Method, device, medium and product for predicting seismic dispersion and attenuation of partially saturated rock

Abstract

The application discloses a partial-saturation rock seismic dispersion attenuation prediction method, device, medium and product. In the method, CT images of a target rock sample in a dry state, a partial water-saturation state and a complete water-saturation state are acquired respectively; image reconstruction is performed according to the CT images in the states to obtain a gas saturation image of the target rock sample in the partial water-saturation state; connected domain marking processing is performed on the gas saturation image to determine each gas package in the gas saturation image and to statistically analyze the radius and volume fraction of each gas package; and based on a partial-saturation rock seismic wave propagation model, the seismic dispersion attenuation of the target rock sample in the partial water-saturation state is predicted according to the radius and volume fraction of each gas package to obtain a prediction result. According to the technical scheme, the fluid distribution characteristics of the partial-saturation rock are accurately described through the CT scanning technology, so that the prediction accuracy and reliability of the seismic dispersion attenuation of the partial-saturation rock are improved.

Description

Technical Field

[0001] This application relates to the field of earthquake rock physics technology, and in particular to a method, device, medium and program product for predicting seismic dispersion attenuation in partially saturated rocks. Background Technology

[0002] Partially saturated rock refers to a rock state in which water and other immiscible fluids coexist in the pores, but the water does not completely fill the pores. The presence of multiple fluids in the pores, and their uneven distribution, complicates the pore structure, affecting the flow and distribution of fluids within the rock.

[0003] When seismic waves propagate through partially saturated rock, the differences in properties of various fluids within the rock generate mesoscale fluid flow. Existing theoretical and experimental studies have shown that mesoscale flow can produce significant seismic wave velocity dispersion and attenuation. However, traditional rock physics models can only assume the pore structure and fluid distribution within partially saturated rock, or rely on predictions based on experimental data, thus affecting the prediction results of seismic dispersion and attenuation in rocks based on these assumptions.

[0004] Therefore, how to provide an accurate and reliable prediction scheme for seismic dispersion attenuation in partially saturated rocks is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a method, device, medium, and product for predicting seismic dispersion attenuation in partially saturated rocks. By using CT scanning technology to accurately characterize the fluid distribution features of partially saturated rocks, the accuracy and reliability of predicting seismic dispersion attenuation in partially saturated rocks are improved.

[0006] According to one aspect of this application, a method for predicting seismic dispersion attenuation in partially saturated rock is provided, the method comprising:

[0007] A first CT image of the target rock sample in a dry state, a second CT image in a partially water-saturated state, and a third CT image in a fully water-saturated state were acquired respectively; wherein the first CT image, the second CT image, and the third CT image were acquired using the same CT scanner under the same scanning conditions;

[0008] Image reconstruction is performed based on the first CT image, the second CT image, and the third CT image to obtain the gas saturation image of the target rock sample under the partial water saturation state.

[0009] The gas saturation image is subjected to connected component labeling to determine each gas element in the gas saturation image, as well as the radius and volume fraction of each gas element.

[0010] Based on the partial saturated rock seismic wave propagation model, the seismic dispersion attenuation of the target rock sample under the partial water saturation state is predicted according to the radius and volume fraction of each air pocket, and the prediction results are obtained.

[0011] According to another aspect of this application, an electronic device is provided, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the partially saturated rock seismic dispersion attenuation prediction method according to any embodiment of this application.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the partially saturated rock seismic dispersion attenuation prediction method according to any embodiment of this application.

[0013] According to another aspect of this application, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the partially saturated rock seismic dispersion attenuation prediction method described in any embodiment of this application.

[0014] The technical solution provided in this application involves acquiring CT images of a target rock sample in dry, partially water-saturated, and fully water-saturated states. Image reconstruction is performed based on the CT images in each state to obtain an air saturation image of the target rock sample in the partially water-saturated state. Connected component labeling is applied to the air saturation image to determine each air pocket, its radius, and volume fraction. Based on a seismic wave propagation model for partially saturated rocks, the seismic dispersion attenuation of the target rock sample in the partially water-saturated state is predicted according to the radius and volume fraction of each air pocket, yielding the prediction result. This technical solution improves the accuracy and reliability of predicting the seismic dispersion attenuation of partially saturated rocks by accurately characterizing the fluid distribution features of partially saturated rocks using CT scanning technology.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for predicting seismic dispersion attenuation in partially saturated rock, provided in Embodiment 1 of this application.

[0018] Figure 2 Two-dimensional slice images of an Indiana limestone sample at 88% water saturation in the XY, YZ, and XZ directions, respectively, provided in Embodiment 1 of this application.

[0019] Figure 3a This is a schematic diagram of the distribution of air pockets in the XY direction provided in Embodiment 1 of this application.

[0020] Figure 3b This is a schematic diagram of the distribution of air pockets in the YZ direction provided in Embodiment 1 of this application.

