Method and device for judging stability of gas-water leading edge of fractured gas reservoir

Abstract

The invention provides a method and a device for judging gas-water front edge stability of a fractured gas reservoir. The method comprises the following steps of: obtaining a water saturation distribution geologic model of the fractured gas reservoir in a target block at different time; performing longitudinal layering or transverse layering on the water saturation distribution geologic model of the fractured gas reservoir in the target block at different time, and storing each layering result as an RGB color picture; the stored RGB color pictures are processed, and corresponding grey-scale maps are obtained; processing each grey-scale image to obtain a corresponding binary image; the fractal dimension of each binary image is calculated; comparing the fractal dimensions of the binary images of the same longitudinal layer or the same transverse layer at different moments; or comparing the fractal dimensions of the binary images of different longitudinal layers or different transverse layers at the same moment so as to judge the stability of the gas-water front of the fractured gas reservoir. According to the method, the irregular degree of the gas-water front edge can be quantitatively represented, and production strategy optimization of a production area can be effectively guided.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development, and particularly relates to a method and device for judging the stability of the gas-water front of fractured gas reservoirs. Background Technology

[0002] In recent years, with the continuous development of fractured gas reservoirs, some wells in these reservoirs have begun to encounter water or have high water cuts, severely restricting their development. Compared to conventional sandstone gas reservoirs, fractured gas reservoirs are characterized by strong heterogeneity, complex spatial structure, large differences in interlayer properties and pressure coefficients, random distribution of fractures, and well-developed natural fractures, mostly medium- to high-angle fractures with large dip angles. The reservoir's water storage capacity is relatively weak, and the fractures mainly function as seepage channels. The development of natural fractures also exacerbates the reservoir's heterogeneity, which is crucial for water breakthrough in gas wells and has a significant impact on the water breakthrough characteristics of oil wells. Furthermore, due to the large difference in water-gas viscosity ratios, water breakthrough in gas wells can cause some gas to be sealed by water, resulting in a significant reduction in gas production, and the remediation methods are extremely difficult. Therefore, it is necessary to control the smooth advancement of the water front in fractured gas reservoirs before water breakthrough, ensuring a smooth overall water intrusion. Accurately assessing the stability of the gas-water front in fractured gas reservoirs is crucial for their development. Summary of the Invention

[0003] The inventors read a large amount of literature in related fields but could not find a solution that could accurately determine the stability of the gas-water front in fractured gas reservoirs. Therefore, the inventors creatively developed a method to quantitatively characterize the irregularity of the gas-water front and thus accurately determine its stability. This provides a basis for timely adjustment of production strategies to reduce uneven water intrusion and can effectively guide the formulation of production strategies in production areas.

[0004] This invention proposes a method for determining the stability of the gas-water front in fractured gas reservoirs, comprising the following steps:

[0005] A geological model of water saturation distribution at different times was obtained for the fractured gas reservoir in the target block;

[0006] The geological model of water saturation distribution at different times in the fractured gas reservoir of the target block is vertically or horizontally layered, and the results of each layer are saved as RGB color images.

[0007] The saved RGB color images are processed to obtain the corresponding grayscale images;

[0008] Each grayscale image is processed to obtain the corresponding binarized image;

[0009] Calculate the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front;

[0010] The fractal dimension of binarized images of the same longitudinal or transverse layer at different times can be compared to determine the stability of the gas-water front of a fractured gas reservoir; or, the fractal dimension of binarized images of different longitudinal or transverse layers at the same time can be compared to determine the stability of the gas-water front of a fractured gas reservoir.

[0011] Furthermore, the geological model of water saturation distribution at different times in the target block fractured gas reservoir is subjected to vertical or horizontal stratification, including:

[0012] The geological model of water saturation distribution at different times in the target block's fractured gas reservoir is vertically stratified according to differences in reservoir properties; or,

[0013] The geological model of water saturation distribution at different times in the fractured gas reservoir of the target block is horizontally stratified according to the location of the well group.

[0014] Furthermore, the geological model of water saturation distribution at different times in the target block's fractured gas reservoir is vertically stratified according to differences in reservoir properties, including:

[0015] The geological model of water saturation distribution at different times in the target block fractured gas reservoir was vertically stratified by utilizing the differences in porosity or permeability.

[0016] Furthermore, each grayscale image is processed to obtain the corresponding binarized image, including:

[0017] The initial threshold for image segmentation is obtained by using the maximum and minimum gray values ​​of the image.

[0018] The foreground and background of the grayscale image are identified using an initial threshold for image segmentation, and then the average grayscale value of the foreground and the average grayscale value of the background are calculated.

[0019] A new threshold for image segmentation is obtained by using the average gray value of the foreground and the average gray value of the background of the grayscale image;

[0020] If the new image segmentation threshold is the same as the initial image segmentation threshold, then the grayscale image is binarized using the new image segmentation threshold to obtain the corresponding binarized image.

