Carbonate rock fracture-cavity filling degree calculation method based on electric imaging image

Abstract

The invention relates to a carbonate rock fracture-cave filling degree calculation method based on an electric imaging image, and belongs to the technical field of oil exploration and development. Graying the electric imaging static image; constructing an electric imaging static image global gray frequency histogram, and determining a matrix gray value; extracting gray value feature information of the electric imaging static image, and judging the number of target depth stratum components according to the maximum value of a gray frequency histogram; adopting a maximum between-class variance method to carry out multiple times of iterative segmentation on the electric imaging static image, recording a threshold value T when g is maximum, and judging the filler type according to image gray value characteristics; and calculating the fracture-cavity filling degree, and smoothing the obtained scatter data to obtain a final fracture-cavity filling degree curve. The difference between different fractured-vuggy components is expressed through the difference characteristics of the gray values of the surface holes on the electric imaging static image, the different fractured-vuggy components are visually divided, and accurate fractured-vuggy filling degree data are provided.

Description

Technical Field

[0001] This invention relates to a method for calculating the degree of filling of fractures and cavities in carbonate rocks based on electrical imaging images, belonging to the field of petroleum exploration and development technology. Background Art

[0002] The degree of filling of fractures and cavities is an important research aspect for evaluating the effectiveness of fractures and cavities in carbonate reservoirs. Fractures and cavities are the main reservoir spaces in carbonate reservoirs, and their filling degree has a significant impact on the oil and gas storage capacity and permeability of fractures and cavities, making it a key factor in improving the evaluation of carbonate reservoirs.

[0003] In recent years, well logging researchers have conducted numerous studies on the effectiveness evaluation of fractures and cavities. Based on the well logging data used, these studies can be categorized into four types: ① Cross-plot analysis using conventional well logging data, such as determining reservoir effectiveness by cross-plotting the porosity of unit conductivity of fracture and cavities with natural gamma ray; ② Core data calibration method, such as identifying the degree of fracture and cavities filling by calibrating uranium-thorium well logging cross-plots with calibrated core data; ③ Evaluation methods using raw electrical conductivity data from electrical imaging, such as fracture and cavities effectiveness evaluation based on the frequency distribution of electrical conductivity in electrical imaging; ④ Multi-data comprehensive evaluation method, such as fracture and cavities effectiveness evaluation based on conventional well logging data combined with coring and electrical imaging data.

[0004] However, the above methods all have certain shortcomings. For example, core data cannot determine the validity of large fractures and cavities; three-porosity logging data cannot accurately calculate secondary porosity; and the discrimination method of electrical imaging porosity spectrum cannot intuitively distinguish formation components. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for calculating the filling degree of carbonate rock fissures and cavities based on electro-imaging images. The method expresses the differences between different components of fissures and cavities by using the difference features of gray values ​​of faces on static electro-imaging images, and intuitively divides different components of fissures and cavities, providing accurate data on the filling degree of fissures and cavities.

[0006] The technical solution adopted in this invention is a method for calculating the degree of filling of fractures and cavities in carbonate rocks based on electro-imaging images, specifically as follows:

[0007] Step 1: Acquire static logging images using electrical imaging, preprocess the static images using electrical imaging, and finally convert the static images using electrical imaging to grayscale.

[0008] Step 2: Construct a global gray-level frequency histogram of the electro-imaging static image to determine the matrix gray-level value;

[0009] Step 3: Extract grayscale feature information of static electro-imaging image according to the specified window length and step size, and determine the number of stratigraphic components at the target depth based on the maximum value of the grayscale frequency histogram;

[0010] Step 4: Use the maximum inter-class variance method to perform multiple iterative segmentation on the electro-imaging static image, record the threshold T when g is maximum, and determine the type of filling material based on the image gray value characteristics;

[0011] Step 5: Calculate the filling degree of the suture hole and make further adjustments based on the effective suture hole porosity;

[0012] Step 6: Smooth the obtained scattered data to obtain the final curve of the filling degree of the pore.

[0013] Furthermore, in step 3, the window length for processing the electro-imaging static image is 5m, and the sliding step size is 0.25m.

[0014] Furthermore, the preprocessing of the electro-imaging in step 1 involves removing background noise from the image using an image opening and closing filtering algorithm.