[0021] Figure 3c This is a schematic diagram of the distribution of air pockets in the XZ direction provided in Embodiment 1 of this application.

[0022] Figure 4 This is a flowchart of a method for predicting seismic dispersion attenuation in partially saturated rock, provided in Embodiment 2 of this application.

[0023] Figure 5 The images show the gas saturation of an Indiana limestone sample at 88% water saturation in the XY, YZ, and XZ directions, respectively. (This is from Embodiment 2 of the present application.)

[0024] Figure 6 This is a flowchart of a method for predicting seismic dispersion attenuation in partially saturated rock, provided in Embodiment 3 of this application.

[0025] Figure 7a This is a comparison chart of the predicted results and experimental data in the XY direction provided in Embodiment 3 of this application.

[0026] Figure 7b This is a comparison diagram in the YZ direction between the predicted results and experimental data of this application, provided as an embodiment of this application.

[0027] Figure 7c This is a comparison diagram in the XZ direction between the predicted results and experimental data of this application, provided as an embodiment of this application.

[0028] Figure 8This is a comparison chart of the prediction results of this application and the prediction results obtained using the random patch saturation model, provided as an embodiment of this application.

[0029] Figure 9 This is a schematic diagram of the device for implementing a partially saturated rock seismic dispersion attenuation prediction method according to an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," "third," "target," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart illustrating a method for predicting seismic dispersion attenuation in partially saturated rock, as provided in Embodiment 1 of this application. This embodiment is applicable to predicting seismic dispersion attenuation within reservoirs with complex microporous structures. The method can be executed by a partially saturated rock seismic dispersion attenuation prediction device, which can be implemented in hardware and / or software and can be configured in a device with data processing capabilities. Figure 1 As shown, the method includes the following steps.

[0034] S110. Acquire a first CT image of the target rock sample in a dry state, a second CT image in a partially water-saturated state, and a third CT image in a fully water-saturated state. The first, second, and third CT images are acquired using the same CT scanner under the same scanning conditions.

[0035] The target rock sample can be a core drilled from the target reservoir section, and its type can be quartz, calcite, dolomite, carbonate rock or other types of rock.

[0036] A dry state refers to a target rock sample with 0% water saturation, a fully water-saturated state refers to a target rock sample with 100% water saturation, and a partially water-saturated state refers to a target rock sample with water saturation between 0% and 100%. Specifically, CT images of the same target rock sample can be obtained in dry, partially water-saturated, and fully water-saturated states by depressurization and water absorption (i.e., gradually increasing water saturation) respectively; alternatively, CT images of the same target rock sample can be obtained in fully water-saturated, partially water-saturated, and dry states by desiccation and drainage (i.e., gradually decreasing water saturation).

[0037] The scanning conditions for a CT scanner when scanning a target rock sample may include tube voltage, tube current, scanning time, slice thickness, pitch, image reconstruction algorithm, and the orientation of the target rock sample. Scanning the same target rock sample under different water saturation states using the same CT scanner and under the same scanning conditions ensures that the first, second, and third CT images have the same resolution, and that the noise level and grayscale range are relatively stable and consistent. This facilitates subsequent comparative analysis of the first, second, and third CT images, improving the reliability of the image analysis results.

[0038] In this application, the acquired CT images are the results of three-dimensional scanning of the target rock sample. Since subsequent processing involves a large amount of computation, to improve computational efficiency, this application selects two-dimensional slice images from three mutually perpendicular planes for analysis.

[0039] For example, Indiana limestone was selected as the target rock sample. A micro-CT scanner was used to scan the sample under dry, drained conditions at different water saturation levels (0%, 88%, and 100%). The sample scanning area was 40 mm in diameter and 38.4 mm in height, with voxel dimensions of 0.022 x 0.022 x 0.022 mm. 3 .like Figure 2 The image shows two-dimensional slice images of an Indiana limestone sample at 88% water saturation in the XY, YZ, and XZ directions.

[0040] S120. Based on the first CT image, the second CT image, and the third CT image, perform image reconstruction to obtain the gas saturation image of the target rock sample under the partially water-saturated state.

[0041] In CT images, the CT values ​​of mineral particles, water, and gas differ significantly, resulting in distinct color variations in their images. The CT value of mineral particles depends on their density and atomic number. Low-density minerals such as quartz, feldspar, and calcite have CT values ​​of approximately 1500–2500 HU, while high-density minerals such as pyrite and barite have CT values ​​of approximately 3000–6000 HU, appearing as bright white in CT images. Pure water has a CT value of HU, while saline water or water containing dissolved substances has a CT value of approximately 0–100 HU, appearing as light gray in CT images. Dry air has a CT value of -1000 HU, while other gases such as CO2 and CH4 have CT values ​​of approximately -1000 to -500 HU, appearing as dark gray in CT images.