[0021] If the new image segmentation threshold is inconsistent with the initial image segmentation threshold, then the new image segmentation threshold is used as the initial image segmentation threshold, and the process of using the initial image segmentation threshold to confirm the grayscale foreground and background is repeated, followed by calculating the average grayscale value of the grayscale foreground, the average grayscale value of the grayscale background, and subsequent steps.

[0022] Furthermore, the fractal dimension of each binarized image is calculated to quantitatively characterize the irregularity of the water vapor front, including:

[0023] Each binarized image is placed on a preset uniformly segmented grid, and the grid size is continuously reduced to calculate the minimum number of grids required to cover the binarized image.

[0024] The fractal dimension of the binarized image is obtained by using the grid size and the minimum number of grids required to cover the binarized image.

[0025] Furthermore, the fractal dimension of the binarized image is obtained using the grid size and the minimum number of grid cells required to cover the binarized image, including:

[0026] Substituting the grid size and the minimum number of grid cells required to cover the binarized image into the following preset formula, we obtain the fractal dimension of the binarized image:

[0027]

[0028] Where d represents the size of each grid cell, N(d) is the minimum number of grid cells required to cover the binarized image, and D is the fractal dimension of the binarized image.

[0029] Furthermore, comparing the fractal dimensions of binarized images of the same vertical or horizontal layer at different times is used to determine the stability of the gas-water front of fractured gas reservoirs, including:

[0030] For the same vertical or horizontal layer, if the fractal dimension of the binarized image at the previous moment is greater than or equal to the fractal dimension of the binarized image at the next moment, then the gas-water front of the fractured gas reservoir is considered stable; otherwise, the gas-water front of the fractured gas reservoir is considered unstable, and the production strategy needs to be adjusted.

[0031] Furthermore, comparing the fractal dimensions of binarized images of different vertical or horizontal layers at the same time point is used to determine the stability of the gas-water front of fractured gas reservoirs, including:

[0032] If the fractal dimensions of the binarized images of two adjacent layers differ at the same time, it is determined that the gas-water front of the fractured gas reservoir is unstable, and the production strategy of the layer with the lower fractal dimension of the binarized image needs to be adjusted.

[0033] In another aspect, the present invention also discloses a device for judging the stability of the gas-water front of a fractured gas reservoir, comprising a geological model acquisition module for water saturation distribution, a layering module, a grayscale image module, a binarized image module, a fractal dimension calculation module, and a judgment module, wherein:

[0034] The water saturation distribution geological model acquisition module is used to obtain the water saturation distribution geological model of the target block fractured gas reservoir at different times;

[0035] The layering module is used to perform vertical or horizontal layering of the geological model of water saturation distribution at different times in the target block fractured gas reservoir, and save each layering result as an RGB color image.

[0036] The grayscale image module is used to process each saved RGB color image to obtain the corresponding grayscale image;

[0037] The binarization image module is used to process each grayscale image to obtain the corresponding binarized image;

[0038] The fractal dimension calculation module is used to calculate the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front.

[0039] The judgment module is used to compare the fractal dimensions of binarized images of the same longitudinal layer or the same transverse layer at different times to determine the stability of the gas-water front of a fractured gas reservoir; or, to compare the fractal dimensions of binarized images of different longitudinal layers or different transverse layers at the same time to determine the stability of the gas-water front of a fractured gas reservoir.

[0040] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows:

[0041] This invention uses geological models of water saturation distribution (i.e., gas-water front morphology) at different development times as input data. Through image processing such as grayscale conversion and binarization, it establishes computer-recognizable data. By calculating the fractal dimension of different input data, it quantitatively characterizes the irregular morphology of the gas-water front at different times, allowing for comparison of the development effects of different strategies and guiding gas field development. This invention utilizes fractal dimension theory, introducing it into the evaluation method of the gas-water front, transforming the qualitative evaluation of the irregularity of the gas-water front into a quantitative characterization. This quantitative characterization method can effectively guide the selection of production strategies in production areas and has broad practical engineering application value. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating a method for determining the stability of the gas-water front in a fractured gas reservoir, as described in Embodiment 1 of the present invention.

[0044] Figure 2This is a schematic diagram of the geological model of water saturation distribution at different times in the target block fractured gas reservoir obtained in Embodiment 1 of the present invention;

[0045] Figure 3 This is a schematic diagram of the results of vertically stratifying the geological model of water saturation distribution at different times in a fractured gas reservoir of a target block using porosity in Embodiment 1 of the present invention.

[0046] Figure 4 This is a schematic diagram of the process steps for obtaining the binarized image corresponding to each grayscale image in Embodiment 1 of the present invention;

[0047] Figure 5 This is a schematic diagram of the corresponding binarized image obtained by processing an RGB color image in Embodiment 1 of the present invention;

[0048] Figure 6 This is a schematic diagram of how, in Embodiment 1 of the present invention, each binarized image is placed on a preset uniformly segmented grid, and the grid size is continuously reduced.