[0015] Furthermore, when constructing the grayscale frequency histogram in steps 2 and 3, the global grayscale frequency histogram is spaced at intervals of 1, and the grayscale frequency histogram within the sliding window length is spaced at intervals of 10; the grayscale frequency histogram is constructed with equally spaced grayscale values ​​on the horizontal axis and frequency on the vertical axis.

[0016] Furthermore, the method for determining the matrix gray value in step 2 is as follows: extract the gray value with the highest frequency among the maxima of the global gray-level frequency histogram.

[0017] Furthermore, the grayscale feature information of the electro-imaging static image in step 3 includes: the grayscale mean square error, grayscale frequency histogram, and maximum grayscale value G. max and minimum gray value G min .

[0018] Furthermore, the steps for determining the quantity of formation components in step 3 are as follows:

[0019] ① Three mean squared error limits were initially set: 0-1, 1-10, and 10-100, representing the current depth segment as the matrix segment, high resistivity segment, and low resistivity segment, respectively. The high resistivity segment and low resistivity segment are the depth segments to be treated where the cavities exist.

[0020] ② Based on the calculated maximum values ​​and number of gray-level frequency histograms within the sliding window length, determine the number of formation components in the high-resistivity and low-resistivity sections as the number of maximum values, denoted as n, and the number of formation components in the matrix section as 1; if in the electro-imaging image, the gray-level frequency histogram data is concentrated on the side with higher gray levels and there is no data on the side with lower gray levels, then the default formation component is 4.

[0021] Furthermore, the formula for the maximum inter-class variance algorithm used in step 4 is: g = w0 × w1 × (u0 - u1) 2

[0022] In the formula, g represents the variance, w0 represents the proportion of face pixels to the total number of pixels, w1 represents the proportion of background pixels to the total number of pixels, u0 represents the average grayscale value of face pixels, and u1 represents the average grayscale value of background pixels.

[0023] Furthermore, in step 4, based on the grayscale value characteristics of the image, the type of filling material is determined by setting the grayscale value of pixels in the grayscale image to 1 if the grayscale value is greater than T, which represents the matrix, and setting the grayscale value of pixels less than T to 0, which represents the pore, thus obtaining the binarized face data.

[0024] Furthermore, the multiple iterative segmentation steps in step 4 are as follows:

[0025] ① Let T0 = 255. Starting from i = 0, for any i in T0... i The grayscale data from ~0 (i = 0, 1, 2, ..., n-1) is thresholded to obtain T. i+1 Then for T i+1 Thresholding is performed on grayscale data ranging from ~0 to obtain T. i+2 By analogy with the loop, all thresholds T are obtained. i ;

[0026] ② Starting from i = n-1, take the grayscale value at T i ~T i-2 The data is segmented using a threshold to obtain T. i ' -1 , replacing T i-1 Then, the updated threshold array T is obtained by looping. i ';

[0027] ③ Calculate the grayscale value at T i If the percentage of data with a value of ~0 is greater than 50%, then T i This is an invalid threshold. In this case, record p = i + 1, and finally determine the threshold array as T. j (j = p, p+1, ..., n-1).

[0028] The specific classification of component types in step 4 is as follows:

[0029] In the electro-imaging, the cavity components, arranged from highest to lowest grayscale value, are high-resistivity components, clay-filled material, and fluid. The high-resistivity segment consists of either clay-filled material and high-resistivity components, or high-resistivity components and fluid; the low-resistivity segment consists of high-resistivity components, clay-filled material, and fluid. To further determine the presence of clay-filled material and fluid in the low-resistivity segment, a discrimination formula for clay-filled material and fluid is established:

[0030]

[0031] In the formula, Tn-1 and T n-2 These are the last and second-to-last thresholds, i.e., the effective threshold and the filler separation threshold, respectively. G min The minimum grayscale value is denoted by ; when P ≥ 0.6, it indicates that the low-resistivity component in the high-resistivity section is fluid; conversely, it indicates that the low-resistivity component in the high-resistivity section is clayey. When G min When the value is 0, it indicates the presence of fluid; otherwise, it indicates that the only low-resistivity substance present is clay.

[0032] Then select the lowest threshold or the two lowest threshold values ​​to separate the muddy backfill material from the fluid, and finally take T. p ~T n-2 or T p ~T n-1 The grayscale range represents the grayscale range of the high-resistivity component.