[0042] In this application, the first CT image is of the target rock sample in a dry state, where the pores are filled with gas, the boundary between the rock matrix and the pores is clear, and the pores appear as black spots or connected networks, clearly identifying primary pores, cracks, and dissolution cavities. The second CT image is of the target rock sample in a partially water-saturated state, where gas and water coexist in the pores, and the wettability affects the appearance of non-uniform patches or finger-like edges. The third CT image is of the target rock sample in a fully water-saturated state, where the pores are completely filled with water, the grayscale of the pore area is significantly brighter, the contrast between the matrix and the pores is reduced, and the image uniformity is enhanced.

[0043] Therefore, in this application, the distribution of fluid in the target rock sample under partial water saturation can be determined by comparing the differences between CT images of the target rock sample under different water saturation states. Specifically, the first CT image under dry state, the second CT image under partial water saturation state, and the third CT image under fully water saturation state can be compared pairwise, such as comparing the first CT image with the second CT image, comparing the first CT image with the third CT image, and comparing the second CT image with the third CT image, to obtain the distribution of pores, water, and gas in the target rock sample, thereby obtaining the gas saturation image of the target rock sample under partial water saturation state.

[0044] S130. Perform connected component labeling processing on the gas saturation image to determine each gas cell in the gas saturation image and the radius and volume fraction of each gas cell.

[0045] Connected component labeling refers to identifying connected regions in a gas saturation image through threshold segmentation. A gas pocket is a region of gas accumulation or a pore space containing gas within the target rock sample. For example, regions in a gas saturation image with CT values ​​below a certain threshold can be considered as gas, and the same region can be labeled as a gas pocket.

[0046] Specifically, the gas saturation image can first be binarized and denoised. A unique label can then be assigned to each independent gas cell in the image based on a four-connected or eight-connected labeling algorithm. The geometric information of each gas cell, such as area, centroid, boundary, and radius, can be determined based on the shape of the pixels occupied by each gas cell. Then, the volume fraction of the gas cell can be determined based on the ratio of the number of pixels occupied by each gas cell to the total number of pixels in the image.

[0047] In this application, to avoid inaccurate radius calculation due to the different shapes of the air bags, the minimum circumscribed circle radius and the maximum inscribed circle radius of each air bag can be obtained first, and then the average of the minimum circumscribed circle radius and the maximum inscribed circle radius can be taken as the radius of the air bag.

[0048] For example, Figures 3a to 3c These are schematic diagrams illustrating the distribution statistics of air pockets in the XY, YZ, and XZ directions, respectively, provided in Embodiment 1 of this application. Figure 3a As shown, from left to right, the distribution charts are: the smallest circumscribed circle radius as the air-bag radius, the average radius as the air-bag radius, and the largest inscribed circle radius as the air-bag radius in the XY direction; Figure 3b As shown, from left to right, the distribution charts are: the smallest circumscribed circle radius as the air-filled radius, the average radius as the air-filled radius, and the largest inscribed circle radius as the air-filled radius in the YZ direction; Figure 3c As shown, from left to right, the distribution statistics are as follows: the smallest circumscribed circle radius as the air bag radius, the average radius as the air bag radius, and the largest inscribed circle radius as the air bag radius in the YZ direction.

[0049] S140. Based on the partially saturated rock seismic wave propagation model, the seismic dispersion attenuation of the target rock sample under the partially water-saturated state is predicted according to the radius and volume fraction of each air pocket, and the prediction result is obtained.

[0050] Seismic wave propagation models for partially saturated rocks can be based on the elastic properties of the rock, the physical properties of the fluid, and their interactions, and are established through experimental verification. Common seismic wave propagation models for partially saturated rocks include Biot theory, the BISQ model, the White model, and the Johnson model.

[0051] In this application, the radius and volume fraction of the air pocket can be used to provide air saturation parameters for the propagation model of seismic waves in partially saturated rocks, and thereby derive the prediction results of seismic dispersion attenuation of the target rock sample under partially water saturated conditions.

[0052] This invention provides a method for predicting seismic dispersion attenuation in partially saturated rocks. The method involves acquiring CT images of a target rock sample in dry, partially water-saturated, and fully water-saturated states. Image reconstruction is performed based on the CT images in each state to obtain the gas saturation image of the target rock sample in the partially water-saturated state. Connected component labeling is applied to the gas saturation image to determine each gas pocket, its radius, and volume fraction. Based on a seismic wave propagation model for partially saturated rocks, the seismic dispersion attenuation of the target rock sample in the partially water-saturated state is predicted according to the radius and volume fraction of each gas pocket, yielding the prediction result. This technical solution improves the accuracy and reliability of predicting seismic dispersion attenuation in partially saturated rocks by accurately characterizing the fluid distribution features of partially saturated rocks using CT scanning technology.