[0049] Figure 7 This refers to the binarized images of the same vertical layer at different times in Embodiment 1 of the present invention;

[0050] Figure 8 This refers to the binarized images of different vertical layers at the same time in Embodiment 1 of the present invention.

[0051] Figure 9 This refers to the binarized images of different lateral layers at the same time in Embodiment 1 of the present invention.

[0052] Figure 10 This is a schematic diagram of the fitting calculation of the fractal dimension of the water vapor front in the first year of development in Embodiment 2 of the present invention;

[0053] Figure 11 This is a schematic diagram of the fitting calculation of the fractal dimension of the water vapor front in the second year of development in Embodiment 2 of the present invention;

[0054] Figure 12 This is a schematic diagram of a device for judging the stability of the gas-water front of a fractured gas reservoir, as shown in Embodiment 3 of the present invention. Detailed Implementation

[0055] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0056] Example 1

[0057] A method for assessing the stability of the gas-water front in fractured gas reservoirs, combined with Figure 1 As shown, it includes the following steps S10-S60:

[0058] Step S10 yields a geological model of the water saturation distribution at different times in the fractured gas reservoir of the target block.

[0059] In some embodiments, the geological model of water saturation distribution at different times in the target block fractured gas reservoir can be obtained directly from external sources, or an initial water saturation model can be used, along with a pre-established production strategy, to allow the initial water saturation model to change over time, forming water saturation models at different times. The resulting geological model of water saturation distribution is shown in the attached figure. Figure 2 As shown in the figure, well names such as KS801 and KeS8-9 are marked. Different colors on the right represent different ranges of water saturation values. For example, the change from red to blue indicates that the water saturation gradually increases from 0 to 1. The geological model of the fractured gas reservoir in the target block generally needs to be obtained through porosity, permeability, gas saturation, pressure, gas-water interpenetration, gas-water viscosity, density, etc. For detailed methods of obtaining the geological model of the fractured gas reservoir in the target block, you can refer to existing technologies, such as Peng Bo, Yang Huiting, Peng Yunhui. Research progress on three-dimensional geological modeling of oil and gas reservoirs [J]. Petrochemical Technology, 2023, 30(12): 175-177, etc.

[0060] Step S20: Perform vertical or horizontal layering on the geological model of water saturation distribution at different times for the fractured gas reservoir in the target block, and save the results of each layer as RGB color images.

[0061] Geological models of water saturation distribution at different times in fractured gas reservoirs within a target block can be vertically stratified according to differences in reservoir properties. For example, based primarily on the understanding of sub-layers after geological interpretation, strata with similar reservoir properties (including porosity, permeability, etc.) can be divided into the same layer Z. i This facilitates subsequent comparison and adjustment of water intrusion levels across different vertical planes. It can also be used to model the water saturation distribution at different times in a fractured gas reservoir within a target block, categorized by well group location X. i The data is then stratified horizontally. Horizontal stratification facilitates subsequent comparison of water invasion levels across different well groups and can guide adjustments to well production. After completing vertical or horizontal stratification, the results are saved as RGB color images (including JPG, PNG, etc.). Figure 3 The results of vertical stratification of the water saturation distribution of the target block fractured gas reservoir at different times using a geological model based on porosity are illustrated.

[0062] Step S30: Process the saved RGB color images to obtain the corresponding grayscale images.

[0063] In order to facilitate the characterization of the irregularity of the gas-water front and to distinguish the target area from the background to reduce noise interference, the saved image format is digitized, that is, the RGB image is converted from a three-dimensional data into a binary two-dimensional matrix.

[0064] In an RGB color image, a color is created by mixing the three primary colors R (red), G (green), and B (blue) in a certain proportion. The preset mixing formula for the three primary colors is:

[0065] Gray=R×0.299+G×0.587+B×0.114

[0066] Convert an RGB color image to an 8-bit grayscale image. The color depth of the points is Gray, ranging from 0 to 255, with 255 for white and 0 for black.

[0067] Step S40: Process each grayscale image to obtain the corresponding binarized image.

[0068] Since the grayscale values ​​of the water and air regions differ, a reasonable intermediate threshold can be calculated to distinguish between them. Specifically, this can be combined with... Figure 4 As shown, the binarized images corresponding to each grayscale image can be obtained by following steps S401-S404:

[0069] Step S401: Obtain the initial threshold for image segmentation using the maximum and minimum gray values ​​of the image.

[0070] For example, based on the maximum and minimum gray values ​​of the grayscale image, let Zmax and Zmin be respectively, and let the initial threshold Y0 = (Zmax + Zmin) / 2.

[0071] Step S402: Use the initial threshold for image segmentation to identify the foreground and background of the grayscale image, and then calculate the average grayscale value of the foreground and the average grayscale value of the background.

[0072] The image is segmented into foreground and background based on the threshold Y0, and the average gray value Z of the foreground in the grayscale image is calculated separately. O The average gray value Z of the grayscale image background B .

[0073] Step S403: Obtain a new threshold for image segmentation using the average gray value of the foreground and the average gray value of the background of the grayscale image.