[0033] Furthermore, the formula for calculating the face rate in step 5 is as follows:

[0034]

[0035] In the formula, S j For grayscale values ​​within the target window less than or equal to T j The percentage of data points for (j = p, p+1, ..., n-1) For grayscale values ​​within the target window less than or equal to T j The total number of data points, where S is the total number of data points within the target window;

[0036] Formula for calculating the degree of filling:

[0037]

[0038] In the formula, DOF represents the degree of filling of the gap within the target window, and S... p S represents the porosity, P is the separation threshold number between the pores and the matrix, and S represents the porosity. n-1 Represents the effective porosity, when there is no fluid or S in the pores. n-1 When <1%, DOF = 100.

[0039] Further, the scattered data obtained in step 6 is smoothed specifically by using a five-point cubic smoothing algorithm. This involves taking five adjacent points in the data for smoothing. Let the sequence be x(i), i = 1, 2, 3, ..., N, where x represents the unprocessed target dataset and y represents the processed dataset. The first two and last two data points in the dataset are processed specifically as follows:

[0040]

[0041]

[0042]

[0043]

[0044] The other data in the dataset is processed as follows:

[0045]

[0046] In the formula, i represents the current data point order, y(i) represents the current smoothing result, and x(i) represents the current data to be processed.

[0047] This invention discloses a method for calculating the degree of filling of fractures and cavities in carbonate rocks based on electro-imaging images. Its advantages include expressing the differences between different components of fractures and cavities through the difference in grayscale values ​​of faces on static electro-imaging images, and intuitively classifying these components to provide accurate data on the degree of filling; by considering the influence of formation background resistance, different components are intuitively and effectively classified in static electro-imaging images based on the mean square error of image grayscale values; and the calculation of the degree of filling is accurate and reliable due to the classification of formation components and the intuitiveness of electro-imaging images. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to the present invention.

[0050] Figure 2 This is a flowchart illustrating the component classification of carbonate rock fracture-cavity formations based on electro-imaging images, as described in this invention.

[0051] Figure 3 This is a diagram illustrating the component division results of the high-resistivity section in this invention;

[0052] Figure 4 This is a graph showing the calculation results of the well logging filling degree from 2940 to 2956 m in well A, an example well of this invention;

[0053] Figure 5 The diagram shows the calculation results of the well logging filling degree in well B (2797.5-2812.5m) in this example well of the present invention.

[0054] Figure 6 This is the electro-imaging grayscale frequency histogram of well A at depths of 2950–2955 m in the example well of this invention;

[0055] Figure 7 The image shows the grayscale frequency histogram of well B in the example well of this invention, from 2809 to 2809.5 m.

[0056] Figure 8 This is a schematic diagram of the iterative separation in step 4 of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] To further understand the invention, the technical solution will be further described below in conjunction with specific embodiments.

[0059] Example 1:

[0060] This embodiment describes a method for calculating the degree of fracture-cavity filling in carbonate rocks based on electro-imaging images. Taking well A in a carbonate reservoir of a certain oilfield as an example, the flowchart of the calculation method is shown below. Figure 1 The specific steps are as follows:

[0061] Step 1: Acquire static logging images using electrical imaging, preprocess the static images using electrical imaging, and finally convert the static images to grayscale. The preprocessing of the static images using electrical imaging involves removing background noise from the images through an opening and closing filtering algorithm.

[0062] Step 2: Construct a global gray-level frequency histogram of the electro-imaging static image to determine the matrix gray-level values. In Step 2, when constructing the gray-level frequency histogram, the global gray-level frequency histogram uses intervals of 1, and the gray-level frequency histogram within the sliding window length uses intervals of 10. The gray-level frequency histogram is constructed with equally spaced gray-level values ​​on the x-axis and frequency on the y-axis. The method for determining the matrix gray-level values ​​is to extract the gray-level value with the highest frequency from the maxima of the global gray-level frequency histogram.