[0053] Example 2

[0054] Figure 4 This is a flowchart of a method for predicting seismic dispersion attenuation in partially saturated rock, provided in Embodiment 2 of this application. This embodiment is an optimization based on the above embodiment, specifically optimizing the CT image reconstruction process. Figure 4 As shown, the method in this embodiment specifically includes the following steps.

[0055] S210. Acquire a first CT image of the target rock sample in a dry state, a second CT image in a partially water-saturated state, and a third CT image in a fully water-saturated state. The first, second, and third CT images are acquired using the same CT scanner under the same scanning conditions.

[0056] S220. Determine the porosity image of the target rock sample based on the first CT image and the third CT image.

[0057] Among them, the porosity image can display the spatial distribution of porosity in a target rock sample. Specifically, the ratio of pore space volume to rock sample volume can be mapped to image pixel values ​​to visually demonstrate the heterogeneity of pores inside the target rock sample.

[0058] In this application, the first CT image and the third CT image can be precisely aligned in space first. For example, rigid or non-rigid registration algorithms can be used to correct the displacement caused by scanning or sample deformation. Then, based on the CT values ​​corresponding to each pixel in the corrected first CT image and the third CT image, the porosity value of each pixel is determined, thereby determining the porosity image of the target rock sample.

[0059] For example, the first CT image and the third CT image can be input into a pre-trained porosity image generation model. The porosity image generation model extracts and fuses the image features of the first CT image and the third CT image to obtain the porosity image of the target rock sample.

[0060] Optionally, before determining the porosity image of the target rock sample based on the first CT image and the third CT image, the method further includes: determining the CT value of each pixel in the first CT image, the CT value of each pixel in the second CT image, and the CT value of each pixel in the third CT image.

[0061] To facilitate the processing of the first, second, and third CT images, the CT value corresponding to each pixel in each CT image can be determined separately.

[0062] The CT value, generated through X-ray scanning and mathematical reconstruction algorithms, reflects the material's ability to absorb X-rays. Specifically, mathematical algorithms, such as filtered back-projection algorithms and iterative reconstruction algorithms, can convert the original projection data into pixel values ​​in an image.

[0063] Optionally, determining the CT value of each pixel in the first CT image, the CT value of each pixel in the second CT image, and the CT value of each pixel in the third CT image includes: determining the CT value of each pixel in the first CT image, the CT value of each pixel in the second CT image, and the CT value of each pixel in the third CT image using the following formula:

[0064]

[0065] In the formula, CT1 represents the CT value of each pixel in the first CT image, CT2 represents the CT value of each pixel in the second CT image, and CT3 represents the CT value of each pixel in the third CT image. Indicates porosity, CT grain CT value represents the CT value of mineral grains. air CT value represents the CT value of air. water CT represents the CT value of water. gas The CT value represents the natural gas, v1 represents the percentage of water in the pore space, and v2 represents the percentage of natural gas in the pore space.

[0066] In this application, since the resolution of a CT scanner may be insufficient to identify the interface between mineral particles, water, and air, and the percentage of mineral particles, water, and air in each pixel is also different, it can be assumed that the CT value of each pixel is approximately equal to the average value of the CT response.

[0067] In the above formulas, porosity The percentage of water in the pore space (v1) and the percentage of gas in the pore space (v2) can be measured experimentally, along with the CT value (CT) of the mineral particles. grain CT value of air air CT value of natural gas gas CT value of water water It is usually a fixed range, which can be determined based on empirical values ​​or experimental measurements.

[0068] Specifically, for the first CT image in a dry state, each pixel contains mineral particles and air. Therefore, the CT value of each pixel in the first CT image can be determined based on porosity, the CT value of mineral particles, and the CT value of air. For the second CT image in a partially water-saturated state, each pixel contains mineral particles, water, and air. Therefore, the CT value of each pixel in the second CT image can be determined based on porosity, the CT value of mineral particles, the CT value of water, and the CT value of air. For the third CT image in a fully water-saturated state, each pixel contains mineral particles and water. Therefore, the CT value of each pixel in the third CT image can be determined based on porosity, the CT value of mineral particles, and the CT value of water.

[0069] Optionally, determining the porosity image of the target rock sample based on the first CT image and the third CT image includes: subtracting the CT value of each pixel in the third CT image from the CT value of each pixel in the first CT image to obtain the first CT value of each pixel; and constructing the porosity image of the target rock sample based on the first CT value of each pixel.

[0070] In the first CT image, each pixel contains mineral particles and air, while in the third CT image, each pixel contains mineral particles and water. Therefore, after calibrating and aligning each pixel in the third CT image with each pixel in the second CT image, the CT value representing the porosity of each pixel can be obtained by subtracting the CT value of each pixel in the first CT image from the CT value of each pixel in the third CT image.