[0074] The new threshold Y for image segmentation is calculated using a preset formula. n+1 Where n takes the values ​​0, 1, 2, 3, ...:

[0075] Y n+1 =(Z O +ZB ) / 2

[0076] In step S404, if the new image segmentation threshold is consistent with the initial image segmentation threshold, the grayscale image is binarized using the new image segmentation threshold to obtain the corresponding binarized image. Otherwise, if the new image segmentation threshold is inconsistent with the initial image segmentation threshold, the new image segmentation threshold is used as the initial image segmentation threshold, and steps S402 to S404 are executed again.

[0077] Specifically, if Yn = Yn+1, that is, Y0 = Y1, then Y1 is the threshold; otherwise, the image is re-segmented into foreground and background according to the threshold Yn+1, and the average gray value of the two is calculated again. After iterative calculation to determine the binarization threshold, the image is binarized. The binarization value is 1 if the gray value is less than the threshold, and 0 if it is greater than the threshold. The white area of ​​the binarized image is 0, and the black area is 1. Figure 5 The diagram illustrates the corresponding binarized image obtained after processing an RGB color image through steps S30 and S40.

[0078] Step S50: Calculate the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front.

[0079] For example, refer to Figure 6 As shown, each binarized image is placed on a preset uniformly segmented grid, the grid size is continuously reduced, and the minimum number of grids required to cover the binarized image is counted; then, the fractal dimension of the binarized image is obtained by using the grid size and the minimum number of grids required to cover the binarized image.

[0080] Specifically, the fractal dimension D of the binarized image can be further calculated to characterize the irregularity of its water vapor front. The image is placed on a uniformly divided grid, where each grid is d×d in size. The minimum number of grids N(d) required to cover this image is calculated. By continuously reducing the grid size d, the boundary delineation becomes clearer and more accurately represents the irregularity of the front. The corresponding required coverage number N(d) can be calculated. To facilitate fitting its fractal dimension, the grid size d and the number of grids N(d) are converted into logarithmic form, and linear fitting is applied. The slope of the resulting straight line is the fractal dimension D, and its formula is:

[0081]

[0082] Where d represents the size of each grid cell, N(d) is the minimum number of grid cells required to cover the binarized image, and D is the fractal dimension of the binarized image.

[0083] In some embodiments, the mesh size can be continuously reduced, but it is generally reduced to (1 / 2) of the original mesh size. 8 This will satisfy the requirement (a straight line segment is sufficient for linear fitting).

[0084] Step S60: Compare the fractal dimensions of binarized images of the same longitudinal layer or the same transverse layer at different times to determine the stability of the gas-water front of the fractured gas reservoir; or, compare the fractal dimensions of binarized images of different longitudinal layers or different transverse layers at the same time to determine the stability of the gas-water front of the fractured gas reservoir.

[0085] Fractal dimension reflects the effectiveness of a complex shape in occupying space. It is a measure of the irregularity of complex shapes. Fractal dimension is used to evaluate the irregular morphology of the gas-water front; the larger the fractal dimension, the more complex the shape it represents. For the same vertical or horizontal layer, if the fractal dimension of the binarized image at the previous moment is greater than or equal to the fractal dimension of the binarized image at the next moment, the gas-water front of the fractured gas reservoir is considered stable; otherwise, the gas-water front of the fractured gas reservoir is considered unstable, and the production strategy needs to be adjusted. For different vertical or horizontal layers at the same moment, if there is a difference in the fractal dimension of the binarized images of adjacent layers, the gas-water front of the fractured gas reservoir is considered unstable, and the production strategy of the layer with the lower fractal dimension in the binarized image needs to be adjusted.

[0086] Specifically, by calculating the fractal dimension of the water vapor front at different times, the development effects under different development periods can be quantitatively compared. For the same vertical K... i Or horizontal Z i For each partition, if the fractal dimension D of the water-gas front at time T(i) (where i is 0, 1, 2, 3, ...) is greater than the fractal dimension D of the water-gas front at time T(i+1), it indicates that the development effect under this production strategy is good, and no adjustment to the production strategy is needed. If the fractal dimension D of the water-gas front at time T(i) is equal to the fractal dimension D of the water-gas front at time T(i+1), it indicates that the development effect under this production strategy remains unchanged, and no adjustment to the production strategy is needed. If the fractal dimension D of the water-gas front at time T(i) is less than the fractal dimension D of the water-gas front at time T(i+1), it indicates that the development effect under this production strategy is starting to deteriorate, the water-gas front is no longer stable, and the production strategy needs to be adjusted in time to reduce uneven water intrusion. Figure 7 This illustrates the same vertical K. i Binarized images of different time points at different layers.