[0063] Step 3: Extract grayscale feature information from the electro-imaging static image according to the specified window length and step size. Determine the number of stratigraphic components at the target depth based on the maximum value of the grayscale frequency histogram. In this embodiment, the window length is 5m and the sliding step size is 0.25m. The window length is designed based on the fact that if the window length is too short, the grayscale value may change little within the window length, even though the window is actually located in a region with significant grayscale changes; while if the window length is too long, it will place two different components of the stratigraphic segment together, leading to misjudgment of grayscale value changes. Therefore, a window length of 5m is used. The step size is chosen to make the grayscale variance curve as continuous as possible, increasing the accuracy and resolution at the boundaries of different stratigraphic segments. The grayscale feature information of the electro-imaging static image includes: grayscale mean square error, grayscale frequency histogram, and maximum grayscale value G. max and minimum gray value G min .

[0064] The steps for determining the quantity of formation components are as follows:

[0065] ① Three mean squared error limits were initially set: 0-1, 1-10, and 10-100, representing the current depth segment as the matrix segment, high resistivity segment, and low resistivity segment, respectively. The high resistivity segment and low resistivity segment are the depth segments to be treated where the cavities exist.

[0066] ② Based on the calculated maxima and their number within the sliding window length range of the gray-level frequency histogram, the number of formation components in the high-resistivity and low-resistivity sections is determined as the number of maxima, denoted as n. The number of formation components in the matrix section is determined to be 1 based on the value of n. Because formations are composed of different components, and the gray-level values ​​of the image change continuously from low to high, for example, the part with the lowest gray-level value in an effective cavity is the center of the effective cavity. The measured resistivity of this part is least affected by other components, or even unaffected. This part often accounts for the largest proportion in the effective cavity. Analogous to other components, the characteristics of these "central parts" on the gray-level histogram are the gray-level values ​​represented by the peaks. If, in the electrical imaging image, the gray-level frequency histogram data is concentrated on the higher gray-level side and there is no data on the lower gray-level side, then the default formation component is 4.

[0067] Step 4: The electro-imaging static image is segmented multiple times using the maximum inter-class variance method. The threshold T when g is maximized is recorded. Based on the image grayscale value characteristics, the type of filling material is determined. The maximum inter-class variance algorithm formula used in face recognition is: g = w0 × w1 × (u0 - u1). 2

[0068] In the formula, g represents the variance, w0 represents the proportion of face pixels to the total number of pixels, w1 represents the proportion of background pixels to the total number of pixels, u0 represents the average grayscale value of face pixels, and u1 represents the average grayscale value of background pixels. In the grayscale image, pixels with grayscale values ​​greater than T are set to 1, representing the matrix, while those with grayscale values ​​less than T are set to 0, representing pores, ultimately yielding binarized face data.

[0069] The steps for iterative segmentation when separating face data are as follows:

[0070] ① Let T0 = 255. Starting from i = 0, for any i in T0... i The grayscale data from ~0 (i = 0, 1, 2, ..., n-1) is thresholded to obtain T. i+1 Then for T i+1 Thresholding is performed on grayscale data ranging from ~0 to obtain T. i+2 By analogy with the loop, all thresholds T are obtained. i ;

[0071] ② Starting from i = n-1, take the grayscale value at T i ~T i-2 The data is segmented using a threshold to obtain T. i ' -1 , replacing T i-1 Then, the updated threshold array T is obtained by looping. i ';

[0072] ③ Calculate the grayscale value at T i The percentage of data with a value of ~0 indicates that the percentage of formation pores (filling material + fluid) cannot be less than 50%. If the percentage is greater than 50%, T i This is an invalid threshold. In this case, record p = i + 1, and finally determine the threshold array as T. j (j = p, p+1, ..., n-1).

[0073] The specific classification of component types in step 4 is as follows:

[0074] In the electro-imaging, the cavity components, arranged from highest to lowest grayscale value, are high-resistivity components, clay-filled material, and fluid. The high-resistivity segment consists of either clay-filled material and high-resistivity components, or high-resistivity components and fluid; the low-resistivity segment consists of high-resistivity components, clay-filled material, and fluid. To further determine the presence of clay-filled material and fluid in the low-resistivity segment, a discrimination formula for clay-filled material and fluid is established:

[0075]