[0071] Specifically, the CT value of each pixel in the gas content image can be represented by the following formula:

[0072]

[0073] S230. Determine the gas content image of the target rock sample based on the second CT image and the third CT image.

[0074] Among them, the gas content image is a visualization of the distribution of natural gas content inside the target rock sample, and different colors or gray levels can be used to represent different gas contents.

[0075] In this application, the second CT image and the third CT image can be precisely aligned in space first. For example, rigid or non-rigid registration algorithms can be used to correct the displacement caused by scanning or sample deformation. Then, based on the CT values ​​corresponding to each pixel in the corrected second CT image and the third CT image, the porosity value of each pixel is determined, thereby determining the gas content image of the target rock sample.

[0076] For example, the second and third CT images can be input into a pre-trained gas content image generation model. The gas content image generation model extracts and fuses the image features of the second and third CT images to obtain the gas content image of the target rock sample.

[0077] Optionally, determining the gas content image of the target rock sample based on the second CT image and the third CT image includes: subtracting the CT value of each pixel in the third CT image from the CT value of each pixel in the second CT image to obtain the second CT value of each pixel; and constructing the gas content image of the target rock sample based on the second CT value of each pixel.

[0078] In the second CT image, each pixel contains mineral particles, water, and natural gas. In the third CT image, each pixel contains mineral particles and water. Therefore, after calibrating and aligning each pixel in the third CT image with each pixel in the second CT image, the CT value representing the natural gas content in each pixel can be obtained by subtracting the corresponding CT value of each pixel in the second CT image from the CT value of each pixel in the third CT image.

[0079] Specifically, the CT value of each pixel in the gas content image can be represented by the following formula:

[0080]

[0081] Since the sum of the percentage of pore space occupied by water (v1) and the percentage of pore space occupied by natural gas (v2) is 1, the above formula can be simplified to:

[0082] S240. Based on the porosity image and the gas content image, determine the gas saturation image of the target rock sample under the partially water-saturated state.

[0083] Gas saturation can be defined as the ratio of gas volume to pore volume. Therefore, in this application, the ratio of the gas content image to the porosity image can be used as the gas saturation image of the target rock sample under partially water-saturated conditions. Furthermore, the gas content image and porosity image can be input into a gas saturation image generation model to extract and fuse features from the gas content image and porosity image respectively, thereby obtaining the gas saturation image of the target rock sample under partially water-saturated conditions.

[0084] Optionally, determining the gas saturation image of the target rock sample under the partially water-saturated state based on the porosity image and the gas content image includes: dividing the CT value of each pixel in the gas content image by the corresponding CT value of each pixel in the porosity image to determine the third CT value of each pixel; and constructing the gas saturation image of the target rock sample under the partially water-saturated state based on the third CT value of each pixel.

[0085] Specifically, the CT value of each pixel in the gas saturation image can be represented by the following formula:

[0086]

[0087] For example, with Figure 2 Taking the two-dimensional slice image of the Indiana limestone at 88% water saturation as an example, two-dimensional slice images of the rock sample at 0% and 100% water saturation were obtained respectively, and processed according to steps S220 to S240 above to obtain the gas saturation image of the Indiana limestone under partially water saturated conditions, as shown. Figure 5 As shown. In Figure 5 In the image, from left to right, are the gas saturation images of the rock sample in the XY, YZ, and XZ directions when the water saturation is 88%. The red dots in the image represent natural gas, and the blue background represents water.

[0088] S250. Perform connected component labeling processing on the gas saturation image to determine each gas cell in the gas saturation image and the radius and volume fraction of each gas cell.

[0089] S260. Based on the partially saturated rock seismic wave propagation model, the seismic dispersion attenuation of the target rock sample under the partially water-saturated state is predicted according to the radius and volume fraction of each air pocket, and the prediction result is obtained.

[0090] This invention provides a method for predicting seismic dispersion attenuation in partially saturated rocks. The method determines the porosity image of a target rock sample based on a first and third CT image; determines the gas content image of the target rock sample based on a second and third CT image; and determines the gas saturation image of the target rock sample under partially water-saturated conditions based on the porosity and gas content images. This technical solution improves the accuracy and reliability of predicting seismic dispersion attenuation in partially saturated rocks by comparing CT images of rock samples under different water saturation conditions to determine the porosity and gas content images.

[0091] Example 3

[0092] Figure 6 This is a flowchart of a method for predicting seismic dispersion attenuation in partially saturated rock, provided in Embodiment 2 of this application. This embodiment is an optimization based on the above embodiment, specifically optimizing the process for predicting seismic dispersion attenuation in partially saturated rock. Figure 6 As shown, the method in this embodiment specifically includes the following steps.