[0087] Simultaneously, the fractal dimensions of different regions at the same time can be compared. Based on the above vertical and horizontal layering, if the vertical K... i Layer fractal dimension greater than K i+1 Layer, then K i If the development effect of the perforation layer is poor, it is necessary to adjust the perforation layer section at K. i Production strategy for layered wells; if K i Layer fractal dimension less than K i+1 Layer, then K iIf the layer development is effective, then the perforation layer segment needs to be adjusted at K. i+1 Production strategy for layered wells; if K i The layer fractal dimension is equal to K i+1 If the water intrusion is uniform and evenly distributed across the layer, no adjustment is needed. If the horizontal Z-axis... i Layer fractal dimension greater than Z i+1 Layer, then Z i If the layer development effect is poor, the well position needs to be adjusted to Z. i Production strategy for layered wells; if Z i Layer fractal dimension less than Z i+1 Layer, then Z i If the layer development is effective, the well location needs to be adjusted to Z. i+1 Production strategy for layered wells; if Z i The layer fractal dimension is equal to Z. i+1 If the water penetrates evenly in layers, no adjustment is needed. Figure 8 The diagram illustrates the binarized images of different vertical layers at the same time. Figure 9 The diagram illustrates the binarized images of different lateral layers at the same time.

[0088] The inventors discovered that fractal theory can reveal the patterns and principles of parts forming complex wholes. Through self-similarity, the relationship between parts and the whole can be analyzed, leading to a better understanding of complex systems. Fractal dimension is an index that characterizes fractal patterns or sets by quantifying their complexity as the ratio of detail changes to scale changes. During water intrusion in gas reservoirs, the morphology of water intrusion exhibits a certain degree of self-similarity at both the pore and macroscopic scales. Furthermore, fractal dimension is an important parameter describing the complexity of fractal structures, and can be used to measure the complexity of an object's geometric shape and spatial structure. Therefore, applying fractal dimension to measure the irregularity of the gas-water front is highly applicable.

[0089] This invention uses geological models of water saturation distribution (i.e., gas-water front morphology) at different development times as input data. Through image processing such as grayscale conversion and binarization, it establishes computer-recognizable data. By calculating the fractal dimension of different input data, it quantitatively characterizes the irregular morphology of the gas-water front at different times, allowing for comparison of the development effects of different strategies and guiding gas field development. This invention utilizes fractal dimension theory, introducing it into the evaluation method of the gas-water front, transforming the qualitative evaluation of the irregularity of the gas-water front into a quantitative characterization. This quantitative characterization method can effectively guide the selection of production strategies in production areas and has broad practical engineering application value.

[0090] Example 2

[0091] To facilitate understanding, the embodiments of the present invention will be further described in detail using a fractured gas reservoir as an example. The specific steps of the present invention are as follows:

[0092] Step S10': Based on the geological model of the fractured gas reservoir in this block, the established production strategy is applied, and the water saturation results for the first and second years of development are obtained according to numerical simulation. The water saturation results are then layered horizontally, and the results are saved as RGB color images in JPG format.

[0093] S20' In order to facilitate the characterization of the irregularity of the gas-water front and to distinguish the target area from the background to reduce noise interference, the saved image format is digitized, that is, the RGB image is converted from a three-dimensional data into a two-dimensional binary matrix.

[0094] In an RGB color image, a color is created by mixing the three primary colors R (red), G (green), and B (blue) in a specific ratio. This is achieved through the formula:

[0095] Gray=R×0.299+G×0.587+B×0.114

[0096] Convert an RGB color image to an 8-bit grayscale image, where the color depth of a point ranges from 0 to 255, with 255 for white and 0 for black.

[0097] S30', based on the maximum and minimum grayscale values ​​of the grayscale image, which are 255 and 0 respectively, let the initial threshold Y0 = 127.5; segment the image into foreground and background according to the threshold Y0, and calculate the average grayscale values ​​of the two as 240 and 14 respectively; calculate the new threshold as 127; since the new threshold is not equal to the initial threshold, the image needs to be segmented into foreground and background again according to the threshold 127, and the average grayscale values ​​of the two are calculated again as 240 and 14 respectively, so the new threshold is 127, which is the same as the previous calculation result. Therefore, the binarization threshold for the first year of development and the second year of development is 127.

[0098] S40', further calculate the fractal dimension D of the binarized image to characterize the irregularity of its water vapor front. Place the image on a uniformly divided grid, where each grid is 1×1 in size. Calculate the minimum number of grids required to cover this image, and then reduce the grid size to... Calculate the required coverage number, then reduce the grid size to [value missing]. To facilitate fitting its fractal dimension, the grid size and number of grids are converted into logarithmic form, and linear fitting is applied. The slope of the resulting straight line is the fractal dimension D, and its formula is:

[0099]

[0100] Figure 10 , Figure 11 The figures illustrate the fractal dimensions of the water vapor front in the first and second years of development, respectively. The final fractal dimension for the water vapor front in the first year of development is 1.04 (Estimated Box Dimension = 1.04), and the fractal dimension for the water vapor front in the second year of development is 1.28 (Estimated Box Dimension = 1.28). In the figures, Corr Coeff is the correlation coefficient calculated from the fitting slope, and MaxErr / Amplitude is the maximum error calculated from the fitting slope. A Corr Coeff closer to 1 indicates a better fitting result, and a smaller MaxErr / Amplitude indicates a better fitting result.