[0076] In the formula, T n-1 and T n-2 These are the last and second-to-last thresholds, i.e., the effective threshold and the filler separation threshold, respectively. Gmin The minimum grayscale value is denoted by ; when P ≥ 0.6, it indicates that the low-resistivity component in the high-resistivity section is fluid; conversely, it indicates that the low-resistivity component in the high-resistivity section is clayey. When G min A value of 0 indicates the presence of fluid; conversely, a value of 0 indicates that the only low-resistivity material present is clay. The significance of the formula for distinguishing between clay-filled materials and fluids lies in determining the proportion of the low-resistivity component's grayscale value within the cavity, between the maximum and minimum grayscale values. If the proportion is large, it proves that the grayscale value of the low-resistivity component is minimally affected by the filling material, and the low-resistivity component within the cavity is fluid; conversely, it proves that the low-resistivity component within the cavity is clay, and the high-resistivity component is high-resistivity filling material. This is based on the premise that fluid grayscale values ​​are extremely small and minimally affected by other components.

[0077] Then select the lowest threshold or the two lowest threshold values ​​to separate the muddy backfill material from the fluid, and finally take T. p ~T n-2 or T p ~T n-1 The grayscale range represents the grayscale range of the high-resistivity component. T p Is it a certain invalid threshold T? i A subsequent threshold T i+1 If at this point there is only one threshold value remaining in the cavity, then there are only two components in the cavity, and T p ~T n-1 This represents the grayscale range of the high-resistivity component filling material; if two threshold values ​​remain in the cavity, then there are three components in the cavity, T. p ~T n-2 T represents the grayscale range of high-resistivity component fillers. n-1 ~T n-2 T represents the area of ​​mud-filled material. n-1 ~0 represents the fluid range.

[0078] Step 5: Calculate the filling degree of the suture cavity and further adjust it based on the effective suture cavity porosity; the formula for calculating the porosity is as follows:

[0079]

[0080] In the formula, S j For grayscale values ​​within the target window less than or equal to T j The percentage of data points for (j = p, p+1, ..., n-1) For grayscale values ​​within the target window less than or equal to T j The total number of data points, where S is the total number of data points within the target window;

[0081] Formula for calculating the degree of filling:

[0082]

[0083] In the formula, DOF represents the degree of filling of the gap within the target window, and S... p S represents the porosity, P is the separation threshold number between the pores and the matrix, and S represents the porosity. n-1 Represents the effective porosity, when there is no fluid or S in the pores. n-1 When <1%, the S n-1 This refers to the face rate S j When j = n-1, DOF = 100.

[0084] Step 6: Smooth the obtained scattered data to obtain the final curve of the filling degree of the cavity. The smoothing of the scattered data specifically involves processing the scattered data using a five-point cubic smoothing algorithm. That is, five adjacent points in the data are selected for smoothing. Let the sequence be x(i), i = 1, 2, 3, ..., N, where x represents the unprocessed target dataset and y represents the processed dataset. The first two and last two data points in the dataset are processed as follows:

[0085]

[0086]

[0087]

[0088]

[0089] The other data in the dataset is processed as follows:

[0090]

[0091] In the formula, i represents the current data point order, y(i) represents the current smoothing result, and x(i) represents the current data to be processed.

[0092] Well A has a reservoir depth of 2940–2960 m, and Well B has a reservoir depth of 2797.5–2812.5 m. Wells A and B produce approximately 105,000 m³ and 300,000 m³ of gas, respectively. The main component of the carbonate reservoirs in these wells is limestone. Based on the clay content curves, Well A is essentially free of clay, while Well B contains less than 5% clay inclusions. Based on porosity spectrum characteristics and conventional logging curves, Well A is a high-resistivity layer, and its reservoir is assessed as a gas-bearing layer; Well B is a low-resistivity layer, and its reservoir is also assessed as a gas-bearing layer.

[0093] In this embodiment, the grayscale of well A is concentrated on the high grayscale level side, such as Figure 6 As shown, there are few gray levels, and Figure 6If the root mean square error is greater than 1, then the number of formation components is set to 4. The three thresholds for well A are 241, 247, and 253, where 0 to 241 represents the fluid gray value range, 242 to 247 represents the clay gray value range, 248 to 253 represents the high resistivity component, and 254 to 255 represents the matrix.

[0094] The gray-level frequency histogram of well B, from 2809 to 2809.5m, is shown below. Figure 7 As shown in the figure, there are two peaks. Figure 7 There are two maxima on the left and right sides, so the number of formation components in the 2809-2809.5m range of well B is 4; the three thresholds in the 2809-2809.5m range of well B are 182, 96 and 42 respectively, where the gray value ranges 0-42, 43-96, 97-182 and 182-255 represent fluid, clay infill, high resistivity component and matrix respectively.