[0093] S310. Acquire a first CT image of the target rock sample in a dry state, a second CT image in a partially water-saturated state, and a third CT image in a fully water-saturated state. The first, second, and third CT images are acquired using the same CT scanner under the same scanning conditions.

[0094] S320. Based on the first CT image, the second CT image, and the third CT image, perform image reconstruction to obtain the gas saturation image of the target rock sample under the partially water-saturated state.

[0095] S330. Perform connected component labeling processing on the gas saturation image to determine each gas cell in the gas saturation image and the radius and volume fraction of each gas cell.

[0096] S340. Based on the partial saturated rock seismic wave propagation model, according to the radius and volume fraction of each air pocket, the plane wave of the target rock sample under the partial water saturation state is analyzed to determine the complex P-wave number and S-wave number.

[0097] Specifically, based on the partial saturated rock seismic wave propagation model, gas patch embeddings can be added piece by piece to the background phase of a fully water-saturated target rock sample to achieve partial saturation, according to the radius and volume fraction of the gas pocket, so as to realize the plane wave analysis of the target rock sample in a partially water-saturated state.

[0098] Among them, the complex P-wave wave number and the S-wave wave number are characteristics of seismic waves propagating in the target rock sample. When seismic dispersion attenuation occurs, the wave number is in complex form.

[0099] Optionally, the propagation model of partially saturated rock seismic waves is as follows:

[0100]

[0101] In the formula, φ0 represents the skeleton porosity, S represents the degree of saturation, u represents the solid displacement, e represents the solid strain, and ξ represents the solid volume strain. H ζ represents the fluid strain in which the pores are saturated with the background phase fluid, and ζ represents the fluid strain increment between the pores. K represents the bulk modulus of the fluid in the embedded body. S ρ represents the bulk modulus of mineral particles. S This indicates the density of mineral particles. U represents the density of the background phase fluid. H U represents the displacement of the background phase fluid. I Indicates the displacement of the fluid within the embedded body. κ represents the viscosity of the background phase fluid. H Indicates the permeability of the background phase fluid. The viscosity of the fluid in the embedded body is represented by N, Z, A, and A'. d Q H Q I R H and R I ρ represents the elastic modulus. 00 ρ 01 ρ 02 ρ 11 and ρ 22 This represents the density coefficient.

[0102] The above formula is the wave control equation. In this application, the analytical solution of the plane wave can be substituted into the above formula to obtain the corresponding Christoffel equation, and the corresponding complex P-wave wave number and S-wave wave number can be obtained by solving the Christoffel equation.

[0103] S350. Based on the complex P-wave number and the S-wave number, predict the seismic dispersion attenuation of the target rock sample under the partially water-saturated state, and obtain the prediction result.

[0104] The prediction results of seismic dispersion attenuation can be represented by the bulk modulus and its corresponding attenuation.

[0105] Optionally, based on the complex P-wave number and the S-wave number, the seismic dispersion attenuation of the target rock sample under the partially water-saturated state is predicted to obtain the prediction result, including obtaining the prediction result using the following formula:

[0106]

[0107] In the formula, K sat The bulk modulus of the target rock sample under the partially water-saturated state is represented by ω, where ω represents the angular frequency and k is the kinematic frequency. p G represents the complex longitudinal wave number. sat This represents the shear modulus of the target rock sample under the partially water-saturated state. k s ρ represents the transverse wave number. f Represents the equivalent fluid density. S H Indicates background phase saturation. S represents the density of the inlay. I Indicates the saturation of the inlay. This indicates the attenuation of the target rock sample under the partially water-saturated state.

[0108] This invention provides a method for predicting seismic dispersion attenuation in partially saturated rocks. This method analyzes plane waves from a target rock sample under partially water saturated conditions based on a partially saturated rock seismic wave propagation model and the radius and volume fraction of each air pocket, determining the complex P-wave number and S-wave number. Based on these numbers, the seismic dispersion attenuation of the target rock sample under partially water saturated conditions is predicted, yielding the prediction result. This technical solution improves the accuracy and reliability of predicting seismic dispersion attenuation in partially saturated rocks by utilizing a partially saturated rock seismic wave propagation model for plane wave analysis.

[0109] Based on the above embodiments, this application verifies the effectiveness and accuracy of the solution provided by comparing the prediction results of this application with experimental data, and comparing the prediction results of this application with the prediction results obtained using the random patch saturation model.

[0110] Regarding the first aspect, such as Figures 7a to 7c The figures shown are comparisons between the prediction results of this application and experimental data in the XY, YZ, and XZ directions, respectively. Figures 7a to 7cIn the diagram, the black solid line represents the prediction result when the minimum circumscribed circle radius is used as the air envelope radius, the blue solid line represents the prediction result when the average radius is used as the air envelope radius, the red solid line represents the prediction result when the maximum inscribed circle radius is used as the air envelope radius, and the red scatter dots represent experimental data. The left graph shows the relationship between frequency and bulk modulus, and the right graph shows the relationship between frequency and attenuation.

