a regression equation between ground crude oil density data and grayscale value. The expression is as follows: p=a 1 ×M+a 2 (1) Among them, p is the ground density of crude oil, M is the gray value, a 1 、a 2 All are regression coefficients; Step 6: Predict the surface crude oil density through the regression equation.

2. A method for predicting ground crude oil density based on fluorescent image processing as claimed in claim 1, It is characterized in that In step 5, linear regression is used for the reservoir oil-bearing wall core sample point data to obtain the regression coefficient a 1 、a 2 .

3. A method for predicting ground crude oil density based on fluorescent image processing as claimed in claim 1, It is characterized in that In step 6, if a fluorescence image of the oil-bearing wall core is obtained at a certain point of the reservoir, the grayscale value of the grayscale image of the oil-bearing wall core can be obtained through steps 1 to 3, and the grayscale value is substituted into formula (1) to calculate the crude oil surface density data at the sampling depth of the oil-bearing wall core.

4. A method for predicting ground crude oil density based on fluorescent image processing as claimed in claim 1, It is characterized in that In the step 1, the fluorescent color image of the oil-bearing wall core of the reservoir is obtained by collecting images of a certain end face formed in the original state of the oil-bearing wall core at a certain reservoir depth position.

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