[0095] exist Figure 4 and Figure 5 In this study, apart from the matrix, the high-resistivity component exhibits extremely high porosity, exceeding 50%. Therefore, this high-resistivity component is considered part of the matrix, and the pores consist of two components. After calculating the root mean square error (RMSE) and P-value, the target reservoirs with an MSE greater than 10 in wells A and B have a P ≥ 0.6, indicating the presence of fluid within the pores of the high-resistivity layers in both wells A and B. Specifically, at the bottom of well B at 2812.25m, P < 0.6, indicating the absence of fluid at this location, while the neutron porosity value of 0 at this point confirms the accuracy of the P calculation.

[0096] After processing the electro-imaging images using this invention, and comparing the filling degree curve values, well A has approximately 30% filling capacity, while well B has approximately 22%. Comparing the total reservoir depth, well A has approximately 4.6m (2942.4–2943.8m, 2952.6–2955.8m), while well B has approximately 6m (2806.5–2812.5m). Regarding the filling material composition, well A contains high-resistivity filling material, while well B contains very little high-resistivity filling material and trace amounts of clay filling material. Based on these data, it can be determined that well B has a higher production capacity than well A.

[0097] By comparing the results, it can be found that the results obtained by this invention are consistent with the gas testing conclusions and the characteristics of conventional well logging curves.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating the degree of filling of fractures and cavities in carbonate rocks based on electro-imaging images, characterized in that, Step 1: Acquire static logging images using electrical imaging, preprocess the static images using electrical imaging, and finally convert the static images using electrical imaging to grayscale. Step 2: Construct a global gray-level frequency histogram of the electro-imaging static image to determine the matrix gray-level value; Step 3: Extract grayscale feature information of static electro-imaging image according to the specified window length and step size, and determine the number of stratigraphic components at the target depth based on the maximum value of the grayscale frequency histogram; Step 4: The electro-imaging static image is segmented multiple times using the maximum inter-class variance method. The threshold T at which the variance is maximum is recorded. Based on the gray value characteristics of the image, the type of filling material is determined. Step 5: Calculate the filling degree of the suture hole and make further adjustments based on the effective suture hole porosity; Step 6: Smooth the obtained scattered data to obtain the final curve of the filling degree of the pore.

2. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1, characterized in that, In step 1, the preprocessing of the electro-imaging is to remove background noise from the image by using an image opening and closing filtering algorithm.

3. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1, characterized in that, When constructing the gray-level frequency histogram in steps 2 and 3, the global gray-level frequency histogram is spaced at intervals of 1, and the gray-level frequency histogram within the sliding window length is spaced at intervals of 10; the gray-level frequency histogram is constructed with equally spaced gray-level values ​​on the horizontal axis and frequency on the vertical axis.

4. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1, characterized in that, The method for determining the matrix grayscale value in step 2 is to extract the grayscale value with the highest frequency from the maximum points of the global grayscale frequency histogram.

5. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1, characterized in that, Step 3 involves the grayscale feature information of the electro-imaging static image, including the grayscale mean square error, grayscale frequency histogram, and maximum grayscale value G. max and minimum gray value G min .

6. A method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1 or 5, characterized in that, The steps for determining the quantity of formation components in step 3 are as follows: ① Three mean squared error limits were initially set: 0-1, 1-10, and 10-100, representing the current depth segment as the matrix segment, high resistivity segment, and low resistivity segment, respectively. The high resistivity segment and low resistivity segment are the depth segments to be treated where the cavities exist. ②Based on the calculated maximum values ​​and their number of gray-scale frequency histograms within the sliding window length, the number of formation components in the high-resistivity and low-resistivity sections is determined as the number of maximum values, denoted as n, and the number of formation components in the matrix section is 1. If, in an electrophysiological imaging image, the gray-level frequency histogram data is concentrated on the higher gray-level side and there is no data on the lower gray-level side, then the default formation composition is 4.