[0111] based on Figures 7a to 7c The following formulas are used to calculate the coefficients of determination between the predicted results and the actual data:

[0112]

[0113] In the formula, R 2 The coefficient of determination is represented by y, where n represents the sample size. i This represents the experimental data of the i-th sample. This represents the prediction result for the i-th sample. This represents the average of n samples.

[0114] If the coefficient of determination R 2 The closer the value is to 1, the better the prediction effect. Table 1 shows the calculation results of the effectiveness determination coefficient, where BR represents the determination coefficient of the prediction result when the minimum circumscribed circle radius is used as the air bag radius, R represents the determination coefficient of the prediction result when the average radius is used as the air bag radius, SR represents the determination coefficient of the prediction result when the maximum inscribed radius is used as the air bag radius, and K represents the bulk modulus, 1 / Q. K This represents the decay corresponding to the bulk modulus.

[0115] Table 1

[0116]

[0117] As can be seen from the determination coefficients in Table 1, the prediction results obtained by the seismic dispersion attenuation prediction method provided in this application have a high degree of agreement with the actual data.

[0118] Regarding the second aspect, such as Figure 8As shown, the left figure compares the predicted bulk modulus results of this application with those obtained using the random patch saturation model, and the right figure compares the predicted attenuation results of this application with those obtained using the random patch saturation model. Specifically, the black solid line represents the prediction result using this application's method in the XY direction, the blue solid line represents the prediction result using this application's method in the XZ direction, the red solid line represents the prediction result using this application's method in the YZ direction, the black dashed line represents the prediction result using the random patch saturation model in the XY direction, the blue dashed line represents the prediction result using the random patch saturation model in the XZ direction, the red dashed line represents the prediction result using the random patch saturation model in the YZ direction, and the red scatter dots represent experimental data.

[0119] based on Figure 8 The determination coefficients for the predicted results and the actual data are calculated using the following formulas:

[0120]

[0121] In the formula, R 2 The coefficient of determination is represented by y, where n represents the sample size and y represents the coefficient of determination. i This represents the experimental data of the i-th sample. This represents the prediction result for the i-th sample. This represents the average of n samples.

[0122] If the coefficient of determination R 2 The closer the value is to 1, the better the prediction effect. Table 2 shows the calculation results of the accuracy determination coefficient, where A represents the prediction method using the random patch saturation model, B represents the prediction method using this application, K represents the bulk modulus, and 1 / Q K This represents the decay corresponding to the bulk modulus.

[0123] Table 2

[0124]

[0125] As can be seen from the determination coefficients in Table 2, the highest determination coefficient between the prediction results and experimental data is 0.4446 when using the random patch saturation model prediction method, and the highest determination coefficient between the prediction results and experimental data is 0.9484 when using the prediction method of this application.

[0126] Example 4

[0127] Figure 9A schematic diagram of the structure of a device 10 that can be used to implement embodiments of this application is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0128] like Figure 9 As shown, device 10 includes at least one processor 11 and a memory, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc., communicatively connected to at least one processor 11. The memory stores computer programs executable by at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of device 10. The processor 11, ROM 12, and RAM 13 are interconnected via bus 14. Input / output (I / O) interface 15 is also connected to bus 14.

[0129] Multiple components in device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0130] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the partially saturated rock seismic dispersion attenuation prediction method.

[0131] In some embodiments, the partially saturated rock seismic dispersion attenuation prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the partially saturated rock seismic dispersion attenuation prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the partially saturated rock seismic dispersion attenuation prediction method by any other suitable means (e.g., by means of firmware).

[0132] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0133] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0134] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0135] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or haptic feedback); and input from the user can be received in any form (including sound input, voice input, or haptic input).

[0136] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0137] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0138] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0139] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0140] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for predicting seismic dispersion attenuation in partially saturated rock, characterized in that, The method includes: A first CT image of the target rock sample in a dry state, a second CT image in a partially water-saturated state, and a third CT image in a fully water-saturated state were acquired respectively; wherein the first CT image, the second CT image, and the third CT image were acquired using the same CT scanner under the same scanning conditions; Image reconstruction is performed based on the first CT image, the second CT image, and the third CT image to obtain the gas saturation image of the target rock sample under the partial water saturation state. The gas saturation image is subjected to connected component labeling to determine each gas element in the gas saturation image, as well as the radius and volume fraction of each gas element. Based on the partial saturated rock seismic wave propagation model, the seismic dispersion attenuation of the target rock sample under the partial water saturation state is predicted according to the radius and volume fraction of each air pocket, and the prediction results are obtained.