[0101] By calculating the fractal dimension of the water-gas front in the first and second years of development, the development effects of the first and second years can be quantitatively compared. The fractal dimension of the water-gas front in the first year of development is 1.04, and that in the second year is 1.28. As the development time increases, the fractal dimension of the water-gas front increases, indicating that the development effect under this production strategy begins to deteriorate, the gas-water front is no longer stable, and the gas reservoir needs to adjust the production strategy in a timely manner to reduce the uneven intrusion of water.

[0102] Example 3

[0103] This invention also discloses a device for judging the stability of the gas-water front of fractured gas reservoirs, combined with... Figure 12 As shown, it includes: a geological model acquisition module for water saturation distribution (10), a layering module (20), a grayscale image module (30), a binarized image module (40), a fractal dimension calculation module (50), and a judgment module (60), wherein:

[0104] The water saturation distribution geological model acquisition module 10 is used to obtain the water saturation distribution geological model of the target block fractured gas reservoir at different times.

[0105] In some embodiments, the geological model of water saturation distribution at different times in the target block fractured gas reservoir can be obtained directly from the outside, or an initial water saturation model can be used, and a pre-established production strategy can be applied, so that the initial water saturation model changes over time to form water saturation models at different times.

[0106] The layering module 20 is used to perform vertical or horizontal layering of the geological model of water saturation distribution at different times in the target block fractured gas reservoir, and save each layering result as an RGB color image.

[0107] Geological models of water saturation distribution at different times in fractured gas reservoirs within a target block can be vertically stratified according to differences in reservoir properties. For example, based primarily on the understanding of sub-layers after geological interpretation, strata with similar reservoir properties (including porosity, permeability, etc.) can be divided into the same layer Z. i This facilitates subsequent comparison and adjustment of water intrusion levels across different vertical planes. It can also be used to model the water saturation distribution at different times in a fractured gas reservoir within a target block, categorized by well group location X. i The data is then stratified horizontally. Horizontal stratification facilitates subsequent comparison of water invasion levels across different well groups and can guide adjustments to well production. After completing vertical or horizontal stratification, the results are saved as RGB color images (including JPG, PNG, etc.).

[0108] The grayscale module 30 is used to process the saved RGB color images to obtain the corresponding grayscale images.

[0109] In order to facilitate the characterization of the irregularity of the gas-water front and to distinguish the target area from the background to reduce noise interference, the saved image format is digitized, that is, the RGB image is converted from a three-dimensional data into a binary two-dimensional matrix.

[0110] In an RGB color image, a color is created by mixing the three primary colors R (red), G (green), and B (blue) in a certain proportion. The preset mixing formula for the three primary colors is:

[0111] Gray=R×0.299+G×0.587+B×0.114

[0112] Convert an RGB color image to an 8-bit grayscale image. The color depth of the points is Gray, ranging from 0 to 255, with 255 for white and 0 for black.

[0113] The binarization image module 40 is used to process each grayscale image to obtain the corresponding binarized image.

[0114] Specifically, the binarization image module 40 is used to obtain an initial threshold for image segmentation using the maximum and minimum gray values ​​of the grayscale image; it is also used to identify the foreground and background of the grayscale image using the initial threshold, and then calculate the average gray value of the foreground and the average gray value of the background; it is also used to obtain a new threshold for image segmentation using the average gray value of the foreground and the average gray value of the background; if the new threshold for image segmentation is consistent with the initial threshold, it uses the new threshold to binarize the grayscale image to obtain the corresponding binarized image; if the new threshold for image segmentation is inconsistent with the initial threshold, it uses the new threshold as the initial threshold for image segmentation, and then executes again the steps of identifying the foreground and background of the grayscale image using the initial threshold, calculating the average gray value of the foreground and the average gray value of the background, and so on.

[0115] The fractal dimension calculation module 50 is used to calculate the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front.

[0116] Each binarized image is placed on a preset uniformly segmented grid. The grid size is continuously reduced, and the minimum number of grids required to cover the binarized image is counted. Then, the fractal dimension of the binarized image is obtained by using the grid size and the minimum number of grids required to cover the binarized image.

[0117] Specifically, the fractal dimension D of the binarized image can be further calculated to characterize the irregularity of its water vapor front. The image is placed on a uniformly divided grid, where each grid is d×d in size. The minimum number of grids N(d) required to cover this image is calculated. By continuously reducing the grid size d, the boundary delineation becomes clearer and more accurately represents the irregularity of the front. The corresponding required coverage number N(d) can be calculated. To facilitate fitting its fractal dimension, the grid size d and the number of grids N(d) are converted into logarithmic form, and linear fitting is applied. The slope of the resulting straight line is the fractal dimension D, and its formula is:

[0118]

[0119] Where d represents the size of each grid cell, N(d) is the minimum number of grid cells required to cover the binarized image, and D is the fractal dimension of the binarized image.