7. A method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1 or 6, characterized in that, The formula for the maximum inter-class variance algorithm used in step 4 is: g = w0 × w1 × (u0 - u1) 2 In the formula, g represents the mean square error, w0 represents the proportion of face pixels to the total number of pixels, w1 represents the proportion of background pixels to the total number of pixels, u0 represents the average gray level of face pixels, and u1 represents the average gray level of background pixels. The steps of multiple iterations of segmentation in step 4 are as follows: ① Let T0 = 255. Starting from i = 0, for any i in T0... i The grayscale data from ~0 (i = 0, 1, 2, ..., n-1) is thresholded to obtain T. i+1 Then for T i+1 Thresholding is performed on grayscale data ranging from ~0 to obtain T. i+2 By analogy with the loop, all thresholds T are obtained. i ; ② Starting from i = n-1, take the grayscale value at T i ~T i-2 The data is segmented using a threshold to obtain T. i ' -1 , replacing T i-1 Then, the updated threshold array T is obtained by looping. i '; ③ Calculate the grayscale value at T i If the percentage of data with a value of ~0 is greater than 50%, then T i This is an invalid threshold. In this case, record p = i + 1, and finally determine the threshold array as T. j (j = p, p+1, ..., n-1); The specific classification of component types in step 4 is as follows: In electro-optical imaging, the fracture cavity components, arranged from largest to smallest grayscale value, are high-resistivity components, clay-filled material, and fluid. Specifically, the high-resistivity segment consists of either clay-filled material and high-resistivity components, or high-resistivity components and fluid; the low-resistivity segment consists of high-resistivity components, clay-filled material, and fluid. A discrimination formula for clay-filled material and fluid is established. In the formula, T n-1 and T n-2 These are the last and second-to-last thresholds, i.e., the effective threshold and the filler separation threshold, respectively. G min The minimum gray value; when P≥0.6, it represents that the low-resistivity component in the high-resistivity section is a fluid; Conversely, it indicates that the low-resistivity component in the high-resistivity section is clayey; when G min When the value is 0, it indicates the presence of fluid; otherwise, it indicates that the only low-resistivity substance present is clay. Then select the lowest threshold or the two lowest threshold values ​​to separate the muddy backfill material from the fluid, and finally take T. p ~T n-2 or T p ~T n-1 The grayscale range represents the grayscale range of the high-resistivity component.

8. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 7, characterized in that, In step 4, based on the grayscale value characteristics of the image, the type of filling material is determined. Specifically, pixels with grayscale values ​​greater than T in the grayscale image are set to 1, which represents the matrix, while those with grayscale values ​​less than T are set to 0, which represents the pores. Finally, the binarized face data is obtained.

9. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 8, characterized in that, The formula for calculating the face rate in step 5 is as follows: In the formula, S j For grayscale values ​​within the target window less than or equal to T j The percentage of data points for (j = p, p+1, ..., n-1) For grayscale values ​​within the target window less than or equal to T j The total number of data points, where S is the total number of data points within the target window; Formula for calculating the degree of filling: In the formula, DOF represents the degree of filling of the gap within the target window, and S... p S represents the porosity, P is the separation threshold number between the pores and the matrix, and S represents the porosity. n-1 Represents the effective porosity, when there is no fluid or S in the pores. n-1 When <1%, DOF = 100.

10. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 9, characterized in that, Step 6 involves smoothing the resulting scattered data. Specifically, this is done using a five-point cubic smoothing algorithm, where five adjacent points are selected for smoothing. Let the sequence be x(i), i = 1, 2, 3, ..., N, where x represents the unprocessed target dataset and y represents the processed dataset. The process involves processing the first two and last two data points in the dataset. The other data in the dataset is processed as follows: In the formula, i represents the current data point order, y(i) represents the current smoothing result, and x(i) represents the current data to be processed.

11. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 7, characterized in that, T p Is it a certain invalid threshold T? i A subsequent threshold T i+1 If at this point there is only one threshold value remaining in the cavity, then there are only two components in the cavity, and T p ~T n-1 This represents the grayscale range of the high-resistivity component filler; If two threshold values ​​remain in the hole at this point, then there are three components in the hole, T. p ~T n-2 T represents the grayscale range of high-resistivity component fillers. n-1 ~T n-2 T represents the area of ​​mud-filled material. n-1 ~0 represents the fluid range.

12. The method for calculating the degree of filling of carbonate rock fissures and cavities based on electro-imaging images according to claim 1, characterized in that, The window is 5m long and the sliding step is 0.25m.

Images