2. The method according to claim 1, characterized in that, Image reconstruction is performed based on the first CT image, the second CT image, and the third CT image to obtain the gas saturation image of the target rock sample under the partially water-saturated state, including: Based on the first CT image and the third CT image, determine the porosity image of the target rock sample; Based on the second CT image and the third CT image, determine the gas content image of the target rock sample; Based on the porosity image and the gas content image, the gas saturation image of the target rock sample under the partially water-saturated state is determined.

3. The method according to claim 2, characterized in that, Before determining the porosity image of the target rock sample based on the first CT image and the third CT image, the method further includes: Determine the CT value of each pixel in the first CT image, the CT value of each pixel in the second CT image, and the CT value of each pixel in the third CT image; Determining the porosity image of the target rock sample based on the first CT image and the third CT image includes: The CT value of each pixel in the third CT image is subtracted from the CT value of each pixel in the first CT image to obtain the first CT value of each pixel. Based on the first CT value of each pixel, a porosity image of the target rock sample is constructed; Determining the gas content image of the target rock sample based on the second CT image and the third CT image includes: The CT value of each pixel in the third CT image is subtracted from the CT value of each pixel in the second CT image to obtain the second CT value of each pixel. Based on the second CT value of each pixel, a gas content image of the target rock sample is constructed; Based on the porosity image and the gas content image, determine the gas saturation image of the target rock sample under the partially water-saturated state, including: The third CT value of each pixel is determined by dividing the CT value of each pixel in the gas content image by the CT value of each pixel in the porosity image. Based on the third CT value of each pixel, an air saturation image of the target rock sample under the partially water-saturated state is constructed.

4. The method according to claim 3, characterized in that, Determining the CT values ​​of each pixel in the first CT image, the second CT image, and the third CT image includes: The CT values ​​of each pixel in the first CT image, the second CT image, and the third CT image are determined using the following formulas: In the formula, CT1 represents the CT value of each pixel in the first CT image, CT2 represents the CT value of each pixel in the second CT image, and CT3 represents the CT value of each pixel in the third CT image. Indicates porosity, CT grain CT value represents the CT value of mineral grains. air CT value represents the CT value of air. water CT represents the CT value of water. gas The CT value represents the natural gas, v1 represents the percentage of water in the pore space, and v2 represents the percentage of natural gas in the pore space.

5. The method according to claim 1, characterized in that, Based on a partially saturated rock seismic wave propagation model, and according to the radius and volume fraction of each air pocket, the seismic dispersion attenuation of the target rock sample under the partially water-saturated state is predicted, yielding prediction results including: Based on the partial saturated rock seismic wave propagation model, the plane wave of the target rock sample under the partial water saturation state is analyzed according to the radius and volume fraction of each air pocket to determine the complex P-wave number and S-wave number. Based on the complex P-wave number and the S-wave number, the seismic dispersion attenuation of the target rock sample under the partially water-saturated state is predicted, and the prediction result is obtained.

6. The method according to claim 5, characterized in that, The propagation model of partially saturated rock seismic waves is as follows: In the formula, φ0 represents the skeleton porosity, S represents the degree of saturation, u represents the solid displacement, e represents the solid strain, and ξ represents the solid volume strain. H ζ represents the fluid strain in which the pores are saturated with the background phase fluid, and ζ represents the fluid strain increment between the pores. K represents the bulk modulus of the fluid in the embedded body. S ρ represents the bulk modulus of mineral particles. S This indicates the density of mineral particles. U represents the density of the background phase fluid. H U represents the displacement of the background phase fluid. I Indicates the displacement of the fluid within the embedded body. κ represents the viscosity of the background phase fluid. H Indicates the permeability of the background phase fluid. The viscosity of the fluid in the embedded body is represented by N, Z, A, and A'. d Q H Q I R H and R I ρ represents the elastic modulus. 00 ρ 01 ρ 02 ρ 11 and ρ 22 This represents the density coefficient.

7. The method according to claim 5, characterized in that, Based on the complex P-wave number and the S-wave number, the seismic dispersion attenuation of the target rock sample under the partially water-saturated state is predicted, and the prediction results are obtained, including: The prediction result is obtained using the following formula: In the formula, K sat The bulk modulus of the target rock sample under the partially water-saturated state is represented by ω, where ω represents the angular frequency and k is the kinematic frequency. p G represents the complex longitudinal wave number. sat This represents the shear modulus of the target rock sample under the partially water-saturated state. k s ρ represents the transverse wave number. f Represents the equivalent fluid density. S H Indicates background phase saturation. S represents the density of the inlay. I Indicates the saturation of the inlay. This indicates the attenuation of the target rock sample under the partially water-saturated state.

8. An electronic device, characterized in that, The device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the partially saturated rock seismic dispersion attenuation prediction method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the partially saturated rock seismic dispersion attenuation prediction method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the partially saturated rock seismic dispersion attenuation prediction method according to any one of claims 1-7.

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