[0120] The judgment module 60 is used to compare the fractal dimensions of binarized images of the same longitudinal layer or the same transverse layer at different times to determine the stability of the gas-water front of the fractured gas reservoir; or, to compare the fractal dimensions of binarized images of different longitudinal layers or different transverse layers at the same time to determine the stability of the gas-water front of the fractured gas reservoir.

[0121] Fractal dimension reflects the effectiveness of a complex shape in occupying space. It is a measure of the irregularity of complex shapes. Fractal dimension is used to evaluate the irregular morphology of the gas-water front; the larger the fractal dimension, the more complex the shape it represents. For the same vertical or horizontal layer, if the fractal dimension of the binarized image at the previous moment is greater than or equal to the fractal dimension of the binarized image at the next moment, the gas-water front of the fractured gas reservoir is considered stable; otherwise, the gas-water front of the fractured gas reservoir is considered unstable, and the production strategy needs to be adjusted. For different vertical or horizontal layers at the same moment, if there is a difference in the fractal dimension of the binarized images of adjacent layers, the gas-water front of the fractured gas reservoir is considered unstable, and the production strategy of the layer with the lower fractal dimension in the binarized image needs to be adjusted.

[0122] Specifically, by calculating the fractal dimension of the water vapor front at different times, the development effects under different development periods can be quantitatively compared. For the same vertical K... i Or horizontal Z i For each partition, if the fractal dimension D of the water-gas front at time T(i) (where i is 0, 1, 2, 3, ...) is greater than the fractal dimension D of the water-gas front at time T(i+1), it indicates that the development effect under this production strategy is good, and no adjustment to the production strategy is needed. If the fractal dimension D of the water-gas front at time T(i) is equal to the fractal dimension D of the water-gas front at time T(i+1), it indicates that the development effect under this production strategy remains unchanged, and no adjustment to the production strategy is needed. If the fractal dimension D of the water-gas front at time T(i) is less than the fractal dimension D of the water-gas front at time T(i+1), it indicates that the development effect under this production strategy is starting to deteriorate, the water-gas front is no longer stable, and the production strategy needs to be adjusted in time to reduce uneven water intrusion. Figure 7 This illustrates the same vertical K. i Binarized images of different time points at different layers.

[0123] Simultaneously, the fractal dimensions of different regions at the same time can be compared. Based on the above vertical and horizontal layering, if the vertical K... i Layer fractal dimension greater than K i+1 Layer, then K i If the development effect of the perforation layer is poor, it is necessary to adjust the perforation layer section at K. i Production strategy for layered wells; if K i Layer fractal dimension less than K i+1 Layer, then K i If the layer development is effective, then the perforation layer segment needs to be adjusted at K. i+1 Production strategy for layered wells; if K i The layer fractal dimension is equal to K i+1 If the water intrusion is uniform and evenly distributed across the layer, no adjustment is needed. If the horizontal Z-axis... i Layer fractal dimension greater than Z i+1 Layer, then Z iIf the layer development effect is poor, the well position needs to be adjusted to Z. i Production strategy for layered wells; if Z i Layer fractal dimension less than Z i+1 Layer, then Z i If the layer development is effective, the well location needs to be adjusted to Z. i+1 Production strategy for layered wells; if Z i The layer fractal dimension is equal to Z. i+1 If the water penetrates evenly in layers, no adjustment is needed. Figure 8 The diagram illustrates the binarized images of different vertical layers at the same time. Figure 9 The diagram illustrates the binarized images of different lateral layers at the same time.

[0124] This invention uses geological models of water saturation distribution (i.e., gas-water front morphology) at different development times as input data. Through image processing such as grayscale conversion and binarization, it establishes computer-recognizable data. By calculating the fractal dimension of different input data, it quantitatively characterizes the irregular morphology of the gas-water front at different times, allowing for comparison of the development effects of different strategies and guiding gas field development. This invention utilizes fractal dimension theory, introducing it into the evaluation method of the gas-water front, transforming the qualitative evaluation of the irregularity of the gas-water front into a quantitative characterization. This quantitative characterization method can effectively guide the selection of production strategies in production areas and has broad practical engineering application value.

[0125] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for judging the stability of the gas-water front of fractured gas reservoirs, which will not be described in detail here.

[0126] The present invention also provides a computer-readable storage medium storing a computer program that executes the above-described method for determining the stability of the gas-water front of fractured gas reservoirs, which will not be described in detail here.

[0127] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0128] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” just as “including,” is interpreted as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.”

Claims

1. A method for determining the stability of the gas-water front in fractured gas reservoirs, characterized in that, Includes the following steps: A geological model of water saturation distribution at different times was obtained for the fractured gas reservoir in the target block; The geological model of water saturation distribution at different times in the fractured gas reservoir of the target block is vertically or horizontally layered, and the results of each layer are saved as RGB color images. The saved RGB color images are processed to obtain the corresponding grayscale images; Each grayscale image is processed to obtain the corresponding binarized image; Calculate the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front; The fractal dimension of binarized images of the same longitudinal or transverse layer at different times can be compared to determine the stability of the gas-water front of a fractured gas reservoir; or, the fractal dimension of binarized images of different longitudinal or transverse layers at the same time can be compared to determine the stability of the gas-water front of a fractured gas reservoir.

2. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 1, characterized in that, The geological model for the water saturation distribution of fractured gas reservoirs in the target block at different times is subjected to vertical or horizontal stratification, including: The geological model of water saturation distribution at different times in the target block's fractured gas reservoir is vertically stratified according to differences in reservoir properties; or, The geological model of water saturation distribution at different times in the fractured gas reservoir of the target block is horizontally stratified according to the location of the well group.

3. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 2, characterized in that, The geological model of water saturation distribution at different times in the target block fractured gas reservoir is vertically stratified according to differences in reservoir properties, including: The geological model of water saturation distribution at different times in the target block fractured gas reservoir was vertically stratified by utilizing the differences in porosity or permeability.

4. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 1, characterized in that, The process of processing each grayscale image to obtain the corresponding binarized image includes: The initial threshold for image segmentation is obtained by using the maximum and minimum gray values ​​of the image. The foreground and background of the grayscale image are identified using an initial threshold for image segmentation, and then the average grayscale value of the foreground and the average grayscale value of the background are calculated. A new threshold for image segmentation is obtained by using the average gray value of the foreground and the average gray value of the background of the grayscale image; If the new image segmentation threshold is the same as the initial image segmentation threshold, then the grayscale image is binarized using the new image segmentation threshold to obtain the corresponding binarized image. If the new image segmentation threshold is inconsistent with the initial image segmentation threshold, then the new image segmentation threshold is used as the initial image segmentation threshold, and the process of using the initial image segmentation threshold to confirm the grayscale foreground and background is repeated, followed by calculating the average grayscale value of the grayscale foreground, the average grayscale value of the grayscale background, and subsequent steps.

5. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 1, characterized in that, The calculation of the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front includes: Each binarized image is placed on a preset uniformly segmented grid, and the grid size is continuously reduced to calculate the minimum number of grids required to cover the binarized image. The fractal dimension of the binarized image is obtained by using the grid size and the minimum number of grids required to cover the binarized image.

6. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 5, characterized in that, The process of obtaining the fractal dimension of the binarized image using the grid size and the minimum number of grids required to cover the binarized image includes: Substituting the grid size and the minimum number of grid cells required to cover the binarized image into the following preset formula, we obtain the fractal dimension of the binarized image: Where d represents the size of each grid cell, N(d) is the minimum number of grid cells required to cover the binarized image, and D is the fractal dimension of the binarized image.

7. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 1, characterized in that, The comparison of the fractal dimension of binarized images of the same longitudinal or transverse layer at different times to determine the stability of the gas-water front of a fractured gas reservoir includes: For the same vertical or horizontal layer, if the fractal dimension of the binarized image at the previous moment is greater than or equal to the fractal dimension of the binarized image at the next moment, then the gas-water front of the fractured gas reservoir is considered stable; otherwise, the gas-water front of the fractured gas reservoir is considered unstable, and the production strategy needs to be adjusted.

8. The method for determining the stability of the gas-water front of a fractured gas reservoir as described in claim 1, characterized in that, The comparison of the fractal dimensions of binarized images of different longitudinal or lateral layers at the same time point to determine the stability of the gas-water front of a fractured gas reservoir includes: If the fractal dimensions of the binarized images of two adjacent layers differ at the same time, it is determined that the gas-water front of the fractured gas reservoir is unstable, and the production strategy of the layer with the lower fractal dimension of the binarized image needs to be adjusted.

9. A device for determining the stability of the gas-water front in a fractured gas reservoir, characterized in that, include: The system includes modules for acquiring geological models of water saturation distribution, layering, grayscale mapping, binarization, fractal dimension calculation, and judgment. The water saturation distribution geological model acquisition module is used to obtain the water saturation distribution geological model of the target block fractured gas reservoir at different times; The layering module is used to perform vertical or horizontal layering of the geological model of water saturation distribution at different times in the target block fractured gas reservoir, and save each layering result as an RGB color image. The grayscale image module is used to process each saved RGB color image to obtain the corresponding grayscale image; The binarization image module is used to process each grayscale image to obtain the corresponding binarized image; The fractal dimension calculation module is used to calculate the fractal dimension of each binarized image to quantitatively characterize the irregularity of the water vapor front. The judgment module is used to compare the fractal dimensions of binarized images of the same longitudinal layer or the same transverse layer at different times to determine the stability of the gas-water front of a fractured gas reservoir; or, to compare the fractal dimensions of binarized images of different longitudinal layers or different transverse layers at the same time to determine the stability of the gas-water front of a fractured gas reservoir.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements any of the methods described in claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs the method of any one of claims 1 to 8.

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